A novel optical fibre device is proposed for real-time measurements of near infrared chromatic refractive index signature of a liquid medium in the 1100-1700 nm range. This inline optical fibre chromatic refractive index analyser is based upon collocated fibre long period gratings written adjacently in the core by femtosecond laser inscription yielding 8 attenuation bands associated with different cladding modes, yielding 8 independent measurements across the entire wavelength range. This fibre device is tested on a series of chemical compounds associated with wines. The results for each compound yielded distinctive chromatic responses. The limits of detection for this fibre devices ranged from 1×10^-3 to 6×10^-5 mol/L for the compounds associated with wines. This demonstrates a potential usefulness of such an optical fibre sensor capable of monitoring in real-time effective chromatic refractive index signatures that is not offered by any current technology.

We propose to use a fiber with a nanostructured square core with a top-hat square-shaped fundamental mode. We present the results of the D-shape fiber fabrication with top-hat square-shaped fundamental mode. We will also show the numerical and experimental analysis of sensors based on such a fiber. A sensor without additional layers is only based on the study of changes in transmitted power. And, SPR and LMR sensors where a layer of metal or dielectric is deposited on the flat surface of the fiber and where the measurement is related to the change in the resonant wavelength.

This work provides a method that combines graphene oxide coating and self-image theory to improve the sensitivity of optical sensors. The sensor is designed specifically to measure the amount of glucose present quantitatively in aqueous solutions that replicate the range of glucose concentrations found in human saliva. COMSOL Multiphysics 6.0 was used to simulate the self-imaging phenomenon using a coreless silica fiber (CSF). For high-quality self-imaging, the second and fourth self-imaging points are usually preferred because of their higher coupling efficiency, which increases the sensor sensitivity. However, managing the fourth self-image is more difficult because it calls for a longer CSF length. As a result, the first and second self-image points were the focus of the simulation in this work. After the simulation, using the Layerby- Layer method, the sensor was constructed to a length that matched the second self-image point (29.12 mm) and coated with an 80 μm/mL graphene oxide layer. When comparing uncoated and graphene oxide-covered sensors to measure glucose in liquids ranging from 25 to 200 mg/dL, one bilayer of polyethyleneimine/graphene demonstrated an eight-fold improvement in sensitivity. The final sensor, built on graphene oxide, showed stability with a low standard deviation of 0.6 pm/min. It also showed sensitivity at 10.403 ± 0.004 pm/(mg/dL) with a limit of detection of 9.15 mg/dL.

A distributed temperature and strain sensing (DTSS) system based on Brillouin backscatter was used to measure changes during a 5 month period on an 9km length of fibre optic cable deployed next to a railway line, including on an embankment and a bridge. By measuring the amplitude and frequency of the Brillouin backscatter it was possible to determine both temperature and strain changes. Most of the strain changes occurred slowly over many weeks, however some rapid strain changes occurred on part of the embankment on one particular day. Temperature effects were dominated by the diurnal and seasonal variations of the air temperature. At a number of points along the cable there were small optical losses that varied with time, but the ability of the system to also carry out regular Rayleigh OTDR measurements meant that these losses could be accounted for, so they had minimal effect on the data.

We demonstrate the application of distributed fiber optic strain sensing based on optical frequency-domain reflectometry for the early identification and location of fatigue cracks in welds in steel tubular test specimens. This involved subjecting two welded tubular specimens, equipped with surface-mounted optical fiber sensors, to a resonant bending load. We continuously measured the strain distributions in the test specimens without any interruption throughout the entire duration of the test. The measurements were carried out with a spatial resolution of 2.6 mm and a strain resolution of 1 με. The test samples were designed so that the fatigue cracks initiated from the inner surface of the pipe at the weld root and propagated through the wall thickness towards the outer surface. The fatigue tests were performed under two stress ranges. In Sample 1 (higher stress), we successfully detected and located the strain concentration region at approximately 5% of the specimen’s lifetime before the breakthrough. For Sample 2 (lower stress) the initiation of the hotspot was detected around 27% of the specimen’s lifetime before the breakthrough. We demonstrate that our method allows for on-the-fly detection and location of fatigue cracks originating from the inner surface of the specimens' wall.

Composite materials are increasingly used in aerospace structures, because of their superior stiffness-to-mass ratio. Due to their ply-stacking nature, they show excellent mechanical performance in the in-plane directions but are much weaker in the out-of-plane direction. Manufacturers and end-users are therefore keen to acquire strength information along that direction. Here, we report on the measurement of three-dimensional strain using fibre Bragg gratings (FBGs) in highly birefringent (HiBi) microstructured optical fibers (MOFs).

By combining two MOF-based FBG sensors, we demonstrated three-dimensional strain-based monitoring of the curing of a fiber reinforced composite subcomponent manufactured by a resin transfer molding (RTM) technique. We also investigated the influence of the presence of the embedded fibers by embedding the MOF in composite coupons manufactured by liquid resin infusion (LRI) and performing a multitude of mechanical tests with these coupons and comparing the mechanical resistance to pristine coupons. It is also the first time that such a comparison has been carried out for this specific combination of optical fiber and composite material system.

In this study, a tailored hybrid sensing configuration integrating fiber Bragg grating and intrinsic Fabry-Perot interferometer is presented. It enables simultaneous discrimination and tracking of internal temperature and pressure changes within 18650 Li-ion batteries (LiBs). The battery undergoes rigorous cycling tests at different operating conditions and a comparison between them is presented. The optical fiber sensors instrumented into the LiB reveal, throughout overall cycling tests, a compelling correlation between internal and external temperature behavior. The application of systematic Incremental Capacity Analysis derivative curves during battery operation exposes crucial insights into the relationship between pressure and temperature physical parameters, and their electrochemical behavior. This optical sensing approach contributes in this way to a nuanced understanding of internal LiB dynamics, with implications for their optimizing performance and safety in diverse applications.

This work introduces a mathematical model for designing optical fiber linear and angular displacement sensors (OFLADS) capable of simultaneously measuring the distance and angle of a finite reflective target such as a mirror. Recently, fiber optical sensors have found applications across industries like medical, military, or aerospace. The advantages of fiber optics, including low manufacturing cost, high sensitivity, and immunity to electromagnetic interference, make them suitable for extreme environments. Reflective fiber sensors, which modulate reflected light intensity to measure displacement, have a simple structure and a wide measurement range. Currently, attention is directed towards OFDS. We developed the mathematical model for the OFLADS design and performed a detailed simulation analysis, including the angular and linear displacement between the reflective target and the OFLADS tip, as well as the light intensity distribution profile. We also validated experimentally the proposed mathematical model with custom-made OFLADS designs.

Accurate and precise measurement of the radiation energy delivered to and absorbed by the patients' tissues is of great importance in radiation therapy quality assurance. Utilizing radioluminescence of selected materials for radiation detection has shown great potential for developing high spatiotemporal resolution dosimeters for radiation therapy applications. Optical fiber-based scintillator dosimeters are promising candidates for small-field and in vivo dosimetry. Scintillation imaging, on the other hand, is a promising method for multi-dimensional dosimetry. There are, however, challenges associated with these techniques, such as mitigating the impact of Cherenkov radiation and ionization quenching of scintillators in certain radiation fields. Here, we present the progress in radioluminescence dosimetry techniques for radiation therapy applications.

Scintillation-based fiber dosimeters are a powerful tool for minimally invasive localized real-time monitoring of the dose rate during Low Dose Rate (LDR) and High Dose Rate (HDR) brachytherapy (BT). This paper shows the design, fabrication and characterization of such dosimeters, consisting of scintillating sensor tips attached to polymer optical fiber (POF). The scintillating sensor tips consist of inorganic scintillators, dispersed in a polymer host. We present the design, fabrication and characterization of those sensor tips. The manufacturing is done by means of a custom compression and transfer moulding process implemented on a commercially available hot embossing machine. We show the manufacturing of 237 sensor tips, which are subsequently attached to the end of the POF using UV-curable adhesive. Finally, we perform dosimetry experiments in water phantoms which show a great potential for in-vivo dosimetry for brachytherapy.

Laser Ablation (LA) is a minimally invasive tumor treatment, particularly valuable when conventional methods are unsuitable. LA utilizes laser energy delivered using an optical fiber, offering advantages like electromagnetic immunity and flexibility of the applicator. Challenges in achieving complete tumor destruction and minimizing collateral damage prompt the need for improved monitoring and LA control. Fiber optic sensors offer advantages over conventional methods, such as small size, electromagnetic immunity and biocompatibility. This study employs fiber Bragg grating array sensors for precise temperature monitoring and implements closed-loop temperature control based on these measurements. Preliminary findings show effective control of temperature, indicating the potential of fiber sensors use in LA as a promising technique to enhance the efficacy of cancer treatment.

We present and discuss an experimental fiber-optic sensing system developed for application in a coal mine. One of the major issues of coal mine engineering is the condition of the rock mass above the roadways in which miners extract coal. The condition is typically monitored using measurement anchors with electronic strain gauges. In the demonstrated system, the electronic sensors are replaced with FBGs. A fundamental cell of the network consists of gratings that are glued to a special groove on the anchor, while the output signal is read out with an interrogation system. The interrogator is realized either using a commercially available device or with a photonic integrated circuit. The system has been assembled and tested both in the anchor laboratory and in a test site in a roadway of a mine. The first characterization results are promising and prove the feasibility of fiber-optic sensors for rock mass monitoring.

A resin cavity at the end face of a single mode optical fiber, monitors the presence of methanol, ethanol and isopropanol vapors with distinct wavelength shift and with ppb sensitivity.

Volatile organic compounds (VOCs) show great potential as biomarkers in non-invasive disease detection. We propose a cascaded Fabry-Perot (FP) fiber sensor for characterizing ethanol and 2-propanol. The sensor fabrication involves splicing a single mode fiber to a silica capillary (SC), followed by cleaving the SC. The SC tip is immersed in a PMMA solution and heated for rapid solvent evaporation, forming a dual cavity. The sensor was tested in the gas phase, using binary hydroalcoholic mixtures containing ethanol and 2-propanol. The response attained was nonlinear, with a maximum sensitivity of -133.7 pm/vol.% and -173.0 pm/vol.% for 2-propanol and the ethanol, respectively, in a range between 35-50 vol.%.

In this work, we present the development of a porphyrin-embedded graphene oxide-based optical fiber sensor using evanescent wave absorption (EWA) spectroscopy for the detection of multiple environmentally hazardous volatile gases. The changes in absorption spectra in the visible region were procured when exposed to the target gases, and the obtained spectroscopic signatures for various gases were examined using an unsupervised machine-learning method. The analysis provides a protocol for grouping the spectra of similarities and forming classes to identify gas molecules. The porphyrin-embedded graphene oxide-coated U-bent fiber probe showed enhanced selection capability compared to the graphene oxide-coated probe. This low-cost photonics platform has enormous potential for developing on-field gas monitoring devices.

Detection of volatile organic compounds (VOCs) with particular attention to the BTEX group consisting of benzene, toluene, ethylbenzene, and xylene has risen as an important task in fields as environmental monitoring and breath analysis. Sensors based on optical detection techniques may represent an alternative to traditional approaches. However, optical sensing has been limited in terms of measurements selectivity as BTEX point out absorption features around 3.3 μm, where strong interferences from hydrocarbons occur. This issue can be eliminated operating at longer wavelength in the region from 13 to 15 μm, where BTEX show distinct and isolated absorption features. However, the investigations in this region have been limited by the lack of suitable laser sources as well as by the low performances of commercial detectors. In this work, a new approach to benzene detection is proposed, employing a long wavelength InAs-based QCL as light source and a quartz tuning fork as detector for TDLAS measurements, in light-induced thermoelastic spectroscopy (LITES) detection scheme. Analyte detection in the range of hundreds of part-per-billion with a shoe-box size design is demonstrated.

Atmospheric composition varies both spatially and temporally, notably in terms of relative humidity and greenhouse gases. Current methods rely on massive and expensive sensors deployed sparsely. Here, we are interested in developing a multi-gas sensor that targets very small atmospheric composition variations. In addition, it intends to be gas specific, lightweight, autonomous and compact. Targeted gases are either greenhouse gases such as CO2, CH4 , and N2O, or toxic gases such as CO and SO2. For this purpose, we have developed a Photoacoustic Spectroscopy sensor, using Mid Infrared QCL laser sources, which achieve a very narrow spectral linewidth and have a good potential for miniaturization. We developed a dedicated electronic device to perform data acquisition. Our prototype is beginning its calibration phase in a lab environment, prior further integration for real time measurement.

Conventional crime scene investigation is slow as traces are collected at the scene and are only subsequently analyzed in the lab. Rapid and contactless detection and examination of various trace materials at the scene without alteration is highly desirable as it avoids degradation of traces, significantly speeds up the investigations, and reduces the time to capture the perpetrator. Furthermore, contactless identification is crucial in guaranteeing the health and safety of crime scene investigators for chemical or explosive threats. Here we present a quantum-cascade laser-based backscattering infrared sensor that allows instantaneous detection and identification of a wide range of forensic relevant samples, from explosives to drugs, and precursors to bodily fluids. The system is based on external-cavity tunable quantum cascade lasers (EC-QCL) using resonant MOEMS (micro-opto-electromechanical system scanners) diffraction gratings, offering kilohertz spectral scan speeds. Two such MOEMS EC-QCL modules are multiplexed in the system and spectrally combined into a single laser beam. The (scalable) approach almost doubles the spectral coverage and increases the selectivity without sacrificing spectral scan speed. We report here on the system design and show first results.

Methane is a significant contributor to global warming so reducing methane emissions, particularly from oil and gas operations, is among the most cost effective, impactful actions governments can take to achieve climate goals. Preventing methane leakage impacts economic productivity and worker safety too. Large-site leak detection requires reliable cost-effective distributed sensors.

Methane leakage is also an issue for several other industries. However, hard wiring is not practical or cost effective and battery power is unacceptable due to the need for regular changes requiring engineers working in hazardous areas at great expense. The sustainability challenge of additional travel associated with device maintenance and disposal of used batteries in the millions is also environmentally unacceptable. Worker safety monitoring with lower-cost portable methane detectors requires bulky, rechargeable battery-powered devices that the industry is seeking to avoid for operational and environmental reasons. Various low-cost sensor technologies have been applied to methane sensing (catalytic, optical - non-dispersive infrared (NDIR), semiconducting metal oxide and electrochemical) with catalytic/pellistor sensors formerly being dominant but in recent years replaced by NDIR sensors overcoming issues of accuracy, susceptibility to poisoning, short lifetimes, power consumption, recalibration and requirement for oxygen presence. It also has the advantage of being a fail-to-safe technology.

In this work, we present an optical NDIR gas sensor that uses a fast-response semiconductor light source/detector optopair operating at <1 mW power consumption, compatible with powering from photovoltaic based energy harvesting. This is a step change from current state-of-the-art gas sensor technologies and orders of magnitude lower than filament/thermopile based detectors. Fabrication of the sensor is discussed, including; semiconductor mid-IR optopair fabrication, mid-IR optical interference filter deposition and injection molded 2-mirror parabolic reflector optical system preparation. Sensor response to methane is discussed and light harvesting operation is demonstrated, enabling compatibility with wireless distributed methane sensor networks.

The monitoring of certain gases is crucial for obtaining Air quality indicators promoting the environmental monitoring. The detection of greenhouse gases (GHG) is especially on the forefront of heath related issues, also dictated by ambitious climate worldwide accords. Currently used spectroscopic solutions remain coslty and bulky, limiting their widespread adoption. We present a study of a MID-IR spectroscopy system developed on a silicon photonics platform, utilizing a Bragg grating mirror cavity, between an optical Bragg source and grating mirror.

Zwitterionic poly(carboxybetaine) brushes have emerged as promising surface functional coatings for antifouling labelfree biosensing in complex biological fluids. This study investigates the effect of storage conditions on highly-dense poly(carboxybetaine methacrylamide) (pCBMAA), poly(sulfobetaine methacrylamide) (pSBMAA), a random copolymer of CBMAA with poly(hydroxypropyl methacrylamide) p(CBMAA-HPMAA), and terpolymer p(CBMAA-SBMAAHPMAA) brushes. Synthesized via surface-initiated atom transfer radical polymerization (SI ATRP) on gold-coated substrates, these brushes were stored in dry, water, or phosphate-buffered saline (PBS) environments at temperatures of 22 °C, 6 °C, or frozen at –20 °C for 44 days. Structural stability of the brushes was assessed using infrared reflection-absorption spectroscopy (IRRAS) and spectroscopic ellipsometry (SE), while fouling resistance and IgG antibody binding capacity were evaluated using surface plasmon resonance (SPR). Although the brushes exhibited minimal detachment from the surface, a slight decrease in swelling ratio was observed across all storage conditions. Furthermore, the storage led to a minor reduction in IgG binding capacity, with freezing at –20 °C showing a more pronounced effect in most cases. Notably, storing all tested carboxybetaine-based brushes in water or in PBS at 6 °C adversely affected fouling, a phenomenon alleviated when stored frozen at –20 °C or in a dry state.

Surface plasmon resonance detections based on phase changes have demonstrated superior sensitivities over the intensity, spectral and angular methods due to the singularity effect (abrupt change of phase value) observed at resonance. The Goos–Hänchen effect, a higher first order derivative of the phase, can be observable as a lateral displacement of the reflected wave at total internal reflection and magnified by the surface plasmons. The GH sensitivity can be further improved through the addition of a phase change material nanolayer beneath the gold. Vanadium dioxide (VO2) belongs to the family of phase change materials that exhibit reversible insulator-metal behavior when heated above 68℃. Adding a thin layer of VO2 below the metal proved to theoretically enhance the sensitivity of a conventional gold-based surface plasmon biosensor (up to 28 times of improvement in comparison with the bare gold configuration).

Environmental sensing is a topic of increasing interest and has triggered much research towards fully integrated sensor solutions. In this context optical measurement approaches in the infrared can provide intrinsic selectivity and sensitivity for integrated gas sensors. Recently, we proposed the possibility of the incorporation of IR-active plasmonic materials to photonic crystal waveguides, which could allow to increase sensitivities and significantly reduce the size of such sensors. Here, we will first present the overall approach and compare two possible specific realizations.

This comparative study investigates the refractive index sensitivity of gold (Au), titanium nitride (TiN), and hafnium nitride (HfN) nanodisk arrays in exciting surface lattice resonances and localized plasmon resonance modes. The plasmonic nanodisk arrays are fabricated using electron beam lithography and optimized for disk diameter and array period. This work provides valuable insights into the comparative performance of TiN, HfN, and Au nanodisk arrays for refractive sensing-based applications.

Plasmonic biosensing is an optical technique that based on refractive index change when the target molecules interact with the sensing surface. Main plasmonic material used in this type of biosensors is gold. Our work is dedicated to design a novel sensing SPR chip with vanadium dioxide (VO2) nanolayer, known for its unique insulator-to-metal phase transition in the near-infrared region. VO2 thin film is deposited using Cross-Beam Pulsed Laser Deposition (CB-PLD) method and gold layer deposition is performed by sputtering. By employing the VO2 nanolayer, we create a highly responsive biosensing interface (with a much-improved sensitivity and also a wide dynamic measurement range). The VO2 layer's ability to modulate the refractive index enables precise control of the excited plasmon resonance. This interaction results in enhancing sensitivity and the capability to detect low-concentration analytes with high accuracy.

Surface plasmon resonance (SPR) optical sensors have attracted considerable attention, finding diverse applications, particularly in the fields of biomedicine and chemistry. While single metal layer based SPR sensors are commonly used, their resolution is limited by broad resonances. Consequently, researchers have dedicated efforts to achieve sharper resonances and heightened sensitivity. In this study, we investigate the use of multilayer structures for bulk sensing and elucidate how they contribute to improving sensor sensitivity. Additionally, we explore thin film sensing, conducting a thorough comparative analysis of monolayer and multilayer structures. Our findings reveal that thin film sensing based on monolayer structures outperforms multilayer stacks. This superiority is linked to the reduced sensitivity observed in the latter, stemming from the compromised coupling between Surface Plasmon Polariton (SPP) modes within the multilayer structure upon the application of the sensing layer. This research not only advances our understanding of SPR sensors but also highlights the potential of tailored multilayer structures in optimizing sensor performance.

Integrating Photonic-Integrated Circuits (PICs) into microfluidic devices for diagnostics faces several challenges such as handling small PICs, ensuring interface access, managing temperature and UV sensitive coatings, and maintaining bubble-free sample transfer. CSEM offers unique services merging cleanroom packaging, microfluidic design, and prototyping, aiding PIC and diagnostics industry partners.
Collaborations with different partners have led to tailored PIC integration solutions for sepsis detection, bioreactor contamination detection, food safety testing, and extracellular vesicle detection. CSEM's packaging for small PICs drastically reduces costs (up to 10-fold) and enhances market competitiveness.
Leveraging simulations for fluid dynamics and chemical interaction optimizes development, increasing detection speed in one case by around 4000 times. CSEM has also pioneered in-line degassing, on-cartridge heating, and liquid storage solutions.
Our upcoming talk will explore these challenges and present solutions within specific use cases.

This study introduces an innovative approach to enhance the utilization of carbon fiber thermosetting composites in advanced structural engineering by addressing the challenges of high manufacturing costs and limited production rates. We develop, deploy and test an ML pipeline utilizing PIC-based sensors (SOI technology, 220 nm thick, fabricated at IMEC’s MPW). They are based on a Bragg structure, packaged using ball lenses and suitable for operating at 180 degrees Celsius and 5 bar pressure. The focus is on accurately predicting two crucial parameters: Cure time and Temperature Overshoot, vital for determining the process duration and part quality. Using advanced tools and sensors, this study achieves a high prediction accuracy of 98% in millisecond scale while effectively handling the outliers. The ML pipeline allows the real-time process optimization of manufacturing process, minimizing the cost, and providing insights into the quality of the composite part through the in-depth monitoring of the process.

This work presents the designs and performance of a series of photonic integrated interrogators developed and investigated for the last few years by the Eastern Europe Design Hub team at Warsaw University of Technology. The interrogators were designed to monitor dynamic signals from fiber Bragg gratings (FBGs) and manufactured in the generic indium phosphide (InP) platform. In particular, the main design assumptions will be presented, along with parameters initially proven by versatile electrical and optical characterization. Possible applications and further development of solutions for integrated photonic interrogators will also be discussed.

The Total Solar Irradiance (TSI) is the principal energy source that propels dynamic changes within the Earth's climate system over extended periods. The variability in TSI plays a crucial role in shaping the long-term evolution of the Earth's climate. This study proposes to introduce a novel approach to investigating irradiation and advancing our understanding of solar radiation's impact on Earth's climate by leveraging circuit technology. Photonic integrated circuits (PICs) represent a pioneering breakthrough in integrating multiple optical components. In contrast to existing sensor platforms, PICs offer increased sensitivity, less interference from electromagnetic noise in measurements, and a more compact design. Most instruments in space that measure TSI are called electrical substitution radiometers (ESR). An ESR usually includes an optical absorbing element connected to a reference heat sink. A poorly conducting thermal link keeps the heat sink at a constant temperature. The instrument compares the heating caused by the radiant power absorbed with the heating caused by an electrical current. The Brazilian National Institute for Space Research (INPE) has initiated a project to create solar observational tools as part of the global effort to comprehend the workings of the Sun and its impact on Earth. The Galileo Solar Space Telescope (GSST) comprises two instrument designs - Irradiance Monitor Module (IMM) - to accomplish the mission objectives. One of the tools is a small radiometer constructed on a photonic integrated circuit. The primary objective of this research is to mitigate instrument uncertainty and elevate temperature measurement techniques in radiometers. By exploring innovative ideas and approaches, this work seeks to redefine state-of-the-art temperature quantification within these crucial scientific instruments. A key aspect of our methodology involves implementing a new instrument design utilizing Mach Zehnder Interferometer (MZI) technology embedded in a substrate for temperature detection. Adopting photonic circuits is a pivotal strategy to provide a more direct and durable solution to temperature sensing. This approach is anticipated to minimize uncertainties associated with electronics and comparators. Here, we will showcase the results of the preliminary manufacturing outcomes. These findings contribute to the ongoing efforts to enhance temperature measurement techniques, aiming to deepen our comprehension of how the Sun's energy influences Earth. Through this work, we anticipate advancing the capabilities of radiometric instruments and paving the way for a more nuanced understanding of the Sun and climate dynamics.

Multiplexed structures out of dual optically coupled 4D microtoroids fabricated with two-photon polymerization and their biosensing-relevant application are discussed. We analyze the sensing performance for detection of small pH value alteration around the neutral solution for independent and optically coupled resonators that are characterized by appearance of the additional signal for back-radiated mode. We demonstrate detectability of pH value changes at the level of 0.01 by using the signal back-radiated mode in the coupled regime together with the material-enhanced spectral shift.

Optical sensing is of preeminent importance for a variety of applications: it can enable detection of harmful or desired contaminants, it can confirm that expected reactions have taken place and it can be used for quantitative analysis of the processes under study. We report on photonic crystal optical sensors based on disorder-induced light confinement in photonic crystal waveguides in silicon nitride, showing that we can make use of fabrication imperfections as a means to add functionalities to the fabricated devices. We prove their suitability for the detection of liquid contaminants at room temperature and investigate their response to refractive index changes. We also show that temperature can be used to tune and modify the quality factor of the cavity resonances, allowing local temperature sensing. Compared to engineered photonic crystal cavities, making use of disorder as a resource allows the spontaneous formation of tens of high-quality optical cavities in a fabricated device that does not require time-consuming optimizations or exact repeatability of the fabrication process - an important result in view of scalability of photonic crystal sensors.

Frequency comb cavity-enhanced spectroscopy has proven itself extremely useful in terms of the effective bandwidth, measurement precision, and acquisition speed. At the same time integrated solutions for both comb source and cavity, based on silicon nitride microring resonators, are readily available. The combination of these two factors motivates our work, in which we demonstrate comb-cavity coupling for cavity-enhanced wideband spectroscopy on-chip. By means of a thermo-optic tuning we can characterize the integrated dispersion of the microring resonator as well as to assess the corresponding lineshape data. THz Kerr frequency comb spanning up to 800 nm makes it possible to overperform industrial table-top tunable sources in terms of the available bandwidth, power consumption, and compactness. We analyze a simple model for the evaluation of the cavity-enhanced spectroscopy performance and perform numerical simulations, which are shown to be in a good agreement with experimental results. Our platform offers intriguing applications in diverse fields, spanning from biology and medicine to photonic design and fundamentals of light-matter interaction.

Optical and acoustic resonators utilizing distributed Bragg reflectors (DBRs) are pivotal in advancing technologies ranging from lasers to quantum computing. Under ideal, smooth interface conditions, the efficiency of these resonators, quantified by their quality factor, primarily depends on the number of DBR pairs. However inhomogeneities of the top layer thickness commonly occur, either during growth or fabrication, and is even more severe when a dielectric material is deposited on the top of the samples, for example in responsive resonators. This study analyzes the effects of such inhomogeneities on the resonators’ quality factor in the case where the nanometer-scale vertical dimension of the inhomogeneities is significantly shorter than their lateral scale. Employing an analytical approach, we demonstrate that such inhomogeneities can significantly degrade the quality factor, imposing a practical limit to its enhancement irrespective of the DBR pairs added.

Bovine brucellosis is an infectious illness caused mainly by Brucella abortus that may affect domestic and wild animals. Accurate and fast diagnosis is critical for disease control and eradication. Thus, we have identified Brucella abortus antibodies in bovine serum by exploring the agglutination process that is carried out when positive samples are mixed with the antigen. In this case, when placed above the on-chip integrated waveguide, the reaction led to scattering spots that indicated the positive serum. The monitoring was performed through optical images over time and analyzed by artificial neural network. Classification models were able to differentiate the positive from the negative samples with 81.25% accuracy. This work may represent a breakthrough for the diagnosis of other infectious diseases.

The sensing behavior of graphitic carbon nitride (g-C3N4) allotropes (fabricated with thermal polymerization of urea or melamine) by monitoring their laser induced room temperature photoluminescence (PL) emission, is demonstrated. Their sensing behavior has been investigated at oxygen atmosphere, upon optical excitation with a laser source (248 and 355 nm) in wavelength for different energy densities. The observed variability of PL features of g-C3N4, such as spectral intensity and wavelength, offers a systematic way to monitor the environmental changes in a reliable manner, opening the path for exploiting g-C3N4, as optical sensing material.

Milk is one of the most important dietary products thanks to its high nutritional value and economic importance, but unfortunately also among the top products facing food fraud and adulteration. Authentication of milk is therefore of high importance, motivating the development of novel sensing techniques. We present the use of broadband diffuse reflection spectroscopy (400 – 1700 nm) in combination with Linear Discriminant Analysis (LDA) to achieve a rapid and nondestructive milk identification. Two case-studies are presented: (1) the differentiation between whole/high fat cow milk, semi-skimmed cow milk and skimmed cow milk, and (2) the differentiation between semi-skimmed regular cow milk and lactose-free milk. Considering a hold-out test set, classification performances exceeding 99.8% were achieved. Consequently, we believe these presented results contribute to an improved milk classification, enhancing food quality monitoring.

Archaeometry research on historic glass primarily relies on understanding their chemical composition. Our research group has extensively used UV-Vis-NIR absorption spectroscopy in situ for this purpose over the past decades. Recent research targeted naturally coloured or decoloured window glass with a post-medieval signature from the Low Countries. We successfully distinguished two subgroups of High Lime-Low Alkali (HLLA or Ca-rich) glass associated with two distinct timeframes: the late 15th to mid-16th centuries and the latter half of the 16th to 17th centuries, respectively. This classification is based on selected optical metrics reflecting variations in ionic and atomic iron contents. The introduction of this innovative tool allows us to address a gap in literature concerning glass composition and consumption in the southern Low Countries spanning the 15th to 17th centuries. Given that recording a single measurement takes less than a minute, the tool facilitates the rapid screening of large collections of glass fragments. We present further validation of the technique by studying archaeological glass fragments from various excavation sites situated in Bree, Mechelen and Ronse. The research findings have confirmed the efficacy of the technique, as glass from different periods between the 15th and 17th centuries exhibited distinctive optical properties. Furthermore, the determined values of the associated optical metrics corresponded with those of previously researched sites, and additionally, they allowed us to fine-tune the mapping between glass purity, production technique and shape. This first-line approach might be beneficial for glass researchers seeking to analyse post-medieval naturally coloured glass using non-destructive methods.

Efficient and sustainable production, recovery and recycling phases of semiconductors (SC) life cycles require noninvasive, inline methods able to identify their composition in material streams. Ideally, the sensor system should be fast and incorporated into conveyor-belt operations. Rapid identification as well as spatial distribution maps would allow for real-time monitoring and quality control of the material stream. Considering these requirements, we suggest the sequential use of fast hyperspectral reflectance imaging (HSI) and Raman spectroscopic sensors for the identification of SC types in a sensor network configuration. We propose spectral proxies based on electronic properties derived from HSI-reflectance (i.e. absorption edge linked to the band gap values) and Raman sensors (i.e. Raman-active phonon modes) for SC identification. We identify potential limitations of each proxy on identifying undoped/doped SC materials, and discuss which process workflows enable optimized SC classification. We demonstrate the multi-sensor approach with SC standards (GaAs, GaSb, InP, 4H-SiC, and Borosilicate) which are relevant for both opto- and power-electronic devices, and showcase the potential of sequential data acquisition by fast HSI-reflectance sensors in the visible to shortwave-infrared (integration times: (4.5–18) ms) and Raman scattering (excitation laser: 532 nm, acquisition times: (0.5–10) s).

Black carbon (BC) pollution particles result from incomplete fossil fuel combustion and are formed by clusters of primary nanoparticles (NPs) with diameters around 50 nm. The impact of BC particles on the environment and climate rise depends on accurately determining optical properties, such as absorption and scattering. The shape and the internal molecular structure of primary NPs influence their optical behavior. In this study, we characterized NPs of genuine soot from direct air filtration, determining their optical properties in water droplets through two-photon microscopy. They are monodispersed in aqueous media, presenting 2P optical features associated with stable molecular aggregates of polycyclic aromatic hydrocarbons (PAHs). The single-photon (1P) spectroscopic features of stable suspensions of pollution NPs in aqueous solution (pH=13) are represented by an optical depth (OD) spectrum relative to the average scattering and absorption in the 300 - 900 nm wavelength range, proportional to λ^-n. The broad 1P absorption spectrum is fitted with three Gaussians. The first, between 500 and 700 nm, is associated with optical transitions between π states in J-type molecular arrangements. The second, between 350 and 450 nm, relates to H-type aggregates. The third could be attributed to optical transitions below 350 nm in weakly perturbed molecular aggregates. Two-photon absorption (2PA) measurements were conducted in the transmission mode of the two-photon microscopy with a tunable laser between 700 and 1050 nm in the infrared. Unlike 1P transitions, these 2P transitions are highly selective to the nature of the aggregates, confined to the spectral region associated with H-aggregated states.

The application of surface treatments to cork stoppers is presently a common practice in the wine industry, designed to achieve maximum performance and optimal costumer experience of premium products. Unfortunately, current coating techniques lack efficient process control tools, often resulting in faulty products being detected too late, already in use, compromising performance, product quality and mining consumer confidence. In this work a fully automated system equipped with machine vision and automatic feeding of corks, was coupled with an imaging LIBS setup and used to perform a benchmarking against conventional quality control methods. Results clearly demonstrate the capability of the new LIBS system to effectively evaluate in real time the quality of silicone-based surface coatings in cork stoppers, effectively working as a tool for process control providing a route for effective optimization.

We introduce an innovative optical measuring device based on laser-induced breakdown spectroscopy (LIBS) for the quantitative analysis of Li in geothermal brines for Li extraction. LIBS allows for contactless and in-situ monitoring of dissolved Li. In the presented technique, we induce plasma on the surface of artificial high saline fluids under flow. We utilize a nanosecond Nd: YAG laser operating at 1064 nm wavelength and 6 ns pulse duration. The pulse laser energy is set to 26 mJ. For each sample, we perform 50 shots at a repetition rate of 1 Hz, and the acquired spectra are averaged. By carefully selecting the parameters of our LIBS system, we achieve efficient measurements on the liquid surface, minimizing common phenomena such as splashes, bubbles, or ripples. We calibrate our system using artificial samples over a wide range from approximately 10 to 1300 mg/L, for Li I line at 670.8 nm, and we derive a calibration curve exhibiting an R² value of 99.98 %. Our LIBS implementation on the flowing fluid surface, coupled with the calibration of the system, enables accurate quantification of Li in high saline fluids with an average relative uncertainty of less than 2 %. The precision of our measurements, compared to reference values obtained using ICP-OES, is high, with a percentage difference ranging from 0.1 % to approximately 7 %. Our findings demonstrate precise quantification of Li in high saline fluids. The accurate Li monitoring achieved through our presented technique, combined with the fast, contactless, and in-situ capabilities of LIBS, paves the way for its integration into Li extraction facilities in geothermal power plants.

Prefabricated concrete has gained significant popularity in the construction industry due to advantages, including cost-effectiveness, reduced construction time, and enhanced quality control. The curing process of prefabricated concrete elements is particularly crucial as it determines their strength development, durability, and overall quality. In this study, we present our work on the development of an optical measurement system for the monitoring and optimization of the prefabrication process. We compare multispectral camera-based methods with the application of multispectral laser scanners in wavelength regions covering the visible and near infrared spectrum. Based on our measurements, we discuss the pros and cons of the investigated setups to monitor the surface of the concrete elements and capture precise and continuous data on the hydration. We further evaluate the possibility to derive process parameters from it such as the ideal time for dismantling of formwork. The remote monitoring capability of our proposed system could enable real-time data acquisition and analysis, allowing to improve the prefabrication process.

Spectral imaging allows seeing subtle optical signatures or light wavelengths invisible for human vision. This has potential for numerous industrial applications and a large economic impact by improving quality inspection, increasing automation and the development of innovative applications. However, current commercial spectral imaging systems (both hyper- and multi- spectral) are either too expensive or inflexible to be used in industrial applications (especially for SMEs: small and medium-sized enterprises), whereas standard RGB cameras are often difficult and mostly impractical to classify those materials that are looking visually similar (e.g., real and fake leaves with similar color, shape, texture). In this paper, we propose a new framework for a multispectral camera design. In particular, we first build a digital twin to simulate the output of a camera given a hardware configuration and details on the scene/object, then we optimize the wavelengths according to a specific application defined by end-users for the best filters selections. By combining the filters, illumination, sensor, lenses, pixel size, etc. from digital twin and optimization, we finally design a multispectral camera for the end-users, which provides a low-cost, flexible solution for industrial users. Experimental results from material classification show that our multispectral camera with 3 spectral bands can achieve similar performances as hyperspectral camera on real leaves separation.

Spectral imaging is a powerful technology that uses spatially referenced spectral signatures to create informative visual maps of sample surfaces that can reveal more than what conventional RGB-visual images can show. Indeed, different spectroscopy modalities can provide different information about the same sample: for instance, Laser- Induced Breakdown Spectroscopy (LIBS) imaging can detect the presence of specific elements on the surface, while Raman imaging can identify the molecular structures and compositions of the sample, both of which have potential applications in various industrial processes, from quality control to material sorting. In the path from science to technology, the increasing accessibility to such solutions and the strong market pull have opened a window of opportunity for innovative multimodal imaging solutions, where information from distinct sources is set to be combined in order to enhance the capabilities of the single modality system. However, the practical implementation of multimodal spectral imaging is still a challenge, despite its theoretical potential, and as such, it is yet to be achieved.

In this work, we will go over multimodal spectral knowledge distillation, a disruptive approach to multimodal spectral imaging techniques that tries to explore the combination of two techniques to capitalize on their individual strengths. In specific, this approach allows us to utilize one technique as an autonomous supervisor for the other, leveraging the higher degree of knowledge and interpretability of one of the techniques to increase the performance and transparency of the other. We present some example scenarios with LIBS and HSI and Raman spectroscopy and LIBS, discussing the impact of this new approach for scientific and technological applications.

The excitation-emission matrix (EEM) is a three-dimensional scan that investigates the fluorescence intensities at each excitation and emission wavelength combination to provide valuable information. During EEM, it is important to eliminate the effects of ambient light in real environments because it can interfere with the EEM measurements. The conventional method subtracts reflected ambient light intensities from the EEM signals. However, this method removes time-invariant components but not time-varying components. To solve this problem, we propose a novel dynamic lighting using the spread spectrum technique. In the proposed method, multiple wavelengths are modulated according to different orthogonal codes and simultaneously irradiated. The fluorescence intensities are then detected and, subsequently, the EEM is reconstructed based on the orthogonal codes. This proposed method allows us to not only eliminate the time-invariant components but also average the fluorescence intensities to suppress the time-varying components. We performed EEM measurements using a sample of nicotinamide adenine dinucleotide + hydrogen (NADH) irradiated with ambient light with a maximum illuminance of 1000 lx. We measured EEM using both our proposed method and the conventional method. We further calculated the correlation coefficient between the EEM under each ambient light intensity and the EEM measured by the conventional method under the condition of no ambient light as a reference. The results showed that the correlation coefficient of the proposed method was higher than that of the conventional method. Therefore, the proposed method is more effective than the conventional method at suppressing the ambient light effect.

The safeguarding of extra virgin olive oil (EVOO) quality throughout its shelf life is imperative due to its vulnerability to quality deterioration caused by auto-oxidation and photo-oxidation. This study investigates machine learning (ML) capabilities applied to fluorescence spectroscopy to detect the ageing of EVOO. The quality of EVOO was assessed by UV-absorption spectroscopy measurements as mandated by European Regulations. In parallel, excitation-emission matrices (EEMs) were measured to determine the predictive potential of ML approaches applied to fluorescence data. First, two excitation wavelengths (480 nm and 300 nm) are identified as exhibiting the maximum relative change in fluorescence intensity, serving as potential indicators of EVOO ageing. Then, ML algorithms were developed to predict olive oil quality based on highly aggregated spectral data at these excitation wavelengths. The algorithms successfully identify still good EVOOs from aged EVOOs with over 90% accuracy, proposing an innovative approach that foregoes the need for detailed chemical analysis. This work shows the potential of ML-based approaches applied to fluorescence to replace traditional, labourintensive analyses. Therefore, it paves the way for the development of a compact, field-deployable fluorescence sensing device for rapid and objective quality control in olive oil production and early detection of oxidation or adulteration, and aiding in the classification of olive oils for market purposes.

Scientific analysis of historic glass objects is needed to enhance our understanding of glass evolutions regarding sourcing and production technology and to distinguish fake from authentic. Furthermore, glass researchers often face the challenge of performing non-destructive analytical methods on site, using portable devices. A recent focus of our research concerned the identification of optical signatures to distinguish various post-medieval glass families. UV-Vis-NIR absorption spectroscopy has proven to be extremely useful as a non-destructive technique for discerning two subgroups of Ca-rich glass (High Lime-Low Alkali or HLLA glass). However, the period of production of Ca-rich glass coincides with other glass composition groups, a pre-requisite is to first exclude non-HLLA materials. To accomplish this, we developed a methodology utilising portable X-ray fluorescence spectrometry (p-XRF). Despite p-XRF’s limited detection capabilities for elements with low atomic numbers, we successfully demonstrated its usefulness as a screening technique for distinguishing different glass types, including Roman Na-rich glass, Industrial Na-rich, K-rich glass and Ca-rich glass. The main rationale behind clustering historic glasses into different chemical groups, is to use heavy elements as proxies for lighter ones. After designing an instrument-specific 3D-printed attachment, we calibrated the recorded p-XRF data against multiple glass standards. Subsequently, we verified the reliability of the empirical calibration for relevant glass major and trace elements by testing it on a sample set whose composition was determined using Laser Ablation Inductively Coupled Mass Spectroscopy (LA-ICP-MS). The defined workflow showed a strong correlation between the corrected p-XRF and the LA-ICP-MS data for the most important light elements (K, Ca, Mn, and Fe), and the heavier glass trace elements (Rb, Sr, and Zr) with R² values exceeding 0.95. Finally, we defined a flowchart based on the calibrated elements to identify the glass composition groups.

We demonstrated a novel technique to improve the signal to noise ratio (SNR) of the frequency domain interferogram of a dual comb spectroscopy (DCS) setup, by about 5 dB, based on temporal shifting of the interfering pulse trains in the two channels. The experimental setup utilized a fiber based DCS architecture where electro-optic modulators (EOM) were used to generate two optical combs (or pulse trains). Due to this carefully adjusted periodic phase shifts, the interferogram now was time-multiplexed, or in other words included a larger number of peaks within a given time frame compared to when an unshifted case. There is nevertheless a tradeoff between the peak power and the bandwidth of the interferogram comb envelope in the spectral domain. The experimental results were also confirmed numerically and a relationship between the SNR improvement and the rate of phase shifting was established. These results open new possibilities in SNR improvement of EOM based multidimensional spectroscopic techniques and provide a powerful resource to execute sparse sampling and other complex techniques to maximize the amount of useful information in the interferogram data of a DCS setup.

Diagnosis of SARS-CoV-2 infection allows for disease intervention, control, and management for COVID-19. The realtime reverse transcriptase-polymerase chain reaction (RT-PCR) is considered the gold standard used to detect the virus. Due to the high testing volumes, the process has a long turnaround time, i.e., 2-3 days. It also requires expensive equipment and involves highly trained staff. Other fast diagnostic methods, such as lateral flow assay based on antibody detection, have limitations such as lower specificity and sensitivity. Thus, there is a critical need for a rapid and low-cost point-ofcare (POC) diagnostic method to accurately diagnose SARS-CoV-2 infections in patients. In this study, three rapid, portable, and cost-effective methods to detect SARS-CoV-2 in human nasopharyngeal swab specimens are developed using surface enhanced Raman spectroscopy (SERS) and deep learning: RNA hybridization, ACE-2 capture, and direct detection. Combining the SERS spectra with a deep learning algorithm, all methods can achieve > 99% accuracy to classify the positive and negative specimens and the test-to-answer time is within 30 min. The RNA hybridization method can achieve a limit of detection of 1000 copies/ml, and the ACE-2 method is capable of differentiating between different variants of SARS-CoV-2 viruses. The direct detection method can additionally quantitatively predict the cycle threshold (Ct) value of RT-PCR tests for positive specimens, demonstrating a diagnostic accuracy of 99.04% in blind tests of 104 specimens. These results indicate that SERS combined with deep learning could be a potential rapid POC COVID-19 diagnostic platform.

Surface-Enhanced Raman spectroscopy (SERS) has proven its powerful ability to precisely characterize biological substances at very low concentrations down to a single molecule level without the use of a ligand or a specific biofunctionalization. SERS substrates are typically made of nanostructures coated with a thin metallic layer to produce plasmonic enhancement of the Raman scattered signal. A variety of nanofabrication technologies enable fast and economical prototyping of SERS substrates. In this work, SERS substrate fabrication based on twophoton polymerization (2PP), metrology, and biosensing characteristics are discussed. An array of polymerized nanopillars is coated by a 25 nm gold layer. Raman spectra of 1,2-bis-(4-pyridyl) ethylene (BPE) molecule at various concentrations are analyzed. The Asymmetric Least Squares (ALS) method is applied to subtract the baselines from the spectra. Next, the standard deviation (STD), enhancement factor (EF), and limit of detection (LOD) for each of the most prominent characteristic peaks of the analyte are calculated separately. The SERS substrates show up to 10⁶ Raman signal enhancement factor, comparable with commercial substrates. Moreover, the fabrication of 50 × 50 μm² SERS substrates based on the 2PP technique takes from less than a minute to 2 hours depending on the fabrication approach and aspect ratio requirements. The process is well-controlled and reproducible for reaching a uniform distribution of nanostructure arrays with an aspect ratio of up to 2.3. Finally, the SERS substrates can be used for a broader range of applications and the characterization of different molecules, like vitamins, bacteria, mycotoxins, and others.

Surface Enhanced Raman Spectroscopy, also known as SERS, is a technology that has developed into an effective, rapid, accurate, and sensitive method for the detection and analysis of any analyte at the ultra-trace level. This is accomplished by the enhancement of the Raman signal. The SERS technique allows for the identification of very low quantities of Raman-active analytes by coupling electromagnetic radiation at optical frequency. There is a substantial amount of interest in the study of the design and development of SERS-active substrates due to the fact that these substrates are of crucial importance in the process of Raman signal amplification. In this study, we have demonstrated EBL fabricated, Au-gratings nanopattern as SERS-active substrate for detection of Rhodamine 6G and a bio-molecule urea up to 10^-9 M concentration. NIR laser source (λ=785nm) was used to collect Raman spectra using Raman set up (Renishaw). As part of normal bodily functions, the liver releases urea into the blood, which is transported to the kidneys and expelled as urine, serving as a biomarker for kidney disease. Gas chromatography and calorimetry can detect low quantities of urea, but they are time-consuming and costly. As a low-cross-section molecule, urea can be adsorbed on noble-metal NPs and detected at trace amounts using SERS. There are a number of other possible applications for the SERS-active substrate that has been presented. These applications include in-situ detection of food and water adulterants, narco-analysis etc.

This paper presents a method for supporting wayfinding in crowded buildings using Visible Light Communication (VLC). Luminaires are repurposed to transmit encoded messages, providing location-based information to users. Tetra chromatic LEDs and OOK modulation efficiently transmit data, while error detection techniques ensure reliable transmission. Users carry receivers that interpret the light signals and perform localization calculations. Wayfinding algorithms guide users with turn-by-turn directions, landmarks, and alerts. The system integrates VLC into an edge/fog architecture, utilizing existing lighting infrastructure for efficient data processing and communication. It enables indoor navigation without GPS, demonstrating self-localization and optimizing routes. This method enhances accessibility and convenience in unfamiliar buildings

This paper analyses the mobility of Autonomous Guided Vehicles (AGV) in dense industrial environments. VLC technology is exploited to ensure data transmission among the infrastructures and the vehicles, providing X2X links. The VLC technology uses tetrachromatic qhite LEDs for simultaneous lighting and data transmission and dedicated pinpin photodiodes as receivers. Indoors positioning based on VLC methodology provides necessary information to enable guidance services. Coding schemes are designed for each VLC link.
The regulation of the AGVs flow along the lanes inside the warehouse is analyzed as the urban traffic flow using a SUMO urban mobility simulator. This tool is used to generate data related to the AGV movement, and a reinforcement learning scheme, combining agent-based modeling and VLC queuing/request/response behaviors, effectively schedules routes. This provides efficient travel and avoids crowded regions. The demonstration of this proof-of-concept is supported on the evaluation of travel time and traffic flows.

Mapping the environment is the basis for measurement tasks, planning processes, monitoring over time, and decision-making. Moreover, the inspection of engineering structures, roads and railways, water and wastewater, and energy infrastructure is essential to ensure safety and sustainability. For an automated mapping and inspection process, the use of robotic systems is indispensable. Due to the complexity of the environments of structures and the requirements for inspection, the automated operation requires real-time navigation, specific sensor solutions, and automated interpretation of the data. In this study, we present various concepts of autonomous measurement robotics for mapping and inspecting challenging environments and damage types. Robotic systems based on unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles are addressed for various scenarios including pipes, dams, bridges, tunnels, coastal areas from the air, and underwater structures. After identifying the demands and needs and presenting the state-of-the-art methods and systems and their identifying limitations, we present ways for an autonomous operation of robotic systems equipped with specific sensors for LiDAR-based subsurface damage detection of cavities or delaminations, underwater laser scanning, high-resolution bathymetry, and multispectral LiDAR for moisture detection. This includes real-time data interpretation, iterative inspection strategies, automated data interpretation, and the strategy for the integration of the human operator. In conclusion, this work gives an overview of existing methods and systems and their limitations for mapping and inspection tasks. Moreover, we show how advanced sensor systems can be used as part of autonomous robotic systems to overcome current limitations for high-quality mapping and inspection of challenging environments and structures.

Unlike robots, humans have no problem working hand in hand. However, when robots try to perform tasks that require precision, they always face a major problem: they are not precise enough in combination. This problem is especially relevant when the application requires high precision. One of the main reasons for this is that the robots usually do not know each other’s position or have an absolute calibration of the common coordinate system with the required accuracy. We will present our approach to introduce precise alignment procedures in a robotic cell using optically assisted methods. Subsequent analysis of the acquired data by tailored optimization algorithms provide an accurate and absolute coordinate system for a robot ensemble. The accuracy is typically limited by the workspace environment. Mostly acoustic vibrations will define the lower limit of the absolute precision to retrieve a global coordinate system within the working environment. In this work we present an analysis of common limitations as well as an algorithmic procedure to retrieve a global orthonormal basis for the robotic workcell, independent of the number of robots in the ensemble. As an intermediate result of our ongoing research, we can demonstrate a repeatable adjustment accuracy of less than 100μm euclidean distance from a common center.

To create a digital twin of a city, all relevant objects, within the city, must be mapped and labelled accordingly. In order to cover the entire city, vehicles and aircraft are used for the mapping task. While airborne mapping can cover areas that are impossible for a vehicle to reach, terrestrial mapping provides a different perspective containing further necessary information about the city. The two datasets are registered using reference points, fused together, and processed so that semantic segmentation can be performed by a neural network. The segmented point cloud can then be exported to create a 3D digital twin of the city. This work presents a process for the construction of a 3D digital city model from airborne and terrestrial mapping data.

In this study, we present the results of computer simulations of heat propagation and noise determination in a micron-sized three-layer thermoelectric detection pixel. The investigation included the absorption of single photons with energies ranging from 0.8 to 7.1 eV in absorbers of varying thicknesses to ensure high absorption efficiency. We examined temporal temperature dependencies in various regions of the detection pixel and evaluated the gradient of the average temperature on the sensor boundaries. The analysis also involved determination of the signal power resulting from photon absorption, the equivalent noise power, and the signal-to-noise ratio.
The findings demonstrated that a detection pixel equipped with either a W or Mo absorber, a Mo heat sink, with surface dimensions of 1 μm × 1 μm, and nanometer-scale layer thicknesses, can reliably detect single photons with energies ranging from 1.65 eV to 7.1 eV. The advantages of thermoelectric detection pixel are a very simple design and absence of stringent requirements on the operating temperature. These characteristics open up broad prospects for their use in large arrays.

Sensing of weak photon fluxes in the short to mid-wave infrared is important for a variety of applications such as optical communication systems, light detection and ranging (LiDAR) and remote gas sensing. For the most demanding applications, avalanche photodiodes (APDs) are regularly employed due to the enhanced sensitivity afforded by their internal avalanche gain. Indium Arsenide (InAs) is a material which exhibits near ideal avalanche multiplication properties and is capable of detecting infrared radiation up to 3 μm at 77 K. Due to exclusive multiplication of electrons, it exhibits incredibly low excess noise factors below 2, regardless of the magnitude of avalanche gain. Furthermore, unlike most APD technologies, its bandwidth is not limited by its avalanche gain, allowing it to operate at high speeds with high gains. Using our recently developed planar process, we report InAs avalanche photodiodes which exhibit high gains in excess of 100 and external quantum efficiencies at 1550 nm of 56 %. Our liquid nitrogen cooled detectors are combined with a low noise current amplifier and the performance of the system is analyzed. Detection of weak 1550 nm laser pulses corresponding to <70 photons per pulse is demonstrated. The detector's noise current is shown to be background limited, hence, detection of lower optical powers could be achieved through further set-up optimization.

There is an increased demand for low noise avalanche photodiodes (APDs) for infrared wavelengths at 1550 nm for long range Light Detection and Ranging applications. Here we present two classes of APD that produce high avalanche gain but with extremely low excess noise factors, F ~ 2. InAs APDs show F < 2 and offer detection wavelength up to 3500 nm, although this drops to ~3000 nm when cooled. For reducing effects of scattering in atmosphere, InAs could be an attractive option. In addition InAs APDs are based on a simple homojunction design, which is relatively easy to grow epitaxially. AlGaAsSb when combined with InGaAs, provides a direct replacement for the traditional InGaAs/InP APDs. It is therefore capable of room temperature performance with excess noise performance similar to Si APDs but operates at 1550 nm. We will present results that show noise equivalent power as low as 69 fW/Hz^0.5.

Single-photon avalanche diode (SPAD) sensors are versatile candidates for applications in low-light imaging and scenarios where high temporal resolution is crucial, like quantum imaging, fluorescence lifetime imaging, and (direct) time-of-flight methods. We demonstrate the improvement in light sensitivity by a factor of 7× for LiDAR (Light Detection and Ranging) by molding application-specific filling factor enhancing microlenses directly onto backside-illuminated SPADs. An 8"- wafer-level process is presented utilizing a mask aligner device for selective UV-curing of highly transparent polymer lenslets only in areas where SPADs are located and rinse uncured material from areas being compatible with postprocessing steps like chip dicing and electrical bonding. In addition to the optical benefits of chip-integrated lenslets, advantages arise from less system integration efforts of separately realized microlenses, especially with respect to tolerance conditions.

Traditional optical metrology techniques face significant challenges when applied to biological specimens, largely due to the specimens' inherent transparency. This often results in inadequate imaging and compromised measurement accuracy. To circumvent these challenges, confocal microscopy has become the preferred method for metrological analysis of such samples. However, it is important to note that confocal microscopy is fundamentally a point -based technique, which inherently limits its field of view (FOV). In this paper, we explore the application of a depth-from-focus method for the 3D mapping of biological tissues in a wide FOV. Although this technique is well-established for reflective and absorbent samples, its use has been limited with transparent, and specifically biological, samples. The lack of critical surface texture in transparent samples hinders the acquisition of the necessary imaging detail when using traditional transillumination or epi-illumination systems. Additionally, conventional high-resolution depth-from-focus systems typically employ microscope objectives that yield a narrow depth of field however limiting the FOV due to high magnification. We address these challenges by introducing two key innovations. First, we implement a ring LED illumination system to enhance the clarity of the sample surface image. Second, we substitute the microscope objective with a photographic lens paired with a larger sensor to expand the FOV. These adaptations, along with a developed focus estimation algorithm, enable high-resolution 3D mapping of biological tissues, overcoming the limitations of existing methods.

The effects of pulsed In-band IR OPO radiations on a thermal matrix imager are investigated using a versatile optical setup permitting different OPO beam sizes and where the imager is used without its native imaging lens. Dazzling effects are examined whether at fixed integration time and various radiant exposures or at fixed radiant power and variable integration time. There is evidence for the mitigation of the dazzling effect with increasing both the radiant power and the integration time. The nature of the scene, its contours, its contrast ratio, its aspect ratio are of major relevance in the dazzling process. Our results indicate that the IR imager enters a nonlinear regime when submitted to IR radiations and toggles to self-protection regime.

Measurement of the far-field parameters of lasers after atmospheric transmission plays an important role in accurately evaluating the atmospheric transmission characteristics of lasers and the overall performance of laser systems. The spot size formed by the laser after long-distance transmission can reach the meter scale, which cannot be measured by the traditional camera due to the limitation of field of view and resolution. In this paper, a measurement far-field laser system for large-size far-field lasers is designed by the detector array method. The following parts are mainly studied, first, the design of the acquisition circuit of the detector array, second, the development of FPGA for data acquisition and network data transmission, and third, the development of the host computer software to complete the remote control, receiving data, image restoration and parameter measurement work.

The acquisition circuit is based on InGaAs photodetectors, operational amplifiers and analog-to-digital converters to design hardware circuits; Xilinx's Artix-7 series xc7a50t chip as the core to complete the multi-channel parallel acquisition and network data transmission based on the W5300 network chip; the upper computer software is based on the MFC platform for the development of the software, and the proposed image restoration algorithm based on the predicted gradient. The upper computer software is developed based on MFC platform, and the image recovery algorithm based on predicted gradient is proposed. Experimental tests show that the hardware circuit works stably, the communication reliability is high, and the data acquisition and transmission frequency reach 100Hz according to the design requirements.

Energy consumption has increased exponentially due to population growth leading to an increasing impact on natural resources. Green hydrogen (H2) offers a safer alternative to fossil fuels, making it a promising alternative for sustainable energy consumption. However, due to H₂'s flammability it is crucial to monitor its concentrations in the environment. Optical sensors have been developed to monitor H₂ concentrations in harsh environments with high sensitivity and remote measurement. In this work, a numerical study and experimental validation of an optical fiber sensor based on Surface Plasmon Resonance (SPR) for H₂ detection are presented. This sensor is composed of a multi-mode fiber with a SPR structure of a metal/dielectric/Pd, where the Pd acts as a sensitive layer. The plasmonic active materials studied are Ag and Au, while TiO₂ and SiO₂ are used as dielectrics, finding that the metal materials have more impact on the SPR band definition, while the dielectric layers have an impact on the band spectral position. The optimized configuration with 25nm/60nm/3nm of Au/TiO₂/Pd was experimentally developed, obtaining a wavelength shift of 19nm for 2kPa of H2, validating the numerical results, and confirming the possibility of using this type of system for H₂ detection.

The pursuit of highly sensitive yet cost-effective technologies has intensified in the realm of optical sensors. This paper presents an innovative approach to refractive index measurement by employing a polished perfluorinated polymer optical fiber sensor. Leveraging the distinct optical properties of speckle patterns formed on the fiber's end-face, this sensor accurately discerns variations in refractive index within its surrounding media. The specific material properties of the fiber render it particularly suited for liquid analysis. The proposed sensors were successfully evaluated for refractive index values up to 1.511, developing robust calibration curves in the process. This research introduces a promising avenue for precise and efficient refractive index measurements, demonstrating the potential of speckle pattern-based perfluorinated polymer optical fiber sensors in diverse applications requiring meticulous liquid analysis.

This work aims to develop polymer-based optical micro-resonator sensors, operating in the visible range and sensitive for homogeneous in-situ detection of pollutants in aqueous medium. This paper demonstrates that using a porous silica cladding (ns = 1.2) enhances significantly the interaction of the evanescent field with the analytes by modifying the propagation properties of the guided optical mode. The results improve sensitivity without complicating the design and avoiding surface chemical functionalization classically used for such application. Detection experiments based on real part refractive index change in the visible range have been conducted using different glucose concentrations. A sensitivity at the state-of-the-art of 255 ± 12 nm/RIU has been achieved at 760 nm for micro-resonator polymer waveguides on porous silica. These promising results enable the use of our devices in sensors to detect both real and imaginary parts of the analyzed medium refractive index, as well as analysis of complex environments.

This work presents the development and application of high-birefringent optical fiber sensors for the internal monitoring of Li-ion pouch cells during their operation. The instrumented optical fiber sensing configuration simultaneously discriminates and tracks temperature and strain shifts at different and consecutive cycling operation conditions which includes a Worldwide Harmonised Light Vehicle Test Procedure. A correlation of the strain data sensed by the optical fiber sensors with the Differential Voltage Analysis of the cell is performed. This study is integrated into the INSTABAT EU-project, and it demonstrates the potential of this advanced technology based on optical fiber in the field of Li-ion cell monitoring, making a substantial contribution to the development of more sustainable energy solutions by improving their safety issues and evaluating their performance in real-time.

We demonstrate an method based on a unique sinusoidally-shaped phased grating for efficient and nearly alignment free detection of both signs and the modulus of the orbital angular momentum (OAM) of light. The OAM detection efficiency is almost same over the whole grating area. The capability and robustness of this method is demonstrated by detection of the optical vortices with OAM topological charge up to 150 using a reflective phase-only liquid crystal on silicon spatial light modulator (SLM).

The research focuses on metal hydride-coated Tilted Fiber Bragg Grating (TFBG) sensors for hydrogen detection. To overcome hysteresis issues linked to traditional sensing materials like palladium, tantalum (Ta) is introduced as an innovative alternative. The study details the establishment of optical constants for these novel materials via ellipsometry, involving the deposition of a nanometer-scale metal stack on a glass substrate. This thin film stack serves as the adhesion, sensing, and capping layer, enabling the selective absorption and desorption of hydrogen molecules, resulting in discernible changes in optical properties. The research includes comprehensive optical constant data, such as the complex refractive index, derived from ellipsometry measurements. Furthermore, the study incorporates these optical constants into a numerical model, examining mode propagation in TFBG sensors coated with Ta-based hydrogen-sensing materials. It explores mode coupling phenomena, characterizes transverse modes, and delves into key parameters, including electric field profiles, mode field diameters, and grating parameters essential for optimizing sensor performance.

This paper introduces Visible Light Communication (VLC) as an integrated approach to improving traffic signal efficiency and vehicle trajectory management at urban intersections. By combining VLC localization services with learning-based traffic signal control, a multi-intersection traffic control system is proposed. VLC utilizes light communication between connected vehicles and infrastructure, enabling joint transmission and data collection via mobile optical receivers. Atmospheric conditions affecting communication quality are considered, with an analysis of outdoor coverage maps. The system aims to reduce waiting times for pedestrians and vehicles while enhancing overall traffic safety. Flexible and adaptive, it accommodates diverse traffic movements during multiple signal phases. Cooperative mechanisms, transmission ranges, and queue/response interactions balance traffic flow between intersections, improving road network performance. Evaluated using the SUMO urban mobility simulator, the multi-intersection scenario demonstrates reduced waiting and travel times for both vehicles and pedestrians. A reinforcement learning scheme, based on VLC queuing/response behaviors, optimally schedules traffic signals. Agents at each intersection control traffic lights using VLC-ready vehicles' communication, calculating strategies to enhance flow and communicate with each other for overall optimization. The decentralized and scalable nature of the proposed approach, particularly for multi-intersection scenarios, is discussed, showcasing its potential applicability in real-world traffic scenarios.

This study proposes an optical fiber sensor with a single mode - no-core - single mode (SNS) structure. The sensor employs zinc oxide (ZnO) as a sensing layer coated over the no-core fiber (NCF) region. ZnO is introduced to absorb volatile organic compound (VOC) biomarkers of diabetes, utilizing an evanescent sensing mechanism. The ZnO colloidal solution used for this purpose was synthesized through aqueous chemical growth. A cost-effective airbrush was used for coating a thin layer of ZnO over the NCF. Several material characterizations, including FE-SEM, UV-Vis, and XRD, were conducted to validate the presence of the ZnO layer over the NCF. The study investigates VOC biomarkers for diabetes, such as acetone, isopropanol, and ethanol, with 100-500 ppm concentrations. These VOCs were chosen because they belong to the same class of alcohol found in the exhaled breath of individuals with diabetes. Notably, the optical fiber sensor exhibited the highest sensitivity to acetone, with a sensitivity of 0.0028 nm/ppm. In addition, the sensor demonstrated selectivity to acetone, highlighting the significant role played by the morphology of the ZnO layer. The results presented in this study suggest that the proposed sensor has the potential for real-time and non-invasive monitoring of acetone vapor, a biomarker in exhaled breath for diabetes. Its advantages include cost-effectiveness and simple fabrication processes, making it a promising tool for diabetes management and diagnosis.

C-reactive protein (CRP) is a protein made by the liver in response to inflammation anywhere in the body. Early and accurate detection of CRP levels is essential for diagnosing various diseases. The proposed method uses a spectroscopy to analyze urine samples and machine learning to classify them as infected or non-infected based on CRP levels. Three machine learning models were employed: Extra Trees, Random Forest, XGBoost, K-Nearest Neighbors and Decision Tree. These models aimed to classify urine samples into two categories: infected (CRP level above 10⁻⁴ μg/mL) and non-infected (CRP level below or equal 10⁻⁴ μg/mL). The accuracy of the best model, Extra Trees is up to 68%. This method has the potential for faster and more user-friendly CRP detection compared to traditional methods.

Sepsis and cancer are some of the causes of morbidity and mortality in hospitals. Prompt detection and administration of the appropriate drug targeting the correct causative agent increases the chance of patient survival. The study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis and urinary changes suggestive of bladder cancer. The method comprises spectra of spectroscopy measurement of patients' urine samples with: urinary tract infection, urosepsis and bladder cancer. To provide reliable classification of results assistance of 27 algorithms were tested. We proved that is possible to obtain up to 95% accuracy of the measurement method with the use of machine learning. The method was validated on urine samples from 93 patients. The advantages of the proposed solution are the simplicity of the sensor, mobility, versatility, and low cost of the test.

This article deals with the issue of fire safety monitoring of wooden buildings. Wooden buildings currently represent a quick and relatively inexpensive option for acquiring a family house. However, with regard to the building material and its flammability, the construction possibilities are strongly regulated by standards. The aim of this paper is to describe the possibilities of monitoring temperature changes during a simulated fire using optical fiber and a distributed temperature measurement system (DTS). The DTS system uses the principle of stimulated Raman scattering, which allows longitudinal temperature measurements with a spatial resolution of 1 m. The increase in spatial resolution was achieved by winding the optical fiber into 5 cm diameter rings, with a fiber length of 3 m in each ring. The measuring rings of optical fiber were attached to fabric arranged in a rectangular grid. This fabric with the rings was then placed at the surveyed locations in the construction panels. During the temperature loading of the test samples with the gas torch, temperature monitoring was carried out both on the reverse side of the samples and in the inner layers. The results showed that this system with conventional multimode optical fibers can measure temperatures ranging from 20 °C to approximately 500 °C. This method offers the possibility to accurately monitor the temperatures of wooden buildings, including the inner layers of the building panels. The proposed method can therefore be used to thoroughly verify the fire safety of wooden buildings and their components.

We report on the realization of novel instruments for absolute vapor pressure measurements: (i) a novel vacuum system in which the saturation vapor pressure of low-volatile substances can be measured directly and accurately, (ii) optomechanical “membrane sandwich” squeeze film pressure sensors with enhanced pressure response and pressure sensitivities on a par with those of the best commercial capacitive diaphragm sensors.

This publication aims to describe the design, implementation, and verification of a fiber-optic wrist sensor based on the Bragg grating (FBG) for monitoring the heart rate (HR) of the human body. One of our primary goals was also to create a sensor suitable for measurements in harsh environments such as magnetic resonance imaging (MRI). For this reason, we used polylactic acid (PLA) material for the encapsulation of Bragg grating itself. This combination results in a sensor solution that is resistant to electromagnetic interference (EMI). Based on their previous agreement, the sensor has been properly and systematically tested based on a group of six volunteers of one sex. The acquired data were then processed and evaluated, using the absolute error (AE), relative error (RE), and its mean. The outcome of this study indicated an RE of 9.07 %, implying promising results and the first step of this study.

Here we investigate the improvement of the precision of measurement means to determine if it is possible to have sufficient sensitivity for detecting effects of elementary particles which would be characteristic of dark matter. A particle has been proposed and is called axion. There would be an interaction between the axions and the photons using the Primakoff effect under strong magnetic field. Radio frequencies in the range of 460 – 810 MHz could be assumed to be enough suitable for the mass of the axion, in case it could exist. It is then interesting to focus on the piezoaxionic effect. If the frequency of the axions could match the natural frequency of a normal mode bulk acoustic of a piezoelectric crystal, one would expect the piezoaxionic effect to occur. One could then rely on the piezoelectric effect to observe the variations on the resonant frequency which can be read out electrically using the best piezoelectric materials such as quartz. Through this example of development and applications in detection, we propose to decrypt this subject and to show how multidisciplinary skills are necessary to hope that small fluctuations can be detectable.

In this study, we demonstrate a multicore fiber interferometer applied for insulin biosensing. This interferometer was produced by splicing a segment of a multicore fiber (MCF) fused to a standard single-mode fiber (SMF). Our investigation depicted that the interferometer's response to changes in refractive index (RI) was influenced by polarization when a thin gold layer was deposited at the end facet of the multicore fiber (MCF). The most significant sensitivity to RI was achieved when using P-polarized light with a ~10 nm-thick gold-coated sample. This gold-coated interferometer was subsequently functionalized for biosensing purposes and employed in the detection of insulin, successfully identifying insulin at a concentration level of 10−8 g/ml.

In this work, we introduce a new concept for an in-line optical fiber temperature and force sensor, which are based on two cascaded Fabry-Perot cavities and the Vernier effect. The sensing cavity is composed of the UV glue, while the reference cavity is composed of the silica fiber. Due to the high thermo-optic coefficient and low Young's modulus of the UV glue in the sensing cavity, this sensor can be used for temperature and force measurements. The obtained temperature and force sensitivity are 8.0 nm/°C and ‒5.44×10⁴ nm/N, respectively.

Measuring flour quality is crucial for consistently achieving high-quality results in baking and food production. It empowers bakers and food manufacturers to make decisions regarding ingredient selection, adjust recipes for optimal performance, and meet consumer expectations. The advantage of optical spectroscopy in assessing flour quality lies in its non-destructive nature, speed, and the capability to analyze multiple components simultaneously without requiring any sample preparation.

We conducted an examination of 46 commercially available flours of various types, including cereals, legumes, tubers, and others. Consequently, their nutraceutical composition varied. For spectroscopic measurements in the 850-1700 nm range, we employed the SpectraPodTM pocket-size spectral sensor. Subsequently, we utilized chemometrics to integrate the measured spectra with the nutraceutical data provided on the flour packaging. Chemometric processing of spectroscopic data enabled us to differentiate the samples based on low and high concentrations of carbohydrates and proteins, with cutoff levels set at 30 g/hg for carbohydrates and 16 g/hg for proteins, achieving an accuracy of 93.5% and 82.6%, respectively.

In the "MiniSpectral" project at Jade University of Applied Sciences Wilhelmshaven, a polymer-based miniaturized spectrometer is being developed. Utilizing advanced injection molding techniques, the spectrometer incorporates a polymer dome as its foundational structure. Integrated onto the dome's surface is a concave diffraction grating, dispersing incident light into individual wavelengths. The relationship between the dome and grating exhibits a generalized Rowland circle radius ratio in three-dimensional space, facilitating a lens effect for focused detection and analysis. To model the optical configuration, Zemax raytracing simulations are employed, complemented by wave optics simulations utilizing Rigorous Coupled-Wave Analysis (RCWA) implemented through Dynamic Link Libraries (DLLs) in Zemax. These simulations play a crucial role in optimizing the spectrometer's performance.

The early detection of different diseases is crucial for their successful treatment. Existing literature has established distinct volatile organic compound (VOC) profiles in organic samples that are associated with different disease conditions. These identified profiles hold significant potential for the development of rapid, non-invasive, and cost-effective disease screening tools, meeting the essential criteria for effective diagnostic measures.

2-propanol is a secondary alcohol that exhibits different associations with a variety of diseases across diverse organic samples. Notably, elevated levels of 2-propanol in urine samples have been linked to type II diabetes, whereas a decrease in its concentration is observed in cases of malignant biliary strictures. In feces, an increase in 2-propanol is associated with colorectal cancer. Furthermore, disturbances in 2-propanol levels in breath have been correlated with various types of cancers. These findings underscore the potential utility of 2-propanol as a biomarker for disease detection across multiple biological matrices.

In this study, we propose a sensor that combines a multimode interference structure with a molecularly imprinted polymer (MIP) functionalization. The multimode sensor is created by fusion splicing a coreless fiber section to a single-mode fiber (SMF). For coating the coreless fiber (CSF), the dip coating technique was utilized, employing a MIP with 2-propanol as the template, and separately, the corresponding non-imprinted polymer (NIP). Our findings reveal that the MIP demonstrates specificity for 2-propanol in the gas phase, showing a sensitivity of 13 pm/wt.% 2-propanol. In contrast, the corresponding NIP lacks sensitivity to 2-propanol. This outcome emphasizes the potential of our proposed sensor design for the selective detection of 2-propanol, thereby highlighting its applicability in gas phase analyses.

Annealings have been performed on Ge0.84Sn0.16 microstructures in order to improve their optical properties by reducing the number of defects at the origin of Sn segregation. An enhancement of the photoluminescence intensity by a factor of 2.4 for annealed microstructures compared to ones without annealing was for instance achieved. Different annealing temperatures were tested to limit Sn segregation above the epitaxy temperature.

Elaboration of topographic maps from Mobile Laser Scanner (MLS) point clouds is a long and tedious manual task. This study presents a method that automates the underlying vectorization process. We propose a road marking vectorization method that can operate on road urban areas acquired from a MLS. The presented technique is based on a bottom-up contour-based technique incorporating prior knowledge at different stages. Our experiment shows that the use of ”road logic” is a key element in the success of the detection and vectorization process.

Hollow-core fibers (HCF) are increasingly being studied and evaluated for telecommunication, as well as sensing application. They seem to have many advantages compare to standard single-mode fibers (SMF), especially in the area of the cyber-security of optical fiber infrastructures and also information they carrying. In our research we mainly focus on cyber-security issues, especially acoustic sensing. In the paper we evaluate sensitivity of negative curve HCF using a Mach–Zehnder interferometry (MZI). Results are compared with SMF (G.657 in this case). Both fibers were in primary coating with similar length. Sensitivity measurements were conducted within the controlled environment of an anechoic chamber. Results show that both fibers are sensitive to acoustic vibration and with post-processing method based on de-noising of the measured signal, the sensitivity can be improved. The clarity of the captured speech signal was assessed using the Speech Transmission Index for Public Address (STIPA).

Bearings are omnipresent in mechatronic systems such as motors and drive trains, as they are the interface between rolling and stationary components. Bearings in critical machinery are continuously monitored to detect faults in an early stage and avoid system damage and downtime. This condition monitoring is typically done with electrical accelerometers or microphone arrays. Optical fiber Bragg grating-based (FBG) sensors are ideal candidates to overcome the shortcomings of their electronic counterparts as they are thin and lightweight, immune to EMI and crosstalk, and have multiplexing capabilities. This implies that a multitude of sensors can be implemented directly on the bearing.

In this work, we report on the quantitative investigation of the repeatability and reliability of FBG-based bearing fault detection. We document the influence of the installation of the fiber on the bearing on the acquired strain signals, and the impact thereof on the selection of an adequate damage indicator. To the best of our knowledge, this is the first time that such an extensive study has been carried out in view of quantifying the repeatability and reliability of FBG-based fault detection in bearings.

In this work, we propose an optical fiber sensor based on a silica capillary section spliced between two sections of single mode fiber. One of the fusion splices is done with a transversal offset, to enable the creation of a Mach-Zehnder interferometer (MZI). Furthermore, the structure, monitored in transmission, also exhibits anti-resonant behavior. This effect is inhibited by submerging the sensor in liquid media, and the MZI becomes dominant. The sensor with a length of 3.75 mm presented a linear response to temperature with a sensitivity of 26.0 pm/°C. Combining this sensor with another with a length of 3.82 mm, in a parallel configuration where both sensors are placed under water, an interferometric pattern with a higher frequency modulated by a lower one is attained, which is produced by the Vernier effect. The sensor, although insensitive to refractive index variations, showed a maximum temperature sensitivity of 2.193 nm/°C, showcasing a magnification factor of 84.3. This sensor may find applications in different fields where an accurate measurement of temperature in liquid media is required, such as chemistry, pharmaceutical, and biological applications.

A growing human population and changing climatic conditions have made food safety a pressing issue to our society. While conventional methods for their analysis are accurate, sensitive, and selective, they are costly, time-consuming and require skilled technicians. Optical fibre sensors have emerged as a versatile alternative for the detection of these compounds with advantages over traditional methods. In this work, we propose a high-sensitivity multimode fibre sensor for RI measurement. Our sensor consists of a section of capillary tube spliced between two sections of SMF and was characterised in regard to its response to variations in RI in a range between 1.3329 and 1.3459 RIU. The resulting spectrum presented several frequencies and the measurements performed at 1558 nm provided a sensitivity of 423.6 nm/RIU, with a resolution of 9.71 x 10^‑5 RIU. Further analysis of the spectrum using a low pass filter with a cutoff frequency of 0.06 nm^-1 provided a maximum sensitivity of 1459.4 nm/RIU, with a resolution of 3.33 x 10^-4 RIU.

This paper introduces a novel approach to high-throughput moving target detection using reservoir computing, which is both proposed and experimentally demonstrated. The implementation involves utilizing a Digital Micromirror Device (DMD) to introduce selected patterns for spatial encoding. During the target detection stage, optical line scan is employed, and a single-pixel detector collects the transmitted signal. Reservoir Computing (RC) is employed to do target classification. Experimental results reveal that shapes of the moving objects can be effectively classified.

Circular economy policies and recycling play a pivotal role in fostering sustainable models for the wood industry capable of reducing the environmental impact of our consumption patterns. The production of Particleboard is a good example of industry that uses high quantities of recycled wood. However, it poses risks since wood often have contaminants that compromise compliance of safety standards. Thus, it is necessary to develop methodologies for rapid analysis of chemical contaminants in wood wastes that allow easy detection of these elements.

In this work, the capability of Laser-induced breakdown spectroscopy (LIBS) to detect a set of heavy metals in wood samples was explored. Some advantages of this technique, such as portability, minimal to no sample preparation, and quick analysis are characteristics that make this method one of the most suitable for this purpose of analysis.

In the majority of cases, the contamination comes from the pigments used in paints, varnishes, or coatings. Titanium (Ti) e.g. is a common element in white pigments and Chromium (Cr) in red and green pigments. To ensure the presence or absence of Cr and Ti, a set of 3 lines was analysed. The results revealed the presence of these elements and that 30% of the samples seem to be highly contaminated. The LIBS technique proved to be a powerful methodogy for decision-making purposes.

To meet the increasingly miniaturized and precise monitoring demands, this paper proposed a compact Fiber Bragg Grating (FBG) accelerometer based on a flexible spring. The designed FBG accelerometer retains the compact structure and good lateral anti-interference capability observed in diaphragm type FBG accelerometers, while significantly enhancing the sensitivity. Firstly, the 3D model and the dynamic model of the accelerometer was designed and constructed. Subsequently, theoretical values for accelerometer sensitivity and resonant frequency were derived. Then, finite element modeling (FEM) was employed to investigate the measurement performance of the accelerometer. Finally, a vibration calibration platform was established to determine the actual operational performance of the accelerometer through a series of acceleration excitation and frequency sweep experiments.

Two-dimensional metal-organic frameworks (MOFs) demonstrate the advantages of organo-inorganic nature such as operation efficiency, and stability for optical sensing. For achieving high selectivity and operational performance optical properties of MOFs nanosheets can be varied by choosing the metal centers and the linking ligands. According to 2D nature, the material could be exfoliated up to monolayer height, which drastically increases the effective surface area of the sensor. The existing challenge is to preserve the lateral size of the membrane while reducing the height of the material. In this work, we have overcome the described challenge and obtained high-quality 2D MOF layers with a record for MOF aspect ratio (21300:1) and demonstrated unique optical sensitivity to solvents of varied polarity. Developed method of membrane fabrication opens the way to produce scalable and freestanding 2D MOF-based atomically thin chemo-optical sensors by an industry-oriented approach.

In this paper we investigate the use of photopolymer material as the sensor medium. This research will focus on the creation of self-written waveguides within the photopolymer and the interaction with the environment. This SWW can be used to measure direction of propagation and angle of incidence upon the polymer material. Under environmental force new SWWs can be measured and recorded within the PVA/AA on a 3D plane. These newly created SWWs, from numerical modelling, can represent the interaction with the surrounding environment. This can be used to measure force and direction of movement.

The research on frequency measurement for the designed resonator used to detect infrared radiation by means of tracking the change in resonance frequency of the resonator with temperature attributed to the infrared radiation from targets is presented in this paper. The research provides the basis for the integrated design of the uncooled infrared MEMS resonance structure and its measuring circuits.

Indoor organic photovoltaic (OPV) cells present a promising technology to fuel smart applications due to their thin, lightweight, and flexible nature, rendering them ideal for wearable and implantable devices. Moreover, they can be manufactured through cost-effective and accessible methods, making them an appealing choice for large-scale production. OPV have the potential to revolutionize the realm of medical applications by providing a dependable and sustainable power source for wearable devices. Using OPVs in indoor settings offers the potential to capture clean energy from easily accessible for indoor light sources, to power self-sufficient devices, improve the visual appeal of designs, support sustainability initiatives, and lower the energy expenses. These indoor organic cells are capable of achieving remarkably high power conversion efficiencies (PCEs), which are fairly comparable with the typical efficiencies of commercial single junction indoor silicon photovoltaic cells, which usually hover around 26%. Notably, indoor OPV cells are seamlessly integrated into wearable devices, to ensure patient comfort and safety. The use of organic materials in the active layer have made the realization of highly efficient, flexible, lightweight, biocompatible, and durable devices possible. In this work, we have used PBDB-TCl:AITC:BTP-eC₉ as an active layer to produce a high efficiency for indoor OPV as well as to control and optimize the operating voltage of 1865 series sensors in order to power the smart IoT health monitoring sensors. The OPV cells embedded with smart sensor devices help in monitoring the human (patient) vitals such as blood-pressure, body temperature and other parameters. It is found that the layer of polymers PBDB-TCl : AITC : BTP-eC9 in the device provides an open-circuit voltage of 0.87V, fill factor of 79.4% and a power conversion efficiency of 19.1% under low light intensity. Inspiringly, an output efficiency of more than 26% under 1000lx has been realized.

Raster masks (binary, object-by-object, or energy fields) of areas of interest are formed by identifying certain informative features in images. To ensure transparency in presenting these features, raster data is vectorized to obtain information in a readable form. Vectorization involves primary and secondary transformations. For two-dimensional images, the immediate change consists of getting points, sections, and polygons from pixels and raster areas of the “substrate”, and the secondary transformation consists of a series of morphological operations on it. A mathematical model of labyrinths (two-dimensional rectilinear), mathematical characteristics of labyrinths and a model of generation of labyrinths with given characteristics are presented. The types of labyrinths from the point of view of satellite monitoring and some examples of artificial and natural origin are given. The “labyrinth effect” for pedestrians and transport is noted, which arises during the restructuring of the territory using remote sensing.

The approach to solving the problem of positioning unmanned transport objects, in particular, railway transport, in conditions of measurement interference uncertainty is considered. Today, a large number of urban (as well as railway) infrastructure facilities generate an unpredictable nature of disturbances acting on navigation sensors of unmanned objects. In this case, the use of both satellite measurements and various sensors located on the site (in particular, a video surveillance system) often becomes impossible. A stable high-precision solution to the problem of positioning unmanned transport objects becomes necessary. To solve this problem, the article proposes to use autonomous linear motion parameter meters as sensitive elements of the navigation complex. It is proposed to use a Kalman filter and a robust filtration method to process noisy measurements, including those with uncertain probability characteristics.