This PDF file contains the front matter associated with SPIE Proceedings Volume 13527, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Multiwavelength laser combiners enhance sensor accuracy by merging multiple laser beams, but conventional designs with dichroic mirrors require complex alignment and bulky optics. We present a miniaturized laser combiner integrating three semiconductor laser diodes, a collimation lens, and a volume phase hologram. Using spectral beam combining, red, green, and blue lasers are merged efficiently into a single output. Its compact design simplifies alignment, enabling seamless integration into portable systems. Simulations show its high performance, low divergence, and potential for further miniaturization. A prototype was built, supporting the simulation results.

This paper presents an advanced photonic detection system for high-resolution millimeter-wave (MMW) imaging, utilizing an up-conversion detection method with a Glow Discharge Detector (GDD) and a photoreceiver. The system employs a 105 GHz MMW beam to illuminate metallic objects, with reflected MMW radiation up converted to an optical signal by the GDD. The optical signal is detected by a low noise photoreceiver, enhancing sensitivity and accuracy. A key innovation in this work is the integration of a near-infrared (NIR) long-pass filter, which significantly improves the signal-to-noise ratio (SNR) by blocking unwanted visible light and isolating MMW-induced NIR wavelengths. Experimental results demonstrate that the NIR filter enhances the system's responsivity and detection efficiency, enabling high-quality grayscale MMW imaging. This system shows significant advancements in MMW imaging, offering enhanced resolution and sensitivity.

The MUSKETEER (Milk adUlteration detection using SpecKlE paTtern and machinE lEaRning) project aims to address the global challenge of fighting milk adulteration, which poses significant health risks for consumers. Traditional methods for milk analysis (eg. ELISA, PCR, chemical tests) are complex, time- and money-consuming. The project goal is the development of a user-friendly platform that employs real-time Artificial Intelligence (AI)- based processing of Speckle Pattern (SP) images to identify adulteration in milk samples non-invasively. SP is the interference pattern produced when laser light illuminates a milk sample, which has a non-uniform refractive index distribution due to the presence of suspended particles. Images of SP acquired by a low-cost industrial camera are rich in information about the sample. In this work, we report an effective method to recognize different types of commercial cow milk and to identify milk dilution with water and 12.5% water-glucose solution. The average intensity and the dimension of the SP grains can be extracted from SP images. By considering both statistical parameters, our system can distinguish between different types of milk and detect diluted samples with both water and glucose, offering a reliable approach to address milk adulteration and ensure the integrity of dairy products on the market.

Optical tactile sensing holds transformative potential for robotics, particularly in collaborative environments where touch perception enhances safety, adaptability, and cognitive interaction. However, traditional tactile technologies based on total internal reflection (TIR) and frustrated total internal reflection (FTIR) - such as those used in touchscreen systems - face significant limitations. These include reliance on multiple infrared light sources and cameras, as well as poor adaptability to the complex, curved geometries often found in robotic systems. To address these challenges, we recently introduced OptoSkin, an advanced optical tactile sensor based on direct Time-of-Flight (ToF) technology, enabling touch and pressure detection. In this study, we investigate how specific material properties, particularly light scattering, influence the sensitivity of contact point detection under direct ToF sensing. Four materials with distinct scattering coefficients ranging from 0.02 cm⁻¹ to 1.1 cm⁻¹ at 940 nm were selected to assess their impact on signal quality across different contact scenarios involving various target surfaces.

In this paper, we present our latest progress in thermal ion-exchange technology, a technique employed for the creation of low-loss optical waveguides characterized by low bend radii and small crossing angles at wavelengths of 1550nm. This milestone has been achieved in commercially available multipurpose panel sized Schott D 263 T eco thin glass, and its effectiveness has been validated through both 3D Finite-Difference Time-Domain (FDTD) simulation and empirical measurement of a range of benchmarking layouts. These advancements pave the way for a novel measurement arrangement for the precise determination of the Verdet constant in optical waveguides, and, conversely, the measurement of an external magnetic field or electrical currents. This compact 2D layout can be seamlessly integrated into glass core substrate-based Printed Circuit Boards (PCBs), serving as a galvanically isolated optical current sensor for both Alternating Current (AC) and Direct Current (DC) in power electronics. This allows for the optical signal detection to take place at a considerable distance from the electrical guide. Such developments for glass core substrate PCB push the boundaries of power electronics, by enabling accurate, reliable, and robust measurements that are galvanically isolated from the electronic circuit. This opens up new possibilities in terms of safety, efficiency, and miniaturization.

This study is the primary research towards physical optical reservoir computing for road surface monitoring. Road surface monitoring traditionally utilizes specialized equipment on a road assessment vehicle to monitor road conditions, such as the profile, the roughness, the cracks, and the friction of the road pavement. These specific road assessment vehicles cannot provide up-to-date road surface conditions between two survey tasks, which limits the decision-making by using road data. Acceleration data collected by the sensors on the vehicles to achieve low-cost and high-efficiency road surface data acquisition based on the mobile vehicle platforms showing potential for road surface monitoring as an alternative approach. In this study, a physical optical fibre computing framework was utilized for road pavement surface fluctuation detection with acceleration data of the vehicles. Specifically, the acceleration data was obtained by an accelerometer on the vehicle for a road distance of 10 km and the corresponding road pavement surface fluctuation data was calculated from the road profile collected by a road assessment vehicle with a laser. The data was extracted from a road dataset. A time-delay optical fibre computing framework uses the normalized acceleration data in the z direction as the input signals and uses the dynamics of the physical optical fibre computing structure to calculate the temporary signals to be trained with linear regression to predict the road pavement surface fluctuations. The results showed that the physical optical computing structure achieved road surface fluctuation prediction while the parameters of the physical optical computing structure have a great influence on the prediction of the road surface data. This study provides insights into the development of physical computing for road surface monitoring in the future.

Quantitative analysis of microplastic (MP) size and concentration is essential for a better understanding of plastic pollution in aquatic environments. Conventional single-particle methods, however, are often time-intensive and can be cumbersome for high-throughput analysis. In this study, we present a novel measurement approach combining Static Light Scattering (SLS) with Machine Learning (ML) algorithms to enable a rapid and accurate analysis of the size and concentration of microplastics in water samples. Polystyrene microplastics ranging from 0.5 to 20 µm are used to evaluate the technique. We employ Principal Component Analysis (PCA) to reduce the dimensionality of scattering intensity patterns, facilitating feature extraction for machine learning classification. We automate the classification process with machine learning approaches such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Decision Trees (DT), Gradient Boosting (GB), Linear Discriminant Analysis (LDA), Linear Regression (LR), and Random Forest (RF). These models are fine-tuned and optimized to accurately determine the size distribution of MPs in water, with KNN and MLP providing more than 96% classification accuracy, outperforming the others. Concentration is subsequently quantified through a linear fitting model based on the specified particle sizes. Our integrated SLS-ML approach offers a scalable, high-throughput alternative to traditional methods after filtering/preprocessing the water, enhancing the efficiency of microplastic monitoring. This technique provides a valuable tool for advancing environmental assessments and offers potential for broader applications in pollution tracking in water and water quality management.

This work presents an analysis of the impact of noise spatial distribution shape reconstruction accuracy employing Monte-Carlo simulations. The findings reveal how noise introduced at the initial segments of the curve negatively affects the reconstructed shape accuracy more than an equivalent amount of noise applied towards the curve end, providing a quantitative estimate for achievable accuracy.

This contribution demonstrates and discusses a recently commercialized optical pressure and temperature sensing device. The sensor’s sensitive element consists of a sapphire substrate comprising two well-defined Fabry-Pérot cavities. One cavity is vacuum-sealed and located close to the front end of the substrate, thus creating a thin diaphragm. Pressure-induced diaphragm deformations lead to changes of the cavity’s optical path length. The second cavity is formed by the bulk material of the remaining substrate. Its optical path length is solely impacted by environmental temperature changes that entail geometry as well as refractive index alterations. Due to the cavities’ serial connection, a superimposed interference signal is observed when coupling broadband light into the sensing element. The sapphire element is packaged in a custom sensor housing designed to withstand temperatures up to 600 °C while the interrogation unit is spatially separated with an optical fiber and thus operates in less challenging conditions. The interrogator comprises a light source and a spectrometer-based detector. Demodulation of the superimposed reflection signal is achieved by analysis of the detected return spectrum, yielding the individual optical path difference of each cavity. The calibrated system provides simultaneous pressure and temperature data with accuracies better than ±0.1% F.S. (3σ) for pressures up to 7 MPa and temperatures up to 600 °C. Therefore, the outstanding performance and ruggedness of the device holds great potential to unlock new markets and industrial applications.

This study evaluates the performance of shape sensing cables in the presence of fiber core failures, a critical issue in scenarios where cable replacement is impractical due to technological and economic constraints. The impact of core failure is quantified by comparing the uncertainty in key parameters, such as curvature and bending angle, between pristine and damaged cables through simulation studies. Results indicate that while core failure degrades performance, shape reconstruction remains achievable.

This paper presents a fiber-optic short-range quasi-distributed strain/deformation sensing device suitable for miniature tactile sensing applications. In particular, the proposed sensor can provide in situ sensing functions required in actuation of robotic grippers mimicking fine motor skills of human fingers, assembly machines with precision mechanics and a small dimensions quality control. The sensing device consists of a short section of an SMF fiber with multiple fs laser-inscribed mirrors. These mirrors are further organized into a reference mirror and an array of equidistantly distributed mirrors. The laser acts as a short-range, high-resolution strain sensing region. The reference mirror, which precedes the sensing array by the length that is greater than the length of the sensing array, provides a foundation for interrogation of optical path length change of individual segments of the sensing array. The sensing assembly is interrogated by a commercially available interrogator operating in the vicinity of the 1550nm. While different distributions of mirrors in the sensing array are possible, within this work we demonstrate a sensor with the sensing region length of 1mm and distribution of sensing mirrors in the array at distances of 90 µm. Individual sensing segments lengths variations were resolved by processing of a back-reflected spectrum, where multiple overlapping spectral fringes were separated and phase-tracked by application of Discrete Fourier Transform. As an application example, a tactile sensing device was fabricated where the sensing fiber, with ten sensing segments was further etched to a diameter of 60 µm. The fiber was then encapsulated into a thin viscoelastic epoxy layer. This sensor structure was studied by recording a deformation along the sensing array while objects with dimensions in range of few hundred µm were pressed upon it and different locations of the sensing region.

Fiber optic sensing is being used to observe hardware anomalies on two categories of semiconductor components. Distributed fiber optic sensors were first attached to a programmable development board. The sensors monitored temperature during specific operations commonly performed on programmable hardware platforms such as a field-programmable gate array (FPGA). The sensors detected temperature changes that were consistent for identifying specific operations. The fiber optic sensors were also used to measure semiconductor package temperatures on power diodes and provided intrinsic high voltage isolation and low electromagnetic susceptibility. Thermal characterization during switching or power cycling is important for monitoring device behavior in power electronic systems. Ultimately, the goal of this project is to prove that fiber optic sensing may be a viable technique to diagnose hardware anomalies that result during specific computing functions, such as malware, or for detecting thermal changes on semiconductors embedded in power module systems.

Due to the increase in energy consumption based on fossil fuels, sustainable alternatives have emerged, and green hydrogen (H2) is one of them. This fuel is a promising eco-friendly energy source but is highly flammable. Therefore, continuous monitoring is essential, where optical sensors can contribute with a fast and remote sensing capability. In this field, plasmonic sensors have demonstrated high sensitivity, but with the plasmonic band in the visible range and low definition in the infrared. It presents a sensing structure for H₂ sensing composed of inexpensive materials (SiO₂ and TiO₂) and Pd as a sensitive medium, which supports Tamm Plasmon Resonance. The structure is numerically optimized to obtain a plasmonic band around 1550nm, which was experimentally validated with a sensitivity of 9.5nm in the presence of 4 vol% H₂ and a response time of 30 seconds. This work aims to emphasize the advantages of this plasmonic technique for gas sensing at the infrared spectral range, allowing remote sensing.

A Joule-assisted active plasmonic technique, which enhances the sensitivity of surface plasmon resonance measurements, is presented. Surface plasmon resonance curves play an important role in a variety of sensing applications due to their extraordinary sensitivity to changes in material properties. The presented technique incorporates a self-modulated device that builds on the well-established surface plasmon resonance method. By introducing a thermally-modulated attenuated total reflection, a sinusoidal Joule heating enables homodyne detection of the temperature-induced changes of the optical constants. An angularly resolved scan of the modulated reflection generates a curve with greater fluctuation in intensity, signifying improved sensitivity. This is confirmed by concurrent comparison of the performance of the modified setup with that of a typical attenuated total reflection sensor. The active plasmonic element enables a heightened susceptibility to changes in the metal surface morphology. The dynamic technique boasts better performance than the conventional technique and is more capable of detecting low-concentration adsorbates.

Exploring the impact of micro-curvatures on SERS performance reveals untapped potential in optomechanical enhancement. We developed a flexible microbending structure, termed a "microbender," to systematically investigate how micro-curvature influences the SERS response. Raman measurements confirmed that bending boosts SERS intensity by increasing light scattering, guiding, and field localization. A curved array with 6 µm radii, 0.8 µm-diameter pillars, and 0.2 µm spacing was shown to produce Raman signals equivalent to those of densely packed planar arrays fabricated at much higher resolution. These findings highlight that by applying micro-curvatures, we can achieve high SERS performance with low-resolution fabrication methods, offering a promising alternative to the current drive for ever-smaller, high-resolution planar SERS structures.

A fundamental biophysical attribute known as Refractive Index (RI) regulates how light travels through cells. We study this biophysical property with a developed surface plasmon probe. Adolescent rodent retina showed an increased RI in the centre while that in rodent pups showed a lower RI in the centre surrounded by a higher RI region. Obtained results can be correlated with the chromatin inversion (heterochromatin in the centre and euchromatin in the periphery) in rodent retina.

We carried out the fabrication of sensor chips containing plasmonic nanostructures over a large-area and employed these sensor chips for surface-enhanced Raman scattering (SERS) based highly sensitive and specific detection of chemical and biological molecules. The SERS sensor chips were fabricated over a large-area with high levels of uniformity. Moreover, the fabrication processes employed for developing the SERS sensor chips were repeatable and reproducible. These SERS sensor chips were characterized using benchtop Raman system and employed for the detection of chemical molecules such as pesticides and dyes, as well as some biological molecules of interest.

The development of novel photonic integrated microchips (PIC) is a promising approach to allow for the convenient detection of key biomarkers in complex matrices through multichannel real-time analysis in a highly compact package. This study reports the successful development and application of a backside released CMOS chip designed for the multichannel real-time detection of biomarkers. Operating at the C-band at approx. 1550 nm, the microchip features three dedicated detection sensors in addition to a reference sensor, enabling simultaneous analysis of multiple biomarkers. The compact and highly miniaturized design of this microchip, with a footprint of just 1 mm², positions it as promising candidate for point-of-care diagnostics and personalized medicine applications. This technology opens a path to transform biomarker detection across various medical fields, offering rapid, reliable, and cost-effective diagnostic solutions. In conclusion, the presented multichannel photonic microchips signify a substantial leap forward in real-time biomarker detection, providing a highly capable platform for future research and clinical applications.

Contactless detection of urea concentration in solution is very important in industrial, chemical, biological and agri-food applications. Urea, naturally contained in wine, when reacting with ethanol especially at high temperature may become the precursor of ethyl carbamate (urethane) which is carcinogen. Close monitoring of urea concentration in wine is necessary to prevent risks and diseases related to high urethane intake. In this work, we propose an optoelectronic measuring system for absorption-based detection of urea in water, in hydroalcoholic solutions with ethanol concentration of 12%, and in white wine with a 12% nominal concentration of ethanol. The urea concentration of the tested samples, contained in a 1-mm-deep rectangular glass capillary, was in the range from 0 up to 40 mg/mL. Two low-cost, low-power LEDs with emission spectra in the near infrared were employed as readout sources. The emission peaks of the selected LEDs were at λ = 1450 nm, where water absorbs more than urea, and at λ = 2350 nm, where urea absorption is higher than that of water. An amplified photodiode allowed to measure the transmitted optical power through the sample. To highlight the specificity of the measures, we also compared results of the optical measurements performed on urea-water solutions and on NaCl-water solutions. We demonstrate that measuring light intensity transmitted through each fluid mixture at the selected wavelengths allows not only to identify different urea concentrations, but also to distinguish samples of water-ethanol solutions from samples of white wine with the same urea concentration. This work represents the Proof of Principle of an optical method for specific urea detection in wine. Future work will be devoted to improving the system operation to enhance the limit of detection and to achieve recognition of urea concentration of the order of 0.001 - 0.003 mg/mL, typical values found in winery products.

With the increasing demand for cheese, a protein-rich food source, there is a need for efficient, non-destructive quality evaluation. Here, we propose the use of laser biospeckle to evaluate cheese and related microorganisms in real time. We used three different kinds of cheese (Red cheddar, Goda, Mozzarella) and lactic acid bacteria at various temperatures. Cheese varieties and bacteria have different temperatures for optimum activity. The cheese was sliced into 1.5mm thick slices, and the lactobacillus powder dissolved in deionized water was illuminated by light from a laser diode at 630nm. The speckle patterns generated by the scattered light from the sample were captured at 15 frames per second (fps) for ten seconds at various temperatures ranging from 10°C to 60°C in 10°C increments, and the correlation between the first frame and the remaining frames was calculated. The differences in correlation depend on the temperature, which reflects the activity of the microorganisms, and this was found to be different depending on the type of microorganism within the cheese. The correlation results show that the activity of lactobacilli is more pronounced at around 30°C and 40°C, and decreases at 60°C, while the activity of Gouda and Mozzarella is slightly reduced at 50°C. The measurement could take only a few seconds, demonstrating its use in the quality evaluation of cheese and other fermented milk products to assess the quality of the products and optimize storage, thereby enhancing the taste.

The skin is a source of biomarkers of several psychiatric conditions. Here, we aim to individuate stress-related skin response with a high-throughput multimodal combination of optical techniques. The cognitive-emotional stress was induced in healthy volunteers with the Montreal Imaging Stress Task. Raman, reflectance, and fluorescence spectra were recorded on their palm before and after stress induction using a multimodal fiber-probe sensor developed by our group. Electrodermal activity was registered throughout the experiment to monitor the psychological stress. A multimodal approach demonstrated 100% accuracy in classifying "no-stress" and "stress" conditions. Several Raman shifts were identified following stress induction. We develop a database of Raman, diffusive reflectance, and fluorescence spectra, as well as fluorescence lifetime properties of stress-responsive molecules to perform to extract single-molecule spectra from the total Raman spectra collected from volunteers’s skin. The preliminary results confirm the potential in application of our multispectral optical sensor for non-invasive detection of psychological stress and psychiatric disease conditions.

The application of Raman spectroscopy to the natural gas composition evaluation represents one of the novel approaches facing the market nowadays. An apparatus tailored to meet the industrial sector requirements is going through its industrialization stage by implementing all the technical and engineering improvements. The system is designed to be ready for industrial production and to guarantee robustness and reliability. It implements self-standing measurements in a non-invasive way and without the need for consumables. The technique is capable to simultaneously measure the content of all the main natural gas molecular species. In particular, the system is calibrated to detect light hydrocarbons up to butanes, carbon dioxide, nitrogen and hydrogen. The Raman emission is excited by a multi-mode, solid-state laser diode with emission centered at 455nm. The source, stabilized in temperature by a TEC, provides an optical power of about 2 W. The beam is focused into a gas cell which can safely operate with an internal pressure up to 17 absolute bars. The latest developments regarding the apparatus are presented in this work. The system performances, in terms of evaluation of the individual molecular components and calculation of the thermodynamics properties, have been characterized over the industrial temperature range from -20°C to 50°C and at a sample pressure up to 17 absolute bars. The results comply with the metrological requirements for all the mixtures tested: natural gas mixtures with different content of hydrocarbons and hydrogen-enriched natural gas.

The influence of the grating type (holographic or ruled), resolution, and polarization on the performance of an External Cavity Diode Laser (ECDL) emitting in the blue spectral region are investigated. The single mode ECDL is designed in Littrow configuration and uses an uncoated low-power GaN Fabry-Perot (FP) laser diode, which forms the gain medium. The emitted Fabry-Perot laser diode beam, which is TE-polarized in the plane of the p-n junction, after collimation, is directed incident on the grating. The frequency-selective element, which is used for longitudinal mode selection, is placed outside the FP laser diode cavity to form the extended cavity laser diode. The first-order diffraction is reflected directly into the FP laser diode, while the zero-order is the output from the ECDL. The slope efficiency, output power, power stability, tuning range, and linewidth of the blue ECDL are investigated using different gratings with different resolutions and orientations. The type and geometrical configuration of the dispersive element and its location relative to the antireflection-coated output facet of the FP laser diode are very important parameters in the performance.

Polygon mirror (PM)-based scanners are one of the most utilized and versatile type of opto-mechatronic scanning systems. Their advantages include a potentially large Field-of-View (FOV), versatility in terms of spectral range, and a high scanning velocity. However, for the latter aspect, PMs must spin with a high rotational velocity. This creates issues that must be addressed, both for the structural integrity and for deformations levels of the polygon and of its assembly. Studying such aspects is the aim of the present work, which builds upon our contributions on the optical [V.-F. Duma, Proc. of the Romanian Acad. A 18, 25-33, 2017] and on the opto-mechanical aspects of PMs [V.-F. Duma, M.-A. Duma, Appl. Sci. 12, 5592, 2022]. Finite Element Analyses (FEA) are performed with regard to the constructive and functional parameters of the system. Upper limits of 60 to 120 krpm are considered, in terms of rotational velocity. The yield stress is determined and analyzed, for useful ranges of other parameters of the device. Several rules-of-thumb are extracted from the analyses, for an optimal design of the scanner. Future work is discussed, for the development of the field of laser scanners in general, and of PMs, in particular.

This study develops an Active Plasmonic Colorimetric (APC) detection chip for quantitative biomolecule detection by observing the dispersion spectrum of plasmons. The active plasmonic detection chip can be applied in Point-of-Care (POC) testing, enabling rapid and convenient diagnostics at the patient’s location. The biosensor integration includes an OLED and a Au nano-grating, The OLED provides stable illumination as a uniform light source. The nano-grating, fabricated using nanoimprint technology is used to excite Surface Plasmon Resonance (SPR). The experimental results show that when the grating period is set to 555nm, the coupling wavelength of the surface plasmon wave exhibits a significant shift in environments with different refractive indices. When the refractive index changes from 1 to 1.33, the coupling wavelength shifts by approximately 190nm. The results indicate that this approach can be used for biomolecule detection. Through wavelength shifts induced by changes in the environmental refractive index, enabling high-sensitivity measurement of biomolecule concentrations. This sensor is expected to be further developed for the detection of Neuron-Specific Enolase (NSE) as a biomarker for lung cancer. Lung cancer is the leading cause of cancer death worldwide, posing a serious threat to life and the economy. The sensor demonstrated a broad linear detection range from 1 ng/mL to 100 ng/mL, with a low detection limit of 200 pg/mL for NSE, as well as excellent selectivity against potential interferences. Future clinical serum matrix analysis will further validate the platform's suitability for sensitive and accurate NSE quantification at clinically relevant levels.

Surface Enhanced Raman Spectroscopy (SERS) is a rapid, precise, and sensitive approach for ultra-trace analyte detection and analysis. Our work employed a chemical synthesis of silver nanoparticles decorated reduced graphene as a SERS-active substrate to detect a food adulterant Malachite Green (MG) in aquatic products at concentrations up to 10^-9 M.

In this study, we propose an integrated detection system that combines Surface Plasmon Resonance (SPR) and Raman spectroscopy for the rapid and quantitative detection of Small Cell Lung Cancer (SCLC). Neuron-Specific Enolase (NSE) is a reliable biomarker for Small Cell Lung Cancer (SCLC), making its accurate detection essential for the early diagnosis of lung cancer. SPR enables real-time detection of biomolecular interactions, providing critical information into molecular affinity and selectivity. Raman spectroscopy identifies chemical bonds and functional groups in proteins, allowing for detailed molecular structure analysis. By analyzing molecular fingerprints in Raman spectra, concentration changes can also be inferred. The results demonstrate that SPR exhibits exceptionally high detection sensitivity, with a Limit of Detection (LOD) of 0.1 pg/mL for NSE. The calibration curve, y = 2.64 + 0.1x with an R² value of 0.98, demonstrates a strong linear correlation. Raman spectroscopy reveals characteristic peaks corresponding to both MOA and NSE. Additionally, mass spectrometry analysis of antigen–antibody binding identifies distinct amide I (1654 cm⁻¹) and amide II (1546 cm⁻¹) peaks. Notably, the amide C–N bonds (1241 cm⁻¹ and 1115 cm⁻¹) were observed only after successful antigen–antibody binding, confirming the specificity of the interaction. The integrated SPR-Raman system demonstrates remarkable sensitivity and accuracy, effectively overcoming nonspecific binding issues associated with SPR and enhancing overall detection precision. This system holds significant potential for the early diagnosis of diseases such as SCLC, as well as broader applications in disease diagnostics and drug development.

Fiber Bragg Grating (FBG)-based throat microphone’s superior background noise suppression makes them ideal for wearable Automatic Speech Recognition (ASR) devices. However, achieving naturalness and intelligibility remains challenging due to the low-pass filtering effects of tissue and bones. This study presents a deep learning framework using a Long Short-Term Memory (LSTM) recurrent neural network for speech enhancement in FBG microphones and explores the impact of microphone placement and sex on ASR performance. The microphone, designed with a 1530.12nm prestrained FBG, captured vocal vibrations from six participants reciting Harvard sentences. An LSTM model trained with spectral mapping restored high-frequency components, reducing the log-spectral distance by 26% and improving the Non-Intrusive Short-Time Objective Intelligibility (NI-STOI) score by 2%. Character Error Rate (CER) and NI-STOI score showed significantly better performance at the lower throat position, emphasizing the importance of optimal microphone placement. Speaker sex, however, had no significant effect on CER or intelligibility.

Photonic micro-ring resonators (MRR) are widely studied for their high sensitivity across applications like environmental monitoring, healthcare, and chemical analysis. Their evanescent field sensing requires partially unembedded waveguides compatible with CMOS processing. Our approach uses local backside etching with an additional buried oxide (BOX) etch to release waveguides while preserving the back-end of line (BEOL) structure, enabling spatial separation of the sensing area and electronics. The BOX etch critically affects sensor performance, as waveguide surface roughness can alter MRR properties and coupling. We analyzed MRR design variations, comparing wet and dry etching techniques for their effects on optical performance across rib and strip waveguides in quasi-TE and quasi-TM modes. Wafer-level measurements show that backside-released MRR achieve high extinction ratios with slightly reduced quality factors, advancing high-sensitivity photonic sensors.

Scientific CMOS (sCMOS) image sensors are a modern alternative to typical CCD detectors and are rapidly gaining popularity in observational astronomy due to their large sizes, low read-out noise, high frame rates, and cheap manufacturing. However, numerous challenges remain in using them due to fundamental differences between CCD and CMOS architectures, especially concerning the pixel-dependent and non-Gaussian nature of their read-out noise. One of the main components of the latter is the Random Telegraph Noise (RTN) caused by the charge traps introduced by the defects close to the oxide-silicon interface in sCMOS image sensors which manifests itself as discrete jumps in a pixel’s output signal, degrading the overall image fidelity. In this work, we present a statistical method, utilizing mixture of Gaussians fitting, to detect and characterize RTN-affected pixels from a series of dark frames. Identifying RTN contaminated pixels enables post-processing strategies that mitigate their impact and development of manufacturing quality metrics.

We present an optical waveguide-based sensor configuration to detect organic pollutants, such as Polychlorinated Biphenyls (PCBs) and Polycyclic Aromatic Hydrocarbons (PAHs) in marine environments. These hazardous substances originate from industrial activities, oil spills, and agricultural runoff, and pose significant threats to aquatic ecosystems. The major challenge in detecting these pollutants is their weight. Typically, these pollutants weigh a few hundred daltons, which is two to three orders lighter than most proteins. Therefore, the detection of these pollutants requires highly sensitive sensors. To achieve high sensitivity and multiplexing capability, we propose a novel refractive index sensor that integrates a ring resonator (RR) with a Mach-Zehnder interferometer (MZI), where the MZI arm acts as the sensing component. Our simulations indicate a sensitivity of approximately 13 rad/RIU for a sensing arm length of 59.6 µm and show potential for low detection limit and multiplexing capability to detect various pollutants at differing concentrations. This innovative approach aims to enhance pollutant detection methods, contributing to understanding their ecological impacts.

In this study, we proposed a novel SPR-Raman biosensing system and utilized it to detect the concentration of umbilical cord blood exosomes for the early detection of cancer. SPR technology detects molecular interactions, analyzing affinity and specificity, while Raman spectroscopy identifies chemical bond characteristics and functional groups in transmembrane proteins and exosome lipid layers. We successfully tested three transmembrane proteins in exosomes and analyzed their interactions, which will aid in the early detection of lung cancer. The experimental results showed that the Limits of Detection (LODs) of SPR technology for the three transmembrane proteins CD63, CD91, and CD151 were 2.4×10⁵ particles/mL, 1.2×10⁷ particles/mL, and 1.2×10⁷ particles/mL, respectively. Among them, CD63 is a common biomarker of exosomes, while CD91 and CD151 are biomarkers of lung cancer. Therefore, detecting CD91 and CD151 in healthy exosomes requires a higher concentration of exosomes, suggesting that these cancer biomarkers are present at lower levels in healthy exosomes. Raman spectroscopy results revealed distinct peaks for transmembrane proteins, lipids, and nucleic acids in exosomes, confirming successful binding. This further validates the specificity of the SPR response, demonstrating the system's capability to accurately detect exosomal components. This system exhibits high accuracy and sensitivity, serving as a powerful tool for early diagnosis and prognosis. By integrating SPR and Raman spectroscopy, it enhances biomolecular detection, enabling precise analysis of molecular interactions and structural characteristics for improved disease detection and monitoring.

The rapid and accurate detection of SARS-CoV-2, the virus responsible for COVID-19, remains a critical priority in public health. Surface Plasmon Resonance (SPR) biosensors have emerged as a powerful tool for real-time, label-free, and highly sensitive detection of viral pathogens. The integration of Recombinase Polymerase Amplification (RPA) with SPR biosensors and Lateral Flow Assay (LFA) enables quantitative detection of SARS-CoV-2 with high specificity and efficiency. This study presents a promising alternative: a fast, sensitive, and specific RPA method combined with CRISPR-Cas12a (Clustered Regularly Iinterspaced Short Palindromic Repeats-Cas12a), which reduces false positives. Enhanced with lateral flow assays, fluorescence for qualitative detection, and SPR for quantitative analysis, this approach achieves a detection limit as low as one DNA copy per reaction. This innovative RPA-CRISPR/Cas12a technique offers a powerful tool for rapid, accurate, and accessible COVID-19 diagnosis, holding great potential for future clinical applications. According to the LFA test results, the Limit of Detection (LOD) for CRISPR-based detection of RPA products was 10³ copies per reaction. Fluorescence intensity detection results indicate that the fluorescence intensity difference is minimal above 10¹ copies per reaction, with an LOD of 10¹ copies per reaction. For SPR quantitative detection, when the flow rate was set to 30 μL/min, the injection of RPA products at different concentrations caused a corresponding change in the SPR angle. The detection results fit a linear model (R² = 0.923), demonstrating that the detection limit could reach 10¹ copies per reaction. This RPA-CRISPR/Cas12a technique offers a powerful tool for rapid, accurate, and accessible COVID-19 diagnosis, with significant potential for clinical applications.

Over the past decades, the global incidence of skin cancer has shown a steady increase. Basal Cell Carcinoma (BCC), the most common type of skin cancer, is also the most prevalent form of malignant tumor among individuals with fair skin. This study focuses on the Autofluorescence (AF) photobleaching kinetics of BCC using optical imaging and aims to develop machine learning algorithms for automated skin lesion classification and diagnostics. During clinical studies, we collected 300 sets of autofluorescence (AF) photobleaching images of basal cell carcinoma. Each sample was subjected to continuous excitation with a 405 nm LED, and filtered autofluorescence images were captured at a rate of one image per second over a 20-second interval. The resulting dataset comprises a time series of intensity images showing an exponential decrease in AF intensity. Acquired time-series data provides information on fluorophore content as well as environmental factors, such as metabolic activity, concentration of Reactive Oxygen Species (ROS) etc. The AF photobleaching parametric images reveal characteristic photobleaching patterns that distinguish BCC tissue from surrounding skin. Preliminary observations indicate that certain regions of the AF decay profile could serve as unique identifiers for diagnostics of basal cell carcinoma. Notably, areas displaying a gradient in the kinetics plots seem to correlate with BCC boundaries, highlighting the potential for regional mapping of lesion borders. Our current focus is on leveraging this photobleaching data to develop a machine learning-based classification model. By training algorithms on AF decay characteristics specific to BCC, we aim to establish a robust, non-invasive diagnostic tool that can accurately distinguish BCC from benign lesions and other malignant forms. The algorithm development process involves feature extraction from the time-series AF data, enabling the identification of patterns that may not be visible to the naked eye.

Bulk and localized sensitivity in Ultraviolet (UV) spectral regime discussed here in comparing way between one dimensional (1-D) and two dimensional (2-D) arrays of aluminum plasmonic nanostructures having narrow-gaps between the nanostructures. All results discussed here while considering oxide layer i.e. Aluminum oxide (Al₂O₃), which was naturally develop a thin layer of some thickness due to the effect of atmosphere. We optimized the height, width and periodicity of both the plasmonic nanostructures in the UV spectral regime. For bulk sensitivity calculation, refractive index of surrounding bulk medium over the plasmonic nanostructures changes from 1.0 to 1.5. For localized sensitivity calculation, surrounding bulk medium over the plasmonic nanostructures takes as air and a thin (1nm) localized layer i.e. SiO₂ is used here for localized sensing of both the plasmonic nanostructures. Along with bulk as well as localized sensing results, thickness of oxide layer effects on shift in plasmon resonance wavelength also discussed. Here, all results simulated with Finite Difference Time Domain (FDTD) technique and the reflection spectra from the plasmonic nanostructures shows the shift in plasmon resonance wavelength.

In this paper, we investigate the properties of polymer ring resonators for environmental monitoring. Optical sensors provide low-cost and real-time gas sensing to monitor air quality. Polymers in combination with whispering gallery mode resonators offer high sensitivity and a broad selection of materials that can be tailored for specific needs. We used SU-8, which is a low-cost, common photoresist, and we tested its sensitivity to relative humidity and temperature. To excite the resonances, we used a 760 nm tunable laser. Our results show that it is important to optimize the gap between the resonator and the waveguide to improve coupling efficiency, which increases the Q factor and, consequently, the sensitivity. However, the resonator width is not crucial. By applying a PMMA coating, we demonstrated that functionalized coatings can improve the selectivity of the sensor.

The distinction between asbestiform (chrysotile) and non-asbestiform (lizardite and antigorite) serpentine polymorphs is critical due to the significant health risks associated with asbestos exposure. Chrysotile, the most common form of asbestos, is known for its fibrous morphology, which can lead to severe respiratory diseases when inhaled, including asbestosis, lung cancer, and mesothelioma. Differentiating asbestiform chrysotile from its non-asbestiform counterparts, lizardite and antigorite, is therefore essential for regulatory, industrial, and environmental purposes. Raman spectroscopy provides a powerful approach for differentiating these polymorphs based on their unique spectral features. However, the characterization of individual spectra remains heavily reliant on operator expertise, which can introduce variability and limit performance. To address this limitation, this study integrates Raman spectroscopy with multivariate analysis techniques, such as Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), to develop a classification model that automates the identification process. The proposed approach aims to recognize diagnostic spectral patterns and to classify chrysotile, lizardite, and antigorite reducing the need for manual interpretation. The model is trained and validated on spectral data obtained from pure mineral samples and from serpentinite samples collected at the tunneling excavation site in Cravasco (Genoa, Italy), in asbestos containing Green Rocks with related potential asbestos exposure for numerous workers. The proposed approach enhances the reliability and efficiency of serpentine polymorphs differentiation, offering a robust tool for standardized and automated characterization of these minerals in real geological settings, allowing better risk assessment for exposed workers and timely adoption of more precautionary measures.

The study proposes a D-shaped Surface Plasmon Resonance (SPR) fiber-optic biosensor for detecting glucose levels in urine. The sensor, designed using gold (Au) and platinum disulphide (PtS₂) as sensing layers, is analyzed through Finite Element Method (FEM) simulations. The Refractive Index (RI) and corresponding glucose concentrations in urine were documented, showing a direct correlation between increasing glucose levels and RI. The biosensor can identify glucose levels between 0 to 15 mg/dL as normal and up to 10 g/dL as highly elevated. The highest wavelength sensitivity (WS) is 10,000 nm/RIU when the refractive index changes from 1.335 to 1.336. This sensor may be essential for detecting and tracking conditions such as renal glycosuria and complications related to diabetes.

This paper proposes a Surface Plasmon Resonance (SPR) biosensor designed to detect hemoglobin (Hb) levels in blood, facilitating the diagnosis of anaemia. The sensor uses a silver (Ag) and cadmium selenide (CdSe) coating, leveraging wavelength interrogation to achieve high sensitivity and resolution. A thin layer of Ag (45nm) and a CdSe film (15nm) serve as sensing layers. Using AI/ML techniques, we identify optimized parameters and calculate Mean Square Error (MSE) and R-squared (R²). Using Bayesian Regularization Artificial Neural Network (BRANN) we minimized the loss function ‘F’ by decaying weight of ANN and solved the problem of overfitting due to fewer number of samples. Also, we obtained best values of MSE and R² compared to others models i.e., 0.9647 and 0.9992 respectively. The maximum sensitivity is achieved in RI range of 1.32919–1.34919, indicating precise detection capability with value of around 6000 nm/RIU. It reaches a highest resolution of 1.6×10⁻⁵ RIU, enabling the detection of alterations in hemoglobin concentration as low as 6.1025 g/L. We have also calculated concentration sensitivity 0.9832 nm/g/L. This study marks a crucial advancement in creating sensitive, label-free optical biosensors for use in medicine.

Ultra-stable, low-noise lasers are indispensable for advanced fiber sensor applications requiring high sensitivity and precision. This work presents a novel Polarization-Maintaining (PM) fiber laser design utilizing a fully spliced PM fiber ring cavity for self-injection locking of a Distributed Feedback (DFB) laser. This configuration eliminates the need for adjustments or maintenance, offering robust, environmentally resilient operation with a Lorentzian linewidth of approximately 75 Hz. Phase and intensity noise levels are suppressed to below –120 dBc/Hz (>10 kHz) and –140 dBc/Hz (>30 kHz), respectively. With an output power of ~8 mW and frequency drift below 0.5 MHz/min, this laser design is ideal for demanding fiber sensor applications, including distributed acoustic sensing and high-resolution interferometry, while being compact and cost-effective.

Optical choppers are widely utilized for the modulation of light beams in a variety of applications. While the most common choppers have rotational disks, we have patented solutions of choppers with shafts [Patent RO 129610-B1/2021], which have the potential of a much higher chopping frequency. The aim of our current, on-going work is to simulate the functioning of such choppers with shafts with different shapes and with slits of different profiles. We utilize PTC CREO to simulate the chopping of laser beams in order to obtain the transmission function of these devices, i.e. the profile of the laser impulses generated by such choppers. The setup of PTC CREO simulations his been considered, including defining the geometry of the system, boundary conditions, frequency and modulation analysis, input and output, as well as data extraction and post-processing. A comparison can be made to transmission functions of other choppers, classical (i.e., with disks with windows with linear margins) [J. of Opt. A: Pure and Appl. Opt. 10, 064008, 2008] or eclipse (i.e., with disks with non-linear margins, outward or inward, which we have patented and developed) [Comm. in Nonlinear Sci. and Numerical Simulation 16, 2218-2224, 2011; Patent RO 126505-B1/2016]. Also, a finite element analysis (FEA) is performed on the chopper. Thus, this study is in the frame of a larger research avenue that comprises the development of several types of optical modulators.

This work presents the development of a temperature sensor fabricated using a 3D laser lithography system, based on a Mach-Zehnder Interferometer (MZI) configuration. The sensor leverages the precise microfabrication capabilities of two-Photon Polymerization (2PP) to create highly sensitive and scalable devices with customizable geometries. The Mach-Zehnder interferometer structure, known for its sensitivity to changes in environmental parameters, was designed to detect temperature variations by exploiting the phase shift induced in the interferometer arms due to thermal expansion or refractive index changes. The sensor's performance was characterized by evaluating the temperature-dependent shifts in interference patterns, demonstrating high accuracy and responsivity. The use of the commercial Nanoscribe system enables the creation of intricate, compact, and robust sensor designs, offering potential applications in various fields, including environmental monitoring, industrial processes, and biomedical diagnostics.

PLATO, the PLAnetary Transits and Oscillations of stars mission, is an ESA mission dedicated to finding and studying extrasolar planetary systems, with a special emphasis on rocky planets around Sun-like stars and their habitable zone, planned to be launched in 2026. Payload is composed of 26 cameras in the same instrument bench in order to cover a large field of view with the highest possible photon statistics. Each PLATO Camera consists of a Telescope (TOU, Telescope Optical Unit), a Focal Plane Assembly (FPA), and a Front-End Electronics box (FEE). This paper is focused on the FPAs which contain four CCD (Charge-Coupled Device) sensors. It presents the model philosophy to reach the flight model for this particular serial production project. The flight FPA design and the different models which have been manufactured will be described. In total, 37 FPAs (holding 144 CCDs) have been manufactured plus 26 mass thermal dummies. We want to remark one of the biggest challenges of this project which is the precise alignment requirements for the CCD sensors within the different cameras.

The PLATO FPA (Focal Plane Assembly) integration and verification plan is considered to be as an iterative process since the complete spacecraft composed of 26 cameras being the integration of these focal plane assemblies identical with no distinction between FAST and NORMAL cameras. The differences in parts such as CCDs (Charge Coupled Device) and TS (Thermal Strap) between the two types do not affect to the steps followed in the AIV (Assembly, Integration and Verification) procedures. During two years, from 2022 to 2024, INTA qualified personnel has been involved in the integration, assembly, alignment and verification for the FPA Flight Models. This serial production resulted in a learning curve derived from continuous processes improvement and personnel expertise. In this paper will be presented the evolution of AIVT (Assembly, Integration, Verification and Test) activities.

PLATO (PLAnetary Transits and Oscillations of stars) is an M-class mission of the European Space Agency’s Science Programme Cosmic Vision 2015-2025 planned to be launched in 2026. PLATO aims to characterize exoplanetary systems by detecting planetary transits and conducting asterosismology of their parent stars. The Payload is composed of 26 camera, each PLATO Camera consists of a Telescope (TOU), a Focal Plane Assembly (FPA), which is comprised of four CCDs (Charge-Coupled Device). Each FPA is associated to a Front-End Electronics (FEE) that drives the readout. The FEE transmits the data to the Data Processing Unit (DPU). Then the data is downloaded to the central Instrument Control Unit (ICU). CCDs performances are very sensitive to any kind of contamination. The presence of particles and molecular contamination on CCD sensitive surface can have an impact on pixels thus potentially degrading the optical performance. The total sensitive area is wide and open, completely exposed to the environment. For that reason, a very exhaustive contamination control during the serial production of the FPA has been followed to achieve the specified cleanliness (molecular and particulate) level focusing in avoiding loss in CCDs performances. This paper describes the control, issues and improvements implemented during the FPA flight models activities related to contamination.

PLATO Focal Plane Assembly (FPA) flight models have been serially produced and tested. Due to the tight schedule, each of the 26 units had to be delivered within two to three months from the start of integration. As a result, from the Assembly, Integration and Verification (AIV) perspective, the vibration acceptance and workmanship test at the FPA level had to be minimized to meet this delivery timeline without causing delays. To adopt this reduced vibration testing approach for flight models while assuming no risks, significant efforts have been made in previous models to ensure that the units met all structural requirements. The objective of this paper is to describe the vibration testing approach used across the different models, from the initial prototype model to Structural and Thermal Model (STM), Qualification Model (QM), and Flight Models (FM).

Methods for detecting mechanical vibrations from speckle images have inspired a novel approach for monitoring microbial growth activity. Unlike mechanical deformations, random microbial movements induce both image displacements and variations in the images, causing rapid correlation decay between sequential frames. Consequently, correlation-based algorithms cannot use the first frame as a reference. This issue was addressed by comparing consecutive frames and accumulating detected shifts to preserve signal continuity. However, this accumulation functions as an integrator, amplifying DC and low-frequency components, potentially introducing unwanted signal drifts. This study examines this challenge and its solution, contributing to the advancement of a reliable optical sensor for rapid bacterial and fungal activity detection, aiding microbiological diagnostics.

This study utilized the Gas in Scattering Media Absorption Spectroscopy (GASMAS) technique to examine the diffusion dynamics of water vapor in commercial mesoporous silica MCM-41 and SBA-15. The GASMAS technique employed an innovative semiconductor laser configuration, utilizing a tunable diode laser at 936 nm to facilitate real-time monitoring of water vapor absorption. This approach is straightforward and economical, yielding localized insights into diffusion behavior. Our findings indicate a notably slow time constant rate, influenced by hydrogen bonding interactions with pore walls, size, and relative humidity.

The fast-developing field of optics is crucial for enhancing large-scale photovoltaic (PV) technologies, especially in micro-concentrator photovoltaic (CPV) systems. This study explores the integration of advanced optics in micro-concentrator photovoltaic (CPV) modules, inspired by performance differences between two generations of CPV modules, “Gen1” and “Gen2.” While “Gen2” enhanced light transmission and reduced Fresnel losses by laminating lenses directly onto the module without a protective glass layer, it exposed vulnerabilities to soiling, thermal stress, and manufacturing defects. This paper explores the convergence of optical design, material advancements, and environmental adaptability to achieve a balance between efficiency and reliability for recently CPV applications that focus on diffuse light transmission and capture. A comparative analysis of different materials and spectral filters was done by evaluating their transmission, reflectance, and absorbance in order to recommend the most suitable spectral filter material and its manufacturing method. This paper highlights the essential importance of interdisciplinary innovation in realizing scalable, efficient, and long-lasting solar energy solutions.

This paper is focused on the transport activities of the Focal Plane Assembly (FPA), one of the units that conform each camera in the payload of the PLATO Mission. The presented activities include a brief description of the transport container and the packing process, as well as the considerations on its design and the analysis of the complete system by a non-linear finite element model (FEM) in LS-DYNA, obtaining results of the displacement and accelerations of both the container and the FPA along its transport MGSE (mechanical ground support equipment). The verification of these results by a test is also included, as well as a comparison with the real transport data obtained from the datalogger included in the container.

Mechanical Ground Support Equipment (MGSE) devoted to transport and handling activities is a set of aluminum parts used to hold together the Focal Plane Assembly (FPA) and the Front-End Electronics (FEE) of the PLATO satellite. It will join them in a fixed position in order to form an independent assembly and to allow the handling and transport in the different facilities of the PLATO Consortium. Prior to the assembly of the MGSE with the flight components is necessary to clean these parts conscientiously in order to avoid cross-contamination (particle and molecular) to the FPA sensors and mechanical parts, some of them with a specific black coating. Acetone and Isopropyl Alcohol (IPA) are the solvents used for this practice including also ultrasonic baths and N₂ gun to remove particles, spots and fluorescence. Although there exist two different types of cameras in the satellite, normal and fast, the differences applied to the parts of the MGSE assembly is only translated to a very specific parts (legs and lower plate). The assembly process implies caution and a good handling and knowledge of the FPA and FEE to avoid any damage with the optical sensors (Charge Coupled Devices - CCDs), flexi cables, thermal straps and coated parts. Procedures and step-by-step documents are made to avoid any mishandling of the complete assembly. Finally, the complete assembly is double bagged with N₂ and packed with specific ad-hoc shaped antistatic foam in a ZARGES Transport container to avoid shocks, loads and exposure to external environmental conditions that can compromise the status of the FPA-FEE assembly.

Fiber-optic technologies (FOT) have been actively developing over the last 40-50 years, demonstrating wide application and high efficiency. The transmission of light pulses instead of electrical signals has significantly expanded the range of optical fiber applications and increased its effectiveness, allowing telecommunication networks to reach a new level of development. The intensive development of optical fiber production technology and the improvement of its modifications have made it possible to create new and higher-quality systems based on them. The most well-known area of optical fiber application is telecommunication networks for data transmission. However, considering its physical properties, the field of use is expanding, and it can be used as a measurement device, i.e., sensors for measuring certain physical quantities. In this regard, there is a need to research this industry for its effective application in various technical tasks.