C-reactive protein (CRP) evaluation in human urine using optical sensor supported by machine learning.

The rapid and sensitive indicator of inflammation in the human body is C-Reactive Protein (CRP). Determination of CRP level is important in medical diagnostics because, depending on that factor, it may indicate, e.g., the occurrence of inflammation of various origins, oncological, cardiovascular, bacterial or viral events. In this study, we describe an interferometric sensor able to detect the CRP level for distinguishing between no-inflammation and inflammation states. The measurement head was made of a single mode optical fiber with a microsphere structure created at the tip. Its surface has been biofunctionalized for specific CRP bonding. Standardized CRP solutions were measured in the range of 1.9 µg/L to 333 mg/L and classified in the initial phase of the study. The real samples obtained from hospitalized patients with diagnosed Urinary Tract Infection or Urosepsis were then investigated. 27 machine learning classifiers were tested for labeling the phantom samples as normal or high CRP levels. With the use of the ExtraTreesClassifier we obtained an accuracy of 95% for the validation dataset. The results of real samples classification showed up to 100% accuracy for the validation dataset using XGB classifier.

An Efficient Bio-Receptor Layer Combined with a Plasmonic Plastic Optical Fiber Probe for Cortisol Detection in Saliva.

Cortisol is a clinically validated stress biomarker that takes part in many physiological and psychological functions related to the body's response to stress factors. In particular, it has emerged as a pivotal tool for understanding stress levels and overall well-being. Usually, in clinics, cortisol levels are monitored in blood or urine, but significant changes are also registered in sweat and saliva. In this work, a surface plasmon resonance probe based on a D-shaped plastic optical fiber was functionalized with a glucocorticoid receptor exploited as a highly efficient bioreceptor specific to cortisol. The developed plastic optical fiber biosensor was tested for cortisol detection in buffer and artificial saliva. The biosensor response showed very good selectivity towards other hormones and a detection limit of about 59 fM and 96 fM in phosphate saline buffer and artificial saliva, respectively. The obtained detection limit, with a rapid detection time (about 5 min) and a low-cost sensor system, paved the way for determining the cortisol concentration in saliva samples without any extraction process or sample pretreatment via a point-of-care test.

Hydroelectric Plant Safety: Real-Time Monitoring Utilizing Fiber-Optic Sensors.

In the context of hydroelectric plants, this article emphasizes the imperative of robust monitoring strategies. The utilization of fiber-optic sensors (FOSs) emerges as a promising approach due to their efficient optical transmission, minimal signal attenuation, and resistance to electromagnetic interference. These optical sensors have demonstrated success in diverse structures, including bridges and nuclear plants, especially in challenging environments. This article culminates with the depiction of the development of an array of sensors featuring Fiber Bragg Gratings (FBGs). This array is designed to measure deformation and temperature in protective grids surrounding the turbines at the Santo Antônio Hydroelectric Plant. Implemented in a real-world scenario, the device identifies deformation peaks, indicative of water flow obstructions, thereby contributing significantly to the safety and operational efficiency of the plant.

Side-Opened Hollow Fiber-Based SPR Sensor for High Refractive Index Detection.

To facilitate the sensor fabrication and sensing operation in microstructured optical fiber-based surface plasmon resonance (SPR) sensors for high refractive index (RI) detection, we propose a special hollow fiber-based SPR sensor that comprises an opening on its body side and a thin gold layer coated on its outer surface. The analyte is able to flow into the hollow core through the side-opening to form new fiber core, with the Gaussian-like mode propagating in it. We investigate the sensing performance of the proposed sensor in a higher RI range of 1.48 to 1.54 at two feasible schemes: one is to only fill the fiber core with analyte (Scheme A), and the other is to directly immerse the sensor in the analyte (Scheme B). The results demonstrate that our sensor exhibits higher wavelength sensitivity at Scheme A with a maximum wavelength sensitivity of 12,320 nm/RIU, while a greater amplitude sensitivity was found at Scheme B with a maximum amplitude sensitivity of 1146 RIU-1. Our proposed sensor features the advantages of simple fabrication, flexible operation, easy analyte filling and replacing, enhanced real-time detection capabilities, high RI detection, and very high wavelength sensitivity and amplitude sensitivity, which makes it more competitive in SPR sensing applications.

Applications of optical sensing and imaging spectroscopy in indoor farming: A systematic review.

As demand for food continues to rise, innovative methods are needed to sustainably and efficiently meet the growing pressure on agriculture. Indoor farming and controlled environment agriculture have emerged as promising approaches to address this challenge. However, optimizing fertilizer usage, ensuring homogeneous production, and reducing agro-waste remain substantial challenges in these production systems. One potential solution is the use of optical sensing technology, which can provide real-time data to help growers make informed decisions and enhance their operations. optical sensing can be used to analyze plant tissues, evaluate crop quality and yield, measure nutrients, and assess plant responses to stress. This paper presents a systematic literature review of the current state of using spectral-optical sensors and hyperspectral imaging for indoor farming, following the PRISMA 2020 guidelines. The study surveyed existing studies from 2017 to 2023 to identify gaps in knowledge, provide researchers and farmers with current trends, and offer recommendations and inspirations for possible new research directions. The results of this review will contribute to the development of sustainable and efficient methods of food production.

Spectral Behavior of Fiber Bragg Gratings during Embedding in 3D-Printed Metal Tensile Coupons and Cyclic Loading.

Additive manufacturing (AM) enables the spatially configurable 3D integration of sensors in metal components to realize smart materials and structures. Outstanding sensing capabilities and size compatibility have made fiber optic sensors excellent candidates for integration in AM components. In this study, fiber Bragg grating (FBG) sensors were embedded in Inconel 718 tensile coupons printed using laser powder bed fusion AM. On-axis (fiber runs through the coupon's center of axis) and off-axis (fiber is at 5° and 10° to the coupon's center of axis) sensors were buried in epoxy resin inside narrow channels that run through the coupons. FBGs' spectral evolutions during embedment in the coupons were examined and cyclic loading experiments were conducted to analyze and evaluate the sensor integration process, complex strain loading, process flaws, and sensing performance. This study also demonstrates that the AM process-born deficiencies such as poor surface finish and staircase effects can be detrimental to the embedded sensors and their sensing performance.

Smart mid-infrared metasurface microspectrometer gas sensing system.

Smart, low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications. Traditionally, electrochemical and nondispersive infrared (IR) gas sensors are designed to detect a single specific analyte. Although IR spectroscopy-based sensors provide superior performance, their deployment is limited due to their large size and high cost. In this study, a smart, low-cost, multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm. The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free, compact ( ~ 1 cm3) and lightweight ( ~ 1 g). The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present. The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100% with 100% accuracy. It also detects hazardous gases at low concentrations with an accuracy of 98.4%. Ammonia can be detected at a concentration of 100 ppm. Additionally, methyl-ethyl-ketone can be detected at its permissible exposure limit (200 ppm); this concentration is considered low and nonhazardous. This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.

Effect of Skin Pigmentation and Finger Choice on Accuracy of Oxygen Saturation Measurement in an IoT-Based Pulse Oximeter.

Pulse oximeters are widely used in hospitals and homes for measurement of blood oxygen saturation level (SpO2) and heart rate (HR). Concern has been raised regarding a possible bias in obtaining pulse oximeter measurements from different fingertips and the potential effect of skin pigmentation (white, brown, and dark). In this study, we obtained 600 SpO2 measurements from 20 volunteers using three UK NHS-approved commercial pulse oximeters alongside our custom-developed sensor, and used the Munsell colour system (5YR and 7.5YR cards) to classify the participants' skin pigmentation into three distinct categories (white, brown, and dark). The statistical analysis using ANOVA post hoc tests (Bonferroni correction), a Bland-Altman plot, and a correlation test were then carried out to determine if there was clinical significance in measuring the SpO2 from different fingertips and to highlight if skin pigmentation affects the accuracy of SpO2 measurement. The results indicate that although the three commercial pulse oximeters had different means and standard deviations, these differences had no clinical significance.

Adaptive Multi-Sensor Joint Tracking Algorithm with Unknown Noise Characteristics.

In this study, to solve the low accuracy of multi-space-based sensor joint tracking in the presence of unknown noise characteristics, an adaptive multi-sensor joint tracking algorithm (AMSJTA) is proposed. First, the coordinate transformation from the target object to the optical sensors is considered, and the observation vector-based measurement model is established. Then, the measurement noise characteristics are assumed to be white Gaussian noise, and the measurement covariance matrix is set as a constant. On this premise, the traditional iterative extended Kalman filter is applied to solve this problem. However, in most actual engineering applications, the measurement noise characteristics are unknown. Thus, a forgetting factor is introduced to adaptively estimate the unknown measurement noise characteristics, and the AMSJTA is designed to improve the tracking accuracy. Furthermore, the lower bound of the proposed algorithm is theoretically proved. Finally, numerical simulations are executed to verify the effectiveness and superiority of the proposed AMSJTA.

SiNx/SiO2-Based Fabry-Perot Interferometer on Sapphire for Near-UV Optical Gas Sensing of Formaldehyde in Air.

Fabry-Perot interferometers (FPIs), comprising foundry-compatible dielectric thin films on sapphire wafer substrates, were investigated for possible use in chemical sensing. Specifically, structures comprising two vertically stacked distributed Bragg reflectors (DBRs), with the lower DBR between a sapphire substrate and a silicon-oxide (SiO2) resonator layer and the other DBR on top of this resonator layer, were investigated for operation in the near-ultraviolet (near-UV) range. The DBRs are composed of a stack of nitride-rich silicon-nitride (SiNx) layers for the higher index and SiO2 layers for the lower index. An exemplary application would be formaldehyde detection at sub-ppm concentrations in air, using UV absorption spectroscopy in the 300-360 nm band, while providing spectral selectivity against the main interfering gases, notably NO2 and O3. Although SiNx thin films are conventionally used only for visible and near-infrared optical wavelengths (above 450 nm) because of high absorbance at lower wavelengths, this work shows that nitride-rich SiNx is suitable for near-UV wavelengths. The interplay between spectral absorbance, transmittance and reflectance in a FPI is presented in a comparative study between one FPI design using stoichiometric material (Si3N4) and two designs based on N-rich compositions, SiN1.39 and SiN1.49. Spectral measurements confirm that if the design accounts for phase penetration depth, sufficient performance can be achieved with the SiN1.49-based FPI design for gas absorption spectroscopy in near-UV, with peak transmission at 330 nm of 64%, a free spectral range (FSR) of 20 nm and a full-width half-magnitude spectral resolution (FWHM) of 2 nm.

Optically levitated micro gyroscopes with an MHz rotational vaterite rotor.

The field of levitated optomechanics has experienced significant advancements in manipulating the translational and rotational dynamics of optically levitated particles and exploring their sensing applications. The concept of using optically levitated particles as gyroscopes to measure angular motion has long been explored but has not yet been proven either theoretically or experimentally. In this study, we present the first rotor gyroscope based on optically levitated high-speed rotating particles. The gyroscope is composed of a micrometer-size ellipsoidal vaterite particle that is driven to rotate at MHz frequencies in a vacuum environment. When an external angular velocity is input, the optical axis deviates from its initial position, resulting in changes in the frequency and amplitude of the rotational signal. By analyzing these changes, the angular velocity of the input can be accurately detected, making it the smallest rotor gyroscope in the world. The angular rate bias instability of the gyroscope is measured to be 0.08°/s and can be further improved to as low as 10-9°/h theoretically by cooling the motion and increasing the angular moment of the levitated particle. Our work opens a new application paradigm for levitated optomechanical systems and may pave the way for the development of quantum rotor gyroscopes.

Monolithic integrated optoelectronic chip for vector force detection.

Sensors with a small footprint and real-time detection capabilities are crucial in robotic surgery and smart wearable equipment. Reducing device footprint while maintaining its high performance is a major challenge and a significant limitation to their development. Here, we proposed a monolithic integrated micro-scale sensor, which can be used for vector force detection. This sensor combines an optical source, four photodetectors, and a hemispherical silicone elastomer component on the same sapphire-based AlGaInP wafer. The chip-scale optical coupling is achieved by employing the laser lift-off techniques and the flip-chip bonding to a processed sapphire substrate. This hemispherical structure device can detect normal and shear forces as low as 1 mN within a measurement range of 0-220 mN for normal force and 0-15 mN for shear force. After packaging, the sensor is capable of detecting forces over a broader range, with measurement capabilities extending up to 10 N for normal forces and 0.2 N for shear forces. It has an accuracy of detecting a minimum normal force of 25 mN and a minimum shear force of 20 mN. Furthermore, this sensor has been validated to have a compact footprint of approximately 1.5 mm2, while maintaining high real-time response. We also demonstrate its promising potential by combining this sensor with fine surface texture perception in the fields of compact medical robot interaction and wearable devices.

Ultrastretchable Segmented Sensors for Functional Human-Machine Interfaces.

The key feature that enables soft sensors to shorten the performance gap between robots and biological structures is their deformability, coupled with their capability to measure physical changes. Robots equipped with these sensors can interact safely and proprioceptively with their environments. This has sparked interest in developing novel sensors with high stretchability for application in human-robot interactions. This study presents a novel ultrasoft optoelectronic segmented sensor design capable of measuring strains exceeding 500%. The sensor features an ultrastretchable segment physically joined with an asymmetrically configured soft proprioceptive segment. This configuration enables it to measure high strain and to detect both the magnitude and direction of bending. Although the sensor cannot decouple these types of deformations, it can sense prescribed motions that combine stretching and bending. The proposed sensor was applied to a highly deformable scissor mechanism and a human-robot interface (HRI) device for a robotic arm, capable of quantifying parameters in complex interactions. The results from the experiments also demonstrate the potential of the proposed segmented sensor concept when used in tandem with machine learning, affording new dimensions of proprioception to robots during multilayered interactions with humans.

Spicy Recipe for At-Home Diagnostics: Smart Salivary Sensors for Point-of-Care Diagnosis of Jaundice.

Even though significant advances have been made, there is still a lack of reliable sensors capable of noninvasively monitoring bilirubin and diagnosing jaundice as the most common neonatal disease, particularly at the point-of-care (POC) where blood sampling from infants is accompanied by serious challenges and concerns. Herein, for the first time, using an easy-to-fabricate/use assay, we demonstrate the capability of curcumin embedded within paper for noninvasive optical monitoring of bilirubin in saliva. The highly selective sensing of the developed sensor toward bilirubin is attributed to bilirubin photoisomerization under blue light exposure, which can selectively restore the bilirubin-induced quenched fluorescence of curcumin. We also fabricated an IoT-enabled hand-held optoelectronic reader to measure and quantify the fluorescence and color signals of our sensor. Clinical analysis on the saliva of 18 jaundiced infants by using our developed smart salivary sensor proved that it is amenable to be widely exploited in POC applications for bilirubin monitoring as there are good correlations between its results with those of reference methods in saliva and blood. Meeting all WHO's REASSURED criteria by our developed sensor makes it a highly promising sensor for smart noninvasive diagnosis and therapeutic monitoring of jaundice, hepatitis, and other bilirubin-induced neurologic diseases at the POC.

From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management.

In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.

Activity-driven synaptic translocation of LGI1 controls excitatory neurotransmission.

The fine control of synaptic function requires robust trans-synaptic molecular interactions. However, it remains poorly understood how trans-synaptic bridges change to reflect the functional states of the synapse. Here, we develop optical tools to visualize in firing synapses the molecular behavior of two trans-synaptic proteins, LGI1 and ADAM23, and find that neuronal activity acutely rearranges their abundance at the synaptic cleft. Surprisingly, synaptic LGI1 is primarily not secreted, as described elsewhere, but exo- and endocytosed through its interaction with ADAM23. Activity-driven translocation of LGI1 facilitates the formation of trans-synaptic connections proportionally to the history of activity of the synapse, adjusting excitatory transmission to synaptic firing rates. Accordingly, we find that patient-derived autoantibodies against LGI1 reduce its surface fraction and cause increased glutamate release. Our findings suggest that LGI1 abundance at the synaptic cleft can be acutely remodeled and serves as a critical control point for synaptic function.

Biominerals and Bioinspired Materials in Biosensing: Recent Advancements and Applications.

Inspired by nature's remarkable ability to form intricate minerals, researchers have unlocked transformative strategies for creating next-generation biosensors with exceptional sensitivity, selectivity, and biocompatibility. By mimicking how organisms orchestrate mineral growth, biomimetic and bioinspired materials are significantly impacting biosensor design. Engineered bioinspired materials offer distinct advantages over their natural counterparts, boasting superior tunability, precise controllability, and the ability to integrate specific functionalities for enhanced sensing capabilities. This remarkable versatility enables the construction of various biosensing platforms, including optical sensors, electrochemical sensors, magnetic biosensors, and nucleic acid detection platforms, for diverse applications. Additionally, bioinspired materials facilitate the development of smartphone-assisted biosensing platforms, offering user-friendly and portable diagnostic tools for point-of-care applications. This review comprehensively explores the utilization of naturally occurring and engineered biominerals and materials for diverse biosensing applications. We highlight the fabrication and design strategies that tailor their functionalities to address specific biosensing needs. This in-depth exploration underscores the transformative potential of biominerals and materials in revolutionizing biosensing, paving the way for advancements in healthcare, environmental monitoring, and other critical fields.

Optical sensing for real-time detection of food-borne pathogens in fresh produce using machine learning.

Contaminated fresh produce remains a prominent catalyst for food-borne illnesses, prompting the need for swift and precise pathogen detection to mitigate health risks. This paper introduces an innovative strategy for identifying food-borne pathogens in fresh produce samples from local markets and grocery stores, utilizing optical sensing and machine learning. The core of our approach is a photonics-based sensor system, which instantaneously generates optical signals to detect pathogen presence. Machine learning algorithms process the copious sensor data to predict contamination probabilities in real time. Our study reveals compelling results, affirming the efficacy of our method in identifying prevalent food-borne pathogens, including Escherichia coli (E. coli) and Salmonella enteric, across diverse fresh produce samples. The outcomes underline our approach's precision, achieving detection accuracies of up to 95%, surpassing traditional, time-consuming, and less accurate methods. Our method's key advantages encompass real-time capabilities, heightened accuracy, and cost-effectiveness, facilitating its adoption by both food industry stakeholders and regulatory bodies for quality assurance and safety oversight. Implementation holds the potential to elevate food safety and reduce wastage. Our research signifies a substantial stride toward the development of a dependable, real-time food safety monitoring system for fresh produce. Future research endeavors will be dedicated to optimizing system performance, crafting portable field sensors, and broadening pathogen detection capabilities. This novel approach promises substantial enhancements in food safety and public health.

A new fabrication method for enhancing the yield of linear micromirror arrays assisted by temporary anchors.

As one of the most common spatial light modulators, linear micromirror arrays (MMAs) based on microelectromechanical system (MEMS) processes are currently utilized in many fields. However, two crucial challenges exist in the fabrication of such devices: the adhesion of silicon microstructures caused by anodic bonding and the destruction of the suspended silicon film due to residual stress. To solve these issues, an innovative processing method assisted by temporary anchors is presented. This approach effectively reduces the span of silicon microstructures and improves the Euler buckling limit of the silicon film. Importantly, these temporary anchors are strategically placed within the primary etching areas, enabling easy removal without additional processing steps. As a result, we successfully achieved wafer-level, high-yield manufacturing of linear MMAs with a filling factor as high as 95.1%. Demonstrating superior capabilities to those of original MMAs, our enhanced version boasts a total of 60 linear micromirror elements, each featuring a length-to-width ratio of 52.6, and the entire optical aperture measures 5 mm × 6 mm. The linear MMA exhibits an optical deflection angle of 20.4° at 110 Vdc while maintaining exceptional deflection flatness and uniformity. This study offers a viable approach for the design and fabrication of thin-film MEMS devices with high yields, and the proposed MMA is promising as a replacement for digital micromirror devices (DMDs, by TI Corp.) in fields such as spectral imaging and optical communication.

MicroLOCK: Highly stable microgel biosensor using locked nucleic acids as bioreceptors for sensitive and selective detection of let-7a.

Chemically modified oligonucleotides can solve biosensing issues for the development of capture probes, antisense, CRISPR/Cas, and siRNA, by enhancing their duplex-forming ability, their stability against enzymatic degradation, and their specificity for targets with high sequence similarity as microRNA families. However, the use of modified oligonucleotides such as locked nucleic acids (LNA) for biosensors is still limited by hurdles in design and from performances on the material interface. Here we developed a fluorogenic biosensor for non-coding RNAs, represented by polymeric PEG microgels conjugated with molecular beacons (MB) modified with locked nucleic acids (MicroLOCK). By 3D modeling and computational analysis, we designed molecular beacons (MB) inserting spot-on LNAs for high specificity among targets with high sequence similarity (95%). MicroLOCK can reversibly detect microRNA targets in a tiny amount of biological sample (2 µL) at 25 °C with a higher sensitivity (LOD 1.3 fM) without any reverse transcription or amplification. MicroLOCK can hybridize the target with fast kinetic (about 30 min), high duplex stability without interferences from the polymer interface, showing high signal-to-noise ratio (up to S/N = 7.3). MicroLOCK also demonstrated excellent resistance to highly nuclease-rich environments, in real samples. These findings represent a great breakthrough for using the LNA in developing low-cost biosensing approaches and can be applied not only for nucleic acids and protein detection but also for real-time imaging and quantitative assessment of gene targeting both in vitro and in vivo.