Detected Research Articles

Temperature-compensated optical fiber sensor for urea detection based on the femtosecond laser-inscribed process

This study demonstrates a low crosstalk temperature-compensated fiber-based urea sensor based on the hybridization of surface plasmon resonance (SPR) and Mach-Zehnder interferometer (MZI). In this sensing structure, an inscribed waveguide and a semi-open cavity are processed by a femtosecond laser in an optical fiber to excite an SPR signal sensitive to refractive index (RI) and an MZI signal highly sensitive to temperature. Based on the temperature detected by the MZI, the influence of temperature on the SPR signal’s RI detection can be calculated and compensated for using a sensitivity matrix. Based on this precise RI detection of the SPR signal, we have achieved selective urea detection by bio-functionalizing the sensor surface using the urease self-assembly method. The sensitivity and limit of detection for urea are 7.98 (nm/mmol/L) and 0.13 mmol/L, respectively, which are better than those in previous studies. Compared to previous temperature compensation studies, this SPR + MZI sensing method using femtosecond laser processing significantly improves the temperature sensitivity of the MZI signal, which reduces crosstalk and ensures a sufficient working range for the SPR signal. Therefore, this sensor is crucial for advancing high-precision temperature compensation detection, high-sensitivity biological detection, and the future application of the femtosecond laser technology.

High-sensitivity fiber temperature sensor based on composite film structure and lossy mode resonance

In this work, we proposed and demonstrated a high-sensitivity optical fiber temperature sensor based on lossy mode resonance (LMR). The sensor is composed of a D-shaped fiber and a thin composite film. The composite film consists of tin dioxide (SnO2) and polydimethylsiloxane (PDMS). PDMS is inserted as an intermediate layer between D-shaped fiber and SnO2 layer, realizing the separation of TM and TE polarization to reduce polarization crosstalk, while also serving as a temperature sensitive layer. The polarization state of the excited LMR can be freely controlled by adjusting the thickness of the composite film. The proposed sensor exhibits a high temperature sensitivity of −1.1883 nm/°C with a measurement range of 10–90 °C. Meanwhile, a fast response time of ∼ 109 ms was observed due to the ultra-thin PDMS interlayer.

Wide-range and ultra-low temperature thermometer based on a silicon resonator.

In this Letter, the silicon-based microring resonator (MRR) was experimentally demonstrated for cryogenic sensing down to 10 K by overcoming the issue of acquiring the optical signals at low temperatures for on-chip optical sensors. A wide-range temperature sensor from 240 to 10 K was obtained. The experimental results show that the device sensitivity decreased from 64.7 pm/K at 240 K to 4.19 pm/K at 10 K. Theoretical analysis indicates that the reduction in sensitivity is attributed to the weakening of thermo-optic effects with the decrease in temperature, which is well consistent with the experimental results. Based on this work, the silicon-based ring resonators, featuring complementary metal oxide semiconductor (CMOS) compatibility, high-quality factors, and ease of chip-scale integration, are a potential platform for ultra-low temperature monitoring.

Optical Biosensors for Detection of Cancer Biomarkers: Current and Future Perspectives.

Optical biosensors are emerging as a promising technique for the sensitive and accurate detection of cancer biomarkers, enabling significant advancements in the field of early diagnosis. This study elaborates on the latest developments in optical biosensors designed for detecting cancer biomarkers, highlighting their vital significance in early cancer diagnosis. When combined with targeted nanoparticles, the bio-fluids can help in the molecular stage diagnosis of cancer. This enhances the discrimination of disease from the normal subjects drastically. The optical sensor methods that are involved in the disease diagnosis and imaging of cancer taken for the present review are surface plasmon resonance, localized surface plasmon resonance, fluorescence resonance energy transfer, surface-enhanced Raman spectroscopy and colorimetric sensing. The article meticulously describes the specific biomarkers and analytes that optical biosensors target. Beyond elucidating the underlying principles and applications, this article furnishes an overview of recent breakthroughs and emerging trends in the field. This encompasses the evolution of innovative nanomaterials and nanostructures designed to augment sensitivity and the incorporation of microfluidics for facilitating point-of-care testing, thereby charting a course towards prospective advancements.

Integrating AI in food contaminant analysis: Enhancing quality and environmental protection

This paper marks a groundbreaking step toward ensuring food safety by applying artificial intelligence (AI) in the detection of food contaminants. It argues that AI offers a significant advantage over traditional methods, addressing both food safety and environmental risk issues. We aim to make rapid, precise online analysis of chemical contaminants a reality. While traditional methods work well, they struggle with the demands for simplicity, large datasets and speed. In contrast, AI excels with its data manipulation and predictive analytics. This paper explores AI's applications and future perspectives in detecting, quantifying and reducing food contaminants, showcasing examples like machine learning, neural networks, and data mining techniques for identifying pests, heavy metals and mycotoxins. Additionally, AI-driven sensor technologies and spectroscopic methods are discussed for improving detection accuracy. AI's real-time detection capabilities can help prevent health crises and economic loss, while its predictive power supports sustainable agriculture by reducing the use of harmful chemicals.

Advances in Selective Detection of Cadaverine by Electronic, Optical, and Work Function Sensors Based on Cu-Modified B12N12 and Al12N12 Nanocages: A Density Functional Theory (DFT) Study.

This work explores Cu-modified B12N12 and Al12N12 nanocages for cadaverine diamine (Cad) detection using advanced density functional theory (DFT) calculations. The study found that Cu modification altered the geometry of the nanocages, increased the dipole moment, reduced the energy gap, and enhanced the reactivity. While pristine B12N12 and Al12N12 were not sensitive to Cad, the modified Cu(b64)B12N12 and Cu(b66)Al12N12 nanocages showed significantly higher electronic sensitivity (Δgap = 39.8% and 35.6%, respectively), surpassing the literature data. However, molecular dynamics (MD) revealed that the Cu(b66)Al12N12 nanocage is not stable in the long term, since the nanocage changes configuration to Cu(b64)Al12N12, which is less sensitive and has an even longer recovery time for Cad sensing. Adsorption energy analysis (Eads) showed a strong interaction of Cad/nanocages, while charge analysis suggested that the nanocages act as Lewis acids, accepting electrons from Cad. UV-vis spectra confirmed that Cu(b64)B12N12 responds optically to the presence of Cad. Furthermore, Cu(b64)B12N12 showed greater sensitivity to Cad compared to NO, H2, H2S, CO, COCl2, N2O, N2 gases, or H2O, showing high selectivity to diamine against interfering gases or water, standing out as a promising material for environmental applications in electronic, optical or work function sensors for cadaverine detection, even in humid environments.

Revitalizing E-waste: Eco-friendly electrochemical sensor for Hg(II) detection enhanced by oxygen vacancy in metal oxide nanostructures based on recycled LCD

In the current work, an innovative eco-friendly sensor using ceria integrated cobalt oxide nanosheets immobilized on LCD monitor (Ce@Co-EcoR) recycled from E-waste is presented. The Ce@Co-EcoR nanocomposite was thoroughly investigated using appropriate characterization techniques. This nanostructured electrode was employed to construct an electrochemical sensor to detect mercury. It showed a very low detection limit of 2.8 ppb, a wide detection ranges from 16 to 620 ppb, and a good sensitivity of 158.28 μA cm2.ppm−1. The sensor applicability was verified by performing interference, repeatability, stability studies. It was also applied to control the purity of sea water. This work underscores the potential of incorporating recycled materials onto sensor technology, not only to control environmental pollution, but also to promote sustainable practices in scientific innovation.

Non-Conventional Data for Farming-Related Air Pollution: Contributions to Modelling and Risk Assessment in the Lombardy Region, Italy

Air pollution is one of the most critical global health concerns today. While emissions from industrial activities and combustion processes are the primary threats to air quality, intensive farming activities also contribute significantly, especially through ammonia emissions that promote the formation of secondary pollutants, such as particulate matter. Advancements in air quality research have been achieved by enhancements in emissions characterisation, modelling techniques, and sensor technology, expanding the availability of air pollution data beyond traditional ground sensor observations, which are often lacking in rural agricultural areas. Accordingly, this paper demonstrates the advantages of integrating traditional and non-conventional data to investigate farming-related air pollution through a case study in the Lombardy Region, Northern Italy. The study incorporates an array of data sources, including ground sensors and atmospheric composition model estimates. The concurrent utilisation of these diverse datasets is explored through machine learning modelling, focusing on assessing the influence of agricultural activities on particulate matter distribution patterns. Finally, the reliability of non-conventional air pollution data for health risk assessment applications is also investigated. The paper critically discusses the main findings based on empirical results, highlighting the significance of integrating multiple data sources to complement traditional air quality monitoring while outlining the main limitations in terms of the accuracy and usability of such non-conventional data.

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Abstract Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100kHz and 20kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

Ultrafast and Reproducible Fiber-Optic Hydrogen Sensor via a Tilted Fiber Grating With Pd/WO3 Nanocoating

Hydrogen, a high-density and clean energy, has been widely used in various critical applications. However, the safety risk caused by hydrogen leakage during storage and transportation is still a non-negligible issue. Therefore, it is necessary to offer hydrogen sensors with fast response and high repeatability, and it will be perfect for achieving in situ monitoring over the lifecycle of hydrogen production and utilization. Here, we propose a compact optical fiber sensor with a short section of the tilted Bragg fiber grating (TFBG) inscribed in the fiber core and a palladium and tungsten trioxide (Pd/WO3) combined film of 40 nm thickness over the fiber surface. The TFBG excites tens of narrow cladding resonances, part of which possess refractive indexes matching that of the Pd/WO3 coating and providing the high sensitivity to the surrounding hydrogen concentration change. The sensor offers improved sensing characteristics, including the fast response time (less than 10 s), high repeatability (over tens of measurement), and excellent linear response (higher than 99.6%) over the 0% to 3% concentration range.

Ultrasensitive optical electric field sensor with temperature compensation for point-wise and quasi-distributed sensing.

A point-wise and quasi-distributed optical sensing technique with the Vernier effect is proposed and achieved by multiplexing Fabry-Perot interferometers (FPIs). The FPIs are fabricated by LiNbO3 (LN) crystals of varying lengths to enable simultaneous measurement of electric field (E-field) and temperature. Compared to the traditional bulk-type optical E-field sensors, this innovative sensor enables E-field measurement without being limited by half-wave voltage and effectively avoids the influence of natural birefringence. By an algorithm based on the Fourier transform, sub-FPIs are addressable, and their interference spectra can be demodulated independently, where any two FPIs are paired to generate the fundamental Vernier effect (FVE) or harmonic Vernier effect (HVE). Thus, the measurement sensitivities of FPIs can be significantly improved by monitoring the spectral shift of the envelope in the superimposed spectra. The quasi-distributed sensing experiment is conducted using three cascaded FPIs, yielding E-field sensitivities of 2.84 nm/E (FVE) and 3.37 nm/E (HVE), with a standard deviation (STD) of spectrum variation after temperature compensation below 3.19 × 10-3. The proposed multiplexing method is significant for practical applications of the E-field quasi-distributed measurement.

Designing of High-Sensitivity Optical Surface Plasmon Resonance Sensor (OSPRS) for Fast and Accurate Diagnosis of Dengue Virus (DeVs)

Designing of High-Sensitivity Optical Surface Plasmon Resonance Sensor (OSPRS) for Fast and Accurate Diagnosis of Dengue Virus (DeVs)

Cyanobacteria can benefit from freshwater salinization following the collapse of dominant phytoplankton competitors and zooplankton herbivores

Abstract Freshwater salinization is an increasing threat to lakes worldwide, but despite being a widespread issue, little is known about its impact on biological communities at the base of the food chain. Here we used a mesocosm set‐up coupled with modern high‐frequency sensor technology to identify short‐ and longer‐term responses of phytoplankton to salinization in an oligotrophic lake. We tested the effects of salinization over a gradient of increasing salt concentrations that can be found in natural lakes exposed to road salt contamination (added salt range: from 0 to 1500 mg Cl− L−1). The high‐frequency chlorophyll‐a (chl‐a) fluorescence measurements showed an increasing divergence of chl‐a concentrations along the salinization gradient over time, with substantially lower concentrations at higher salt levels. At the sub‐daily scale, we found a profound suppression of day–night signal cycles with increasing salinity, which could be related to physiological stress due to the impairment of photosynthesis via effects on the photosystem II or potential changes in the active migration of phytoplankton. Community analyses revealed a similar decline pattern for the total phytoplankton biomass and a collapse of the total zooplankton biomass. Interestingly, we found a loss of phytoplankton diversity coupled with a compositional re‐organization involving the loss of dominant green algae but increased biomass of salt‐tolerant cyanobacteria. Altogether, these results suggest that specific cyanobacterial taxa can benefit from freshwater salinization following the collapse of dominant phytoplankton competitors and zooplankton herbivores. The results also highlight the value of autonomous sensor technology to capture novel, small‐scale ecological responses to freshwater salinization, and thereby to track fast changes in primary producer communities.

Enhancing performance of microbolometers by utilizing low-temperature polycrystalline silicon

In response to the urgent need for advanced noncontact temperature sensing technologies to mitigate pandemic transmission, there has been a notable surge in global demand. Thermal cameras, combined with infrared sensors, are critical not only for high-resolution imaging but also for cost-effective commercialization. Amorphous silicon-based microbolometers offer advantages in terms of integration and cost compatibility with conventional silicon processes. However, they suffer from limitations in their electrical properties, particularly in the noise-equivalent temperature difference. This study examines the effectiveness of low-temperature polycrystalline silicon (poly-Si) as an active material for microbolometer cells compared to amorphous silicon, focusing on improving the temperature coefficient of resistance (TCR) and lowering the noise density. Our investigation reveals that various parameters, such as dehydrogenation temperatures ranging from 350 to 550 °C, diverse laser annealing techniques (including single, step and multishot methods), and laser power density levels ranging from 150 to 300 mJ/cm2, influence the grain size trends of poly-Si. Using these methods, we produced poly-Si films with grain sizes ranging from 15 to 40 nm, which were used as the active layer in bolometer cells. The final part of our study assessed the TCR and noise density in devices with different poly-Si grain sizes. The TCR/noise density ratio was 3.5 times better in poly-Si devices compared to amorphous silicon devices. This study evaluates poly-Si as an active material for microbolometers, paving the way for future research and development in next-generation infrared sensor technology.

The Fuse Platform: Integrating data from IoT and other Sensors into an Industrial Spatial Digital Twin

Abstract. Digital Twins as virtual representations of industrial assets are being used to assimilate varied sources of data for improved awareness and decision making in operations and process optimisation. This paper explores the integration of IoT sensors into a spatial digital twin called Fuse that Woodside Energy has been building for the assets it operates. We describe the Fuse platform and its knowledge graph data core that is used to organise and inter-relate data for presentation within 3D visualisations, domain-specific contexts and immersive augmented reality presentations. The key contribution here is the use of a knowledge graph to link diverse data sources so as to contextualise sensor data for actionable insights. One area of significant innovation has been the development and use of new Internet of Things (IoT) devices which have been enabled by advances in sensor technology, connectivity, and cloud computing. These new tailored data sources are complimenting existing plant and resource planning data for improved asset monitoring, predictive maintenance and process automation.

Rapid Synthesis of Uniformly Small Nickel Nanoparticles for the Surface Functionalization of Epitaxial Graphene

AbstractNickel nanoparticles (Ni NPs) combined with carbon nanomaterials are of significant interest due to their wide range of applications, including catalysis, hydrogen storage, and sensor technologies. However, it is challenging to develop an efficient process to produce small and stable Ni NPs ideal for functionalizing graphene or substrates with complex geometries. For this purpose, a rapid, simple, and cost‐effective method is presented for synthesizing uniformly small Ni NPs. The process involves cooling aqueous solutions of Ni(OAc)2 and cetyltrimethylammonium bromide (CTAB) to ≈1 °C, followed by the rapid addition of NaBH4. Crucial parameters, such as temperature and stirring rate, are precisely controlled to ensure uniform particle growth, with the reaction completing in just a few minutes. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) characterizations reveal spherical NPs with an average diameter of ≈11 nm and a narrow size distribution. Additionally, epitaxial graphene (EG) samples are functionalized with the synthesized NPs and their arrangement on the surface and their stability upon thermal annealing are investigated. X‐ray photoelectron spectroscopy (XPS) and scanning tunneling microscopy (STM) measurements demonstrate the degradation of CTAB, along with the recovery of Ni(0) under mild conditions (below 350 °C), with the NPs maintaining structural stability up to approximately 550 °C.

Design of Digital Lux Meter Based on Arduino Using EL7900 Photodiode Sensor

Aims: To design and manufacture a digital lux meter to the illumination intensity (E) detection tool that is more efficient, relatively cheaper, and can be carried because it has a smaller shape and size compared to tools on the market. Study Design: Design of lux meter digital based on Arduino uno using an EL 7900 sensor. Place and Duration of Study: Physics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University, from June 2024 to September 2024. Methodology: The method used is to design electronic components so that it can become a digital lux meter. The main processor uses Arduino. The microcontroller functions as a data processor which is the output of the EL 7900 sensor. The HC-SR04 sensor is a distance sensor whose function is to measure the distance measured on the design tool. The detection results from the sensor are then processed in the microcontroller and displayed on the OLED. Results: Lux meter digital based on Arduino uno using EL 7900 sensors has been produced. The results of the research show that the further the measurement distance from the light source, the illumination intensity (E) decreases. This research also finds that the illumination intensity will increase if the source power is increased. Based on calibration testing between the design tool and the reference tool, it shows very good results with an error rate of 0.06%. Field test results showed that the level of accuracy of the illumination intensity between the design tool and the reference tool at a distance of 150 cm from the light source with different light source power reached 99.48%. Conclusion: In this research, digital lux meter to the illumination intensity (E) detection tool, relatively cheaper and can be carried because it has a smaller shape and size compared to tools on the market. Based on calibration testing, it shows very good results with an error rate of 0.06%.

Near-surface particulate backscattering observations with bio-optical Lagrangian drifters

Abstract Particulate matter concentration is an essential ocean variable (EOV) useful for understanding biogeochemical processes, improving ocean productivity estimates, and constraining coupled physical and biogeochemical numerical models. While direct observations of this parameter are difficult to obtain, the optical backscattering coefficient of marine particles (bbp) can be used as a reliable proxy. However, most in-situ multi-spectral bbp measurements are ship-borne or obtained from moored systems, thus with a limited spatial and temporal coverage. Utilizing Surface Velocity Programme (SVP) drifting buoys with self-contained backscatter sensors designed for extended deployment periods offers a promising solution for collecting data in challenging marine environments. In this study, we presented for the first time the integration of a commercially available, high-frequency and multispectral optical backscatter sensor into an SVP drifter, the so-called BO-SVP drifter designed to collect bbp measurements near the ocean surface. bbp measurements obtained by the BO-SVP drifter were reliable over a wide range of environmental conditions, showing a good agreement with independent datasets (relative bias < 10%; relative standard deviation < 36%). The BO-SVP drifter captures the satellite sub- and pixel scale variability by combining the Lagrangian approach and a high frequency sampling. This configuration enables the acquisition of measurements across numerous pixels in a single day, enhancing validation activities for ongoing and future satellite products and the quantification of associated uncertainties. The measurements acquired by these platforms can offer valuable insights into particle distribution at fine spatial scales, daily variability, and its relationship with water masses type and ocean dynamics.

Retraction Note: Design of optical sensors based on computer vision in basketball visual simulation system

Retraction Note: Design of optical sensors based on computer vision in basketball visual simulation system

Short Lysine-Containing Tripeptide as Analgesic Substance: The Possible Mechanism of Ligand–Receptor Binding to the Slow Sodium Channel

A possible molecular mechanism of the ligand–receptor binding of Ac-Lys-Lys-Lys-NH2 (Ac-KKK-NH2) to the NaV1.8 channel that is responsible for nociceptive signal coding in the peripheral nervous system is investigated by a number of experimental and theoretical techniques. Upon Ac-KKK-NH2 application at 100 nM, a significant decrease in the effective charge carried by the NaV1.8 channel activation gating system Zeff is demonstrated in the patch-clamp experiments. A strong Ac-KKK-NH2 analgesic effect at both the spinal and supraspinal levels is detected in vivo in the formalin test. The distances between the positively charged amino groups in the Ac-KKK-NH2 molecule upon binding to the NaV1.8 channel are 11–12 Å, as revealed by the conformational analysis. The blind docking with the NaV1.8 channel has made it possible to locate the Ac-KKK-NH2 binding site on the extracellular side of the voltage-sensing domain VSDI. The Ac-KKK-NH2 amino groups are shown to form ionic bonds with Asp151 and Glu157 and a hydrogen bond with Thr161, which affects the coordinated movement of the voltage sensor up and down, thus modulating the Zeff value. According to the results presented, Ac-KKK-NH2 is a promising candidate for the role of an analgesic medicinal substance that can be applied for pain relief in humans.