Advanced Sensors Research Articles

On-Site Electrochemical Sensor for Carbamazepine Monitoring in Aquatic Environments

Abstract This work analyzes the detection of carbamazepine (CBZ), a commonly used pharmaceutical that exhibits extended persistence in aquatic habitats, by the application of advanced sensor technologies. Owing to its chemical stability, CBZ is resistant to natural degradation, posing significant ecological risks in aquatic environments. Conventional techniques for CBZ detection, such as high-performance liquid chromatography and spectrophotometry, require intricate laboratory configurations and significant sample preparation, restricting their use for fast in situ monitoring. To tackle these issues, we created a portable electrochemical sensor using screen-printed electrodes on a flexible polyester-based substrate. Under optimized conditions, the sensor demonstrated high sensitivity in a linear detection range from 1 to 20 µM with a detection limit of 0.8 M by differential pulse voltammetry technique. Recovery range was found to be 93% to 107%, which further confirmed reliability, showing consistency in CBZ detection across varied concentration levels in wastewater. Preliminary results on continuous measurements have been carried out, demonstrating a promising application for prolonged analysis in real-settings.

Oxygen Vacancy Mediated-Bismuth Molybdate/Graphitic Carbon Nitride Type II Heterojunction Chemiresistor for Efficient NH3 Detection at Room Temperature.

Metal oxide-based chemiresistive gas sensors are expected to play a significant role in assessing human health and evaluating food spoilage. However, the high operating temperature, insufficient limit of detection (LOD), and long response/recovery time restrict their broad application. Herein, 3D Bi2MoO6/2D Eg-C3N4 heterocomposites are developed for advanced NH3 gas sensors with RT operational mode. Utilizing the synergetic engineering of micro-nanostructure, surface oxygen vacancies, and well-defined Type II n-n heterojunctions, BMOCN3 demonstrated superior NH3 sensing properties at 23 °C, including the high response (S = 13.6 at 10 ppm), fast response/recovery speed (8/30 s), excellent selectivity, and low LOD (166 ppb). Based on the experimental, DFT, and MD studies, the improved sensing performance can be ascribed to accelerated charge transfer, superior redox capacity, and improved adsorption/desorption kinetics. Moreover, the practical application in rapid exhaled NH3 biomarker detection of the as-fabricated gas sensor was preliminarily verified. This work highlights that the novel synergetic engineering can effectively modulate the electronic structure and charge transfer, offering a rational solution for room temperature chemiresistive gas sensors.

An advanced deep learning-driven Terahertz metamaterial sensor for distinguishing different red wines

Terahertz time-domain spectroscopy (THz-TDS) encounters two issues in the detection field, which refer to the detection target for solid samples caused by the strong absorption of water and the large content target substance led by the low sensitivity. Fortunately, terahertz metamaterial (THz-MM) that can carry liquid samples and amplify signals solves the above problems well. In addition, the THz-MM can achieve the detection of trace substances through the resonance peak shift generated by designing suitable structures. However, since most researchers focus on designing complex structures rather than analyzing data, deep learning (DL) that can mine new features from original features and construct decision models is used to research the rich information in THz-MM sensor data. In the current research, a flexible transmissive THz-MM in the shape of a circle (‘O’ shape) was designed by depositing the gold on the polyimide substrate. Firstly, the structures referring to substrate thickness (ST), metal thickness (MT) and ring width (RW) were optimized, and the performances referring to principle, stability and sensitivity were evaluated. Next, the best THz-MM (ST: 16 µm, MT: 0.2 µm, RW: 6 µm) was prepared and characterized from morphology, thickness and consistency. Then, different concentrations of anthocyanins (R2: 0.9982) and tannic acid (R2: 0.9736) were successfully predicted by combining the resonance peak shifts. Finally, resonance peak descriptors were constructed and combined with DL referring to a fully connected neural network (FCNN) model to successfully identify different varieties of red wines (Precision: 91.11 %; Recall: 90.74 %, F1-score: 90.83 %; Accuracy: 90.74 %). Overall, the current research presents an advanced DL-driven THz-MM sensor, which promotes the process of THz-TDS technology in the food detection field

Real-time Neurochemical Sensing of Hydrogen Peroxide Production in Brain Tissue using Advanced Nanostructured Au Microelectrodes Functionalized with Ruthenium Purple Film

Real-time monitoring of H2O2 is essential for understanding its role in neurological physio(path)ology. In this study, we developed an advanced electrochemical sensor utilizing nanostructured gold wire microelectrodes functionalized with a Ruthenium Purple (RP) film, a polynuclear transition metal hexacyanoferrate analog of the prototypical Prussian blue (PB). The RP film exhibits enhanced stability in neutral buffer solutions and reduced interference from biologically relevant cations such as Na+, Mg2+, and Ca2+. By integrating gold nanoparticles onto a nanoporous gold surface, we significantly increased the electroactive surface area of the microwire (ρ=26.1 ± 10.2). The electrodeposition of a thin RP film (4.23 ± 1.2 nm) resulted in a highly selective H2O2 sensor, operating at an optimal potential of -0.05 V vs. Ag/AgCl, with a linear detection range of 0.5-500 µM, sensitivity of 1.18 ± 0.37 µA µM-1 cm-2, and a low limit of detection (LOD) of 66.6 ± 34.9 nM. Adding a Nafion® layer enhanced the sensor's operational stability, maintaining optimal performance over three hours under physiological conditions of pH and ion concentration. We validated the sensor's capability to monitor transient changes in H2O2 concentration in brain tissue by assessing its response to exogenously applied H2O2. Furthermore, we demonstrated that glutamate receptor activation in hippocampal slices induces a rapid and transient increase in extracellular H2O2 levels, underscoring the sensor's potential for studying oxidative stress in neurobiological contexts.

Enhanced adsorption and sensing properties of Ptn(n=1,3)-doped SnS2 monolayers for warfare toxic gases: A DFT Study

Toxic gases have been extensively employed in warfare, spanning from the deployment of irritant tear gas to fatal nerve gas. These chemical armaments have caused significant damage and casualties in times of conflict. This study explores the adsorption properties and sensing mechanisms of three chemical warfare agents(chlorine Cl2, phosgene COCl2, chloropicrin CCl3NO2) on both pristine SnS2 and doped SnS2 using density functional theory (DFT). The results highlight the superior adsorption energy, charge transfer efficiency, and band structure of doped Ptn/SnS2 (n=1,3) compared to pristine SnS2. Pt/SnS2 exhibits exceptional adsorption capabilities for all three war gases. Under specific conditions, Pt3/SnS2(cluster) emerges as an ideal material for detecting Cl2 and CCl3NO2, while also serving as an effective adsorbent for COCl2 gas. This research serves as a valuable reference for sensor performance studies grounded in first-principles theory, showcasing the potential of doped Pt3/SnS2 as a viable sensor for warfare gases. The insights from this study contribute to the development of advanced poison gas sensors, facilitating improvements in poison gas detection and prevention capabilities.

Remotely geolocating static events observed by citizens using data collected by mobile devices

The increasing use of smartphones has led to a surge in crowdsourcing initiatives, where citizens easily collect and upload information using advanced sensors, leveraging the collective efforts of the crowd. However, these devices face accuracy issues that must be addressed before they can be used effectively in certain applications. While most research has focused on GNSS-based positioning errors, compass-based orientation errors have received far less attention. This paper presents a novel method for determining the geolocation of static events by combining contributions from multiple volunteers located around a target event. Instead of straight lines, the method applies funnel-shaped regions to represent the azimuths toward the target location and encompasses various phases to address errors stemming from measured azimuths. These compass-based orientation errors have attracted less attention compared to GNSS-based positioning errors. The approach mitigates the uncertainty in azimuth measurements, producing a set of regions that contain the target location with different levels of confidence. Its reliability was tested in three case studies with known target locations, using metrics such as area and compactness. The results were promising, indicating that the developed approach is particularly useful in applications that do not require a high degree of accuracy (common in many crowdsourcing projects), such as the geolocation of forest fire ignitions − once in the vicinity they can be easily spotted. Implementing this approach within a system that collates citizens’ contributions can furnish invaluable information to authorities, empowering prompt action during emergency scenarios.

Multimodal Human–Robot Interaction Using Gestures and Speech: A Case Study for Printed Circuit Board Manufacturing

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, including gesture recognition via Leap Motion and Tap Strap, and speech recognition. The gesture recognition system achieved an average accuracy of 95.42% and 97.58% for each device, respectively. The speech control system, called Cellya, exhibited a markedly reduced Word Error Rate of 22.22% and a Character Error Rate of 11.90%. Furthermore, a scalable user management framework, the decentralized multimodal control server, employs biometric security to facilitate the efficient handling of multiple users, regulating permissions and control privileges. The platform’s flexibility and real-time responsiveness are achieved through advanced sensor integration and signal processing techniques, which facilitate intelligent decision-making and enable accurate manipulation of manufacturing cells. The results demonstrate the system’s potential to improve operational efficiency and adaptability in smart manufacturing environments.

Automated Fire Sensing and Fire Extinguisher Module

The Hazard of fire and fire explosions poses significant risks to both property and human safety, necessitating immediate action to minimize damage. Conventional fire detection methods that rely on human observation may result in delays, particularly in unattended areas. To address these challenges, a specialized fire detection and extinguishing system is being developed with the capability to autonomously detect and extinguish fires. This system will also promptly notify individuals through messages and phone calls upon fire detection, improving response times and ensuring immediate awareness of fire incidents. Equipped with Arduino microcontrollers, the system integrates advanced sensors for precise detection of heat and smoke, allowing accurate pinpointing of fire locations. The use of Arduino technology provides adaptable functionality in various environments, ranging from residential to commercial settings. Effective communication of fire alerts is essential for promptly notifying firefighters and occupants, facilitating timely intervention and safe evacuations. The objective is to create an efficient system that harnesses modern technology to enhance fire safety measures, ultimately reducing fire related risks and effectively protecting lives.

Zirconium–Polycarboxylato Gel Systems as Substrates to Develop Advanced Fluorescence Sensing Devices

This study presents the development of zirconium polycarboxylate gel systems as substrates for advanced fluorescence sensing devices. Zirconium-based metal–organic gels (MOGs) offer a promising alternative due to the robustness of the Zr–O bond, which provides enhanced chemical stability. In this work, zirconium polycarboxylate gels were synthesized using green solvents in a rapid room temperature method. Fluorescein, naphthalene-2,6-dicarboxylic acid, and 4,4′,4″,4‴-(porphine-5,10,15,20-tetrayl)tetrakisbenzoic acid were incorporated as fluorophores to give the gel luminescent properties, enabling it to be used as a sensor. These fluorophores produce specific changes in the perceived color and intensity of the fluorescence emission upon interaction with different analytes in a solution, allowing a qualitative identification of different solvents and compounds. However, the fragile structure of neat gels hinders reproducible quantitative analysis of fluorescence emission. Therefore, to increase their mechanical stability during manipulation, a composite material was developed by combining the MOGs with quartz microcrystals, which proved to be a more reliable fluorescent system. The results show that the material can identify univocally different solvents and analytes in aqueous solutions by the quantitative analysis of the emission intensities. This work presents an innovative approach to create advanced fluorescence sensors with improved mechanical properties and stability using zirconium polycarboxylate gels and multiple fluorophores.

Effects of sputtering process and annealing on the microstructure, crystallization orientation and piezoelectric properties of ZnO films

Ceramic piezoelectric films play a crucial role in sensors, acoustic wave devices, and micro-electromechanical systems (MEMS) due to their superior piezoelectric properties. This study focuses on optimizing ZnO piezoelectric films through radio frequency (RF) magnetron sputtering under a combination of varied deposition parameters such as tuning the target-substrate distance, sputtering power, and pressure effectively to control the atomic bombardment, collisions, and surface diffusion of sputtered particles, thereby influencing the surface morphology, grain size, thickness and chemical composition of the ZnO films. Furthermore, annealing heat treatment enhances the crystallization quality and piezoelectric properties of the ZnO films. Results show that annealing the films sputtered at a magnetron power of 50 W at 300 °C maximizes a favorable c-axis (002) orientation. Piezoelectric performance confirms that the annealed ZnO films exhibits enhanced piezoelectric amplitude, mechanical-electrical signal response, stable linear piezoelectric behavior, and a 180° phase flip under voltage excitation. This work is promising to lay a foundational solution for the development of advanced sensors with excellent piezoelectric properties.

Powering a molecular delivery system by harvesting energy from the leaf motion in wind.

Smart agriculture tools as well as advanced studies on agrochemicals and plant biostimulants aim to improve crop productivity and more efficient use of resources without sacrificing sustainability. Recently, multiple advanced sensors for agricultural applications have been developed, however much less advancement is reported in the field of precise delivery of agriculture chemicals. The organic electronic ion pump (OEIP) enables electrophoretically-controlled delivery of ionic molecules in the plant tissue, however it needs external power-supplies complicating its application in the field. Here, we demonstrate that an OEIP can be powered by wind-driven leaf motion through contact electrification between a natural leaf and an artificial leaf. This plant-hybrid triboelectric nanogenerator (TENG) directly charges the OEIP, enabling proton delivery into a pH indicator solution, which triggers visible color changes as a proof-of-concept. The successful delivery of up to 44 nmol of protons was revealed by pH measurements after 17 h autonomous operation in air flow moving the plant and artificial leaves. Several control tests indicated that the proton delivery was powered uniquely by the charges generated during leaf fluttering. The OEIP-TENG combination opens the potential for targeted and self-powered long-term delivery of relevant chemicals in plants, with the possibility of enhancing growth and resistance to abiotic stressors. &#xD.

IoT based real-time water quality monitoring system in water treatment plants (WTPs)

This study presents the development and implementation of an Internet of Things (IoT)-based real-time water quality monitoring system tailored for water treatment plants (WTPs). The system integrates advanced sensor technologies to continuously monitor key water quality parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), and temperature. Data collected by these sensors is transmitted through a robust communication network to a centralized monitoring platform that utilizes cloud-based storage and analytics. The system's design includes a PLC-based control mechanism, allowing for flexible setup modifications and the easy addition of new monitoring parameters. The IoT-based system, powered by a low-energy 29-W configuration, offers accurate and reliable data with a minimal error margin of 0.1–0.2 across various parameters. The research highlights the system's ability to provide real-time alerts, historical data logging, and remote monitoring, all of which contribute to enhanced operational efficiency, proactive maintenance, and informed decision-making. This innovative approach to water quality management not only improves the effectiveness of WTP operations but also ensures environmental sustainability and public health safety. The study underscores the significant potential of IoT technologies in revolutionizing water quality monitoring practices.

Diamond-coated quartz crystal microbalance sensors: Challenges in high yield production and enhanced detection of ethanol and sars-cov-2 proteins

This study presents the technological progress in the deposition of diamond thin films on quartz crystal microbalance (QCM) sensors. The linear antenna microwave plasma chemical vapour deposition (CVD) technique effectively grows thin diamond films on QCM substrates (Dia-QCM) differently oriented on the substrate holder in the deposition chamber, resulting in single-sided and double-sided coated QCMs. Each of these coated QCMs offers a distinctive advantage for sensing applications. The double-sided coated QCM sensors exhibited the most effective performance in ethanol detection, demonstrating approx. a 3-fold and 12-fold higher response than single-sided diamond-coated and bare gold QCM sensors, respectively. Furthermore, the single-sided Dia-QCM aptasensors demonstrated superior performance compared to bare gold QCM sensors, with a 2-fold higher response and a lower detection limit for S-RBD protein (LODDia-QCM = 0.09pg/mL vs. LODAu-QCM = 0.10pg/mL). In experiments conducted in human plasma, the Dia-QCM aptasensor demonstrated the ability to detect S-RBD protein at concentrations as low as 50pg/mL, with high percentage recoveries. These results highlight the potential of linear antenna microwave plasma CVD for the mass production of advanced diamond-coated QCM sensors with different diamond film morphologies (porous, micro- or nanocrystalline) for various applications.

Superior piezoelectric performance in holmium-substituted high-TC Bi5Ti3FeO15

High-temperature piezoelectric materials are essential for advanced sensor technologies, yet achieving both superior piezoelectric properties and a wide operating temperature range remains a persistent challenge. Bismuth titanate-ferrite (Bi5Ti3FeO15) is a promising candidate due to its notably high Curie temperature (TC) of approximately 761 °C. However, its low intrinsic piezoelectric response and limited poling efficiency, attributed to insufficient direct-current (dc) resistivity, have restricted its broader utilization. To address these issues, we explored compositional modifications by partially substituting A-site bismuth ions with holmium ions (Bi5-xHoxTi3FeO15, BTF-100xHo), aiming to enhance both piezoelectric performance and electrical resistivity. Comprehensive characterization was performed on the holmium-substituted ceramics, examining their crystal structure, microstructural attributes, and piezoelectric properties. Our findings reveal that the holmium-substituted Bi5Ti3FeO15 compositions exhibit significantly improved piezoelectric behavior in comparison to their unmodified counterparts. The optimized BTF-4Ho composition demonstrates a high piezoelectric coefficient (d33 = 23.1 pC/N) and an elevated TC of 795 °C. Moreover, the BTF-4Ho composition maintains stable electromechanical coupling across a wide temperature range, underscoring its potential for high-temperature sensor applications.

Wearable Robotic Exoskeletons for Assisted Mobility: Enhancing Rehabilitation and Quality of Life for Neuromuscular Disorders

Wearable robotic exoskeletons represent a significant advancement in assistive technology, offering enhanced mobility and improved quality of life for individuals with neuromuscular disorders. This research explores the integration of robotics and biomechanical engineering to develop exoskeleton systems tailored for rehabilitation and daily mobility assistance. Key areas of investigation include the use of sensor-driven actuators for real-time movement adaptation, the role of artificial intelligence (AI) in customizing therapy sessions, and the impact of these devices on neuroplasticity during rehabilitation. By analyzing real-world applications in conditions such as spinal cord injuries, muscular dystrophy, and stroke recovery, the study evaluates the effectiveness of exoskeletons in restoring functional movement and independence. Ethical considerations, including accessibility, cost, and long-term usability, are also addressed. This research underscores the transformative potential of wearable robotic exoskeletons, emphasizing their role in bridging the gap between assistive mobility and therapeutic rehabilitation. With the integration of AI and advanced sensors, these systems hold promise for reshaping rehabilitation paradigms, improving patient outcomes, and enhancing the overall quality of life for individuals with neuromuscular impairments.

Evaluation of vermicompost and vermicompost tea application on corn (Zea mays) growth and physiology using optical plant sensors

Corn is a vital global crop, yet its cultivation demands extensive agrochemical inputs, prompting the need for sustainable alternatives. This study investigates the impact of vermicompost (VC) and vermicompost tea (VCT) applications on corn growth, physiology, and resistance to Fall Armyworm (FAW) infestation using advanced optical plant sensors. Six treatments were employed: V0 (control), VC1, VCT100, VC1 + VCT50, VC3, and VC3 + VCT50. During the growing season, plant growth parameters, such as height, chlorophyll content, and spectral reflectance were measured using a chlorophyll meter, fluorometer, porometer, and spectroradiometer. Results indicated that VC-treated plants exhibited superior growth and higher chlorophyll content than control or untreated plants. The VC1 + VCT50-treated plants showed robust resistance to FAW, with no infestation throughout the season, while VC1-treated plants showed delayed attack by FAW. Soil chemical analysis showed that VC and VCT treatments had similar nutrient concentrations as the control. Plant nutrient content was higher in VCT100 compared to all treatments. These findings suggest that the combined application of VC and VCT, particularly at specific application rates, can enhance corn plant health, mitigate pest damage, and optimize yield potential.

AI-Driven Rehabilitation Robots: Enhancing Physical Therapy for Stroke and Injury Recovery

AI-driven rehabilitation robots are transforming physical therapy by providing personalized, precise, and adaptive support for patients recovering from strokes and injuries. This research explores the integration of Artificial Intelligence (AI) into robotic systems to enhance physical rehabilitation outcomes, focusing on key areas such as motor skill recovery, real-time performance tracking, and patient engagement. Utilizing machine learning algorithms and biomechanical data, these robots can tailor therapy sessions to individual needs, dynamically adjusting resistance, movement patterns, and feedback. Advanced sensor technology enables the robots to monitor patient progress, ensuring accurate assessments and adaptive interventions. This study also examines the role of AI in promoting neuroplasticity through repetitive, task-specific training, a critical component of stroke recovery. Ethical considerations, including data privacy and accessibility, are analyzed to address barriers to widespread adoption. By bridging robotics, AI, and clinical practice, this research highlights the potential of AI-driven rehabilitation robots to revolutionize physical therapy, offering scalable and effective solutions that improve recovery rates and enhance the quality of care.

Chemiresistive Gas Sensors Made with PtRu@SnO2 Nanoparticles for Machine Learning-Assisted Discrimination of Multiple Volatile Organic Compounds.

Volatile organic compounds (VOCs) constitute key pollutants in the environment, and exposure to them is associated with negative health impacts. The vigilant monitoring of these pernicious VOCs is imperative for their timely detection and for curtailing the likelihood of both immediate and prolonged exposure, thus safeguarding against the deterioration of environmental quality. In this study, porous PtRu nanoalloys are successfully synthesized via a hydrothermal method and innovatively integrated with SnO2 nanoparticles to significantly enhance the performance of gas sensors. Density functional theory (DFT) calculations substantiated the pivotal role of PtRu nanoalloys in amplifying the sensitivity of SnO2 to acetone. A primary challenge in VOC surveillance is achieving the selectivity required for sensors to accurately identify specific compounds. By employing machine learning algorithms, with a particular emphasis on particle swarm optimization-support vector machine (PSO-SVM), we attained a classification accuracy of 100% in distinguishing between acetone, ethanol, methanol, and formaldehyde. This study demonstrates the potential for creating advanced sensors with selective detection of VOCs.

Surface facet engineering of ZnIn2S4 via supercritical hydrothermal synthesis for enhanced NO gas sensing performance

In this study, we investigate the novel application of ZnIn2S4 as an NO gas detection device by precisely modulating its surface facets through crystal growth control in a supercritical water environment. The supercritical hydrothermal synthesis successfully transforms ZnIn2S4 from a flower-like structure into a hexagonal plate morphology, driven by the preferential growth of the basal plane (003) surface facet. This morphological control, which is unattainable in a subcritical environment, is evidenced by a substantial increase in the (003)/(011) facet ratio from 0.52 to 1.98 with rising temperature. NO detection results indicate that this surface morphology modification significantly accelerates sensor response, attributed to enhanced interaction between the ZnIn2S4 surface and NO gas, as well as reduced diffusion limitations compared to the flower-like morphology. The hexagonal plates exhibit a remarkably fast response time of approximately 25 s, in contrast to 181 s for the flower-like counterpart. These findings underscore the crucial role of surface facet engineering in optimizing the gas-sensing properties of ZnIn2S4, highlighting its potential for advanced gas sensor applications.

Enhancing the Pressure-Sensitive Electrical Conductance of Self-Assembled Monolayers.

The inherent large HOMO-LUMO gap of alkyl thiol (CnS) self-assembled monolayers (SAMs) has limited their application in molecular electronics. This work demonstrates significant enhancement of mechano-electrical sensitivity in CnS SAMs by external compression, achieving a gauge factor (GF) of approximately 10 for C10S SAMs. This GF surpasses values reported for conjugated wires and DNA strands, highlighting the potential of CnS SAMs in mechanosensitive devices. Conductive atomic force microscopy (cAFM) investigations reveal a strong dependence of GF on the alkyl chain length in probe/CnS/Au junctions. This dependence arises from the combined influence of molecular tilting and probe penetration, facilitated by the low Young's modulus of alkyl chains. Theoretical simulations corroborate these findings, demonstrating a shift in the electrode Fermi level toward the molecular resonance region with increasing chain length and compression. Introducing a rigid graphene interlayer prevents probe penetration, resulting in a GF that is largely independent of the alkyl chain length. This highlights the critical role of probe penetration in maximizing mechano-electrical sensitivity. These findings pave the way for incorporating CnS SAMs into mechanosensitive and mechanocontrollable molecular electronic devices, including touch-sensitive electronic skin and advanced sensor technologies. This work demonstrates the potential of tailoring mechanical and electrical properties of SAMs through molecular engineering and interface modifications for optimized performance in specific applications.