Sensor Applications Research Articles

Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

Fabrication and optical characterization of porous silicon heterostructure as matrix for sensing organic solvent via resonance peak and Q factor shift

Porous silicon heterostructures with an extensive photonic band gap, featuring a microcavity defect layer, have been successfully fabricated and applied as sensor devices for detecting organic solvent species. These sensors utilize both the resonance peak shift and the inverse Q-factor as sensing parameters. Similar to conventional porous silicon microcavities immersed in various organic solvents, the resonance peak exhibits a linear dependence on the solvent refractive index. However, in the proposed structure, the inverse Q-factor also displays a comparable linear trend with the refractive index, but with greater sensitivity to solvent mixtures. This increased sensitivity arises because the inverse Q-factor deviates from linearity when the prior solvent is not adequately removed from the porous structure before reflectance measurements — a condition not commonly detected by the resonance peak shift. To improve the performance of these sensors, the porous structures were passivated by thermal oxidation. For sensor applications, it is crucial that the contrast in the effective refractive index between the high and low porosity layers of the passivated structures be sufficiently large. Otherwise, when the porous matrix is immersed in solvents, it loses its resonance peak, rendering the structure ineffective for sensor applications.

Enhanced Photodetection Performance of WSe2/V2O5 Nanocomposite on Flexible Substrate: Synergistic Advantages and Improved Efficiency.

The two-dimensional (2D) chalcogenide WSe2/V2O5 composite nanostructures were synthesized using the hydrothermal method and extensively characterized with various spectroscopic techniques. X-ray diffraction analysis confirmed the hexagonal crystal structure exhibiting space symmetry of P63/mmc. Scanning electron microscopy images provided insights into the irregular and nonuniform morphology. Optical spectrum analysis indicated a band gap value of 2.01 eV for 15% WSe2/V2O5 nanostructures, as determined by the Wood and Tauc equation. Photoluminescence (PL) excitation spectra at emission wavelengths of 550 and 750 nm exhibited broad emission attributed to self-trapped excitons for V2O5 and WSe2 nanostructures. Under excitation at λexc = 365 nm, PL emission spectra displayed distinct peaks at 550 and 750 nm, demonstrating the ability to emit vivid red light. A device optimized for photoresponsivity (R) of approximately 7.80 × 10-1 A W-1 and detectivity (D) of around 8.65 × 1011 Jones, and quantum efficiency of approximately 3.42 × 10-2 A W-1 were achieved at a wavelength of 390 nm while using a lamination sheet as a substrate. These findings underscore the capability of devices for efficient photoconversion at specified wavelengths, indicating potential applications in sensing, imaging, and optical communication. The photoresponsivity of the device remained stable at 3.38 × 10-3 A W-1 at 0° and 3.09 × 10-3 A W-1 at 55° bending angle. This indicates the resilience of device to mechanical strain, making it ideal for flexible and wearable sensor applications. The structural, morphological, and optical characterizations confirm the suitability of luminescent WSe2/V2O5 chalcogenide for practical optoelectronic applications, especially in display technologies.

Observation of Body‐Centered‐Cubic Cu Nanocluster with Partial Cu(0) Character

The structure of nanoparticles is a key determinant of their material properties. The synthesis of non‐face‐centered‐cubic (FCC) Cu nanoclusters (NCs) with Cu(0) character is still challenging due to their instability. Herein, a Cu(0)‐containing Cu NC is first reported to contain a metallic framework of 23 Cu atoms with a body‐centered‐cubic Cu@Cu8 core, stabilized by 6 triphenylphosphine (TPP) and 18 4‐methylbenzenethiolate ligands, as determined by single‐crystal X‐ray crystallography. The observed structure stands in stark contrast to the FCC and other structures reported for Cu NCs. Moreover, this newly formed NC could react with Au+ ion with controlled concentration, rapidly yielding another identifiable and bright red‐emitting AuCu14 cluster upon irradiation. Based on this structural conversion, a new “turn‐on” type of photoluminescent probe can be developed for the detection of Au+ ion with an ultrafast response time (30 s) at a low detection limit (0.13 ppm). This work provides a new way of understanding the growth of metal clusters and exploring their applications in luminescent sensors.

Tear-Based Ocular Wearable Biosensors for Human Health Monitoring.

Wearable tear-based biosensors have garnered substantial interest for real time monitoring with an emphasis on personalized health care. These biosensors utilize major tear biomarkers such as proteins, lipids, metabolites, and electrolytes for the detection and recording of stable biological signals in a non-invasive manner. The present comprehensive review delves deep into the tear composition along with potential biomarkers that can identify, monitor, and predict certain ocular diseases such as dry eye disease, conjunctivitis, eye-related infections, as well as diabetes mellitus. Recent technologies in tear-based wearable point-of-care medical devices, specifically the state-of-the-art and prospects of glucose, pH, lactate, protein, lipid, and electrolyte sensing from tear are discussed. Finally, the review addresses the existing challenges associated with the widespread application of tear-based sensors, which will pave the way for advanced scientific research and development of such non-invasive health monitoring devices.

Chemical spray pyrolysis deposited ZnO/ZIF-8 and cobalt doped ZnO/ZIF-8 composite thin films for highly sensitive and selective ammonia sensing at room temperature

ZnO is a widely studied metal oxide for gas sensor applications concerning its biocompatibility and physio-chemical properties. However, the high operating temperature and low response are drawbacks. ZIF-8, the metal-organic framework of Zinc and imidazolate, is renowned for its gas adsorption property. The poor stability of ZIF-8 also limits its application as an effective gas sensor. Thus, a composite thin film of ZnO and ZIF-8 is deposited on a glass substrate using the chemical spray pyrolysis technique to detect ammonia at room temperature (300 K). The composite thin film is stable, has a good response, and is repeatable. Further, to enhance the sensor response at room temperature, Co is doped to the ZnO/ZIF-8 composite thin films. The sensor response for 1 ppm of ammonia for the Co doped ZnO/ZIF-8 is 301 at room temperature. Along with the gas adsorption capability of ZIF-8 providing more active sites, the change in morphology from flakes to spherical grains on doping with Co provided higher active surface area and free electrons and all together instigated fast redox reaction and conduction mechanism in ZnO. Hence, the Co doped ZnO/ZIF-8 composite thin films have an enhanced response of 117,285 to 50 ppm of ammonia.

Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Realizing accurate determination of in situ soil organic matter (SOM) content and soil moisture (SM) content in the field is of great importance for improving agricultural production efficiency. However, the feature information of a single sensor is limited, and there is still a certain gap in modeling accuracy compared to traditional laboratory methods. The variations of SM in the field also interfere with data collection, limiting the application of single sensor. In order to realize the efficient detection of in-situ SOM and SM content, this study developed an online detection system for SOM and SM content based on the fusion of characteristic wavelengths, visible images and thermal imaging image features. Firstly, based on a vehicle-mounted platform, a visible-thermal imaging camera and a characteristic wavelength integration device were integrated in a deep pine plow to realize the simultaneous acquisition of in-situ soil multi-sensor data. Then, a lightweight multimodal network was constructed to obtain thermal visible light image features and characteristic wavelength features through branch networks, achieving deep fusion of different modal data and predicting SOM and SM content. Finally, output the forecasting results of SOM and SM content, and transmit the predictive information to the cloud platform for storage. It was verified that the Multi-modal proposed in this study worked best in the laboratory environment, with a predicted R2 of 0.91 and RMSE of 2.9 g/kg for SOM, and a predicted R2 of 0.92 and RMSE of 0.77 % for SM. Compared with single image or characteristic spectral data, the fusion of visible image and characteristic wavelength effectively improved the prediction accuracy of SOM. The real-time prediction of SM was realized by fusing thermal imaging data, and the elimination of the moisture effect of visible images and characteristic wavelengths was also realized with the help of deep fusion of Multi-modal network. After field validation, the R2 of the Multi-modal system was 0.84 and the RMSE was 5.0 g/kg for SOM, and the R2 of the SM was 0.88 and the RMSE was 1.03 %. Despite the differences in soil types between the validation field and the sampling field, the system still demonstrated strong generalization and achieved high accuracy prediction of in-situ SOM and SM in the field, which effectively improves the efficiency and applicability of field. The efficiency and applicability of soil testing effectively improve the efficiency and provide technical guidance for precision management in the field.

Use of an inertial sensor and a force platform to assess static balance in participants affected by multibacillary leprosy.

Leprosy is a chronic, slowly developing infectious disease that affects the peripheral nerves, specifically Schwann cells. Individuals with the multibacillary type exhibit a propensity for developing chronic pain and a decrease in sensitivity in the plantar region, which directly interferes with balance maintenance. The evaluation of static balance in this population is made through the measurement of the center of pressure (COP) oscillations. Therefore, there is a need to investigate the association between postural control and COP oscillations using a force platform and finding accelerations of the center of mass (COM) from inertial sensors for reliable and portable balance assessment in leprosy patients. To validate the application of inertial sensors for patients with leprosy by establishing a correlation with the outcomes obtained from a force platform. This is an observational study with a case-control design, in which 30 participants with leprosy and 30 healthy participants were recruited to evaluate static balance using an inertial sensor and a force platform. Participants underwent balance assessment under two conditions (Eyes Open: OE and Eyes Closed: CE), and data from the platform and sensor were processed using Matlab computational routines. The data were quantified using four parameters: Total Displacement (TD), Area, Antero-Posterior Displacement (APdisp), and Medio-Lateral Displacement (MLdisp). The evaluated parameters showed significantly different values between the groups, where the Leprosy group exhibited significantly higher values compared to the control group, both in the OE and CE conditions for all four parameters. The sensor corroborated the differences demonstrated by the platform and followed the same trend for medio-lateral displacements and accelerations. It can be observed that the evaluated parameters exhibited a varied correlation ranging from moderate to large between the platform and the sensor. Among the four variables, MLdisp had the lowest correlation. The results partially confirmed the first hypothesis of concurrent validation, showing a moderate to large correlation between the force platform and the inertial sensor. The second hypothesis of clinical validation was also partially confirmed, as not all group differences observed in the COP measurements from the force platform were reflected in the COM measurements from the inertial sensor. Specifically, the force platform indicated greater oscillations in participants with multibacillary leprosy compared to controls, a finding statistically confirmed by the sensor for all measures except MLdisp. This research confirmed the concurrent validity of the inertial sensor with the force platform and its clinical validation, demonstrating that this instrument can be applied in clinical settings due to its low cost and ease of use. The findings may contribute to public health by identifying postural control tools for patients with multibacillary leprosy.

Fiber‐Integrated Diamond Quantum Sensor for High‐Voltage Current Measurements

AbstractIn power network systems, there is an urgent demand for highly accurate and miniaturized sensors, owing to their high safety level and limited installation space. Current sensors in high‐voltage grids are required to accommodate harsh environments and provide accurate measurements of several kiloamperes. Thus, this study proposed an integrated quantum diamond sensor to facilitate high‐accuracy, large‐dynamic‐range current measurements. The design incorporated optical fiber and directional microwave (MW) antennas to drive the diamond sensor, which significantly reduced the size and power consumption on the high‐voltage side. Remote‐control and demodulation systems are installed more than 10 m away from the low‐voltage side. The proposed approach achieved zero power consumption on the high‐voltage side and ensured efficient signal transmission. A passive diamond probe manufactured using microfabrication processes facilitated miniaturization and practical deployment. Through parameter optimization, a magnetic detection sensitivity of 4.86 nT·Hz−1/2 is achieved at a safe distance of 11 m, which can be further optimized to 0.77 nT·Hz−1/2 with enhanced MW power. This sensor achieved a current measurement error of ±0.4% in the 1000 A measurement range. Thus, this study provides a new solution for the application of diamond quantum sensors in power systems.

Chemical Sensor Technologies for Sustainable Development: Recent Advances, Classification, and Environmental Monitoring

AbstractOne of the significant issues of concern in recent times is detecting, quantifying, and monitoring various chemical contaminants in the environment. Numerous inorganic and organic substances are discharged into the environment from various anthropogenic activities, thereby polluting the ecosystems' air, water, soil, and other components. These contaminants have been documented to have deleterious effects on humans, animals, and the environment. Therefore, the need for monitoring and continual quantification of the contents of these contaminants in various environmental matrices becomes paramount. Many diverse sensors are fabricated for multiple utilizations, including liquids and gas sensors, wireless sensors, electronic tongues, and noses for detecting volatile airborne substances, pollutants, and effluents. This review focuses on cutting‐edge technology used in chemical sensors for environmental protection and sustainable development. It also discusses the fabrication and development of chemical sensors and their working principles. In addition, highlights about the applications of chemical sensors in environmental monitoring are also elaborated. This review article discusses emerging sensor technologies and their application in ecological monitoring. Further, the recognition of chemical warfare agents and homeland security applications with the help of bioelectronic nose and tongue is discussed. Finally, some challenges associated with chemical sensors and future trends are also attempted.

A Photovoltaic Self-Powered Volatile Organic Compounds Sensor Based on Asymmetric Geometry 2D MoS2 Diodes

Transition metal dichalcogenides have gained considerable interest for vapour sensing applications due to their large surface-to-volume ratio and high sensitivity. Herein, we demonstrate a new self-powered volatile organic compounds (VOC) sensor based on asymmetric geometry multi-layer molybdenum disulfide (MoS2) diode. The asymmetric contact geometry of the MoS2 diode induces an internal built-in electric field resulting in self-powering via a photovoltaic response. While illuminated by UV-light, the sensor exhibited a high responsivity of ∼60% with a relatively fast response time of ∼10 sec to 200 ppm of acetone, without an external bias voltage. The MoS2 VOC diode sensor is a promising candidate for self-powered, fast, portable, and highly sensitive VOC sensor applications.

Construction of electrochemical sensor of Cu2O-CuO-W3O-W2C composite material for rapid and sensitive detection of vanillic acid

Vanillic acid is a phenolic compound which has antioxidant properties and plays an essential role in the prevention of several degenerative diseases, including cancer and atherosclerosis. This work reports the development and application of a electrochemical sensor for vanillic acid detection by a composite material (Cu2O-CuO-W3O-W2C) using polyoxometal-based metal–organic frameworks (POMOFs) as precursor through thermal decomposition. The synthesized Cu2O-CuO-W3O-W2C have high electrical conductivity and excellent electrocatalytic performance. The electrochemical sensor displays a linear response between the current density and concentration of vanillic acid (50–1100 μM) with a lowest detection limit of 1.439 μM under optimal conditions. The sensor was also demonstrated for the determination of vanillic acid in different real samples (fruit juice and fruit peel) and satisfactory results were obtained.

Design of an Improved Model for Multimodal Data Fusion Using XGBoost-LSTM-CNN and Proximal Policy Optimizations

Applications in healthcare, finance, and real-time sensor applications call for much stronger demands of improving accuracy and efficiency in the analysis of multimodal data. Current methods have deficiencies in fusing multiple types of data such as time-series, spatial, and categorical data, arising due to limitations in capturing sequential dependencies, spatial patterns, and feature importance simultaneously. It also discusses these challenges through a novel solution based on a hybrid ML-DL approach, with an integration of reinforcement learning and advanced probabilistic models. First, the method that comes to mind is the XGBoost-LSTM-CNN Hybrid Model, wherein three drivers of performance improvements come together, namely, XGBoost, with much-appreciated capability for outstanding handling of structured tabular data, LSTM due to its proficiency in capturing long-term temporal dependencies, and the strength of CNN in spatial feature extraction. These results improve the predictive accuracy for multimodal datasets. After that, further enhanced multimodal fusion by the CAMT selectively pays attention to the critical features across the modalities, enhancing the accuracy of the contextual time-series predictions. Subsequently, PPO enables real-time model adaptation by dynamic optimization of model parameters and improvement of predictions through continuous learning. KF-BNN reduces noise and uncertainty in time-series data by fusing the filtering capability of a Kalman filter with probabilistic modeling via Bayesian neural networks to give reliable predictions. It also provides federated learning via FedAvg with online gradient descent for distributed model training in a manner that ensures privacy, continuous model updates without having to centralize sensitive data samples. These approaches show significant improvements along many axes, with accuracy improvements of up to 4.5% and MAE reductions of 12% relative to the baseline models. Proposed models provide a valid framework for analyzing multimodal data, thereby increasing precision, recall, and overall adaptiveness in dynamic real time, hence pushing the state-of-the-art in multimodal fusion and timeseries prediction. This research probably will influence those areas dependent on distributed, multimodal, and sequential data samples.

Noise sensors - Part 1: Usage for the redesign of neighbourhoods

An interdisciplinary research project at the Stuttgart University of Applied Sciences with participation of the departments of urban planning, surveying and geoinformatics, business psychology and acoustics, present alternative urban scenarios. Different aspects for the redesign of the district, especially measures to reduce the individual motorised transport, were investigated. Virtual reality and augmented reality experiments are used in public spaces, to show redesign possibilities. Surveys of people, who had the experience of the future scenarios via the AR application and VR application, were carried out. Additionally, acoustical surveys and measurements in this research project were performed using so called noise sensors. These sensors based on MEMS microphones were developed for a simple and cost-effective measurement of external noise in a citizen sciences project. The measurement results of the noise sensors are saved online and can be accessed on the homepage "Sensor.Community". One of the noise sensors was used in the project area over a longer period. Measurement values were generated in third octave bands at intervals of 5 s and are available for further evaluations, e.g. for the calculation of indoor noise levels. The paper presents the research project and reports about the application of the noise sensor.

N-n type In2O3@-WO3 heterojunction nanowires: enhanced NO2 gas sensing characteristics for environmental monitoring.

Solvothermal synthesis of 1D n-In2O3@n-WO3 heterojunction nanowires (HNWs) and their NO2 gas sensing characteristics arereported. The n-In2O3@n-WO3 HNWs have been well-characterised using XRD, Raman spectroscopy, XPS, SEM and HRTEManalyses. The NO2 sensing performance of n-In2O3@n-WO3 HNWs showed superior performance compared withpristine WO3 NWs. Due to the distinctive configuration of WO3-In2O3 heterojunctions, the n-In2O3@n-WO3 HNWs demonstrated remarkable sensitivity reaching 182% in response towards 500ppb of NO2 gas at operating temperature of 200°Cwhich is nearly 3.5 times greater than the response observed with pristine WO3 (50%). Moreover, the n-In2O3@n-WO3 HNWs also exhibited fast response (8-13s)/recovery (54-62s) time characteristics. A plausible sensing mechanism has been discussed. The enhancement in sensor characteristics shows that n-In2O3@n-WO3 HNWs could serve as a promising material for high-performance NO2 gas sensors for real-time environmentalmonitoringapplications. This work could provide new understandings of the sensing mechanism of n-In2O3@n-WO3-based heterojunction nanowires, which can be applied to the design of novel n-n type MOS heterojunction materials for the application of low-temperature real-time high-performance NO2 sensors.

Portable self-powered electrochemical aptasensor for ultrasensitive and real-time detection of microcystin-RR based on hydrovoltaic-photothermal coupling effect

Coupling different energy harvesting technologies to obtain an excellent output signal is essential for the development of high-performance self-powered electrochemical sensors. Herein, a novel hydrovoltaic-photothermal coupling self-powered electrochemical aptasensing platform was designed for sensitive detection of microcystin (MC-RR) with a digital multimeter as a direct visual readout strategy. The straightforward ultrasonic method was employed to synthesize polyaniline (PANI) and bismuth oxybromide (BiOBr) nanosheets, which were then integrated as active components in a hydrovoltaic device. The unique layer structure of two-dimensional (2D) nanomaterials BiOBr can create flexible interlayer spaces to accommodate various ions and water molecules, which was beneficial to construct evaporation-driven channels. Meanwhile, the exceptional photothermal characteristics of polyaniline could accelerate the water evaporation rate, consequently boosting the migration speed of charge carriers and increasing output signal. Moreover, a digital multimeter was connected to the constructed sensor for real-time displaying the output signal. With the assistance of aptamer, a novel self-powered electrochemical aptasensing platform was constructed for sensitive detection of MC-RR. Under optimum conditions, the output signal of the hydrovoltaic-photothermal coupling cell was linearly related to the logarithm of MC-RR concentration in the range of 1 fM to 1 nM with a detection limit of 0.31 fM (S/N = 3). Furthermore, this sensor also exhibited many advantages such as high selectivity, good repeatability and portability. Such novel strategy not only offers a completely new general approach to construct high-performance self-powered devices for the detection of MC-RR, but also provides a new strategy for advancing the miniaturization and field application of self-powered electrochemical sensors.

Exploring the physical properties of Mn2VSi and Mn2VGe Heusler alloys under high pressure conditions: a theoretical investigation using GGA and TB-mBJ calculation

The investigation into Mn2VSi and Mn2VGe Heusler alloys has yielded crucial insights into their structural, mechanical, thermal, electronic, and magnetic attributes. Employing Density Functional Theory (DFT) with Generalized Gradient Approximation (GGA) and Tran Blaha modified Becke Johnson (TB-mBJ) method, these alloys were comprehensively analyzed. Structurally, both compounds exhibited stability in the regular Heusler structure, with precise lattice constants and pressure-volume variations. Elastic properties indicated mechanical stability and ductile behavior, with enhanced material strength under elevated pressure. Thermodynamic assessments revealed behavior under extreme conditions, while electronic properties showed half-metallic behavior and semiconducting characteristics. Magnetic moments decreased with pressure, signaling a magnetic transition state in alloys. This theoretical study provides valuable insights into Mn2VSi and Mn2VGe compounds, offering potential applications in spintronics and magnetic sensors.

Electrochemical characterization of carbon black in different redox probes and their application in electrochemical sensing

Carbon black - materials rich in carbon nanostructures - have been successfully applied as modifiers of electrochemical transducers, rivalling other carbon nanomaterials for cost and ease-of-use. Despite the remarkable promise of this nanomaterial, no study has yet comparatively characterised a wide range of different grades of carbon black for their utility in electrochemical sensors. Here, we explore several commonly-studied carbon black grades (N220, N234, N326, N330, N339, N375, N550, N660 and Lamp Black-101), alongside relatively newer grades (Printex®-200, Printex® G, Printex® XE-2B, and Printex® Zeta) for their application in electrochemical sensors. The effects of coating glassy carbon electrodes with carbon black on electrode performance were studied by cyclic voltammetry using three redox probes: ferri-/ferrocyanide (anionic probe molecules), ferrocenemethanol (neutral) and hexaammineruthenium (cationic). Raman Spectroscopy characterisation of the different grades associated a lower degree of graphitisation with superior electrode modifiers. Generally, modification increased the anodic peak current for ferri-/ferrocyanide probes; and lowered anodic potential for ferri-/ferrocyanide and hexaammineruthenium probes. Increases in peak current and potential observed at ferrocenemethanol are consistent with the increased tendency for this probe to adsorb to the surface of modified electrodes. N330 and Printex® XE-2B displayed the best electrocatalytic properties in terms of enhanced peak currents and lowered anodic overpotentials for the redox probes. CB grades were used to modify screen-printed carbon electrodes and the obtained sensors examined for anodic detection of reduced nicotinamide adenine dinucleotide (NADH) cofactor by cyclic voltammetry. Printex® XE-2B significantly improved the detection of NADH and was further used for chronoamperometric detection of NADH at low overpotentials. Grades N220, N375, N550 and P-G showed their suitability as enzyme scaffolds for sensor fabrication, as determined by their preservation of the activity of a NAD-dependent aldehyde dehydrogenase.

Electronic Properties of ZnO Nanowires: A First-Principles Analysis Using Two-Probe Methodology

In this study, the electrical characteristics of pure ZnO nanowires are investigated using density functional theory (DFT) inside the non-equilibrium Green's function (NEGF) paradigm. We clarify the underlying electronic behaviours by a thorough analysis of I-V curves, density of states (DOS) spectra, and transmission spectra. All calculations are carefully carried out using the Quantum Wise Atomistic Tool Kit programme and the Generalised Gradient Approximation (GGA). Our results show that localised d orbitals dominantly contribute to ZnO nanowire transmission properties. We also investigate the effect of doping with copper on these characteristics. Gradual doping from pristine to 2 and 4 Cu atoms shows significant enhancements, indicating a clear relationship between electrical properties and doping concentration. Notably, the device's potential as a resistor is highlighted by the greater linearity of current with copper doping, demonstrating adherence to Ohm's law. Moving beyond basic research, we tackle real-world issues in sensing applications, including the quick identification of copper molecules in water. We provide light on the Au-ZnO-Au structure's potential for sensor applications by thoroughly discussing its electron transport properties. In conclusion, this study advances our knowledge of the electrical characteristics of ZnO nanowires and how doping modifies them, opening the door to their application in a variety of electronic and sensing devices.

Electrosynthesis and characterization of poly(rhodamine B) coatings on 3d printed polylactic acid doped carbon black (PLA-CB) electrodes for promising sensor applications

Electrosynthesis and characterization of poly(rhodamine B) coatings on 3d printed polylactic acid doped carbon black (PLA-CB) electrodes for promising sensor applications