Search Results (586)

In this study, we propose a bidirectional chemical sensor platform based on a reconfigurable ion-sensitive field-effect transistor (R-ISFET) architecture. The device incorporates Ni-silicide Schottky barrier source/drain (S/D) contacts, enabling ambipolar conduction and bidirectional turn-on behavior for both p-type and n-type configurations. Channel polarity is dynamically controlled via the program gate (PG), while the control gate (CG) suppresses leakage current, enhancing operational stability and energy efficiency. A dual-control-gate (DCG) structure enhances capacitive coupling, enabling sensitivity beyond the Nernst limit without external amplification. The extended-gate (EG) architecture physically separates the transistor and sensing regions, improving durability and long-term reliability. Electrical characteristics were evaluated through transfer and output curves, and carrier transport mechanisms were analyzed using band diagrams. Sensor performance—including sensitivity, hysteresis, and drift—was assessed under various pH conditions and external noise up to 5 V_pp (i.e., peak-to-peak voltage). The n-type configuration exhibited high mobility and fast response, while the p-type configuration demonstrated excellent noise immunity and low drift. Both modes showed consistent sensitivity trends, confirming the feasibility of complementary sensing. These results indicate that the proposed R-ISFET sensor enables selective mode switching for high sensitivity and robust operation, offering strong potential for next-generation biosensing and chemical detection. Full article

Nanomaterials’ special features enable their extensive application in chemical and biochemical nanosensors for food assays; food packaging; environmental, medicinal, and pharmaceutical applications; and photoelectronics. The analytical strategies based on novel nanomaterials have proved their pivotal role and increasing interest in the assay of key food components. The choice of transducer is pivotal for promoting the performance of electrochemical sensors. Electrochemical nano-transducers provide a large active surface area, enabling improved sensitivity, specificity, fast assay, precision, accuracy, and reproducibility, over the analytical range of interest, when compared to traditional sensors. Synthetic routes encompass physical techniques in general based on top–down approaches, chemical methods mainly relying on bottom–up approaches, or green technologies. Hybrid techniques such as electrochemical pathways or photochemical reduction are also applied. Electrochemical nanocomposite sensors relying on conducting polymers are amenable to performance improvement, achieved by integrating redox mediators, conductive hydrogels, and molecular imprinting polymers. Carbon-based or metal-based nanoparticles are used in combination with ionic liquids, enhancing conductivity and electron transfer. The composites may be prepared using a plethora of combinations of carbon-based, metal-based, or organic-based nanomaterials, promoting a high electrocatalytic response, and can accommodate biorecognition elements for increased specificity. Nanomaterials can function as pivotal components in electrochemical (bio)sensors applied to food assays, aiming at the analysis of bioactives, nutrients, food additives, and contaminants. Given the broad range of transducer types, detection modes, and targeted analytes, it is important to discuss the analytical performance and applicability of such nanosensors. Full article

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

A field experiment in growing spring wheat (Triticum aestivum L.—cv. ‘Monsun’) under organic, integrated and conventional farming systems was conducted over the period of 2020–2022 at the Czesławice Experimental Farm (Lubelskie Voivodeship, Poland). The first experimental factor analyzed was the farming system: A. organic system (control)—without the use of chemical plant protection products and NPK mineral fertilization; B. conventional system—the use of plant protection products and NPK fertilization in the range and doses recommended for spring wheat; C. integrated system—use of plant protection products and NPK fertilization in an “economical” way—doses reduced by 50%. The second experimental factor was irrigation strategy: 1. no irrigation—control; 2. double irrigation; 3. multiple irrigation The aim of the research was to determine the physical, chemical, and enzymatic properties of loess soil under spring wheat crops as influenced by the factors listed above. The highest organic C content of the soil (1.11%) was determined in the integrated system with multiple irrigation of spring wheat, whereas the lowest one (0.77%)—in the conventional system without irrigation. In the conventional system, the highest contents of total N (0.15%), P (131.4 mg kg⁻¹), and K (269.6 mg kg⁻¹) in the soil were determined under conditions of multiple irrigation. In turn, the organic system facilitated the highest contents of Mg, B, Cu, Mn, and Zn in the soil, especially upon multiple irrigation of crops. It also had the most beneficial effect on the evaluated physical parameters of the soil. In each farming system, the multiple irrigation of spring wheat significantly increased moisture content, density, and compaction of the soil and also improved its total sorption capacity (particularly in the integrated system). The highest count of beneficial fungi, the lowest population number of pathogenic fungi, and the highest count of actinobacteria were recorded in the soil from the organic system. Activity of soil enzymes was the highest in the integrated system, followed by the organic system—particularly upon multiple irrigation of crops. Summing up, the present study results demonstrate varied effects of the farming systems on the quality and health of loess soil. From a scientific point of view, the integrated farming system ensures the most stable and balanced physicochemical and biological parameters of the soil due to the sufficient amount of nutrients supplied to the soil and the minimized impact of chemical plant protection products on the soil. The multiple irrigation of crops resulting from indications of soil moisture sensors mounted on plots (indicating the real need for irrigation) contributed to the improvement of almost all analyzed soil quality indices. Multiple irrigation generated high costs, but in combination with fertilization and chemical crop protection (conventional and integrated system), it influenced the high productivity of spring wheat and compensated for the incurred costs (the greatest profit). Full article

Zinc oxide (ZnO) is a wide bandgap semiconductor of great scientific and technological interest due to its high exciton binding energy and outstanding structural and optical properties, making it an ideal material for applications in optoelectronics, sensors, and photocatalysis. This study presents the rapid synthesis of highly crystalline ZnO nanostructures using two alternative routes: (1) direct thermal decomposition of zinc acetate and (2) a physical-green route assisted by Mangifera indica extract. Both routes were subjected to identical calcination thermal conditions (400 °C for 2 h), allowing for an objective comparison of their effects on structural, vibrational, morphological, and optical characteristics. X-ray diffraction analyses confirmed the formation of a pure hexagonal wurtzite phase in both samples, highlighting a higher crystallinity index (91.6%) and a larger crystallite size (35 nm) in the sample synthesized using the physical-green route. Raman and FTIR spectra supported these findings, revealing greater structural order. Electron microscopy showed significant morphological differences, and UV-Vis analysis showed a red shift in the absorption peak, associated with a decrease in the optical bandgap (from 3.34 eV to 2.97 eV). These results demonstrate that the physical-green route promotes significant improvements in the structural and functional properties of ZnO, without requiring changes in processing temperature or the use of additional chemicals. Full article

This article proposes a new Flower Patch Nectar Sensor to address the problem of detecting and measuring nectar in artificial flowers used in experiments on pollinator behavior. Traditional methods have focused mainly on recording the visits of pollinators to the flowers, without addressing the dynamic variations in nectar in terms of volume and concentration. The proposed approach provides more detailed information about the nectar consumption by bees and allows for the determination of the optimal time to refill the flowers. This study introduces an innovative method that uses electrodes and an oscillator circuit to measure the volume of nectar present in the flower. The system correlates the concentration of nectar with a frequency signal that can be processed by a microcontroller. It was evaluated using initial volumes ranging from 1 $\mathsf{\mu }$L to 4 $\mathsf{\mu }$L, demonstrating its ability to accurately detect variations in nectar, even up to the point where the frequency approaches zero. The results confirm that this method allows us to identify how the reward offered to pollinators (represented by nectar) varies over time, in terms of concentration, under both controlled and natural conditions. Additionally, graphs are presented that show the relationship between an initial volume of 4 $\mathsf{\mu }$L and variations in the frequency signal over a period of 25 min, highlighting the influence of these factors on nectar dynamics. This work not only introduces an innovative approach for the dynamic monitoring of nectar in artificial flowers but also lays the groundwork for future studies on the physical and chemical modeling of nectar in response to environmental conditions. Full article

Real-time, high-resolution monitoring of chemically diverse water pollutants remains a critical challenge for smart water management. Here, we report a fully integrated, multi-modal nano-sensor array, combining graphene field-effect transistors, Ag/Au-nanostar surface-enhanced Raman spectroscopy substrates, and CdSe/ZnS quantum dot fluorescence, coupled to an edge-deployable CNN-LSTM architecture that fuses raw electrochemical, vibrational, and photoluminescent signals without manual feature engineering. The 45 mm × 20 mm microfluidic manifold enables continuous flow-through sampling, while 8-bit-quantised inference executes in 31 ms at <12 W. Laboratory calibration over 28,000 samples achieved limits of detection of 12 ppt (Pb²⁺), 17 pM (atrazine) and 87 ng L⁻¹ (nanoplastics), with R² ≥ 0.93 and a mean absolute percentage error <6%. A 24 h deployment in the Cherwell River reproduced natural concentration fluctuations with field R² ≥ 0.92. SHAP and Grad-CAM analyses reveal that the network bases its predictions on Dirac-point shifts, characteristic Raman bands, and early-time fluorescence-quenching kinetics, providing mechanistic interpretability. The platform therefore offers a scalable route to smart water grids, point-of-use drinking water sentinels, and rapid environmental incident response. Future work will address sensor drift through antifouling coatings, enhance cross-site generalisation via federated learning, and create physics-informed digital twins for self-calibrating global monitoring networks. Full article

Inverse opal (IO) structures based on photonic colloidal crystal (PCC) templates are types of materials that possess unique optical properties due to their ordered arrays. These materials have the ability to manipulate the propagation of light, producing unique reflection spectra and structural colours. Due to these properties, IOs have been used as optical sensors for various applications such as the detection of physical, chemical, and biological entities. This review begins with a brief introduction of PCCs, IOs and their preparation procedures. The recent advancements in the applications of IOs for sensing temperature, pH, humidity, chemical compounds (such as organic solvents and heavy metal ions), and biological entities (such as tumour cells, viruses and bacteria) are then discussed in detail. The review also explores strategies and techniques aimed at enhancing the sensitivity and lowering the limit of detection of IO-based sensors. Finally, it addresses the current challenges, existing limitations, and prospective future directions in the development and deployment of IO-based sensors. Full article

This research presents an advanced autonomous HVAC control system tailored for a chemical fiber factory, emphasizing the human-centric principles and collaborative potential of Industry 5.0. The system architecture employs several functional levels—actuator and sensor, process, model, critic, fault detection, and specification—to effectively monitor and predict indoor air pressure differences, which are critical for maintaining consistent product quality. Central to the system’s innovation is the integration of digital twins and physical AI, enhancing real-time monitoring and predictive capabilities. A virtual representation runs in parallel with the physical system, enabling sophisticated simulation and optimization. Development involved custom sensor kit design, embedded systems, IoT integration leveraging Node-RED for data streaming, and InfluxDB for time-series data storage. AI-driven system identification using Nonlinear Autoregressive with eXogenous inputs (NARX) neural network models significantly improved accuracy. Crucially, incorporating airflow velocity data alongside AHU output and past pressure differences boosted the NARX model’s predictive performance (R² up to 0.9648 on test data). Digital twins facilitate scenario testing and optimization, while physical AI allows the system to learn from real-time data and simulations, ensuring adaptive control and continuous improvement for enhanced operational stability in complex industrial settings. Full article

Machine learning (ML) is transforming the evaluation of 3D printing materials, enabling more efficient and accurate assessment of material properties, including their sustainable life cycle. ML algorithms can analyze vast amounts of data from previous printing processes to predict the performance of different materials (including those used in multi-material printing) under different conditions. This predictive ability helps in selecting the most suitable materials for specific printing tasks, optimizing the mechanical, chemical, and overall quality of the final product. Furthermore, by integrating real-time data from sensors during the printing process, ML can continuously monitor and adjust parameters, ensuring optimal material utilization and reducing waste. ML models can identify and correct defects in printed materials by recognizing patterns associated with defects, thus improving the reliability of 3D-printed objects. This approach reduces the need for expensive and time-consuming physical tests. This accelerates the pace of 3D printing development but also increases the precision of material selection and processing, contributing to more efficient use of materials and energy for printing. Full article

Zearalenone (ZEN) is a common mycotoxin widely found in food crops such as corn. The toxicity of ZEN is manifested as multiple hazards to reproduction, genes, cells, and immune systems. Long-term exposure may have a serious impact on health, so it has received extensive attention due to its potential harm to human and animal health. In order to ensure food safety, countries have formulated corresponding ZEN content limit standards and promoted the development of efficient and rapid detection technologies. This paper reviews the research progress of ZEN detection in food based on nanoenzyme electrochemical sensors. Firstly, the basic situation of ZEN was introduced, including its physical and chemical properties, toxicity, and related regulations and standards. Secondly, the advantages and disadvantages of traditional detection methods and new detection technologies are analyzed, and the application progress of electrochemical sensors in ZEN detection is discussed, especially aptamer electrochemical sensors, immune-electrochemical sensors, and nanoenzyme electrochemical sensors. In this paper, the advantages of nanoenzyme electrochemical sensors in ZEN detection are discussed in detail, especially in terms of sensitivity, selectivity, and rapid detection. However, nanoenzyme electrochemical sensors still face some challenges in practical applications, such as high production costs, control of signal amplification effects, and safety issues of nanomaterials. Finally, this paper looks forward to the future development direction of nanoenzyme electrochemical sensors and proposes possible solutions to further improve their stability, reduce costs, and optimize sensing performance. Full article

Colorimetric gas sensors are based on a color changing reaction of a sensor dye upon exposure to an analyte. For most sensor applications, the sensor dye must be immobilized in a sensor matrix. The choice of matrix significantly influences the dye’s response due to different physical and chemical effects. Ideal matrix materials should be transparent, stable, compatible with the sensor dye, and processable. Polymers are often applied as matrix materials, as they can be easily applied to sensor structures. In this study, we present a method to examine the impact of polymers of different structures and functionalities on sensor dyes. Therefore, 18 polymers are studied in combination with the pH indicator bromocresol green regarding their sensitivity to ammonia. The measurement setup is based on a camera as a detector of the color changing reaction of the sensor materials and allows for the simultaneous measurement of the sensor materials. Furthermore, the response and regeneration time, the stability, and the influence of the environmental parameters humidity and temperature on the colorimetric reaction are investigated. The study demonstrates that polymers as sensor matrices have an influence on the response of sensor dyes, due to their different properties, such as polarity. This has to be considered when choosing a suitable sensor matrix. Full article

Oil pollution in water bodies is a substantial environmental concern that poses severe risks to human health, aquatic ecosystems, and economic activities. Rising energy consumption and industrial activity have resulted in more oil spills, damaging long-term ecology. The aim of the review is to discuss problems, effects, and methods of monitoring and sensing oil pollution in water. Oil can destroy the aquatic habitat. Once oil gets into aquatic habitats, it changes both physically and chemically, depending on temperature, wind, and wave currents. If not promptly addressed, these processes have severe repercussions on the spread, persistence, and toxicity of oil. Effective monitoring and early identification of oil pollution are vital to limit environmental harm and permit timely reaction and cleanup activities. Three main categories define the three main methodologies of oil spill detection. Remote sensing utilizes satellite imaging and airborne surveillance to monitor large-scale oil spills and trace their migration across aquatic bodies. Accurate real-time detection is made possible by optical sensing, which uses fluorescence and infrared methods to identify and measure oil contamination based on its particular optical characteristics. Using sensor networks and Internet of Things (IoT) technologies, wireless sensing improves early detection and response capacity by the continuous automated monitoring of oil pollution in aquatic settings. In addition, the effectiveness of advanced artificial intelligence (AI) techniques, such as deep learning (DL) and machine learning (ML), in enhancing detection accuracy, predicting leak patterns, and optimizing response strategies, is investigated. This review assesses the advantages and limits of these detection technologies and offers future research directions to advance oil spill monitoring. The results help create more sustainable and efficient plans for controlling oil pollution and safeguarding aquatic habitats. Full article

Oil spills represent a critical environmental hazard with far-reaching ecological and economic consequences, necessitating the development of sophisticated modelling approaches to predict, monitor, and mitigate their impacts. This study presents a computationally efficient and physically grounded modelling framework for simulating oil spill drift in marine environments, developed using Python coding. The proposed model integrates core physical processes—advection, diffusion, and degradation—within a simplified partial differential equation system, employing an integrator for numerical simulation. Building on recent advances in marine pollution modelling, the study incorporates real-time oceanographic data, satellite-based remote sensing, and subsurface dispersion dynamics into an enriched version of the simulation. The research is structured in two phases: (1) the development of a minimalist Python model to validate fundamental oil transport behaviours, and (2) the implementation of a comprehensive, multi-layered simulation that includes NOAA ocean currents, 3D vertical mixing, and support for inland and chemical spill modelling. The results confirm the model’s ability to reproduce realistic oil spill trajectories, diffusion patterns, and biodegradation effects under variable environmental conditions. The proposed framework demonstrates strong potential for real-time decision support in oil spill response, coastal protection, and environmental policy-making. This paperwork contributes to the field by bridging theoretical modelling with practical response needs, offering a scalable and adaptable tool for marine pollution forecasting. Future extensions may incorporate deep learning algorithms and high-resolution sensor data to further enhance predictive accuracy and operational readiness. Full article

Dichromated Gelatin (DCG) was first used in optics in 1872 by Lord Rayleigh. Then, in 1968, Shankoff suggested its use as a photosensitive material to record interference diffraction gratings and holograms. Diffraction efficiencies of nearly 100% were achieved. This review discusses some physical and chemical characteristics of DCG films; the fabrication methods of DCG films; and some of the applications of DCG films in holography, holography in real time, solar concentrators, optical elements, and relative humidity sensors. Full article