Search Results (626)

Unmanned aerial vehicles (UAVs) equipped with lightweight gas sensors offer a promising approach for greenhouse-gas emission monitoring. However, airflow generated by multirotor propellers can disturb the atmosphere and influence measured gas concentrations. This study investigates rotor-induced downwash effects on vehicle exhaust plume measurements using experimental and numerical approaches. Experiments were conducted with a stationary diesel vehicle at idle, while a propeller system reproduced UAV downwash at rotor-sensor separation distances of 0.5–5.5 m above a fixed CO₂ sensor. A low-cost Feather-based sensing platform was evaluated against a commercial IoTSens monitoring station. CFD simulations were performed in OpenFOAM^® using a compressible multi-species solver, Large Eddy Simulation (LES), and a Multiple Reference Frame (MRF) approach. Experiments showed CO₂ reductions of up to 52.7%, while CFD predicted reductions of 50.4–88.5%. Both approaches showed decreasing rotor-wake influence with increasing separation distance, with strongest effects below approximately 2–3 m. Experimentally, downwash effects became weak between 3.5 and 5.5 m, consistent with reduced plume–wake interaction predicted by CFD. These findings highlight the importance of accounting for rotor-induced downwash when designing UAV-based gas monitoring missions. Full article

Conventional diagnosis of chronic kidney disease (CKD) relies predominantly on invasive blood-based examinations, limiting the scalability of kidney health screening in resource-constrained environments. This study presents embedded engineering framework for non-invasive, breath-based CKD staging framework supported by machine learning and implemented on a low-cost embedded platform. To account for physiological sex differences in baseline creatinine production, estimated glomerular filtration rate (eGFR) values and breath ammonia concentrations were derived from two independent clinical cohorts using sex-specific MDRD equations (incorporating the standard male formula and the $0.742$ female correction factor, respectively) and creatinine–BUN conversion models, with male- and female-parameterized algorithms developed in parallel. The resulting feature space was analyzed using four unsupervised clustering approaches to stratify subjects into five clinically meaningful kidney function stages. Stage-specific ammonia thresholds were implemented within an Arduino Nano-based prototype equipped with an MQ-137 gas sensor and OLED display, enabling real-time point-of-care classification. Dataset-level classification accuracy reached 82% for the male algorithm and 92% for the female algorithm. Hospital-based validation on 29 patients (22 male, 7 female) yielded a real-world testing accuracy of 90.5% ($20/22$) for male patients and 71.4% ($5/7$) for female patients, a discrepancy largely attributable to the small female sample size. Because the current evaluation lacks healthy control subjects and is constrained by sample size, these empirical results serve primarily to demonstrate hardware-software functional integration and real-world deployment feasibility rather than definitive clinical efficacy. Despite these preliminary, sample-limited clinical datasets, results suggest this approach holds promise as an accessible, non-invasive screening complement to conventional diagnostic pathways, particularly in low-resource healthcare settings. Full article

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies. Full article

Listeria monocytogenes (LM), one of the most virulent foodborne pathogens, poses a serious threat to public health due to its strong environmental adaptability and high pathogenicity. Rapid, sensitive, and real-time detection of LM is of great importance. Chemiresistive gas sensors have attracted enormous attention in LM detection owing to their advantages of low cost, simple structure, fast response, and easy miniaturization, which can achieve indirect detection of LM by recognizing its specific metabolic volatile organic compounds. This review summarizes the recent progress in chemiresistive gas sensors for the detection of LM metabolites. First, the metabolic characteristics of LM and the typical volatile organic compound (3-hydroxy-2-butanone) as its characteristic biomarker are introduced. Then, the performance and sensing mechanisms of different types of chemiresistive gas sensors for LM metabolite detection are summarized and elaborated systematically. The application of chemiresistive gas sensors for the detection of actual samples and the progress in the design of related detection devices are introduced. Finally, the current challenges faced by chemiresistive gas sensors in LM metabolite detection and their future development prospects are discussed. This review provides a comprehensive reference for the research and practical application of chemiresistive gas sensors in Listeria monocytogenes detection. Full article

Increasingly stringent emission regulations in combustion systems drive the demand for robust, cost-effective gas sensors capable of operating under harsh, high-temperature conditions. Thermoelectric ceramic gas sensors represent a promising approach. Their application, however, may be limited owing to a low response. In this work, calcium cobaltite (CCO), a p-type thermoelectric oxide with a Seebeck coefficient higher than most metals used for thermocouples, is investigated as an alternative material to enhance sensor performance. CCO powder was synthesized via the mixed oxide route and deposited as dense ceramic films onto alumina substrates at room temperature using the powder aerosol deposition method (PAD). The thermoelectric properties of the deposited films were characterized up to 850 °C, with the Seebeck coefficient showing only minor dependencies on variations in oxygen and water vapor concentrations. Following these results, planar exothermic gas sensors based on Au/CCO thermocouples utilizing laser-cut polyimide masks for patterning the PAD films were fabricated and compared to reference sensors with screen-printed metallic Au/Pt thermocouples. Laboratory gas measurements with CO and hydrocarbons demonstrated that the Au/CCO-based sensors exhibited an average sensitivity increase by a factor of 8–9, while maintaining high linearity and low cross-sensitivity to variations in oxygen and humidity. Furthermore, the additive response to gas mixtures was confirmed. Initial tests in real flue gas from a wood-burning stove showed an excellent correlation between the sensor signal and relevant flue gas components (measured using precise gas analyzers). The presented results highlight the potential of calcium cobaltite as a thermoelectric material for high-temperature gas sensors based on the exothermic principle and demonstrate the suitability of the PAD method for fabricating fine-structured functional films. Full article

High-performance ammonia (NH₃) sensors play a critical role in environmental protection and noninvasive medical diagnosis. This work reports a new NH₃ sensor based on Au-microsphere-decorated polyaniline (PANI) manufactured by a precise in situ inkjet printing method on a MEMS micro-hotplate. The in situ oxidative polymerization of aniline was performed directly on the MEMS platform using AuCl₃ as a bifunctional oxidant and precursor, with a hierarchical morphology of microspheres (~750 nm) and nanorods (~250 nm). Reduced from Au³⁺ in the polymerization reaction, Au microparticles achieve substantial catalytic promotion by virtue of chemical sensitization and spillover effect. The optimized Au–PANI MEMS sensor exhibits a superlative response of 201% toward 1 ppm NH₃ at room temperature, with an ultra-low theoretical limit of detection (LOD) of 0.42 ppb. Furthermore, the device demonstrates rapid response/recovery kinetics (76 s/72 s), exceptional selectivity against common interfering gases (SO₂, CO, H₂, etc.), and robust long-term stability with high response retention over two months. This research provides a scalable, cost-effective strategy for the mass production of miniaturized, high-sensitivity gas sensors for industrial and healthcare applications. Full article

Wide-scale, spatio-temporal quantification of soil CO₂ efflux is essential for understanding terrestrial carbon dynamics, predicting climate change, and evaluating the carbon balance in managed and natural ecosystems. Rising global temperatures, changing land use patterns, and other activities aimed at boosting crop productivity have resulted in an increase in microbial activity, increasing the impact of soil on gas exchange. Therefore, it is important to measure ${\mathrm{CO}}_2$ gas exchange in situ, over wide areas and extended periods without manual intervention. However, current approaches such as remote sensing lacks sufficient spatial and depth resolution, while other direct measurements such as eddy covariance demand expensive infrastructure, limiting wide-scale deployment. In this work, we propose a low-cost, battery-operated ${\mathrm{CO}}_2$ sensing system that provides long-term and scalable monitoring of soil respiration and carbon flux, with the promise for high-resolution measurements. Our innovative design features a PVC-based gas chamber that periodically opens and closes to allow for gas exchange, and a sensor module with low-cost temperature, moisture, pressure, and ${\mathrm{CO}}_2$ sensors, with a low-power wireless LoRa network for real-time monitoring. Our system was rigorously validated through multiple outdoor deployments, over long periods to demonstrate its practicality. We observe that temperature, air pressure, and humidity trends show responsiveness to the environment. We also observe that ${\mathrm{CO}}_2$ emission flux rate vary significantly across different modules, underscoring the need for fine-grained spatial and temporal resolution in monitoring. Full article

Low-cost multi-pollutant sensors make personal exposure monitoring affordable, but assuring their data quality in the field is the bottleneck, while current devices leave it to remote servers: the field unit is a passive terminal that cannot self-check its sensors, takes days to accept a new one, and loses quality control whenever connectivity drops. We develop Zhiwei, an on-device edge AI agent for personal exposure monitoring that brings the reasoning loop onto the device, so it can diagnose its own sensors without a reference, onboard new ones through a declarative skill package with a capability-association graph, and keep working offline through a three-tier cloud-to-rule-engine fallback. We validate these capabilities, rather than field exposure tracking, in a 30-day fixed indoor deployment in Beijing of 1,896,789 records at 99.9% completeness. The agent decided on its own, without a reference, which channels to trust, identifying that the nominal ozone channel measures total oxidizing gas rather than ozone alone, a conclusion the manufacturer’s datasheet independently confirms, while the PM_2.5 and NO₂ channels were separately corroborated as relatively usable against a nearby station (r = 0.90 and 0.86). Under a simulated cloud outage, it kept data collection uninterrupted by handing inference to the on-device local model. This is a single fixed indoor site and a design-and-functional validation; evaluation under mobile, rapidly changing microenvironments is future field work. Zhiwei shows that an environmental sensing device can manage its own data quality autonomously on-device, a prerequisite for trustworthy personal exposure monitoring. Full article

This study investigates the application of Artificial Neural Network (ANN), Long Short-Term Memory network (LSTM) as a representative of Recurrent Neural Network (RNN), and one-dimensional Convolutional Neural Network (1D-CNN) for time series prediction, demonstrated through a use case of low-cost gas sensor readings from transient signals. Despite the widespread use of these architectures in IoT forecasting applications, there is a lack of systematic comparative studies that evaluate their performance under identical experimental conditions, particularly in energy-constrained sensing scenarios. The primary objective is to evaluate the trade-offs between model accuracy, computational cost, and memory requirements under energy-efficient data acquisition scenarios. A comprehensive experimental analysis was conducted using 186 recorded transient samples, where all models were trained and evaluated under consistent preprocessing, identical data splits, and uniform hyperparameter settings. Performance was assessed using RMSE, MAE, $R^2$, training time, and model size as key evaluation metrics under varying input sequence lengths. The results show that the LSTM model achieved the highest accuracy, with an RMSE of 3.69%, $R^2$ of 0.85 and scaled MAE of 0.04, effectively capturing long-term temporal dependencies. The 1D-CNN exhibited a balanced compromise between accuracy and training efficiency, while the ANN provided the shortest training time but lower overall performance. Reducing the number of input readings from 186 to as few as 10–20 resulted in only a 2–4% increase in RMSE, with model size reductions of up to 50%, making such configurations particularly suitable for edge or embedded IoT devices. The findings demonstrate that artificial neural networks can maintain high prediction accuracy even under reduced data conditions, contributing to the development of low-power, resource-efficient sensing systems for intelligent and distributed IoT environments. Full article

This paper presents the design, development, and large-scale deployment of a vehicular IoT-based methane sensing system for urban environmental monitoring. The proposed solution integrates a low-cost metal oxide semiconductor (MOS) methane sensor with a dual chamber gas sampling mechanism, embedded processing, and wireless communication via 4G/5G networks using a smartphone as a gateway. Methane concentration data are collected from sensors installed in moving vehicles, georeferenced in real time using GNSS, and transmitted to a cloud-based platform for storage and analysis. Field experiments were conducted in the metropolitan region of São Paulo, Brazil, using 16 instrumented vehicles over a 20-month period, covering approximately 192,274 km and generating more than 48 million measurements. The results reveal spatially consistent methane concentration patterns and identify urban areas with elevated levels exceeding global background concentrations. A comparative analysis with a commercial infrared-based mobile methane monitoring system showed consistent agreement in the identification of spatial methane concentration patterns and potential emission hotspots. These results demonstrate the effectiveness of the proposed system for scalable urban methane monitoring. Full article

Recent demand for reliable, low-power, and cost-effective gas sensors has spurred research into chemiresistive materials that operate under ambient conditions. PEDOT and PEDOT:PSS combined with semiconductor metal oxides (SMOs) have attracted attention due to their complementary properties: polymer flexibility and stability, alongside oxide reactivity and robustness. This review highlights the integration of PEDOT and PEDOT:PSS with n- and p-type SMOs, concentrating on fabrication techniques, sensing mechanisms, and performance indicators, such as sensitivity, selectivity, and response time. Emphasis is placed on heterojunction engineering, morphology control, and the influence of particle size and environmental factors. Despite notable progress, challenges persist in long-term stability, selectivity in mixed gases, and performance under varying conditions. Interface engineering and composite optimisation show promise, with potential applications in environmental monitoring, industrial safety, and wearable diagnostics. Full article

Low-cost gas sensor arrays are attractive for mixed-gas monitoring, but deployment-oriented modeling remains challenging because mixed-gas responses are nonlinear, cross-sensitive, and strongly dependent on sensor dynamic states. Existing electronic-nose models often rely on handcrafted response descriptors or generic sequential networks, which may either compress transient response information or introduce unnecessary computational cost. This work proposes SDRMixer, a lightweight sensor-specific framework for mixed-gas concentration quantification. SDRMixer uses a parameter-free sparse dynamic response encoding to organize the original sensor response, baseline-referenced excitation, and smoothed response kinetics into a physically meaningful dynamic response field. A compact temporal-feature mixer is then applied over fixed response-stage tokens for simultaneous multi-gas regression. To improve calibration coverage, a response-consistent augmentation strategy is used during model training. The proposed framework is evaluated on a previously reported mixed-gas sensor array dataset containing NO₂, NH₃, CH₄, and CO₂ mixtures. Both augmentation-enriched calibration domain benchmarking and original-measurement-based validation are conducted to assess prediction performance, computational efficiency, and stability on measured calibration samples. The results show that SDRMixer provides a good trade-off between accuracy and efficiency compared with generic deep learning architectures and compact gas-sensing baselines. These findings indicate that explicit dynamic response encoding combined with lightweight temporal-feature mixing is an effective modeling strategy for compact mixed-gas quantification within the investigated calibration domain. Full article

In response to the EURO 7 regulation, which mandates near-continuous monitoring of pollutant gas emissions from every vehicle during real driving conditions, this research reports the development of a micro portable emissions monitoring system (µPEMS) for monitoring tailpipe mass emissions of NO_x, CO, and CO₂. It consists of low-cost MOX sensors installed in the exhaust pipe to detect pollutant concentrations, complemented with engine operation data from the vehicle’s On-Board Diagnostics (OBD) system. Issues of sensor drift, cross-sensitivity, and varying sampling frequency were addressed. Readings from this µPEMS prototype exhibited high correlation (R² > 0.87) with experimental data obtained under real driving conditions using a well-accepted PEMS for the cases of three vehicles (gasoline, diesel, and hybrid). This innovation enables new alternatives to regulate vehicular emissions. It also provides valuable real-time data for improving ecodriving, vehicle technology, and national emission inventories. Full article

Blockchain-based sensor networks rely on public-key cryptography for transaction verification, auditability, and data integrity. However, widely used public-key mechanisms are quantum-vulnerable in the presence of Cryptographically Relevant Quantum Computers (CRQCs), requiring sensor-to-blockchain transactions to address both post-quantum security and exposure control. This paper proposes a post-quantum sensor-to-blockchain transaction framework that minimizes CRQC-aware exposure while preserving low-cost auditability. It defines a transaction workflow that represents sensor data through hash-based commitments instead of storing raw measurements on-chain. The workflow combines Module-Lattice-Based Digital Signature Algorithm (ML-DSA)-based authentication, threshold-based authorization, Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM)-protected relay communication, and an event-based smart contract (EBSC) for compact audit recording. A Quantum Exposure Score (QES) is introduced as a transaction-level metric to quantify CRQC-induced exposure across cryptographic, relay, key-lifecycle, migration-readiness, and authorization dimensions. The framework is evaluated using differential pulse voltammetry (DPV) electrochemical sensor data, Constrained Application Protocol (CoAP) communication, and a Ganache-based blockchain, with scalability runs of up to 10,000 sensor transactions and ablation baselines. Compared with full on-chain storage, EBSC reduces gas consumption by approximately 80%, while QES decreases from 100 in the classical open scenario to 4 in the full framework. These results demonstrate that the proposed design provides a practical path for post-quantum secure sensor-to-blockchain transactions. Full article