Search Results (5,796)

This study develops high-performance ammonia sensors based on composites of metal-organic frameworks (MOF-808 and MOF-818) with reduced graphene oxide (rGO). Two sensor architectures were fabricated: interdigital electrodes and silicon micropillar arrays. The MOF-808/rGO composite demonstrated superior sensing performance for 40 ppm NH₃ at room temperature, with faster response kinetics and higher sensitivity compared to pristine rGO and MOF-818/rGO. Silicon micropillar array sensors showed enhanced performance through optimized periodic arrangements, while oxygen plasma surface modification improved both sensor types. Comprehensive testing confirmed that the MOF-808/rGO sensor maintains reliable NH₃ detection at concentrations as low as 5 ppm under high humidity conditions, exhibiting excellent stability and selectivity. These findings provide valuable insights for developing advanced gas sensors for environmental monitoring applications. Full article

Buildings account for a substantial proportion of global energy consumption and greenhouse-gas emissions, largely due to the widespread use of conventional heating, ventilation, and air-conditioning (HVAC) systems. Hybrid systems that integrate passive and active technologies have emerged as a promising strategy for reducing energy demand while maintaining adequate indoor environmental conditions. This study evaluates the thermal and airflow performance of a hybrid air-conditioning system (HACS) that combines transparent thermal insulation (TTI) filled with R-410A refrigerant and a pig-fat-based organic phase-change material (PCM). Experimental measurements of heat flux, temperature, airflow velocity, and CO₂ concentration were conducted in a controlled prototype system. In parallel, computational simulations were performed using computational fluid dynamics (CFD) and multizone airflow modeling. The hybrid system incorporates a TTI container acting as a solar absorber and a galvanized-steel PCM container filled with 10 kg of pig fat used as latent heat storage. Heat-flux measurements were obtained using an HFS-5 sensor connected to a data acquisition system, while airflow velocity and temperature were monitored with analog data loggers. Indoor CO₂ concentrations were recorded using a dedicated CO₂ meter and simulated using CONTAMW software version 3.4.0.8. The experimental results show that the TTI and PCM containers reached average heat-flux values of 77.04 W/m² and 55.31 W/m², respectively. Airflow within the system is induced by buoyancy forces arising from temperature gradients generated by heat transfer processes at the surfaces of the TTI and PCM, resulting in a mixed air stream with an average temperature of 37.54 °C during winter operation. Recorded CO₂ concentrations remained between 290 and 413 ppm, indicating high indoor air quality levels. The overall experimental campaign spanned 6 years and 3 months. CFD simulations confirmed the airflow patterns and heat-transfer behavior observed experimentally. The findings demonstrate that hybrid air-conditioning systems combining refrigerant-filled transparent insulation with bio-based phase-change materials can effectively enhance passive thermal performance while maintaining acceptable indoor air quality. The integration of photovoltaic-powered ventilation systems could further the operational autonomy and overall energy efficiency of such hybrid systems. Full article

Brassica napus L. is a globally important crop and its productivity is constrained by multiple abiotic stresses (salinity, drought, and heat). Calcineurin B-like proteins (CBLs) act as calcium sensors and play key roles in regulating ion homeostasis and stress-responsive signaling pathways, thereby contributing to plant adaptation under unfavorable environmental conditions. Here, through detailed bioinformatics analyses, the BnCBL gene family has been identified along with its role in tolerance to multiple abiotic stresses. The identified 17 BnCBLs comprised four groups, as in Arabidopsis thaliana. The predicted molecular weights of the CBL proteins ranged from approximately 24.35 kDa (BnCBL3 and -9) to 29.7 kDa (BnCBL5), with protein lengths spanning 213 (BnCBL3, -9, -10, -12 and -15) to 260 amino acids (BnCBL5). Sequence, promoter, and structural analyses showed that BnCBL proteins harbor palmitoylation and myristoylation motifs in their EF-hand domains, contain hormone- and stress-responsive cis-elements, and exhibit characteristic post-translational modification sites and tertiary structures. RNA-seq and RT-qPCR expression analyses showed that several BnCBL genes (BnCBL2, -6, -9, -10, and -15) exhibit differential expression (3~6-fold) under NaCl, drought, and heat stresses, as well as in response to phytohormones (IAA, GA₃, ABA, and JA). In addition, BnCBL2, -3, -6, -8, -9, -11, -12 and -16 showed significant expression (around 7-fold) against biotic stresses (Sclerotinia sclerotiorum (Lib.) de Bary and Plasmodiophora brassicae (Woronin, 1877), indicating their roles in both biotic and abiotic stress tolerance and potential utility in biotechnological breeding of stress-enduring B. napus cultivars. Full article

This Special Issue based on eight Articles/Reviews focuses on low-cost chemical sensor technologies, bio-chemical sensors, advanced active materials, sensing nanomaterials, sensor nodes, wireless sensor networks for chemical sensing, functional characterization, miniaturized transducers, advanced proofs of concept, and chemical detection applications. Promising advanced materials such as metal oxide nanostructures, carbon nanomaterials, composite heterostructures, multilayered coatings, and more have been explored for chemical sensing applications and environmental sustainability. Sensing solutions have been applied in the context of bio-chemical detection and gas monitoring, representing the current state of the art. Full article

Air quality affects large urban areas, where rapid urban development and human activities place constant pressure on ecosystems and public health. In this context, large-scale air quality assessment, supported by short-term forecasts, can provide useful information for environmental management and decision-making in urban areas, thus supporting evidence-based urban environmental management. The aim of this work is to design an affordable, smart real-time air pollution monitoring and prediction system for urban planning in overpopulated locations, which is deeply related to community health. The system focuses on real-time monitoring and forecasting of air quality. Prediction tasks were limited to gaseous pollutants CO and CO₂. Measurements were obtained over four months from a low-cost sensor platform installed in a highly populated neighborhood district in Baghdad, Iraq. Air quality prediction of gas concentrations was done using three types of time-series algorithms: Long Short-Term Memory, or LSTM; Gated Recurrent Unit, or GRU; and Temporal Convolutional Network, or TCN, models. Among these, the LSTM architecture showed more stable behavior and a higher predictive ${\mathrm{R}}^2$, ranging from 98.2% to 98.9%. Generally, the findings suggest that combining low-cost sensing technologies with artificial intelligence can offer a feasible and scalable solution for urban air quality monitoring. This approach may support cost-effective strategies for monitoring air quality in resource-constrained urban environments. Full article

To address the cross-sensitivity and non-linear coupling issues caused by the coexistence of hydrogen, carbon monoxide, ammonia, and nitrogen dioxide in industrial environments, a flow-through quantitative detection system based on a MEMS gas sensor array was designed and constructed. The steady-state peak sampling method was employed for feature extraction from high-dimensional time-series data, and regression prediction models were developed using a traditional BP neural network and BP neural networks optimized by four swarm intelligence algorithms (ALA, AOO, SFOA, and SDO). The experimental results indicate that the intelligent optimization algorithms excel in decoupling the “cross-response” phenomenon, with all optimized models outperforming the traditional BP network. Among them, the SDOBP (Sledge Dog Optimizer-BP) model demonstrated the best overall performance, achieving the highest accuracy in carbon monoxide and hydrogen detection, with the Root Mean Square Error for hydrogen reduced to 2.17, an 84.2% improvement over the traditional model. The system achieves high-precision quantitative inversion of multi-component gases in complex environments, providing an effective means for industrial environmental safety monitoring. Full article

With the growing security requirements of sensor nodes in Internet of Things (IoT) systems, conventional silicon-circuit-based physical unclonable functions (PUFs) still face limitations in circuit overhead, design complexity, and system integration. To address these challenges, this paper proposes a lightweight gas sensor PUF (GS-PUF) design based on a Ni-doped SnO₂ nanoscale gas sensor array. The proposed method exploits both the unavoidable process randomness introduced during sensor fabrication and the device-to-device electrical response variations induced by gas–material interactions as entropy sources, thereby enabling high-quality PUF response generation. At the device level, Ni-SnO₂ nanomaterials are prepared by electrostatic spray deposition (ESD), and an indirectly heated gas sensor array is constructed to enhance the sensitivity and stability of the sensing response. At the algorithmic level, a random resistance balancing algorithm based on multi-sensor combinational comparison is proposed. By randomly comparing the summed resistances of multiple sensor clusters, a 128-bit multi-bit PUF response is generated, while the uniformity and independence of the output bits are effectively improved. Experimental results demonstrate that the proposed GS-PUF exhibits excellent randomness, uniqueness, and reliability: the information entropy of the PUF responses is greater than 0.99, approaching the ideal value; the probabilities of output bits “1” and “0” are 0.4988 and 0.5012, respectively, indicating a well-balanced distribution; the inter-device uniqueness reaches 49.8%, close to the ideal value of 50%; all items in the NIST randomness test suite are passed, with all p-values exceeding 0.01 and the minimum p-value being 0.0368, confirming a high level of statistical randomness confidence. In addition, long-term measurements under fixed laboratory conditions show that the PUF response reliability remains above 96%. Compared with other sensor-based PUFs, the proposed method provides a lightweight sensing-security integration approach for IoT sensor nodes by reusing intrinsic gas-sensor response variations and avoiding an additional dedicated silicon PUF circuit. Full article

Currently, hydrogen has become an indispensable topic when discussing the energy transition. Determining the amount of hydrogen produced or lost through leaks is a critical issue. Recently, with the emergence of the low-cost MQ-8 resistive semiconductor sensor, which is sensitive to hydrogen and responds with an output voltage $V_{out}$, there has been considerable interest in its use in small laboratory experiments. The combination of the volumetric method, the MQ-8 sensor, and the BME280 sensor (for temperature, pressure, and humidity) is of significant interest and has industrial applications. This work presents an in-depth study of the combination of the traditional volumetric method with the MQ-8 and BME sensors. Sensor validation metrics were evaluated to ensure the reliability of the results. The pressure remained approximately constant due to the system configuration. The results indicate that for a current of 1 A, it is possible to determine the approximate volume of hydrogen as a function of the sensor’s output voltage. For low currents ranging from 0.76 to 250 mA, the results indicate that it is possible to determine the approximate hydrogen flow rate as a function of the voltage detected by the sensor. With further investigation, it will be possible to propose the use of MQ-8 and BME280 sensors in environments containing hydrogen. Full article

A highly sensitive ppb-level resistive H${}_2$ gas sensor was fabricated based on Pt/PtO and Pd/PdO${}_x$ co-decorated WO${}_3$ nanofibers prepared via electrospinning and calcination. The optimized sensor based on 2 at% Pt–2 at% Pd co-decorated WO${}_3$ nanofibers exhibited reliable detection toward 100 ppb H${}_2$ at an optimized operating temperature of 170 ${}^{\circ }$C. Upon 2 at% Pd decoration, the response of the WO${}_3$-based sensor increased from 1, corresponding to almost no response, to 55 (R${}_a$/R${}_g$) toward 100 ppm H${}_2$. Further introduction of 2 at% Pt reduced the optimal operating temperature of the 2 at% Pd-decorated WO${}_3$-based sensor from 200 ${}^{\circ }$C to 170 ${}^{\circ }$C and enhanced the response by approximately twofold. The optimal sensor exhibits excellent linear response characteristics, high selectivity, good response repeatability, and long-term operational stability. The enhanced sensing performance is attributed to the catalytic capability and possible spillover-related effects of Pd/PdO${}_x$ and Pt/PtO toward H${}_2$/O${}_2$, as well as depletion-layer modulation induced by the heterostructures between Pt/PtO and WO${}_3$, and Pd/PdO${}_x$ and WO${}_3$. These synergistic catalytic and electronic sensitization effects collectively contribute to the high sensitivity toward H${}_2$. These results indicate that the proposed resistive H${}_2$ sensor holds significant potential for practical hydrogen-sensing applications. Full article

In the Kingdom of Saudi Arabia (KSA) and other arid regions, higher education institutions account for a significant share of energy consumption and greenhouse gas (GHG) emissions. Improving the environmental performance of higher education institutions is important to achieving nationwide impact reduction. This study evaluates the water, energy, and carbon (WEC) footprint of higher education campuses in arid environments. Qassim University (QU), KSA, is a leading public institution of higher education and research in Buraydah City and was selected for this study. A comprehensive assessment based on the GHG Protocol was conducted for the period 2022–2025, covering Scope I, II, and III emissions. This study analyzed institutional data on water use, wastewater, electricity consumption, transportation, waste generation, and air travel. The results show that total water consumption increased from 354,747 m³ in 2022 to 547,268 m³ in 2025, with per capita use rising from 46.2 to 61.7 L/c/day. Net water demand, including irrigation, reached 877,456 m³ in 2025. The declining trend in energy consumption between 2022 and 2025 reflects significant (33%) energy savings with the use of sensors and the overall tendency towards sustainability. Correspondingly, Scope II emissions decreased significantly from 147.2 million kg CO₂/year to 99.1 million kg CO₂/year and were the dominant CO₂ contributor (60–75% of total emissions). In contrast, Scope III emissions from commuting staff and students increased, with transport-related emissions rising from 36.4 million kg CO₂/year in 2022 to 52.2 million kg CO₂/year in 2025. This study also evaluated current and potential CO₂ emission reduction scenarios targeting energy and transportation systems on the QU campus. The findings indicate that the deployment of a 5.1 MW solar energy system can generate approximately 8.6 million kWh annually, resulting in a reduction of around 4000 tCO₂ and contributing to nearly 43% of the 2030 emission reduction target. In addition, transportation-focused strategies—including modal shift, vehicle electrification, and hybrid learning approaches—demonstrate significant mitigation potential, with total reductions reaching up to 18,700 tCO₂ by 2030. Overall, this study contributes to the limited body of knowledge on WEC footprint assessments on university campuses in arid regions and provides a baseline for future sustainability planning. Full article

Hydrogen-blended natural gas (HBNG) is widely regarded as a transitional pathway for decarbonizing urban gas systems. However, the coupled evolution from buried pipeline leakage to pre-ignition flammable cloud formation has not yet been systematically integrated across research stages. This review synthesizes experimental, numerical, and data-driven studies on leak source-term dynamics, subsurface migration through porous media, surface breakthrough and escape, accumulation in semi-enclosed spaces, and pre-ignition flammable cloud development. Hydrogen blending modifies the density, diffusivity, flammability limits, and ignition sensitivity of the gas mixture, thereby influencing breakthrough time, stratification behavior, and the available early-warning window before ignition. The hazard evolution is jointly governed by pipeline pressure, leak orifice size, burial depth, soil heterogeneity, soil moisture content, spatial confinement, and ventilation conditions. Six major research gaps are identified, including fragmented stage-specific investigations, limited full-scale multiphysics experimental data, insufficient characterization of heterogeneous soils, inadequate high-resolution gas-cloud measurements, weak integration with quantitative risk assessment, and delayed full-lifecycle integrity management. To address these gaps, this review proposes a coherent, mechanism-informed analytical framework for urban HBNG pipeline safety and further provides a numerical parameter-transfer example showing how surface breakthrough outputs can be converted into aboveground velocity, mass flux, and species-concentration boundary conditions. This framework integrates dynamic mechanistic parameters into high-consequence area zoning, sensor placement, ventilation interlocking, and full-lifecycle integrity management, thereby supporting safer engineering deployment of HBNG systems. Full article

Agricultural soils are a major source of greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) and methane (CH₄), highlighting the need for cost-effective and field-applicable monitoring solutions. This study developed and evaluated a low-cost real-time monitoring system for soil CO₂ and CH₄ emissions by integrating surface emission chambers, low-cost gas sensors, a solar-powered energy supply, and IoT-based wireless communication. Three acrylic chambers with different heights (40, 60, and 80 cm) were fabricated to investigate the influence of chamber geometry on measurement performance. System performance was assessed through simultaneous measurements against a Biogas 5000 analyzer under simulated conditions and during field deployment in a sugarcane cultivation area in Khon Kaen Province, Thailand. Relative agreement was used to compare the developed system with the reference instrument. The results showed that relative agreement varied with chamber height for both gases. Under simulated conditions, the 80 cm chamber achieved the highest overall relative agreement for CO₂ and CH₄, underscoring the importance of sufficient headspace volume in chamber-based measurements. Field experiments confirmed the system’s capability for continuous CO₂ monitoring in an agricultural environment. However, CH₄ emissions were not detected during the study period, likely due to drought-induced, well-aerated soil conditions. The developed system demonstrated stable autonomous operation, low energy consumption, and ease of installation, making it suitable for long-term field applications. Overall, the proposed platform provides a practical and scalable approach for real-time soil GHG monitoring and offers strong potential for integration into precision agriculture and climate-smart farming systems to support GHG mitigation strategies. Full article

In early-warning scenarios for thermal runaway in energy storage batteries, carbon monoxide (CO) may interfere with hydrogen detection and reduce the reliability of signal interpretation. To mitigate CO cross-interference under representative mixed-gas conditions and improve sensing stability, a fiber-optic microelectromechanical systems (MEMS) hydrogen sensor based on a Pd–Cu alloy-sensitive layer was developed. The sensor employs a single-cantilever structure and a reflective Fabry–Pérot (F–P) interferometer for optical readout. Comparative experiments were carried out using sensors coated with pure Pd and Pd–Cu-sensitive layers under pure H₂, CO background interference, and temperature-fluctuation conditions. The Pd–Cu sensor exhibited a good linear response over 0–500 ppm H₂, with a sensitivity of 0.0845 nm/ppm. Under a mixed atmosphere of 200 ppm H₂ and 500 ppm CO, the Pd–Cu sensor measured 198 ppm, whereas the pure Pd sensor measured 176 ppm, corresponding to relative errors of approximately 1% and 12%, respectively. In addition, the Pd–Cu sensor showed faster response/recovery behavior and better output stability after temperature compensation. These results indicate that, under the investigated conditions, the selected Pd–Cu-sensitive layer can effectively reduce CO-induced interference and improve the accuracy and stability of fiber-optic MEMS hydrogen sensing, supporting its feasibility for representative early-warning-related monitoring scenarios in energy storage batteries. Full article

With the widespread adoption of cryptocurrencies, the ability to conduct continuous offline payments has increasingly become a critical technological requirement. In network-constrained scenarios, current dual-offline payment technologies are useful for single transactions. However, their limitations in continuous payment scenarios have become increasingly evident, making them unable to meet real-world application needs. This has prompted the industry to demand more urgent innovations in research on continuous offline payment capabilities. To address these challenges, this paper proposes a continuous dual-offline payment system capable of supporting multiple continuous payments. The system integrates elliptic curve cryptography (ECC) and zero-knowledge proof (ZKP) technology to generate secure asset credentials, ensuring both immutability and privacy credentials throughout the offline payment lifecycle. A dynamic credential decomposition mechanism enables the splitting of input credentials into change credentials and receipt credentials, facilitating uninterrupted dual-offline payments between hardware wallets. Additionally, it incorporates a batch verification scheme based on smart contracts, utilizing zero-balance verification and chained hash tracing to ensure payment uniqueness and prevent double-spending attacks, thereby guaranteeing the verifiability and validity of payment settlements. Experimental evaluations demonstrate that the proposed system reduces gas consumption per payment and improves execution efficiency during batch processing, combining high security with strong performance. This research provides a feasible solution for the application of digital currencies in offline scenarios, carrying significant theoretical value and practical significance for driving technological innovation and application expansion in the cryptocurrency field. In addition to cryptocurrency payments, the proposed system is also applicable to IoT and sensor network environments. Many IoT devices operate in disconnected or network-limited areas and require secure micro-transactions. Our dual-offline payment mechanism supports such scenarios, as the main cryptographic operations are lightweight enough for typical IoT hardware. This further extends the practical value of our system beyond traditional cryptocurrency payments. Full article

Covalent organic frameworks (COFs) are one of the most important crystalline structures, having high porosity, and are mostly composed of lighter elements, such as H, C, N, O, etc., with covalent bonds between them. They are chemically synthesized in a repetitive arrangement and create a highly effective porous surface area that plays a fundamental role in various applications including sensing and biomedical applications. This study offers an overview of COFs in sensing and biomedical applications and provides a detailed overview of various synthesis procedures of COFs. Next, we explore their innovative sensing performances in the cases of various gases, ions and metals. Finally, it is emphasized that the major biomedical applications of COFs have been addressed regarding diseases and treatment strategies. Overall, this review offers an overview of COFs’ capabilities and promising behaviors in enhancing and revolutionizing sensing and biomedical technologies. Full article