Search Results (183)

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

The mechanism by which quorum sensing (QS) enhances stress resistance in enterohemorrhagic Escherichia coli (E. coli) O157:H7 remains unclear. We employed optimized exogenous QS signal N-acyl-homoserinelactones (AHL) (100 μM 3-oxo-C6-AHL, 2 h) in EHEC O157:H7 strain EDL933, which was validated with endogenous yenI-derived AHL, to investigate QS-mediated protection against acid stress. RNA-seq transcriptomics identified key upregulated genes (e.g., rmf). Functional validation using isogenic rmf knockout mutants generated via λ-Red demonstrated abolished stress resistance and pan-stress vulnerability. Mechanistic studies employing qRT-PCR and stress survival assays established Ribosomal Hibernation Factor (RMF) as a non-redundant executor in a SdiA–RMF–RpoS axis, which activates ribosomal dormancy and SOS response to enhance EHEC survival under diverse stresses. For the first time, we define ribosomal hibernation as the core adaptive strategy linking QS to pathogen resilience, providing crucial mechanistic insights for developing EHEC control measures against foodborne threats. Full article

Point-of-care (POC) diagnostic technologies have become essential for the real-time monitoring and management of chronic wounds, where maintaining a moist environment and controlling pH levels are critical for effective healing. In this study, a flexible pH sensor based on a graphene/molybdenum disulfide (graphene/MoS₂) composite interdigitated electrode (IDE) structure was fabricated using pulsed laser ablation. The pH sensor, with an active area of 30 mm × 30 mm, exhibited good adhesion to the polyethylene terephthalate (PET) substrate and maintained structural integrity under repeated bending cycles. Precise ablation was achieved under optimized conditions of 4.35 J/cm² laser fluence, a repetition rate of 300 kHz, and a scanning speed of 500 mm/s, enabling the formation of defect-free IDE arrays without substrate damage. The influence of laser processing parameters on the surface morphology, electrical conductivity, and wettability of the composite thin films was systematically characterized. The fabricated pH sensor exhibited high sensitivity (~4.7% change in current per pH unit) across the pH 2–10 range, rapid response within ~5.2 s, and excellent mechanical stability under 100 bending cycles with negligible performance degradation. Moreover, the sensor retained > 95% of its stable sensitivity after 7 days of ambient storage. Furthermore, the pH response behavior was evaluated for electrode structures with different pitches, demonstrating that structural design parameters critically impact sensing performance. These results offer valuable insights into the scalable fabrication of flexible, wearable pH sensors, with promising applications in wound monitoring and personalized healthcare systems. Full article

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T_C) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T_C. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T_r-3T_C), derived from T_C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T_r-3T_C. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H₂O m⁻² s⁻¹ and 0.555 mmol H₂O m⁻² s⁻¹ for the respective years, indicating strong interannual stability. Full article

This work represents the first use of a phosphonium salt-functionalized β-Cyclodextrin polymer (β-CDP) as a highly selective sensing membrane for monitoring the safety of drinking water against perchlorate ions (ClO₄⁻) using electrochemical impedance spectroscopy (EIS). Structural confirmation via ¹H NMR, ¹³C NMR, ³¹P NMR, and FT-IR spectroscopies combined with AFM and contact angle measurements demonstrate how the enhanced solubility of modified cyclodextrin improves thin film quality. The innovation lies in the synergistic combination of two detection mechanisms: the “Host-Guest” inclusion in the cyclodextrin cavity and anionic exchange between the bromide ions of the phosphonium groups and perchlorate anions. Under optimized functionalization conditions, EIS reveals high sensitivity and selectivity, achieving a record-low detection limit (LOD) of ~10⁻¹² M and a wide linear range of detection (10⁻¹¹ M–10⁻⁴ M). Sensing mechanisms at the functionalized transducer interfaces are examined through numerical fitting of Cole-Cole impedance spectra via a single relaxation equivalent circuit. Real water sample analysis confirms the sensor’s practical applicability, with recoveries between 96.9% and 109.8% and RSDs of 2.4–4.8%. Finally, a comparative study with reported membrane sensors shows that β-CDP offers superior performance, wider range, higher sensitivity, lower LOD, and simpler synthesis. Full article

To address the growing demand for environmentally friendly flexible sensors, here, a composite hydrogel of nanocellulose (NC) and polyvinyl alcohol (PVA) was designed and fabricated using Camellia oleifera shells as a sustainable alternative to petroleum-based raw materials. Firstly, NC was extracted from Camellia oleifera shells and modified with 2-chloropropyl chloride to obtain a nanocellulose-based initiator (Init-NC) for atomic transfer radical polymerization (ATRP). Subsequently, sulfonyl betaine methacrylate (SBMA) was polymerized by Init-NC initiating to yield zwitterion-functionalized nanocellulose (NC-PSBMA). Finally, the NC-PSBMA/PVA hydrogel was fabricated by blending NC-PSBMA with PVA. A Fourier transform infrared spectrometer (FT-IR), proton nuclear magnetic resonance spectrometer (¹H-NMR), X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), universal mechanical testing machine, and digital source-meter were used to characterize the chemical structure, surface microstructure, and sensing performance. The results indicated that: (1) FT-IR and ¹H NMR confirmed the successful synthesis of NC-PSBMA; (2) SEM, TEM, and alternating current (AC) impedance spectroscopy verified that the NC-PSBMA/PVA hydrogel exhibits a uniform porous structure (pore diameter was 1.1737 μm), resulting in significantly better porosity (15.75%) and ionic conductivity (2.652 S·m⁻¹) compared to the pure PVA hydrogel; and (3) mechanical testing combined with source meter testing showed that the tensile strength of the composite hydrogel increased by 6.4 times compared to the pure PVA hydrogel; meanwhile, it showed a high sensitivity (GF = 1.40, strain range 0–5%; GF = 1.67, strain range 5–20%) and rapid response time (<0.05 s). This study presents a novel approach to developing bio-based, flexible sensing materials. Full article

In this study, we develop a graphene-trenched silicon Schottky junction for humidity sensing. This novel structure comprises suspended graphene bridging etched trenches on a silicon substrate, creating both free-standing and substrate-contacting regions of graphene that enhance water adsorption sensing. Suspended graphene is intrinsically insensitive to water adsorption, making it difficult for adsorbed H₂O to effectively dope the graphene. In contrast, when graphene is supported on the silicon substrate, water molecules can effectively dope the graphene by modifying the silicon’s impurity bands and their hybridization with graphene. This humidity-induced doping leads to a significant modulation of the Schottky barrier at the graphene–silicon interface, which serves as the core sensing mechanism. We investigate the current–voltage (I–V) characteristics of these devices as a function of trench width and relative humidity. Our analysis shows that humidity influences key device parameters, including the Schottky barrier height, ideality factor, series resistance, and normalized sensitivity. Specifically, larger trench widths reduce the graphene density of states, an effect that is accounted for in our analysis of these parameters. The sensor operates under both forward and reverse bias, enabling tunable sensitivity, high selectivity, and low power consumption. These features make it promising for applications in industrial and home safety, environmental monitoring, and process control. Full article

The development of ultraflexible and sensitive gas sensors is critical for advancing next-generation environmental monitoring and healthcare diagnostics. In this work, we demonstrate an ultraflexible chemiresistive nitrogen dioxide (NO₂) sensor integrated with a photopatterned porous poly(3-hexylthiophene) (P3HT)/SU-8 blend film as an active sensing layer. The porous microarchitecture was fabricated via high-resolution photolithography, utilizing SU-8 as a photoactive porogen to template a uniform, interconnected pore network within the P3HT matrix. The engineered porosity level ranged from 0% to 36%, substantially improving gas diffusion kinetics to enlarge the accessible surface area for analyte adsorption. Our sensor exhibited a marked enhancement in sensitivity at an optimized porosity of 36%, with the current response at 30 ppm NO₂ increasing from 354% to 3201%, along with a detection limit of 0.7 ppb. The device further exhibited a high selectivity against common interfering gases, including NH₃, H₂S, and SO₂. Moreover, the porous structure imparted excellent mechanical durability, maintaining over 90% of its initial sensing performance after 500 bending cycles at a 1 mm radius, underscoring its potential for integration into next-generation wearable environmental monitoring platforms. Full article

The exploration of the coupling trend between urban agglomeration development (UAD) and land surface temperature (LST) expansion is of great significance, and it is of scientific value for the regulation of the thermal environment of urban agglomerations, the optimization of urban spatial planning, and the achievement of sustainable urban development. This study employs an array of remote sensing datasets from multiple sources—employing a multi-faceted approach encompassing an overall coupling situation analysis model, a coordination and evaluation system, a geographically weighted spatial autocorrelation algorithm, and landscape pattern quantification indicators—to explore the mutual feedback mechanism and spatial coupling characterization of LST and UAD in the Guanzhong Plain Urban Agglomeration (GZPUA). The results of the study can provide data support for urban spatial planning and thermal environment regulation. The results indicate the following findings: (1) In the GZPUA, the nighttime light (NTL) and land surface temperature (LST) centroids show a significant tendency toward approaching one another, with a spatial offset decreasing from 45.0 km to 9.1 km at the end, indicating a strengthening trend in the photothermal system’s coupling synergy. (2) The coordination of light and heat in the study area exhibits significant non-equilibrium development, with a dynamic trend of urban development space shifting towards the southwest. It confirms the typical regional response law of rapid urbanization. (3) The Moran’s I index of the photothermal system in the study area increased from 0.289 to 0.335, an increase of 15.9%. The proportion of “high–high” (H-H)/“low–low” (L-L)-type regions with clustering distribution of cold and hot spots reaches 58.01%, and their spatial continuity characteristics are significantly enhanced, indicating a significant trend of spatial structural integration between urban heat island effect and construction land expansion. Full article

Rapeseed (Brassica napus L., 2n = 38) is an important oil crop worldwide, providing vegetable oil and biofuel. Despite improvements in breeding, rapeseed’s harvest index and yield remain lower than other major crops. Plant height (PH) and first-branch height (FBH) are crucial plant architecture traits affecting yield, lodging resistance and efficiency of mechanical harvesting. Phenotypic analysis of 125 rapeseed accessions across four environments revealed wide variation in PH (100–198 cm) and FBH (15.56–112.4 cm), with high broad-sense heritability (H² = 81.59% for PH, 77.69% for FBH), and significant positive correlations between traits. To understand the genetic control of PH and FBH, a genome-wide association study (GWAS) of a natural population was conducted, covering 2,131,705 genome variants across four environments. The 13 QTLs for PH and 15 for FBH were identified. Meta-analysis revealed that 28.57% of these loci overlapped with previously reported QTLs. Haplotype analysis confirmed significant effects of these loci on the traits. Candidate genes for PH and FBH, respectively, were identified based on linkage disequilibrium and functional predictions. However, five novel loci lacked nearby annotated genes. The candidate genes are linked to traits in Arabidopsis and other species, as well as to phytohormone response and cell development, and cell development. Notably, MOS1 gene copies (BnaA03G0481200ZS and BnaC07G0459400ZS) were associated with PH and FBH, indicating their multifunctional potential. Additionally, BnaA05G0163200ZS, with no functional annotation, emerged as a crucial gene for plant architecture. This study provides new genetic insights and may enhance marker-based breeding for ideotypes in rapeseed. Full article

The application of hydrogels in flexible sensing has received increasing attention, but the simultaneous preparation of hydrogels with good structural stability, strain sensing sensitivity, freezing resistance, and drying resistance remains a challenge. Based on this, a GG-nanocellulose/sodium alginate/polyacrylamide composite hydrogel with a hierarchical network structure was constructed by one-step synthesis by incorporating graphene oxide (GO) and glycerol into the hydrogel. The hydrogel remained structurally intact after 100 compression cycles. In addition, the hydrogel was dried at 30 °C for 24 h. The mass retention rate was 48%, the melting peak was as low as −13.87 °C, and the hydrogel remained flexible and stable at low temperatures. GO modulated the network structure arrangement of the hydrogel through various mechanisms, thereby conferring to the hydrogel an excellent sensing performance, with a sensitivity (GF) of 2.21. In conclusion, this hierarchical network hydrogel has good drying, freezing, and sensing properties, which provides a new viable strategy for monitoring motion signals. Moreover, the hydrogel is predicted to function as a dressing, thereby facilitating the absorption of heat from the skin’s surface, with the aim of alleviating the discomfort associated with joint and muscle injuries caused by strenuous exercise. Full article

This study introduces a novel dual-battery architecture with intelligent auto-switching control, designed to ensure uninterrupted operation of agricultural sensing systems in environments with unpredictable energy availability. The proposed system integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries with advanced switching algorithms—specifically, the Dynamic Load Balancing–Power Allocation Optimisation (DLB–PAO) and Dynamic Load Balancing–Genetic Algorithm (DLB–GA)—tailored to maximise sensor operational longevity. By optimizing the dual-battery configuration for real-world deployment and conducting comparative evaluations across multiple system designs, this work advances an innovative engineering solution with significant practical implications for sustainable agriculture and remote sensing applications. Unlike conventional single-battery systems or passive redundancy approaches, the architecture introduces active redundancy, adaptive energy management, and fault tolerance, substantially improving operational continuity. A functional prototype was experimentally validated using realistic load profiles, demonstrating seamless battery switching, extended uptime, and enhanced energy reliability. To further assess long-term performance under continuous Internet of Things (IoT) operation, a simulation framework was developed in MATLAB/Simulink, incorporating battery degradation models and empirical sensor load profiles. The experimental results reveal distinct performance improvements. A baseline single-battery system sustains 28 h of operation with 31.2% average reliability, while a conventional dual-battery configuration extends operation to 45 h with 42.6% reliability. Implementing the DLB–PAO algorithm elevates the average reliability to 91.7% over 120 h, whereas the DLB–GA algorithm achieves near-perfect reliability (99.9%) for over 170 h, exhibiting minimal variability (standard deviation: 0.9%). The integration of intelligent auto-switching mechanisms and metaheuristic optimisation algorithms demonstrates a marked enhancement in both reliability and energy efficiency for soil nutrient monitoring systems. This method extends the lifespan of electronic devices while ensuring reliable energy storage over time. It creates a practical foundation for sustainable IoT agricultural systems in areas with limited resources. Full article

Copper-thiolate nanostructures, formed through the self-assembly of cysteine (Cys) and glutathione (GSH) with copper ions, offer a versatile platform for redox-active applications due to their structural stability and chemical functionality. In this study, Cu-Cys-GSH nanoparticles were synthesized and employed to develop a capacitive biosensor for the ultralow concentration detection of hydrogen peroxide (H₂O₂). The detection mechanism leverages a Fenton-like reaction, where H₂O₂ interacts with Cu-Cys-GSH nanoparticles to generate hydroxyl radicals (·OH) through redox cycling between Cu²⁺ and Cu⁺ ions. These redox processes induce changes in the sensor’s surface charge and dielectric properties, enabling highly sensitive capacitive sensing at gold interdigitated electrodes (IDEs). The influence of chirality on sensing performance was investigated by synthesizing nanoparticles with both L- and D-cysteine enantiomers. Comparative analysis revealed that the stereochemistry of cysteine impacts the catalytic activity and sensor response, with Cu-L-Cys-GSH nanoparticles exhibiting superior performance. Specifically, the biosensor achieved a linear detection range from 1.0 fM to 1.0 pM and demonstrated an ultra-sensitive detection limit of 21.8 aM, outperforming many existing methods for H₂O₂ detection. The sensor’s practical performance was further validated using milk and saliva samples, yielding high recovery rates and confirming its robustness and accuracy for real-world applications. This study offers a disposable, low-cost sensing platform compatible with sustainable healthcare practices and facilitates easy integration into point-of-care diagnostic systems. Full article

The agro-pastoral ecotone of Gansu Province, a critical component of the ecological security barrier in northern China, is characterized by pronounced ecological fragility and climatic sensitivity. Investigating vegetation dynamics in this region is essential for balancing ecological conservation and sustainable development. This study integrated MODIS/NDVI remote sensing data (2000–2020), climate, land, and anthropogenic factors, employing Sen’s slope analysis, coefficient of variation (Cv), Hurst index, geodetector modeling, and partial correlation analysis to systematically unravel the spatio-temporal evolution and driving mechanisms of vegetation coverage. Key findings revealed the following: (1) Vegetation coverage exhibited a significant increasing trend (0.05 decade⁻¹), peaking in 2018 (NDVI = 0.71), with a distinct north–south spatial gradient (lower values in northern areas vs. higher values in southern regions). Statistically significant greening trends (p < 0.05) were observed in 55.42% of the study area. (2) Interannual vegetation fluctuations were generally mild (Cv = 0.15), yet central regions showed 2–3 times higher variability than southern/northwestern areas. Future projections (H = 0.62) indicated sustained NDVI growth. (3) Climatic factors dominated vegetation dynamics, with sunshine hours and precipitation exhibiting the strongest explanatory power (q = 0.727 and 0.697, respectively), while the elevation–precipitation interaction achieved peak explanatory capacity (q = 0.845). (4) NDVI correlated positively with precipitation in 43.62% of the region (r_mean = 0.47), whereas average temperature, maximum temperature, ≥10 °C accumulated temperature, and sunshine hours suppressed vegetation growth (r_mean = −0.06 to −0.42), confirming precipitation as the primary driver of regional vegetation recovery. The multi-scale analytical framework developed here provides methodological and empirical support for precision ecological governance in climate-sensitive transitional zones, particularly for optimizing ecological barrier functions in arid and semi-arid regions. Full article

This work presents a vibration-based non-contact monitoring method to classify the physical activity of a mouse inside a home cage. A novel tuned-beam sensing device is developed to measure low-amplitude activity-induced cage vibrations. The sensing device uses a mechanical beam structure to enhance a six-axis IMU that increases the signal-to-noise ratio (SNR) by 20 to 40 times in a relevant environment. A sophisticated classification algorithm is developed to process vibration sequences with a variable time frame that utilizes multi-level discrete wavelet transformation (MLDWT) to extract time–frequency features and optimize signal properties. The extracted features are classified by a convolutional neural network–long short-term memory (CNN-LSTM) machine learning model to determine the activity class. The ground truth is obtained with a camera-based system using EthoVision XT from Noldus and a custom post-processor. The method is developed on a dataset containing 300 h of vibration measurements with camera-based reference and includes two separate home cages and two individual mice. The method classifies the activity types Resting, Stationary Activity, Walking, Activity in Feeder, and Drinking with an accuracy of 86.81% and an average F1 score of 0.798 using a 9 s time frame. In long-term monitoring, the proposed method reproduces behavioral patterns such as sleep and acclimatization as accurately as the reference method, enabling home cage monitoring in the husbandry environment with a low-cost sensor. Full article