Search Results (226)

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

Carbon nanotube field-effect transistors (CNTFETs) are becoming a strong competitor for the next generation of high-performance, energy-efficient integrated circuits due to their near-ballistic carrier transport characteristics and excellent suppression of short-channel effects. However, CNT FETs with large diameters and small band gaps exhibit obvious bipolarity, and gate-induced drain leakage (GIDL) contributes significantly to the off-state leakage current. Although the asymmetric gate strategy and feedback gate (FBG) structures proposed so far have shown the potential to suppress CNT FET leakage currents, the devices still lack scalability. Based on the analysis of the conduction mechanism of existing self-aligned gate structures, this study innovatively proposed a design strategy to extend the length of the source–drain epitaxial region (L_ext) under a vertically stacked architecture. While maintaining a high drive current, this structure effectively suppresses the quantum tunneling effect on the drain side, thereby reducing the off-state leakage current (I_off = 10⁻¹⁰ A), and has good scaling characteristics and leakage current suppression characteristics between gate lengths of 200 nm and 25 nm. For the sidewall gate architecture, this work also uses single-walled carbon nanotubes (SWCNTs) as the channel material and uses metal source and drain electrodes with good work function matching to achieve low-resistance ohmic contact. This solution has significant advantages in structural adjustability and contact quality and can significantly reduce the off-state current (I_off = 10⁻¹⁴ A). At the same time, it can solve the problem of off-state current suppression failure when the gate length of the vertical stacking structure is 10 nm (the total channel length is 30 nm) and has good scalability. Full article

This study presents a photoresist-free patterning method for solution-processed indium zinc oxide (IZO) thin films using two photochemical exposure techniques, namely pulsed ultraviolet (UV) light and UV-ozone, and a plasma-based method using oxygen (O₂) plasma. Pulsed UV light delivers short, high-intensity flashes of light that induce localised photochemical reactions with minimal thermal damage, whereas UV-ozone enables smooth and uniform surface oxidation through continuous low-pressure UV irradiation combined with in situ ozone generation. By contrast, O₂ plasma generates ionised oxygen species via radio frequency (RF) discharge, allowing rapid surface activation, although surface damage may occur because of energetic ion bombardment. All three approaches enabled pattern formation without the use of conventional photolithography or chemical developers, and the UV-ozone method produced the most uniform and clearly defined patterns. The patterned IZO films were applied as active layers in bottom-gate top-contact thin-film transistors, all of which exhibited functional operation, with the UV-ozone-patterned devices exhibiting the most favourable electrical performance. This comparative study demonstrates the potential of photochemical and plasma-assisted approaches as eco-friendly and scalable strategies for next-generation IZO patterning in electronic device applications. Full article

Mechanosensitive ion channels such as OSCA1.2 enable cells to sense and respond to mechanical forces by translating membrane tension into ionic flux. While lipid rearrangement in the inter-subunit cleft has been proposed as a key activation mechanism, the contributions of other domains to OSCA gating remain unresolved. Here, we combined the genetic encoding of the photoactivatable crosslinker p-benzoyl-L-phenylalanine (BzF) with functional Ca²⁺ imaging and molecular dynamics simulations to dissect the roles of specific residues in OSCA1.2 gating. Targeted UV-induced crosslinking at positions F22, H236, and R343 locked the channel in a non-conducting state, indicating their functional relevance. Structural analysis revealed that these residues are strategically positioned: F22 interacts with lipids near the activation gate, H236 lines the lipid-filled cavity, and R343 forms cross-subunit contacts. Together, these results support a model in which mechanical gating involves a distributed network of residues across multiple channel regions, allosterically converging on the activation gate. This study expands our understanding of mechanotransduction by revealing how distant structural elements contribute to force sensing in OSCA channels. Full article

Voltage-gated potassium channels Kv7.1, encoded by the gene KCNQ1, play critical roles in various physiological processes. In cardiomyocytes, the complex Kv7.1-KCNE1 mediates the slow component of the delayed rectifier potassium current that is essential for the action potential repolarization. Over 1000 KCNQ1 missense variants, many of which are associated with long QT syndrome, are reported in ClinVar and other databases. However, over 600 variants are of uncertain clinical significance (VUS), have conflicting interpretations of pathogenicity, or lack germline information. Computational prediction of the damaging potential of such variants is important for the diagnostics and treatment of cardiac disease. Here, we collected 1750 benign and pathogenic missense variants of Kv channels from databases ClinVar, Humsavar, and Ensembl Variation and tested 26 bioinformatics tools in their ability to identify known pathogenic or likely pathogenic (P/LP) variants. The best-performing tool, AlphaMissense, predicted the pathogenicity of 195 VUSs in Kv7.1. Among these, 79 variants of 66 wildtype residues (WTRs) are also reported as P/LP variants in sequentially matching positions of at least one hKv7.1 paralogue. In available cryoEM structures of Kv7.1 with activated and deactivated voltage-sensing domains, 52 WTRs form intersegmental contacts with WTRs of ClinVar-listed variants, including 21 WTRs with P/LP variants. ClinPred and paralogue annotation methods consistently predicted that 21 WTRs of KCNE1 have 34 VUSs with damaging potential. Among these, 8 WTRs are contacting 23 Kv7.1 WTRs with 13 ClinVar-listed variants in the AlphaFold3 model. Analysis of intersegmental contacts in CryoEM and AlphaFold3 structures suggests atomic mechanisms of dysfunction for some VUSs. Full article

Remote photoplethysmography (rPPG) enables non-contact physiological measurement for emotion recognition, yet the temporally sparse nature of emotional cardiovascular responses, intrinsic measurement noise, weak session-level labels, and subtle correlates of valence pose critical challenges. To address these issues, we propose a physiologically inspired deep learning framework comprising a Multi-scale Temporal Dynamics Encoder (MTDE) to capture autonomic nervous system dynamics across multiple timescales, an adaptive sparse α-Entmax attention mechanism to identify salient emotional segments amidst noisy signals, Gated Temporal Pooling for the robust aggregation of emotional features, and a structured three-phase curriculum learning strategy to systematically handle temporal sparsity, weak labels, and noise. Evaluated on the MAHNOB-HCI dataset (27 subjects and 527 sessions with a subject-mixed split), our temporal-only model achieved competitive performance in arousal recognition (66.04% accuracy; 61.97% weighted F1-score), surpassing prior CNN-LSTM baselines. However, lower performance in valence (62.26% accuracy) revealed inherent physiological limitations regarding a unimodal temporal cardiovascular analysis. These findings establish clear benchmarks for temporal-only rPPG emotion recognition and underscore the necessity of incorporating spatial or multimodal information to effectively capture nuanced emotional dimensions such as valence, guiding future research directions in affective computing. Full article

In this study, a novel silicon carbide (SiC) double-trench MOSFET (DT-MOS) combined Schottky barrier diode (SBD) and MOS-channel diode (MCD) is proposed and investigated using TCAD simulations. The integrated MCD helps inactivate the parasitic body diode when the device is utilized as a freewheeling diode, eliminating bipolar degradation. The adjustment of SBD position provides an alternative path for reverse conduction and mitigates the electric field distribution near the bottom source trench region. As a result of the Schottky contact adjustment, the reverse conduction characteristics are less influenced by the source oxide thickness, and the breakdown voltage (BV) is largely improved from 800 V to 1069 V. The gate-to-drain capacitance is much lower due to the removal of the bottom oxide, bringing an improvement to the turn-on switching rise time from 2.58 ns to 0.68 ns. These optimized performances indicate the proposed structure with both SBD and MCD has advantages in switching and breakdown characteristics. Full article

We introduce a novel system for automatic assessment of newborn and preterm infant behavior—including activity levels, behavioral states, and sleep–wake cycles—in clinical settings for streamlining care and minimizing healthcare professionals’ workload. While vital signs are routinely monitored, the previously mentioned assessments require labor-intensive direct observation. Research so far has already introduced non- and minimally invasive solutions. However, we developed a system that automatizes the preceding evaluations in a non-contact way using deep learning algorithms. In this work, we provide a Gated Recurrent Unit (GRU)-stack-based solution that works on a dynamic feature set generated by computer vision methods from the cameras’ video feed and patient monitor to classify the activity phases of infants adapted from the NIDCAP (Newborn Individualized Developmental Care Program) scale. We also show how pulse rate variability (PRV) data could improve the performance of the classification. The network was trained and evaluated on our own database of 108 h collected at the Neonatal Intensive Care Unit, Dept. of Neonatology of Pediatrics, Semmelweis University, Budapest, Hungary. Full article

This study introduces a comprehensive physical modeling framework for phototransistors based on quasi-two-dimensional transition metal dichalcogenides, with a particular emphasis on MoS₂. By integrating electromagnetic simulations of optical absorption with semiconductor transport calculations, the model captures both dark and photocurrent behaviors across diverse operating conditions. For 20 nm MoS₂ films, the model reproduces the experimental transfer characteristics with a threshold voltage accuracy better than 0.1 V and achieves quantitative agreement with photocurrent and dark current values across the full range of gate voltages, with the worst-case deviation not exceeding a factor of seven. Additionally, the model captures a three-order-of-magnitude increase in the photocurrent as the MoS₂ thickness varies from 4 nm to 40 nm, reflecting the strong thickness dependence observed experimentally. A key insight from the study is the critical role of defect states, including traps, impurities, and interfacial imperfections, in governing the dark current and photocurrent under channel pinch-off conditions (Vg < −1.0 V). The model successfully replicates the qualitative trends observed in experimental devices, highlighting how small variations in film thickness, doping levels, and contact geometries can significantly influence device performance, in agreement with published experimental data. These findings underscore the importance of precise defect characterization and optimization of material and structural parameters for 2D-material-based phototransistors. The proposed modeling framework serves as a powerful tool for the design and optimization of next-generation phototransistors, facilitating the integration of 2D materials into practical electronic and optoelectronic applications. Full article

Electroencephalography (EEG) is a non-invasive and portable way to capture neurophysiological activity, which provides the basis for brain–computer interface systems and more innovative applications, from entertainment to security. However, the acquisition of EEG signals often suffers from noise contamination and even signal interruption problems due to poor contact of the electrodes, body movement, or heavy noise. Such heavily contaminated and lost signal segments are usually removed manually, which can hinder practical system deployment and application performance, especially in scenarios where continuous signals are required. In our previous work, we proposed the weighted gate layer autoencoder (WGLAE) and demonstrated its effectiveness in learning dependencies in EEG time series and encoding relationships among EEG channels. The WGLAE adopts a gate layer to encourage the AE to approximate multiple relationships simultaneously by controlling the data flow of each input variable. However, it only applies a sequential control scheme without taking into account the physical meaning of EEG channel locations. In this study, we investigate the gating mechanism for WGLAE and validate the importance of having a proper gating scheme for learning relationships between EEG channels. To this end, several gate control mechanisms are designed that embed EEG channel locations and their corresponding underlying physical meanings. The influences introduced by the proposed gate control mechanisms are examined on an open dataset with different scales and associated with various stimuli. The experimental results suggest that the gating mechanisms have varying influences on reconstructing EEG signals. Full article

Electrospinning techniques have been widely applied in diverse applications, such as biocompatible membranes, energy storage systems, and triboelectric nanogenerators (TENGs), with the capability to incorporate other functional materials to achieve specific purposes. Recently, gas sensors incorporating doped semiconducting materials fabricated by electrospinning have been extensively investigated. TENGs, functioning as self-powered energy sources, have been utilized to drive gas sensors without external power supplies. Herein, a self-powered triboelectric ethanol sensor (TEES) is fabricated by integrating a TENG and an ethanol gas sensor into a single device. The proposed TEES exhibits a significantly improved response time and lower detection limit compared to published integrated triboelectric sensors. The device achieves an open-circuit voltage of 51.24 V at 800 rpm and a maximum short-circuit current of 7.94 μA at 800 rpm. Owing to the non-contact freestanding operating mode, the TEES shows no significant degradation after 240,000 operational cycles. Compared with previous studies that integrated TENGs and ethanol sensors, the proposed TEES demonstrated a marked improvement in sensing performance, with a faster response time (6 s at 1000 ppm) and a lower limit of detection (10 ppm). Furthermore, ethanol detection is enabled by modulating the gate terminal of an IRF840 metal-oxide semiconductor field-effect transistor (MOSFET), which controls the illumination of a light-emitting diode (LED). The LED is extinguished when the electrical output decreases below the setting value, allowing for the discrimination of intoxicated states. These results suggest that the TEES provides a promising platform for self-powered, high-performance ethanol sensing. Full article

This study analyzes human sleep disorders using non-contact approaches. The proposed approach analyzes periodic limb movement disorder (PLMD) under sleep conditions. This was conceptualized as data capture using a non-contact approach with ultrasonic sensors. The model was designed to estimate PLMD and classify it using real-time sleep data and a machine learning-based random forest classifier. Hardware schemes play a vital role in capturing sleep data in real time using ultrasonic sensors. A field-programmable gate array (FPGA)-based accelerator for a random forest classifier was designed to analyze PLMD. This is a novel approach that aids subjects in taking further medications. Verilog HDL was used for PLMD estimation using a Xilinx Vivado 2021.1 simulation and synthesis. The proposed method was validated using a Xilinx Zynq-7000 Zed board XC7Z020-CLG484. Full article

The contact interfaces of a press-pack insulated-gate bipolar transistor (PP-IGBT) module under fluctuating thermal stress will undergo minor friction and mutual sliding during service, which results in damage to the contact surface and a decline in the thermal performance of the contact interface. Therefore, the temperature inside the module will continue to increase, leading to eventual failure. In this work, a life prediction method based on thermal resistance degradation within a PP-IGBT module is established. The junction temperature can be determined via power loss and a resistance-capacitance (RC) thermal network model, and a life prediction model of the PP-IGBT module is developed based on thermal resistance degradation. The method considers the service quality under power cycling conditions and the influence of the self-accelerating effect of damage accumulation at the contact interface of the PP-IGBT module on fatigue life. The experimental results verify that the proposed PP-IGBT module life prediction method can effectively predict service life under power cycling conditions. Full article

The Neotropical brown stink bug (NBSB), Euschistus heros, is the most prevalent sucking soybean pest in Brazil, and control of it largely relies on the application of synthetic insecticides such as ethiprole, a phenylpyrazole insecticide targeting GABA-gated chloride channels encoded by the Rdl (resistant to dieldrin) gene. This study monitored 41 NBSB populations collected between 2021 and 2024 and revealed, for the first time, the presence of a mutation, A301S, in NBSB RDL receptors commonly known to confer target-site resistance to channel blockers such as phenylpyrazoles. Laboratory contact bioassays with ethiprole at 150 g a.i./ha (ethiprole label dose) revealed that most populations were quite susceptible, despite rather high resistance allele frequencies in some populations. Genotyping results confirmed that susceptible and A301S heterozygous genotypes largely dominate in frequency compared to homozygous resistant individuals, which exhibited high survivorship (84%) when exposed to discriminating rates of ethiprole in laboratory bioassays, while susceptible and heterozygote individuals showed lower survival rates (13% and 34%, respectively), suggesting an incompletely recessive trait conferring ethiprole resistance. Furthermore, we developed a TaqMan assay for molecular genotyping to monitor the spread of resistance allele frequency and to inform resistance management strategies for sustainable NBSB control using highly effective phenylpyrazole insecticides such as ethiprole. Full article

This study introduces an enhancement-mode AlGaN/GaN metal-insulator-semiconductor high-electron-mobility transistor (MIS-HEMT) featuring a self-aligned p-GaN gate structure, fabricated using a gate-first process. The key innovation of this work lies in simplifying the fabrication process by utilizing gate metallization for both electrical contact and etching mask functions, enabling precise self-alignment. A highly selective Cl₂/N₂/O₂ inductively coupled plasma (ICP) etching process was optimized to etch the p-GaN layer in the access regions, with a selectivity ratio of 33:1 and minimal damage to the AlGaN barrier. Additionally, a novel, non-annealed ohmic contact formation technique was developed, leveraging ICP etching to create nitrogen vacancies that facilitate contact formation without requiring thermal annealing. This technique streamlines the process by combining ohmic contact formation and mesa isolation into a single lithographic step. Incorporating a SiNx gate dielectric layer led to a 4.5 V threshold voltage shift in the fabricated devices. The resulting devices exhibited improved electrical performance, including a wide gate voltage swing (>10 V), a high on/off current ratio (~10⁷), and clear pinch-off characteristics. These results demonstrate the effectiveness of the proposed fabrication approach, offering significant improvements in process efficiency and manufacturability. Full article