Gate Modulation Research Articles

Impacts of Cavity Thickness and Insulating Material on Dielectric Modulated Trench Junction-less Double Gate Field Effect Transistor for Biosensing Applications

Introduction: This work represents the influence of gate dielectric, and the nano-cavity gap of a dielectric modulated trench gate Junction-less Double Gate Field Effect Transistor (JL-DGFET) on the different performance indicators is investigated considering the Low-Frequency Noise. Methods: It is noted that the gate dielectric and the nanogap, both parameters, have a substantial influence on the sensing capacity and performance of noise of the device. Results: A double gate suitable dielectric material and cavity thickness can effectively improve the biosensor’s sensitivity with a minimum amount of noise. Conclusion: The sensitivity is found to increase up to 9.5 for dielectric constant, k = 3.57 and 6.5 for dielectric constant, k = 2.1.

Flexible graphene textile-based sensor integrates programmable DNAzyme and logic gates for efficient small extracellular vesicle-miRNA detection

Nucleic acid signal amplification technologies and their cascade systems possess powerful ability to amplify signals. However, their application in biological computing, which necessitates a strict logical relationship, requires further investigation. To address this need, we developed a flexible sensing substrate consisting of carbon fiber cloth/graphene walls (CFC/GW), integrated with a 3D DNA tetrahedron (DNA-Te)-assisted bioplatform. This platform serves as the foundation for two types of Boolean logic gates: the “AND” and “OR” gate. The output system of the Cu2+ + DNAzyme logic gate module, activated by multiple miRNAs, was combined with the CFC/GW/Au-Te-based detection system to precisely identify small extracellular vesicle (sEV)-derived miRNAs in ultratrace quantities. Leveraging DNA Te-assisted DNAzyme molecular logic circuits, we designed higher-order Boolean logic operations to effectively identify and accurately read sEV-miRNAs in complex mixed samples. By integrating a material with low-background-signal and high-performance material with the original branch-strand migration logic gate system, we achieved an intelligent sensing platform that integrates capture, recognition, detection, and interpretation of sEV-miRNAs. We successfully demonstrated the response of the logic gate module of sEV-miRNAs in clinical samples, with detection results maintaining high consistency with conventional qRT-PCR methods. Our DNA logic gate platform boasts a simple design, adaptability, expandable complexity, and compatibility with various systems, enabling diverse possibilities for the nucleic acid-based development of intricate transduction networks in biomedical fields. This platform paves the way for more effective diagnosis, treatment, and monitoring of diseases, marking a significant advancement in nucleic acid signal amplification technologies and their utilization in biological computing.

Laser Liftoff Enabled Batch Fabrication of Ultrathin Graphene Hall Devices.

When optimizing the interface between low-dimensional materials and flexible substrates, the surface properties are essential for preserving their exceptional electronic properties. This study presents an approach to fabricating flexible low-dimensional electronic devices using a laser liftoff process that enables optimal interface quality and batch fabrication. Graphene Hall sensors on 6-μm polyimide substrates were batch-fabricated to demonstrate the utility of this approach. The sensors showed a linear response to magnetic fields up to 25 mT and an average current-normalized sensitivity of 140 V AT-1, outperforming other flexible graphene Hall devices without gate modulation or encapsulation. Owing to the ultrathin substrate, sensor-bending tests showed stable electrical characteristics under strain. The developments in this study represent a step toward extending the applications of low-dimensional flexible electronics.

Real-time prediction of structural responses in deep-water jacket platforms using Ada-STLNet: A comprehensive analysis with prototype monitoring data

Real-time prediction of the mechanical behaviors based on prototype monitoring data is crucial for ensuring the integrity and safety of deep-water jacket platforms. As complex nonlinear systems, these platforms’ response exhibit varying temporal trends, dynamic spatial correlations, and load dependencies. This makes accurate prediction of future axial force and bending moment responses a significant challenge. In this paper, a novel deep learning method called Adaptive Spatio-Temporal Load Network (Ada-STLNet) is proposed aiming to address the issue of multi-node axial force and bending moment response prediction of deep-water jacket platform. Our model utilizes an enhanced graph convolution (EGCN) and self-attention mechanism for efficient spatial correlation modeling in multi-node response, LSTM for long sequence temporal feature capture. It integrates spatial and temporal, along with load-aware modules to capture intricate patterns in structural responses. Additionally, a multi-gated coupling mechanism with two independent gating modules is designed to adaptively represent the complex dependencies of structural responses, ensuring model stability and robustness. Extensive experiments conducted on prototype structural monitoring data from a deep-water jacket platform in the South China Sea demonstrate that Ada-STLNet outperforms six traditional models, including HA, SVR, KNN, MLP, LSTM, and GRU, across various prediction steps. The proposed model exhibits superior accuracy, particularly in short-term predictions, achieving up to 62.80% improvement in MAE and 48.11% in RMSE for axial force predictions compared to the second-best model. Ablation studies confirm the effectiveness of each component within Ada-STLNet. This research highlights the potential of Ada-STLNet for reliable and accurate prediction of structural responses, contributing significantly to the field of marine engineering.

Study of Vertical Phototransistors Based on Integration of Inorganic Transistors and Organic Photodiodes.

We investigate the inorganic/organic hybrid vertical phototransistor (VPT) by integrating an atomic layer deposition-processed ZnO (ALD-ZnO) transistor with a prototype poly(3-hexylthiophene):[6,6]-phenyl-C61-butyric acid methyl ester (P3HT:PC61BM) blend organic photodiode (OPD) based on an encapsulated source electrode geometry, and discuss the device mechanism. Our preliminary studies on reference P3HT:PC61BM OPDs show non-ohmic electron injection between the ALD-ZnO and P3HT:PC61BM layers. However, the ALD-ZnO layer enables the accumulation of photogenerated holes under negative bias, which facilitates electron injection upon illumination and thereby enhances the external quantum efficiency (EQE). This mechanism underpins the photoresponse in the VPT. Furthermore, we demonstrate that the gate field in the VPT effectively modulates electron injection from the ALD-ZnO layer to the top OPD, resulting in the VPT operating as a non-ohmic OPD in the OFF state and as an ohmic OPD in the ON state. Benefiting from the unique transistor geometry and gate modulation capability, this hybrid VPT can achieve an EQE of 45,917%, a responsivity of 197 A/W, and a specific detectivity of 3.4 × 1012 Jones under 532 nm illumination and low drain-source voltage (Vds = 3 V) conditions. This transistor geometry also facilitates integration with various OPDs and the miniaturization of the ZnO channel area, offering an ideal basis for the development of highly efficient VPTs and high-resolution image sensors.

Oriented triplex DNA as a synthetic receptor for transmembrane signal transduction

Signal transduction across biological membranes enables cells to detect and respond to diverse chemical or physical signals, and replicating these complex biological processes through synthetic methods is of significant interest in synthetic biology. Here we present an artificial signal transduction system using oriented cholesterol-tagged triplex DNA (TD) as synthetic receptors to transmit and amplify signals across lipid bilayer membranes through H+-mediated TD conformational transitions from duplex to triplex. An auxiliary sequence, complementary to the third strand of the TD, ensures a controlled and preferred outward orientation of cholesterol-tagged TD on membranes. Upon external H+ stimuli, the conformational change triggers the translocation of the third strand from the outer to the inner membrane leaflet, resulting in effective transmembrane signal transduction. This mechanism enables fluorescence resonance energy transfer (FRET), selective photocleavage, catalytic signal amplification, and logic gate modulation within vesicles. Our findings demonstrate that these TD-based receptors mimic the functional dynamics of natural G protein-coupled receptors (GPCRs), providing a foundation for advanced applications in biosensing, cell signaling modulation, and targeted drug delivery systems.

ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction.

The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation, efficiently utilizing rich, high-dimensional information remains a significant challenge. Our work introduces ChemXTree, a novel graph-based model that integrates a Gate Modulation Feature Unit (GMFU) and neural decision tree (NDT) in the output layer to address this challenge. Extensive evaluations on benchmark data sets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree's superior performance, surpassing or matching the current state-of-the-art models. Visualization techniques clearly demonstrate that ChemXTree significantly improves the separation between substrates and nonsubstrates in the latent space. In summary, ChemXTree demonstrates a promising approach for integrating advanced feature extraction with neural decision trees, offering significant improvements in predictive accuracy for drug discovery tasks and opening new avenues for optimizing molecular properties.

Optical gating characteristic time of measurement with 20 picoseconds resolution for an ultrafast image intensifier

In order to study the optical gating time characteristic of an ultra-fast gated image intensifier, a precision measuring system with 20 ps (picoseconds) trigger jitter is established. The sequential gate images of the image intensifier with a time interval of 20 ps are obtained at 0.125 ns ∼2 ns (nanoseconds) gating electrical pulse modes. These images show that the intensifier has the familiar normal “iris” turn-on and abnormal “iris” turn-off behavior. The turn- on time is about 130 ps and the turn-off time is about 170 ps. In 1 ns and 2 ns gating modes, the fully-on time of optical gating almost is equal to the width of the electrical gating pulse, In 0.5 ns, 0.25 ns and 0.125 ns modes, the fully-on time is less than the width of the electrical gating pulse, especially in the 0.125 ns gating mode, and the image in fully-on phase is not observed. The width at 90% of the peak of the optical gating time response curve may better characterize the fully-on time of optical gating.

Low‐temperature SOI SiGe/Si superlattice FinFET with omega‐shaped channel and self‐allied silicide for 3D sequential IC

AbstractIn this letter, to improve the performance and reduce leakage currents of bulk low‐temperature multi‐layer SiGe/Si superlattice (SL) fin field‐effect transistors (FinFETs), a p‐type omega‐shaped channel (Ω‐channel) SL FinFET is realized by etching a Si/Si0.7Ge0.3 triple‐layer stacked structure in a replacement metal gate (RMG) module on a silicon‐on‐insulator (SOI) substrate. In addition, a self‐allied Ni0.9Pt0.1 silicide process and a low‐thermal‐budget (≤400°C) integration procedure were performed on the Ω‐channel SL FinFET. Test results demonstrate that the on‐state current (Ion) is increased by 5.5 times (from 78 to 429 µA/µm) and the off‐state current (Ioff) is reduced by 78.8% (from 5.2 × 10−3 to 1.1 × 10−3 µA/µm) when compared with the corresponding currents of traditional bulk SL FinFETs.

Модель та метод навчання детектора об’єктів на аерозображенні з оптимізацією робастності та кількості обчислень

The subject of research is Neural network-based object detectors, which are widely used for video image analysis. An increasing number of tasks now demand data processing directly at the source, which limits the available computational resources. However, the vulnerability of neural networks to noise, adversarial attacks, and weight error injections significantly diminishes their robustness and overall effectiveness. The relevant task is to develop models that provide both computational efficiency and robustness against perturbations. This paper investigates a model and method for enhancing the robustness of neural network detectors under limited resources. The objective is to design a model that allocates resources optimally while maintaining stability. To achieve this, the study employs techniques such as dynamic neural networks, robustness optimization, and resilience strategies. The following results were obtained. A detector with a feature extractor based on ViT-S/16, modified with gate modules for dynamic examination was developed. The model was trained on the RSOD dataset and meta-learned on the adaptation results to various perturbations. The model's resistance to random bit inversions in weights (10 % of weights) and to adversarial attacks with perturbation amplitudes up to 3/255 (L∞ norm) was tested. Conclusion. The proposed detector model incorporating dynamic examination and optimized robustness, reduced floating-point operations by over 20 % without loss of accuracy. A novel method for training the detector was developed, combining the RetinaNet loss function with the loss function of gate blocks and applying meta-learning on the adaptation results for various types of synthetic perturbations. Testing demonstrated an increase in accuracy by 11.9 % under the influence of error injection and by 13.2 % under the influence of adversarial attacks.

Ferroelectric Polarization Enhanced Optoelectronic Synaptic Response of a CuInP2S6 Transistor Structure.

Neuromorphic computing can simulate brain function and is a pivotal element in next-generation computing, providing a potential solution to the limitations brought by the von Neumann bottleneck. Optoelectronic synaptic devices are highly promising tools for simulating biomimetic nervous systems. In this study, we developed an optoelectronic neuromorphic device with a transistor structure constructed using ferroelectric CuInP2S6. Essential synaptic behaviors in this device are observed in response to light and electrical stimuli. The optoferroelectric coupling is revealed, and the highly tunable gate modulation of the charge carrier is realized in a single device. On this basis, the light adaptation of the biological eyes and smarter Pavlovian dogs was implemented successfully and enhanced by ferroelectric polarization. The gate voltage application promotes the migration of additional Cu+ ions in the in-plane direction, thus enhancing the synaptic performance of electrical stimulation. Meanwhile, the processing ability of convolutional kernel noise images in ferroelectric devices has been achieved. Our results offer the important observation and application of ferroelectric polarization-enhanced synaptic properties of a transistor structure and have great potential in promoting the development of two-dimensional van der Waals materials and devices.

Feature differences reduction and specific features preserving network for RGB-T salient object detection

In RGB-T salient object detection, effective utilization of the different characteristics of RGB and thermal modalities is essential to achieve accurate detection. Most of the previous methods usually only focus on reducing the differences between modalities, which may ignore the specific features that are crucial for salient object detection, leading to suboptimal results. To address the above issue, an RGB-T SOD network that simultaneously considers the reduction of modality differences and the preservation of specific features is proposed. Specifically, we construct a modality differences reduction and specific features preserving module (MDRSFPM) which aims to bridge the gap between modalities and enhance the specific features of each modality. In MDRSFPM, the dynamic vector generated by the interaction of RGB and thermal features is used to reduce modality differences, and then a dual branch is constructed to deal with the RGB and thermal modalities separately, employing a combination of channel-level and spatial-level operations to preserve their respective specific features. In addition, a multi-scale global feature enhancement module (MGFEM) is proposed to enhance global contextual information to provide guidance information for the subsequent decoding stage, so that the model can more easily localize the salient objects. Furthermore, our approach includes a fully fusion and gate module (FFGM) that utilizes dynamically generated importance maps to selectively filter and fuse features during the decoding process. Extensive experiments demonstrate that our proposed model surpasses other state-of-the-art models on three publicly available RGB-T datasets remarkably. Our code will be released at https://github.com/JOOOOKII/FRPNet.

Symmetry-adapted Markov state models of closing, opening, and desensitizing in α 7 nicotinic acetylcholine receptors.

α7 nicotinic acetylcholine receptors (nAChRs) are homopentameric ligand-gated ion channels with critical roles in the nervous system. Recent studies have resolved and functionally annotated closed, open, and desensitized states of these receptors, providing insight into ion permeation and lipid binding. However, the process by which α7 nAChRs transition between states remains unclear. To understand gating and lipid modulation, we generated two ensembles of molecular dynamics simulations of apo α7 nAChRs, with or without cholesterol. Using symmetry-adapted Markov state modeling, we developed a five-state gating model. Free energies recapitulated functional behavior, with the closed state dominating in absence of agonist. Open-to-nonconducting transition rates corresponded to experimental open durations. Cholesterol relatively stabilized the desensitized state, and reduced open-desensitized barriers. These results establish plausible asymmetric transition pathways between states, define lipid modulation effects on the α7 nAChR conformational cycle, and provide an ensemble of structural models applicable to rational design of lipidic pharmaceuticals.

Ammonia gas sensors based on multi-wall carbon nanofiber field effect transistors by using gate modulation

Ammonia gas sensors play a critical role in environmental monitoring and healthcare applications due to the harmful effects of ammonia exposure. In this study, I present a novel approach to ammonia detection using field-effect transistors (FETs) based on Multi-Wall Carbon Nanofibers (MWCNFs), known for their outstanding electrical properties. I fabricate and characterize MWCNF-FET sensors, utilizing interdigitated Source-Drain electrodes directly deposited onto aligned semiconducting MWCNFs. By systematically investigating the effect of gate bias on sensor performance, I demonstrate that applying a positive gate voltage enhances the sensor's gas response by up to 55 % across varying concentrations of ammonia. Additionally, I show that appropriate gate modulation significantly improves the device's reversibility, ensuring reliable, repeatable measurements. These findings highlight the potential of MWCNF-FETs in developing high-performance, gate-tunable ammonia gas sensors for real-world applications.

TRPML1 gating modulation by allosteric mutations and lipids

Transient Receptor Potential Mucolipin 1 (TRPML1) is a lysosomal cation channel whose loss-of-function mutations directly cause the lysosomal storage disorder mucolipidosis type IV (MLIV). TRPML1 can be allosterically regulated by various ligands including natural lipids and small synthetic molecules and the channel undergoes a global movement propagated from ligand-induced local conformational changes upon activation. In this study, we identified a functionally critical residue, Tyr404, at the C-terminus of the S4 helix, whose mutations to tryptophan and alanine yield gain- and loss-of-function channels, respectively. These allosteric mutations mimic the ligand activation or inhibition of the TRPML1 channel without interfering with ligand binding and both mutant channels are susceptible to agonist or antagonist modulation, making them better targets for screening potent TRPML1 activators and inhibitors. We also determined the high-resolution structure of TRPML1 in complex with the PI(4,5)P2 inhibitor, revealing the structural basis underlying this lipid inhibition. In addition, an endogenous phospholipid likely from sphingomyelin is identified in the PI(4,5)P2-bound TRPML1 structure at the same hotspot for agonists and antagonists, providing a plausible structural explanation for the inhibitory effect of sphingomyelin on agonist activation.

ω-Grammotoxin-SIA inhibits voltage-gated Na+ channel currents.

ω-Grammotoxin-SIA (GrTX-SIA) was originally isolated from the venom of the Chilean rose tarantula and demonstrated to function as a gating modifier of voltage-gated Ca2+ (CaV) channels. Later experiments revealed that GrTX-SIA could also inhibit voltage-gated K+ (KV) channel currents via a similar mechanism of action that involved binding to a conserved S3-S4 region in the voltage-sensing domains (VSDs). Since voltage-gated Na+ (NaV) channels contain homologous structural motifs, we hypothesized that GrTX-SIA could inhibit members of this ion channel family as well. Here, we show that GrTX-SIA can indeed impede the gating process of multiple NaV channel subtypes with NaV1.6 being the most susceptible target. Moreover, molecular docking of GrTX-SIA onto NaV1.6, supported by a p.E1607K mutation, revealed the voltage sensor in domain IV (VSDIV) as being a primary site of action. The biphasic manner in which current inhibition appeared to occur suggested a second, possibly lower-sensitivity binding locus, which was identified as VSDII by using KV2.1/NaV1.6 chimeric voltage-sensor constructs. Subsequently, the NaV1.6p.E782K/p.E838K (VSDII), NaV1.6p.E1607K (VSDIV), and particularly the combined VSDII/VSDIV mutant lost virtually all susceptibility to GrTX-SIA. Together with existing literature, our data suggest that GrTX-SIA recognizes modules in NaV channel VSDs that are conserved among ion channel families, thereby allowing it to act as a comprehensive ion channel gating modifier peptide.

Low-Symmetry Van der Waals Dielectric GaInS3 Triggered 2D MoS2 Giant Anisotropy via Symmetry Engineering.

Low-symmetry structures in van der Waals materials have facilitated the advancement of anisotropic electronic and optoelectronic devices. However, the intrinsic low symmetry structure exhibits a small adjustable anisotropy ratio (1-10), which hinders its further assembly and processing into high-performance devices. Here, a novel 2D anisotropic dielectric, GaInS3 (GIS), which induces isotropic MoS2 to exhibit significant anisotropic optical and electrical responses is demonstrated. With the excellent gate modulation ability of 2D GIS (dielectric constant k ∼12), MoS2 field effect transistor (FET) shows an adjustable conductance ratio from isotropic to anisotropic under dual-gate modulation, up to 106. Theoretical calculations indicate that anisotropy originates from lattice mismatch-induced charge density deformation at the interface. Moreover, the MoS2/GIS photodetector demonstrates high responsivity (≈4750 A W-1) and a large dichroic ratio (≈167). The anisotropic van der Waals dielectric GIS paves the way for the development of 2D transition metal dichalcogenides (TMDCs) in the fields of anisotropic photonics, electronics, and optoelectronics.

Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear Spiking Neural Membrane Systems.

Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image segmentation), a novel model based on convolutional nonlinear spiking neural P systems. NSNPRIS features NSNPFusion and Language Gate modules to enhance feature interaction during encoding, along with an NSNPDecoder for feature alignment and decoding. Experimental results on RefCOCO, RefCOCO[Formula: see text], and G-Ref datasets demonstrate that NSNPRIS performs better than mainstream methods. Our contributions include advances in the alignment of pixel and textual features and the improvement of segmentation accuracy.

TRPML1 gating modulation by allosteric mutations and lipids.

Transient Receptor Potential Mucolipin 1 (TRPML1) is a lysosomal cation channel whose loss-of-function mutations directly cause the lysosomal storage disorder mucolipidosis type IV (MLIV). TRPML1 can be allosterically regulated by various ligands including natural lipids and small synthetic molecules and the channel undergoes a global movement propagated from ligand-induced local conformational changes upon activation. In this study, we identified a functionally critical residue, Tyr404, at the C-terminus of the S4 helix, whose mutations to tryptophan and alanine yield gain- and loss-of-function channels, respectively. These allosteric mutations mimic the ligand activation or inhibition of the TRPML1 channel without interfering with ligand binding and both mutant channels are susceptible to agonist or antagonist modulation, making them better targets for screening potent TRPML1 activators and inhibitors. We also determined the high-resolution structure of TRPML1 in complex with the PI(4,5)P2 inhibitor, revealing the structural basis underlying this lipid inhibition. In addition, an endogenous phospholipid likely from sphingomyelin is identified in the PI(4,5)P2-bound TRPML1 structure at the same hotspot for agonists and antagonists, providing a plausible structural explanation for the inhibitory effect of sphingomyelin on agonist activation.

Gate modulation of barrier height of unipolar vertically stacked monolayer ReS2/MoS2 heterojunction

This study investigates vertically stacked CVD grown ReS2/MoS2 unipolar heterostructure device as Field Effect Transistor (FET) device wherein ReS2 on top acts as drain and MoS2 at bottom acts as source. The electrical measurements of ReS2/MoS2 FET device were carried out and variation in Ids (drain current) Vs Vds (drain voltage) for different Vgs (gate voltage) revealing the n-type device characteristics. Furthermore, the threshold voltage was calculated at the gate bias voltage corresponding to maximum transconductance (gm) value which is ~ 12 V. The mobility of the proposed ReS2/MoS2 heterojunction FET device was calculated as 60.97 cm2 V−1 s−1. The band structure of the fabricated vDW heterostructure was extracted utilizing ultraviolet photoelectron spectroscopy and the UV–visible spectroscopy revealing the formation of 2D electron gas (2DEG) at the ReS2/MoS2 interface which explains the high carrier mobility of the fabricated FET. The field effect behavior is studied by the modulation of the barrier height across heterojunction and detailed explanation is presented in terms of the charge transport across the heterojunction.