NERC standards based cascaded FO controller for frequency regulation in restructured model with flow resources.

The paper establishes the effective utilization of North American Electric Reliability Corporation control performance standards for cascaded fractional order controller in dispensing regulation ancillary service to the deregulated structures having intermittent generating units. The flow resources that highly contribute in faster regulatory facility include photovoltaic and wind systems. For a two area network, the control performance standard 1and balancing authority ACE limits, the successor to CPS2 are assumed to be the control inputs to the fuzzy logic base and its tuned output gain is fed to the cascaded proportional-integral-derivative double staged FOPID controller for best fallouts in dynamic stability and frequency response leveraged under diverse market contracts. Reduced depletion and depreciation in the generating unit equipment by restricted operation of the turbine valves in accordance to the generation load mismatch is the foremost virtue of the suggested performance metrics. The concurrent feed of the above mentioned standards to the fuzzy logic base against varying disturbances, contrary to thermal constraints in iterative sequence, grades to uniform reliability and consistent generation-load balance. The effectiveness of the suggested controller is verified in MATLAB forum against existing classical, cascaded and intelligent centered FO controllers manifested with artificial hummingbird algorithm owing to its proven supremacy and robustness in literature. The analytical effects in transient mode of operation is evaluated with integral time absolute error as fitness function as it epitomizes the reduction of larger errors at the initial and longer transient response state.

Fuzzy-based collective pitch control for wind turbine via deep reinforcement learning.

Wind turbines (WTs) have highly nonlinear and uncertain dynamics due to aerodynamic complexity, mechanical factors, and fluctuations in wind conditions. Turbulence and wind shear add complexity to modelling, especially in constant power region (region 3). Thus, an effective control design demands a deep understanding of the nonlinearities and uncertainties. This paper suggests a novel model-free reinforcement learning (RL) collective pitch angle controller to operate efficiently in region 3. The proposed controller stabilizes generator speed, maximizes power output, and minimizes fluctuations while accommodating system uncertainties, nonlinearity, and pitch limits. The disparity between WT dynamics due to wind speed perturbations and uncertainties is measured using a gap-metric criterion. The controller design adopts a deep deterministic policy gradient (DDPG) algorithm to train six agents in a medium-fidelity WT environment at different mean wind speeds to ensure the controller's robustness. Initially, imitation learning is used for efficient sample collection to fasten training convergence. Afterwards, the agent learns by interacting with the environment. After the training, the pitch control outputs from multi-trained agents are processed by a fuzzy system to have smooth transitions under different operating conditions. The resulting fuzzy DDPG (F-DDPG) controller is deployed to obtain the optimal pitch control action. The performance of the proposed F-DDPG controller is compared to the gain-scheduled PI (GSPI), Linear-Quadratic-Regulator (LQR), and single-DDPG-agent controllers. The controllers are simulated in high-fidelity onshore and offshore 5-MW WT environments using the OpenFAST/MATLAB simulation tools. The results reveal the superiority of the proposed controller in generalizing its optimal performance in different operating conditions.

A Mixture of Negative-, Zero-, and Positive-Differential Transconductance Switching from Tellurium/Indium Gallium Zinc Oxide Heterostructures.

Conventional transistors have long emphasized signal modulation and amplification, often sidelining polarity considerations. However, the recent emergence of negative differential transconductance, characterized by a drain current decline during gate voltage sweeping, has illuminated an unconventional path in transistor technology. This phenomenon promises to simplify the implementation of ternary logic circuits and enhance energy efficiency, especially in multivalued logic applications. Our research has culminated in the development of a sophisticated mixed transconductance transistor (M-T device) founded on a precise Te and IGZO heterojunction. The M-T device exhibits a sequence of intriguing phenomena, zero differential transconductance (ZDT), positive differential transconductance (PDT), and negative differential transconductance (NDT) contingent on applied gate voltage. We clarify its operation using a three-segment equivalent circuit model and validate its viability with IGZO TFT, Te TFT, and Te/IGZO TFT components. In a concluding demonstration, the M-T device interconnected with Te TFT achieves a ternary inverter with an intermediate logic state. Remarkably, this configuration seamlessly transitions into a binary inverter when it is exposed to light.

Exploring the decision-making process of female genital cosmetic procedures in Iranian women and constructing and validating a results-based logic model for a healthy public policy: a study protocol.

BACKGROUND: Female genital cosmetic procedures have grown rapidly in most parts of the world. Professional organizations have issued warnings about the complications and long-term consequences of these practices. To be able to adopt the right health policies, it is necessary to know why women decide to perform these procedures. Therefore, the present study will be aim to discover the decision-making process involved in performing female genital cosmetic procedures for Iranian women and construct and validate a results-based logic model for healthy public policy. METHODS: The present study was conducted in three phases. In the initial phase, a qualitative study will be conducted with the Corbin and Strauss ground theory approach. The participants in the study will be healthy women who desire or have undergone female genital cosmetic procedures without medical indications. In this phase, purposive and theoretical sampling will guide recruitment and data collection. The data will be collected via semi-structured interviews, field notes and observations of individual interactions. The data will be analysed using the approach of Corbin and Strauss (2015). MAXQDA 2007 software was used for managing the process of data analysis. In the second phase, the development of a results-based logic model for a healthy public policy is performed based on the findings of the first phase of the study, interviews with key informants and a review of the results of the literature in this field. Finally, validation of the designed program will be performed by the nominal group technique with the presence of a group of experts in the third phase. DISCUSSION: The findings of this study, by identifying women's main concerns related to the studied phenomenon, the existing context, participants' reactions and the consequences of the adopted reactions, can be very important in designing a program that fits Iran's cultural characteristics. In this research, a program using a logical model will be presented that is suitable for policymakers, planners and healthcare service providers to be implemented in the social-cultural context of the study.

Female genital cosmetic procedures refer to a group of cosmetic procedures that change the structure and healthy appearance of the female external genitalia to improve sexual performance or body image. The desire to perform these techniques has become popular in most parts of the world. However, scientific societies have warned about the efficiency, effectiveness and side effects of these techniques. According to these points, the present study aims to discover the decision-making process of performing FGCPs for Iranian women and to construct and validate a program for healthy public policy. This study will be performed in three stages. First, a qualitative study and interviews with healthy women who desire or have undergone female genital cosmetic procedures will be performed. In the following, based on the findings of the first stage, interviews with key informants and a review of literature, a program will be presented to reduce or prevent these procedures, and then this program will be validated. Using the designed program, healthcare practitioners will be able to provide women with more effective advice and guidance to make correct and informed decisions. In addition, this program will enable planners and policymakers to take steps to reduce the demand for these actions and make informed decisions by women by changing and adjusting the conditions and context.

J-MISFET Hybrid Dual-Gate Switching Device for Multifunctional Optoelectronic Logic Gate Applications.

High-performance and low operating voltage are becoming increasingly significant device parameters to meet the needs of future integrated circuit (IC) processors and ensure their energy-efficient use in upcoming mobile devices. In this study, we suggest a hybrid dual-gate switching device consisting of the vertically stacked junction and metal-insulator-semiconductor (MIS) gate structure, named J-MISFET. It shows excellent device performances of low operating voltage (<0.5 V), drain current ON/OFF ratio (∼4.7 × 105), negligible hysteresis window (<0.5 mV), and near-ideal subthreshold slope (SS) (60 mV/dec), making it suitable for low-power switching operation. Furthermore, we investigated the switchable NAND/NOR logic gate operations and the photoresponse characteristics of the J-MISFET under the small supply voltage (0.5 V). To advance the applications further, we successfully demonstrated an integrated optoelectronic security logic system comprising 2-electric inputs (for encrypted data) and 1-photonic input signal (for password key) as a hardware security device for data protection. Thus, we believe that our J-MISFET, with its heterogeneous hybrid gate structures, will illuminate the path toward future device configurations for next-generation low-power electronics and multifunctional security logic systems in a data-driven society.

Controllable and reusable seesaw circuit based on nicking endonucleases.

Seesaw circuits are essential for molecular computing and biosensing. However, a notable limitation of seesaw circuits lies in the irreversible depletion of components, precluding the attainment of system recovery and rendering nucleic acid circuits non-reusable. We developed a brand-new method for creating controllable and reusable seesaw circuits. By using the nicking endonucleases Nt.BbvCI and Nt.Alwi, we removed "functional components" while keeping the "skeletal components" for recurrent usage. T-inputs were introduced, increasing the signal-to-noise ratio of AND logic from 2.68 to 11.33 and demonstrating compatibility. We identified the logic switching feature and verified that it does not impair circuit performance. We also built intricate logic circuits, such as OR-AND gate, to demonstrate the versatility of our methodology. This controllable reusability extends the applications of nanotechnology and bioengineering, enhancing the practicality and efficiency of these circuits across various domains.

LollipopE: Bi-centered lollipop embedding for complex logic query on knowledge graph.

Answering complex First-Order Logic (FOL) query plays a vital role in multi-hop knowledge graph (KG) reasoning. Geometric methods have emerged as a promising category of approaches in this context. However, existing best-performing geometric query embedding (QE) model is still up against three-fold potential problems: (i) underutilization of embedding space, (ii) overreliance on angle information, (iii) uncaptured hierarchy structure. To bridge the gap, we propose a lollipop-like bi-centered query embedding method named LollipopE. To fully utilize embedding space, LollipopE employs learnable centroid positions to represent multiple entities distributed along the same axis. To address the potential overreliance on angular metrics, we design an angular-based and centroid-based metric. This involves calculating both an angular distance and a centroid-based geodesic distance, which empowers the model to make more informed selections of relevant answers from a wider perspective. To effectively capture the hierarchical relationships among entities within the KG, we incorporate dynamic moduli, which allows for the representation of the hierarchical structure among entities. Extensive experiments demonstrate that LollipopE surpasses the state-of-the-art geometric methods. Especially, on more hierarchical datasets, LollipopE achieves the most significant improvement.

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics.

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment's findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4-5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

The dilemmas and possible solutions for CAR-T cell therapy application in solid tumors.

Chimeric antigen receptor T (CAR-T) cell therapy, as an adoptive immunotherapy, is playing an increasingly important role in the treatment of malignant tumors. CAR-T cells are referred to as "living drugs" as they not only target tumor cells directly, but also induce long-term immune memory that has the potential to provide long-lasting protection. CD19.CAR-T cells have achieved complete response rates of over 90 % for acute lymphoblastic leukemia and over 60 % for non-Hodgkin's lymphoma. However, the response rate of CAR-T cells in the treatment of solid tumors remains extremely low and the side effects potentially severe. In this review, we discuss the limitations that the solid tumor microenvironment poses for CAR-T application and the solutions that are being developed to address these limitations, in the hope that in the near future, CAR-T cell therapy for solid tumors can attain the same success rates as are now being seen clinically for hematological malignancies.

Singleton {NOT} and Doubleton {YES; NOT} Gates Act as Functionally Complete Sets in DNA-Integrated Computational Circuits.

A functionally complete Boolean operator is sufficient for computational circuits of arbitrary complexity. We connected YES (buffer) with NOT (inverter) and two NOT four-way junction (4J) DNA gates to obtain IMPLY and NAND Boolean functions, respectively, each of which represents a functionally complete gate. The results show a technological path towards creating a DNA computational circuit of arbitrary complexity based on singleton NOT or a combination of NOT and YES gates, which is not possible in electronic computers. We, therefore, concluded that DNA-based circuits and molecular computation may offer opportunities unforeseen in electronics.

Dual-Aptamer Recognition of DNA Logic Gate Sensor-Based Specific Exosomal Proteins for Ovarian Cancer Diagnosis.

Clinical diagnosis of ovarian cancer lacks high accuracy due to the weak selection of specific biomarkers along with the circumstance biomarkers localization. Clustering analysis of proteins transported on exosomes enables a more precise screening of effective biomarkers. Herein, through bioinformatics analysis of ovarian cancer and exosome proteomes, two coexpressed proteins, EpCAM and CD24, specifically enriched, were identified, together with the development of an as-derived dual-aptamer targeted exosome-based strategy for ovarian cancer screening. In brief, a DNA ternary polymer with aptamers targeting EpCAM and CD24 was designed to present a logic gate reaction upon recognizing ovarian cancer exosomes, triggering a rolling circle amplification chemiluminescent signal. A dynamic detection range of 6 orders of magnitude was achieved by quantifying exosomes. Moreover, for clinical samples, this strategy could accurately differentiate exosomes from healthy persons, other cancer patients, and ovarian cancer patients, enabling promising in situ detection. By accurately selecting biomarkers and constructing a dual-targeted exosomal protein detection strategy, the limitation of insufficient specificity of traditional protein markers was circumvented. This work contributed to the development of exosome-based prognosis monitoring in ovarian cancer through the identification of disease-specific exosome protein markers.

Promoting a Patient-Centered Understanding of Safety in Acute Mental Health Wards: A User-Centered Design Approach to Develop a Real-Time Digital Monitoring Tool.

BACKGROUND: Acute mental health services report high levels of safety incidents that involve both patients and staff. The potential for patients to be involved in interventions to improve safety within a mental health setting is acknowledged, and there is a need for interventions that proactively seek the patient perspective of safety. Digital technologies may offer opportunities to address this need. OBJECTIVE: This research sought to design and develop a digital real-time monitoring tool (WardSonar) to collect and collate daily information from patients in acute mental health wards about their perceptions of safety. We present the design and development process and underpinning logic model and programme theory. METHODS: The first stage involved a synthesis of the findings from a systematic review and evidence scan, interviews with patients (n=8) and health professionals (n=17), and stakeholder engagement. Cycles of design activities and discussion followed with patients, staff, and stakeholder groups, to design and develop the prototype tool. RESULTS: We drew on patient safety theory and the concepts of contagion and milieu. The data synthesis, design, and development process resulted in three prototype components of the digital monitoring tool (WardSonar): (1) a patient recording interface that asks patients to input their perceptions into a tablet computer, to assess how the ward feels and whether the direction is changing, that is, "getting worse" or "getting better"; (2) a staff dashboard and functionality to interrogate the data at different levels; and (3) a public-facing ward interface. The technology is available as open-source code. CONCLUSIONS: Recent patient safety policy and research priorities encourage innovative approaches to measuring and monitoring safety. We developed a digital real-time monitoring tool to collect information from patients in acute mental health wards about perceived safety, to support staff to respond and intervene to changes in the clinical environment more proactively.

Fuzzy machine learning logic utilization on hormonal imbalance dataset.

In this research work, a novel fuzzy data transformation technique has been proposed and applied to the hormonal imbalance dataset. Hormonal imbalance is ubiquitously found principally in females of reproductive age which ultimately leads to numerous related medical conditions. Polycystic Ovary Syndrome (PCOS) is one of them. Treatment along with adopting a healthy lifestyle is advised to mitigate its consequences on the quality of life. The biological dataset of hormonal imbalance "PCOS" provides limited results that is whether the syndrome is present or not. Also, there are input variables that contain binary responses only, to deal with this conundrum, a novel fuzzy data transformation technique has been developed and applied to them thus leading to their fuzzy transformation which provides a broader spectrum to diagnose PCOS. Due to this, the output variable has also been transformed. Hence, a novel fuzzy transformation technique has been employed due to the limitation of the dataset leading to the transition of binary classification output into three classes. An adaptive fuzzy machine learning logic model is developed in which the inference of the transformed biological dataset is performed by the machine learning techniques that provide the fuzzy output. Machine learning techniques have also been applied to the untransformed biological dataset. Both implementations have been compared by computation of the relevant metrics. Machine learning employment on untransformed biological dataset provides limited results whether the syndrome is present or absent however machine learning on fuzzy transformed biological dataset provides a broader spectrum of diagnosis consisting of a third class depicting that PCOS might be present which would ultimately alert a patient to take preventive measures to minimize the chances of syndrome development in future.

Coumarin derivative-functionalized nanoporous silica as an on-off fluorescent sensor for detecting Fe3+ and Hg2+ ions: a circuit logic gate.

A highly efficient fluorescent sensor (S-DAC) was easily created by functionalizing the SBA-15 surface with N-(2-Aminoethyl)-3-Aminopropyltrimethoxysilane followed by the covalent attachment of 7-diethylamino 3-acetyl coumarin (DAC). This chemosensor (S-DAC) demonstrates selective and sensitive recognition of Fe3+ and Hg2+ in water-based solutions, with detection limits of 0.28 × 10-9 M and 0.2 × 10-9 M for Hg2+ and Fe3+, respectively. The sensor's fluorescence characteristics were examined in the presence of various metal ions, revealing a decrease in fluorescence intensity upon adding Fe3+ or Hg2+ ions at an emission wavelength of 400 nm. This sensor was also able to detect ferric and mercury ions in spinach and tuna fish. The quenching mechanism of S-DAC was investigated using UV-vis spectroscopy, which confirmed a static-type mechanism for fluorescence quenching. Moreovre, the decrease in fluorescence intensity caused by mercury and ferric ions can be reversed using trisodium citrate dihydrate and EDTA as masking agents, respectively. As a result, a circuit logic gate was designed using Hg2+, Fe3+, trisodium citrate dihydrate, and EDTA as inputs and the quenched fluorescence emission as the output.

Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification.

The accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with standard methods. A total of five datasets related to classification problems obtained from the UCI Machine Learning Repository were used. The results showed that the proposed method had higher accuracy than standard imputation methods. Moreover, triangular method performed better than Gaussian fuzzy membership function. This showed that the combination of nearest neighbor data and fuzzy membership function was more effective in handling missing values and improving classification accuracy.

A logic-incorporated gene regulatory network deciphers principles in cell fate decisions.

Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms.

There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.

Digital smart internal fixation surgery for coronal process basal fracture with normal joint spaces or radius-shortening: occult factor of radius-ulna load sharing.

BACKGROUND: Reasonable postoperative humeroradial and humeroulnar joint spaces maybe an important indicator in biomechanical stability of smart internal fixation surgery for coronoid process basal fractures (CPBF). The aim of this study is to compare elbow articular stresses and elbow-forearm stability under smart internal fixations for the CPBF between normal elbow joint spaces and radius-shortening, and to determine the occult factor of radius-ulna load sharing. METHODS: CT images of 70 volunteers with intact elbow joints were retrospectively collected for accurate three-dimensional reconstruction to measure the longitudinal and transverse joint spaces. Two groups of ten finite element (FE) models were established prospectively between normal joint space and radius-shortening with 2.0mm, including intact elbow joint and forearm, elbow-forearm with CPBF trauma, anterior or posterior double screws-cancellous bone fixation, mini-plate-cancellous bone fixation. Three sets of physiological loads (compression, valgus, varus) were used for FE intelligent calculation, FE model verification, and biomechanical and motion analysis. RESULTS: The stress distribution between coronoid process and radial head, compression displacements and valgus angles of elbow-forearm in the three smart fixation models of the normal joint spaces were close to those of corresponding intact elbow model, but were significantly different from those of preoperative CPBF models and fixed radius-shortening models. The maximum stresses of three smart fixation instrument models of normal joint spaces were significantly smaller than those of the corresponding fixed radius-shortening models. CONCLUSIONS: On the basis of the existing trauma of the elbow-forearm system in clinical practice, which is a dominant factor affecting radius-ulna load sharing, the elbow joint longitudinal space has been found to be the occult factor affecting radius-ulna load sharing. The stability and load sharing of radius and ulna after three kinds of smart fixations of the CPBF is not only related to the anatomical and biomechanical stability principles of smart internal fixations, but also closely related to postoperative elbow joint longitudinal space.

Gate Voltage- and Bias Voltage-Tunable Staggered-Gap to Broken-Gap Transition Based on WSe2/Ta2NiSe5 Heterostructure for Multimode Optoelectronic Logic Gate.

Two-dimensional (2D) materials with superior properties exhibit tremendous potential in developing next-generation electronic and optoelectronic devices. Integrating various functions into one device is highly expected as that endows 2D materials great promise for more Moore and more-than-Moore device applications. Here, we construct a WSe2/Ta2NiSe5 heterostructure by stacking the p-type WSe2 and the n-type narrow gap Ta2NiSe5 with the aim to achieve a multifunction optoelectronic device. Owing to the large interface potential barrier, the heterostructure device reveals a prominent diode feature with a large rectify ratio (7.6 × 104) and a low dark current (10-12 A). Especially, gate voltage- and bias voltage-tunable staggered-gap to broken-gap transition is achieved on the heterostructure device, which enables gate voltage-tunable forward and reverse rectifying features. As results, the heterostructure device exhibits superior self-powered photodetection properties, including a high detectivity of 1.08 × 1010 Jones and a fast response time of 91 µs. Additionally, the intrinsic structural anisotropy of Ta2NiSe5 endows the heterostructure device with strong polarization-sensitive photodetection and high-resolution polarization imaging. Based on these characteristics, a multimode optoelectronic logic gate is realized on the heterostructure via synergistically modulating the light on/off, polarization angle, gate voltage, and bias voltage. This work shed light on the future development of constructing high-performance multifunctional optoelectronic devices.

Nonvolatile Reconfigurable Logic Device Based on Photoinduced Interfacial Charge Trapping in van der Waals Gap.

Due to the increasing complexity in miniaturization of electronic devices, reconfigurable field-effect transistors (RFETs) have emerged as a solution. Although the foundational concepts of RFETs have matured over two decades, ongoing breakthroughs are needed to address challenges such as improving the device performance as well as achieving balanced symmetry between n-type and p-type transport modes with long-term stability. Herein, we present a nonvolatile WSe2-based RFET that utilizes photoassisted interfacial charge trapping at the h-BN and SiO2 interface. Unlike typical RFETs with two gate electrodes, our RFETs achieved polarity control with a single operating gate activated exclusively under white-light exposure. The threshold voltage was tunable, ranging from 27.4 (-31.6 V) to 0.9 (+19.5 V), allowing selective activation of n-type (p-type) operation at VGS = 0 V. Additionally, our WSe2-based RFETs show superior repeatability and long-term stability. Leveraging these advantages, various reconfigurable logic circuits were successfully demonstrated, including complementary inverters and switch circuits as well as pull-up and pull-down circuits, highlighting the potential of WSe2 FETs for future advancements of integrated circuits.