Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose Nanofibers.

Manipulating the three-dimensional (3D) structures of cells is important for facilitating to repair or regenerate tissues. A self-assembly system of cells with cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs) has been developed to fabricate various cell 3D structures. To further generate tissues at an implantable level, it is necessary to carry out a large number of experiments using different cell culture conditions and material properties; however this is practically intractable. To address this issue, we present a graph-neural network-based simulator (GNS) that can be trained by using assembly process images to predict the assembly status of future time steps. A total of 24 (25 steps) time-series images were recorded (four repeats for each of six different conditions), and each image was transformed into a graph by regarding the cells as nodes and the connecting neighboring cells as edges. Using the obtained data, the performances of the GNS were examined under three scenarios (i.e., changing a pair of the training and testing data) to verify the possibility of using the GNS as a predictor for further time steps. It was confirmed that the GNS could reasonably reproduce the assembly process, even under the toughest scenario, in which the experimental conditions differed between the training and testing data. Practically, this means that the GNS trained by the first 24 h images could predict the cell types obtained 3 weeks later. This result could reduce the number of experiments required to find the optimal conditions for generating cells with desired 3D structures. Ultimately, our approach could accelerate progress in regenerative medicine.

2D van der Waals Heterostructure with Tellurene Floating-Gate for Wide Range and Multi-Bit Optoelectronic Memory.

Intensive research on optoelectronic memory (OEM) devices based on two-dimensional (2D) van der Waals heterostructures (vdWhs) is being conducted due to their distinctive advantages for electrical-optical writing and multilevel storage. These features make OEM a promising candidate for the logic of reconfigurable operations. However, the realization of nonvolatile OEM with broadband absorption (from visible to infrared) and a high switching ratio remains challenging. Herein, we report a nonvolatile OEM based on a heterostructure consisting of rhenium disulfide (ReS2), hexagonal boron nitride (hBN) and tellurene (2D Te). The 2D Te-based floating-gate (FG) device exhibits excellent performance metrics, including a high switching on/off ratio (∼106), significant endurance (>1000 cycles) and impressive retention (>104 s). In addition, the narrow band gap of 2D Te endows the device with broadband optical programmability from the visible to near-infrared regions at room temperature. Moreover, by applying different gate voltages, light wavelengths, and laser powers, multiple bits can be successfully generated. Additionally, the device is specifically designed to enable reconfigurable inverter logic circuits (including AND and OR gates) through controlled electrical and optical inputs. These significant findings demonstrate that the 2D vdWhs with a 2D Te FG are a valuable approach in the development of high-performance OEM devices.

Advancement in nanomaterials for environmental pollutants remediation: a systematic review on bibliometrics analysis, material types, synthesis pathways, and related mechanisms.

Environmental pollution is a major issue that requires effective solutions. Nanomaterials (NMs) have emerged as promising candidates for pollution remediation due to their unique properties. This review paper provides a systematic analysis of the potential of NMs for environmental pollution remediation compared to conventional techniques. It elaborates on several aspects, including conventional and advanced techniques for removing pollutants, classification of NMs (organic, inorganic, and composite base). The efficiency of NMs in remediation of pollutants depends on their dispersion and retention, with each type of NM having different advantages and disadvantages. Various synthesis pathways for NMs, including traditional synthesis (chemical and physical) and biological synthesis pathways, mechanisms of reaction for pollutants removal using NMs, such as adsorption, filtration, disinfection, photocatalysis, and oxidation, also are evaluated. Additionally, this review presents suggestions for future investigation strategies to improve the efficacy of NMs in environmental remediation. The research so far provides strong evidence that NMs could effectively remove contaminants and may be valuable assets for various industrial purposes. However, further research and development are necessary to fully realize this potential, such as exploring new synthesis pathways and improving the dispersion and retention of NMs in the environment. Furthermore, there is a need to compare the efficacy of different types of NMs for remediating specific pollutants. Overall, this review highlights the immense potential of NMs for mitigating environmental pollutants and calls for more research in this direction.

Improvement in energy performance from the construction of inlet guide vane and diffuser vane geometries in an axial-flow pump.

Advanced inlet guide vane (IGV) and diffuser vane (DV) geometries were constructed in an effort to increase the energy performance of an axial-flow pump at the best efficiency point (BEP). DV setting angles were also investigated to increase energy performance at the off-design points. By integrating the advantages of an adjustable IGV, combinations of adjustable IGV and DV geometries were constructed and thoroughly analyzed by way of energy loss. The asymmetrical geometry of the IGV, upgraded through the use of a hydrofoil profile, resulted in higher hydraulic performance compared to that of the reference model. The efficiency and total head at the BEP increased significantly with the implementation of the new DV, by 1.456% and 5.756% over those of the reference model, respectively. Using the new DV reduced the unsteady turbulent flow behind the trailing edge of the DV under all flow rate conditions, resulting in a reduction in vibration and noise. The positive setting angles of the DV increased the energy performance in the high-flow-rate region, whereas the negative DV setting angles produced a good performance in the low-flow-rate region. Combining an adjustable IGV with an adjustable DV model resulted in a significant increase in the total head, with more optimal energy performance provided by the positive IGV setting angles. At the BEP and under high-flow-rate conditions, the low-velocity zone is closely related to high entropy generation. Furthermore, these high-entropy generation regions follow the trajectory of the low-velocity zones. However, the low-velocity zone is not strongly associated with the high-entropy generation region when operating under low-flow-rate conditions.

Comparison between U-shaped structural deep learning models to detect landslide traces.

Landslides endanger lives and public infrastructure in mountainous areas. Monitoring landslide traces in real-time is difficult for scientists, sometimes costly and risky because of the harsh terrain and instability. Nowadays, modern technology may be able to identify landslide-prone locations and inform locals for hours or days when the weather worsens. This study aims to propose indicators to detect landslide traces on the fields and remote sensing images; build deep learning (DL) models to identify landslides from Sentinel-2 images automatically; and apply DL-trained models to detect this natural hazard in some particular areas of Vietnam. Nine DL models were trained based on three U-shaped architectures, including U-Net, U2-Net, and U-Net3+, and three options of input sizes. The multi-temporal Sentinel-2 images were chosen as input data for training all models. As a result, the U-Net, using an input image size of 32 × 32 and a performance of 97 % with a loss function of 0.01, can detect typical landslide traces in Vietnam. Meanwhile, the U-Net (64 × 64) can detect more considerable landslide traces. Based on multi-temporal remote sensing data, a different case study in Vietnam was chosen to see landslide traces over time based on the trained U-Net (32 × 32) model. The trained model allows mountain managers to track landslide occurrences during wet seasons. Thus, landslide incidents distant from residential areas may be discovered early to warn of flash floods.

Prenatal diagnosis of autosomal recessive renal tubular dysgenesis caused by variants in the ACE gene: Two fetuses with anhydramnios.

INTRODUCTION: Autosomal recessive renal tubular dysgenesis (ARRTD) is a rare genetic disorder with a very high mortality rate. The typical symptoms of the disease during pregnancy are oligohydramnios, anhydramnios, and nearly all affected fetuses die after birth or have a stillbirth in late gestation, which can adversely increase maternal risks. METHODS: Oligohydramnios/anhydramnios can make both amniocentesis for diagnostic testing and morphological evaluation via ultrasound more difficult. In cases of oligohydramnios/anhydramnios suspicious for urinary tract anomalies, amnioinfusion is a meaningful technique that facilitates sampling of amniotic fluid for genetic diagnosis. RESULTS: We report two cases of fetuses with anhydramnios and invisible urinary bladder. Clinical exome sequencing from amniotic fluid revealed a biparentally inherited homozygous pathogenic nonsense ACE variant c.2503G 〉 T [p.Glu853Ter] in proband 1 and a biparentally inherited homozygous pathogenic nonsense ACE variant c.2992C 〉 T [p.Gln998Ter] in proband 2. The prognosis was poor and the patients elected to terminate the pregnancies. Additional post-mortem histopathological examination from the renal tissue of the second fetus showed renal tubular hypoplasia. CONCLUSION: To our knowledge for the first time, we describe the prenatal diagnosis of ARRTD in Vietnam, and highlight the benefit of detecting ACE variants associated with ARRTD in fetuses with oligohydramnios/anhydramnios through amnioinfusion and amniocentesis, which improves genotype-phenotype correlations and provides valuable information for reproductive counseling.

Ultrahigh-porosity Ranunculus-like MgO adsorbent coupled with predictive deep belief networks: A transformative method for phosphorus treatment.

Phosphorus is a nonrenewable material with a finite supply on Earth; however, due to the rapid growth of the manufacturing industry, phosphorus contamination has become a global concern. Therefore, this study highlights the remarkable potential of ranunculus-like MgO (MO4-MO6) as superior adsorbents for phosphate removal and recovery. Furthermore, MO6 stands out with an impressive adsorption capacity of 596.88 mg/g and a high efficacy across a wide pH range (2-10) under varying coexisting ion concentrations. MO6 outperforms the top current adsorbents for phosphate removal. The process follows Pseudo-second-order and Langmuir models, indicating chemical interactions between the phosphate species and homogeneous MO6 monolayer. MO6 maintains 80 % removal and 96 % recovery after five cycles and adheres to the WHO and EUWFD regulations for residual elements in water. FT-IR and XPS analyses further reveal the underlying mechanisms, including ion exchange, electrostatic, and acid-base interactions. Ten machine learning (ML) models were applied to simultaneously predict multi-criteria (sorption capacity, removal efficiency, final pH, and Mg leakage) affected by 15 diverse environmental conditions. Traditional ML models and deep neural networks have poor accuracy, particularly for removal efficiency. However, a breakthrough was achieved by the developed deep belief network (DBN) with unparalleled performance (MAE = 1.3289, RMSE = 5.2552, R2 = 0.9926) across all output features, surpassing all current studies using thousands of data points for only one output factor. These captivating MO6 and DBN models also have immense potential for effectively applying in the real water test with error < 5 %, opening immense horizons for transformative methods, particularly in phosphate removal and recovery.

Migrant entrepreneurship support in Europe: a PRISMA systematic literature review.

Background: This systematic literature review (SLR) analyzes migrant entrepreneurship support in Europe through three research questions (RQs) to understand 1) migrant entrepreneur characteristics in the European context, 2) challenges encountered by migrant entrepreneurs in European host countries, and 3) policies supporting migrant entrepreneurship in Europe. This review addresses gaps in current knowledge in academia as well as issues that policymakers and practitioners face when addressing migrant entrepreneurship support. Methods: This SLR employed a search protocol to retrieve published sources from 1970 to 2021, via Scopus (27 March 2022) and Web of Science (7 April 2022). Inclusion criteria targeted migrant entrepreneurship support studies while exclusion criteria eliminated domestic migration and non-European contexts. The authors worked iteratively, aligning the data with the RQs to reduce bias, and adapted Bourdieu's forms of capital to create an analytical framework for the sources included in the SLR, with a table for each RQ to synthesize relevant data for analysis. Results: The review examined 91 peer-reviewed papers, with a focus on migrant entrepreneurship support in Europe, covering characteristics, challenges, and support policies. It classified migrant entrepreneur challenges and characteristics into financial, human, and social capital, as well as external factors. Common challenges include the local culture and language, network, funding, and adapting to local business practices. Migrant entrepreneurs' stability relates to time in the host country and local language proficiency and reflects past entrepreneurial experience and education. Supportive mechanisms involve local networks, financing, and mentoring. Conclusions: The SLR's limitations encompass possible oversight of pertinent studies, along with potential bias in data extraction, analysis, and subjectivity due to thematic analysis. Nonetheless, the findings suggest the following research agenda for migrant entrepreneurship support: evaluating and enhancing human and social capital, sharing information, designing support programs, addressing in-group/out-group bias in support programs, and exploring bottom-up migrant entrepreneurship support approaches.

Explorative Image Analysis of Methylene Blue Interactions with Gelatin in Polypropylene Nonwoven Fabric Membranes: A Potential Future Tool for the Characterization of the Diffusion Process.

This manuscript explores the interaction between methylene blue dye and gelatin within a membrane using spectroscopy and image analysis. Emphasis is placed on methylene blue's unique properties, specifically its ability to oscillate between two distinct resonance states, each with unique light absorption characteristics. Image analysis serves as a tool for examining dye diffusion and absorption. The results indicate a correlation between dye concentrations and membrane thickness. Thinner layers exhibit a consistent dye concentration, implying an even distribution of the dye during the diffusion process. However, thicker layers display varying concentrations at different edges, suggesting the establishment of a diffusion gradient. Moreover, the authors observe an increased concentration of gelatin at the peripheries rather than at the center, possibly due to the swelling of the dried sample and a potential water concentration gradient. The manuscript concludes by suggesting image analysis as a practical alternative to spectral analysis, particularly for detecting whether methylene blue has been adsorbed onto the macromolecular network. These findings significantly enhance the understanding of the complex interactions between methylene blue and gelatin in a membrane and lay a solid foundation for future research in this field.

Fabricated magnetic adsorption - Forward osmosis membrane hybrid system for hydroponic irrigation from rich arsenic-containing heavy metal water stream.

Solidification of soluble arsenic from extremely acidic water and direct use of recovery water have been the major challenges in global water management, with the urgent need for new treatment system development. Thus, magnetic adsorption - fertilizer drawn forward osmosis (FDFO) hybrid system with a novel adsorbent and fertilizer mixture to solve the drawbacks of each process was developed with the ultimate goals of metal removal and direct reuse for hydroponic irrigation. Magnetic metal-organic framework-based adsorbent (CMM) was synthesized with various promising capabilities, i.e., wide pH range efficiency, strong pH adjustment, good stability, fast adsorption (1 h), and oxidation (40 min), high capacity (175 and 126 mg/g for As(III), As(V)), strong magnetization (75 emu/g), complete separation by a magnet, excellent interference-tolerance and reusability. In the FDFO system, a massive water volume (50 times higher than the initial draw solution with suitable nutrients for hydroponics irrigation with acceptable NaCl levels was obtained for the first time up to now. However, low As(III) rejection (50%) required the FDFO process to improve more. After integrating with magnetic adsorption, nearly 100% of As was removed. The pH of feed solutions adjusted from extremely acidic to close to neutral conditions further solidified metal by precipitation and membrane separation processes, leading to almost no detection of metals in the final draw solution. Also, favorable nutrients and excellent reusability were obtained. This hybrid process would generally offer an environmentally sustainable and high efficiency for decontaminating As-containing heavy metal water for hydroponic irrigation.

Application of MnFe2O4 magnetic silica-covered ethylenediaminetetraacetic acid-functionalized nanomaterials to the draw solution in forward osmosis.

Forward osmosis (FO) is an emerging and promising water treatment technology. However, selection of an optimal draw solution (DS) is essential for efficient FO process operations. In this study, the potential of ethylenediaminetetraacetic acid (EDTA) functionalized SiO2-covered magnetic nanoparticles (MNPs) as DS in FO process were investigated. The MNPs were synthesized and characterized for their morphology, size distribution, magnetic behavior, and dispersibility. Investigations were carried out to determine the effects of DS concentration and MNPs type, utilizing bare, SiO2 covered, and EDTA coated MNPs at concentrations of 20, 40, and 60 g/L. Furthermore, water flux generation capability and rejection efficiency of octanoic acid (OC) was evaluated with EDTA-MNPs as DS in FO mode (active layer facing feed solution) and PRO mode (active layer facing draw solution). Our results showed that a maximum water flux of 9.59 ± 2 LMH in FO mode, and 11.104 ± 2 LMH in PRO mode was achieved using 60 g/L of EDTA-MNPs. Additionally, we investigated the reusability of the EDTA-coated MNPs and found that their recovery was higher than (>90%) with no aggregation. The stability of EDTA-MNPs was due to strong covalent linkages between their four carboxylate groups and the hydrophilic SiO2 surface layer, which resulted in steric hindrance and prevented their aggregation. Finally, we assessed the rejection efficiency of OC at different pH values and found that it was low (30-39%) at pH values below pKa and high (90-97%) at pH values above pKa. Owing to internal concentration polarization, the rejection of OC in FO mode was (10-20%) higher than in PRO mode. The findings demonstrate EDTA-coated MNPs have significant potentials as an effective DS in FO process .

Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions.

Predicting drug-drug interactions (DDIs) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs. This problem can be formulated as predicting labels (i.e., side effects) for each pair of nodes in a DDI graph, of which nodes are drugs and edges are interacting drugs with known labels. State-of-the-art methods for this problem are graph neural networks (GNNs), which leverage neighborhood information in the graph to learn node representations. For DDI, however, there are many labels with complicated relationships due to the nature of side effects. Usual GNNs often fix labels as one-hot vectors that do not reflect label relationships and potentially do not obtain the highest performance in the difficult cases of infrequent labels. In this brief, we formulate DDI as a hypergraph where each hyperedge is a triple: two nodes for drugs and one node for a label. We then present CentSmoothie , a hypergraph neural network (HGNN) that learns representations of nodes and labels altogether with a novel "central-smoothing" formulation. We empirically demonstrate the performance advantages of CentSmoothie in simulations as well as real datasets.

Influence of inflow directions and setting angle of inlet guide vane on hydraulic performance of an axial-flow pump.

Inlet flow direction significantly affects the hydraulic performance of an axial-flow pump. Usually, the research papers ignore this phenomenon, resulting in discrepancies between simulation and experimental results. This study examines the influence of inflow direction in five cases (0%, 5%, 10%, 15%, and 30% pre-swirl intensities) to determine the relationship between the pre-swirl intensity and the hydraulic performance of the axial-flow pump. Based on this, changing the setting angle of the inlet guide vane (IGV) is proposed and thoroughly investigated to reduce the effect of inflow direction. In this study, the influence of clearances in IGV blades on hydraulic performance is also investigated in detail. Numerical simulations are performed using ANSYS-CFX and a shear stress transport reattachment modification (SST k-[Formula: see text]) turbulence model with small y+ values at all walls. Specifically, the hydraulic performance curves and internal flow characteristics, including contours and streamlines, are assessed and analyzed. The inflow direction significantly impacts the hydraulic efficiency of the axial-flow pump. Increased pre-swirl intensity causes more loss in the IGV passage. The internal flow field and performance are not affected by the clearance at the hub and shroud of the IGV. However, the tip clearance of the impeller causes a decrease in hydraulic efficiency due to the tip leakage vortex. By adjusting the setting angle of the IGV, the efficiency and head gradually increase from a negative to a positive setting angle. Additionally, 30° is considered the critical setting angle for IGV.

Mechanism of chorismate dehydratase MqnA, the first enzyme of the futalosine pathway, proceeds via substrate-assisted catalysis.

MqnA, the only chorismate dehydratase known so far, catalyzes the initial step in the biosynthesis of menaquinone via the futalosine pathway. Details of the MqnA reaction mechanism remain unclear. Here, we present crystal structures of Streptomyces coelicolor MqnA and its active site mutants in complex with chorismate and the product 3-enolpyruvyl-benzoate, produced during heterologous expression in Escherichia coli. Together with activity studies, our data are in line with dehydration proceeding via substrate assisted catalysis, with the enol pyruvyl group of chorismate acting as catalytic base. Surprisingly, structures of the mutant Asn17Asp with copurified ligand suggest that the enzyme converts to a hydrolase by serendipitous positioning of the carboxyl group. All complex structures presented here exhibit a closed Venus flytrap fold, with the enzyme exploiting the characteristic ligand binding properties of the fold for specific substrate binding and catalysis. The conformational rearrangements that facilitate complete burial of substrate/product, with accompanying topological changes to the enzyme surface, could foster substrate channeling within the biosynthetic pathway.

Application of deep learning models to detect coastlines and shorelines.

Identifying and monitoring coastlines and shorelines play an important role in coastal erosion assessment around the world. The application of deep learning models was used in this study to detect coastlines and shorelines in Vietnam using high-resolution satellite images and different object segmentation methods. The aims are to (1) propose indicators to identify coastlines and shorelines; (2) build deep learning (DL) models to automatically interpret coastlines and shorelines from high-resolution remote sensing images; and (3) apply DL-trained models to monitor coastal erosion in Vietnam. Eight DL models were trained based on four artificial-intelligent-network structures, including U-Net, U2-Net, U-Net3+, and DexiNed. The high-resolution images collected from Google Earth Pro software were used as input data for training all models. As a result, the U-Net using an input-image size of 512 × 512 provides the highest performance of 98% with a loss function of 0.16. The interpretation results of this model were used effectively for the coastline and shoreline identification in assessing coastal erosion in Vietnam due to sea-level rise in storm events over 20 years. The outcomes proved that while the shoreline is ideal for observing seasonal tidal changes or the immediate motions of current waves, the coastline is suitable to assess coastal erosion caused by the influence of sea-level rise during storms. This paper has provided a broad scope of how the U-Net model can be used to predict the coastal changes over vietnam and the world.

SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug-drug interactions.

MOTIVATION: Predicting side effects of drug-drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to express high-order relationships among two interacting drugs and a side effect. The idea of these methods is that each side effect is caused by a unique combination of latent features of the corresponding interacting drugs. However, in reality, a side effect might have multiple, different mechanisms that cannot be represented by a single combination of latent features of drugs. Moreover, DDI data are sparse, suggesting that using a sparsity regularization would help to learn better latent representations to improve prediction performances. RESULTS: We propose SPARSE, which encodes the DDI hypergraph and drug features to latent spaces to learn multiple types of combinations of latent features of drugs and side effects, controlling the model sparsity by a sparse prior. Our extensive experiments using both synthetic and three real-world DDI datasets showed the clear predictive performance advantage of SPARSE over cutting-edge competing methods. Also, latent feature analysis over unknown top predictions by SPARSE demonstrated the interpretability advantage contributed by the model sparsity. AVAILABILITY AND IMPLEMENTATION: Code and data can be accessed at https://github.com/anhnda/SPARSE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A new deep learning approach based on bilateral semantic segmentation models for sustainable estuarine wetland ecosystem management.

Nowadays, estuarial areas have been strongly affected by the construction of electrical power dams from upstream, downstream urbanization and many types of hazards along the coastal regions. It has resulted in significant changes in estuarine wetland ecosystems between rainy and dry seasons. To avoid estuary vulnerability, monitoring and evaluation of the estuarine ecosystems are very critical tasks. The main goal of this research is to propose and implement a novel deep learning method in monitoring various ecosystems in estuarine regions. The processing speed and accuracy of common neural networks is improved more than ten times through spatial and context paths integrated into a novel Bilateral Segmentation Network (BiSeNet). The multi-sensor and multi-temporal satellite images (including Sentinel-2, ALOS-DEM, and NOAA-DEM images) served as input data. As a result, four BiSeNet models out of 20 trained models achieved a greater than 90% accuracy, especially for interpreting estuarine waters, intertidal forested wetlands, and aquacultural lands in subtidal regions. These models outperformed Random Forest and Support Vector Machine approaches. The best one was used to map estuarine ecosystems from 12 satellite images over a five-year period in the largest estuary in northern Vietnam. The ecosystem changes between dry and rainy seasons were analyzed in detail to assess the ecological succession in estuaries. Furthermore, this model can potentially update new estuarine ecosystem types in other estuarine areas across the world, making possible real-time monitoring and assessing estuarine ecological conditions for sustainable management of wetland ecosystem.

Progress in Sensors for Monitoring Reinforcement Corrosion in Reinforced Concrete Structures-A Review.

Non-destructive monitoring methods and continuous monitoring systems based on them are crucial elements of modern systems for the management and maintenance of assets which include reinforced concrete structures. The purpose of our study was to summarise the data on the most common sensors and systems for the non-destructive monitoring of reinforced concrete structures developed over the past 20 years. We considered systems based on electrochemical (potentiometry, methods related to polarisation) and physical (electromagnetic and ultrasonic waves, piezoelectric effect, thermography) examination methods. Special focus is devoted to the existing sensors and the results obtained using these sensors, as well as the advantages and disadvantages of their setups or other equipment used. The review considers earlier approaches and available commercial products, as well as relatively new sensors which are currently being tested.

Large-Area MoS2 via Colloidal Nanosheet Ink for Integrated Memtransistor.

2D transition metal dichalcogenides (TMDs) exhibit intriguing properties for applications in optoelectronics and electronics, among which memtransistors received extensive attention as multifunctional devices. For practical applications of 2D TMDs, large-area fabrication of the materials via reliable processes, which is in trade-off with their quality, has been a long-standing issue. Here, a simple and effective way is proposed to fabricate large-area and high-quality molybdenum disulfide thin films using MoS2 colloidal ink through a spray coating, followed by a postsulfurization process. High-quality MoS2 thin films exhibit excellent optical and electrical properties that can be utilized in field-effect transistors (FETs) and memtransistor arrays. The MoS2 FETs show an average on/off ratio of 5 × 106 and a high electron mobility of 10.34 cm2  V-1  s-1 , which can be understood by the healing of sulfur vacancies, recrystallization, and the removal of the carbon contamination of the MoS2 . These MoS2 -based memtransistors present stable operations with a high switching ratio tuned by back gate and light illumination, which is promising for multiple-levels memory and complex neuromorphic computing. This study demonstrates a new strategy to fabricate 2D TMDs with large-area and high quality for integrated optoelectronic and memory device applications.

Electrically and Optically Controllable p-n Junction Memtransistor Based on an Al2 O3 Encapsulated 2D Te/ReS2 van der Waals Heterostructure.

The exploration of memtransistors as a combination of a memristor and a transistor has recently attracted intensive attention because it offers a promising candidate for next-generation multilevel nonvolatile memories and synaptic devices. However, the present state-of-the-art memtransistors, which are based on a single material, such as MoS2 or perovskite, exhibit a relatively low switching ratio, require extremely high electric fields to modulate bistable resistance states and do not perform multifunctional operations. Here, the realization of an electrically and optically controllable p-n junction memtransistor using an Al2 O3 encapsulated 2D Te/ReS2 van der Waals heterostructure is reported. The hybrid memtransistor shows a reversible bipolar resistance switching behavior between a low resistance state and a high resistance state with a high switching ratio up to 106 at a low operating voltage (<10 V), high cycling endurance, and long retention time. Moreover, multiple resistance states are achieved by applying different bias voltages, gate voltages, or light powers. In addition, logical operations, including the inverter and AND/OR gates, and synaptic activities are performed by controlling the optical and electrical inputs. The work offers a novel strategy for the reliable fabrication of p-n junction memtransistors for multifunctional devices and neuromorphic applications.