Graphene-driven correlated electronic states in one dimensional defects within WS.

Tomonaga-Luttinger liquid (TLL) behavior in one-dimensional systems has been predicted and shown to occur at semiconductor-to-metal transitions within two-dimensional materials. Reports of one-dimensional defects hosting a Fermi liquid or a TLL have suggested a dependence on the underlying substrate, however, unveiling the physical details of electronic contributions from the substrate require cross-correlative investigation. Here, we study TLL formation within defectively engineered WS atop graphene, where band structure and the atomic environment is visualized with nano angle-resolved photoelectron spectroscopy, scanning tunneling microscopy and spectroscopy, and non-contact atomic force microscopy.

Raman Signature of Stripe Domains in Monolayer WMoS Alloys.

We study the Raman signature of stripe domains in monolayer WMoS alloys, characterized using experimental techniques and density functional theory (DFT) calculations. These stripe domains were found in star-shaped monolayer WS exhibiting a high concentration of molybdenum (Mo) atoms in its central region, and unique Raman peaks that were not previously reported. We attribute these peaks to the splitting of the original doubly degenerate E modes, arising from the lower symmetry of the W-Mo stripe domains.

Giant Valley Zeeman Splitting in Vanadium-Doped WSe Monolayers.

2D dilute magnetic semiconductors (DMS) based on transition metal dichalcogenides (TMD) offer an innovative pathway for advancing spintronic technologies, including the potential to exploit phenomena such as the valley Zeeman effect. However, the impact of magnetic ordering on the valley degeneracy breaking and on the enhancement of the optical transitions g-factors of these materials remains an open question. Here, a giant effective g-factors ranging between ≈-27 and -69 for the bound exciton at 4 K in vanadium-doped WSe monolayers, obtained through magneto-photoluminescence (PL) experiments is reported.

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods.

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods.

A substitutional quantum defect in WS discovered by high-throughput computational screening and fabricated by site-selective STM manipulation.

Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime.

MedChatZH: A tuning LLM for traditional Chinese medicine consultations.

Generative Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, including Question-Answering (QA) and dialogue systems. However, most models are trained on English data and lack strong generalization in providing answers in Chinese. This limitation is especially evident in specialized domains like traditional Chinese medical QA, where performance suffers due to the absence of fine-tuning and high-quality datasets.

Tuning the Fermi Level of Graphene by Two-Dimensional Metals for Raman Detection of Molecules.

Graphene-enhanced Raman scattering (GERS) offers great opportunities to achieve optical sensing with a high uniformity and superior molecular selectivity. The GERS mechanism relies on charge transfer between molecules and graphene, which is difficult to manipulate by varying the band alignment between graphene and the molecules. In this work, we synthesized a few atomic layers of metal termed two-dimensional (2D) metal to precisely and deterministically modify the graphene Fermi level.

Reversing the Natural Drug Resistance of Gram-Negative Bacteria to Fusidic Acid via Forming Drug-Phospholipid Complex.

Drug resistance substantially compromises antibiotic therapy and poses a serious threat to public health. Fusidic acid (FA) is commonly used to treat staphylococcal infections, such as pneumonia, osteomyelitis and skin infections. However, Gram-negative bacteria have natural resistance to FA, which is almost restrained in cell membranes due to the strong interactions between FA and phospholipids.

Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods.

The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms.

Drug-free and multifunctional sodium bicarbonate/hyaluronic acid hybrid dressing for synergistic healing of infected wounds.

Skin wounds are susceptible to microbial infections which commonly lead to the delayed wound healing. Rapid clearance of pathogens from the wound is of great significance and importance for efficient healing of the infected wounds. Herein, we report a multifunctional hybrid dressing, which simply combines sodium bicarbonate (NaHCO) and hyaluronic acid (HA) for the synergistic wound healing.

AptaDB: a comprehensive database integrating aptamer-target interactions.

Aptamers have emerged as research hotspots of the next generation due to excellent performance benefits and application potentials in pharmacology, medicine, and analytical chemistry. Despite the numerous aptamer investigations, the lack of comprehensive data integration has hindered the development of computational methods for aptamers and the reuse of aptamers. A public access database named AptaDB, derived from experimentally validated data manually collected from the literature, was hence developed, integrating comprehensive aptamer-related data, which include six key components: (i) experimentally validated aptamer-target interaction information, (ii) aptamer property information, (iii) structure information of aptamer, (iv) target information, (v) experimental activity information, and (vi) algorithmically calculated similar aptamers.

Network-Based Methods and Their Applications in Drug Discovery.

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery.

mtADENet: A novel interpretable method integrating multiple types of network-based inference approaches for prediction of adverse drug events.

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and accelerate drug safety assessment. ADEs are mainly caused by unintended interactions with primary or additional targets (off-targets). In this study, we proposed a novel interpretable method named mtADENet, which integrates multiple types of network-based inference approaches for ADE prediction.

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.

The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions.

Observation of Room-Temperature Exciton-Polariton Emission from Wide-Ranging 2D Semiconductors Coupled with a Broadband Mie Resonator.

Two-dimensional exciton-polaritons in monolayer transition metal dichalcogenides (TMDs) exhibit practical advantages in valley coherence, optical nonlinearities, and even bosonic condensation owing to their light-emission capability. To achieve robust exciton-polariton emission, strong photon-exciton couplings are required at the TMD monolayer, which is challenging due to its atomic thickness. High-quality () factor optical cavities with narrowband resonances are an effective approach but typically limited to a specific excitonic state of a certain TMD material.

A new paradigm in lignocellulolytic enzyme cocktail optimization: Free from expert-level prior knowledge and experimental datasets.

Effectively pairing diverse lignocellulolytic enzyme cocktails with intricately structured lignocellulosic substrates is an enduring challenge for science and technology. To date, extensive trial-and-error remains the primary approach and no deep-learning methods were developed to address it due to limited experimental data and incomplete expert-level knowledge of enzyme-cocktail-substrate structure-dynamics-function relationships. Here, a novel model is developed to tackle this issue in efficient, cost-effective, and high-throughput manners.

Effects of post-transfer annealing and substrate interactions on the photoluminescence of 2D/3D monolayer WS/Ge heterostructures.

The ultraflat and dangling bond-free features of two-dimensional (2D) transition metal dichalcogenides (TMDs) endow them with great potential to be integrated with arbitrary three-dimensional (3D) substrates, forming mixed-dimensional 2D/3D heterostructures. As examples, 2D/3D heterostructures based on monolayer TMDs (, WS) and bulk germanium (Ge) have become emerging candidates for optoelectronic applications, such as ultrasensitive photodetectors that are capable of detecting broadband light from the mid-infrared (IR) to visible range. Currently, the study of WS/Ge(100) heterostructures is in its infancy and it remains largely unexplored how sample preparation conditions and different substrates affect their photoluminescence (PL) and other optoelectronic properties.

Charge Transfer Modulation in Vanadium-Doped WS /Bi O Se Heterostructures.

The field of photovoltaics is revolutionized in recent years by the development of two-dimensional (2D) type-II heterostructures. These heterostructures are made up of two different materials with different electronic properties, which allows for the capture of a broader spectrum of solar energy than traditional photovoltaic devices. In this study, the potential of vanadium (V)-doped WS is investigated, hereafter labeled V-WS , in combination with air-stable Bi O Se for use in high-performance photovoltaic devices.

Investigation of Anti-Alzheimer's Mechanisms of Sarsasapogenin Derivatives by Network-Based Combining Structure-Based Methods.

Alzheimer's disease (AD), a neurodegenerative disease with no cure, affects millions of people worldwide and has become one of the biggest healthcare challenges. Some investigated compounds play anti-AD roles at the cellular or the animal level, but their molecular mechanisms remain unclear. In this study, we designed a strategy combining network-based and structure-based methods together to identify targets for anti-AD sarsasapogenin derivatives (AAs).

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles.

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs.

Precision Modification of Monolayer Transition Metal Dichalcogenides via Environmental E-Beam Patterning.

Layered Transition Metal Dichalcogenides (TMDs) are an important class of materials that exhibit a wide variety of optoelectronic properties. The ability to spatially tailor their expansive property-space (e.g.

Spatial Control of Substitutional Dopants in Hexagonal Monolayer WS : The Effect of Edge Termination.

The ability to control the density and spatial distribution of substitutional dopants in semiconductors is crucial for achieving desired physicochemical properties. Substitutional doping with adjustable doping levels has been previously demonstrated in 2D transition metal dichalcogenides (TMDs); however, the spatial control of dopant distribution remains an open field. In this work, edge termination is demonstrated as an important characteristic of 2D TMD monocrystals that affects the distribution of substitutional dopants.