Enhancing low-temperature HS sensing performance of SnO quantum wires transition metal doping and oxygen vacancy engineering.

Doping impurity atoms into metal oxide semiconductors plays a crucial role in modulating both their electronic and chemical properties at active sites. Tin oxide (SnO) quantum wires (QWs), with their large surface area and numerous exposed active sites, have shown significant potential as sensing materials for gas sensors. However, challenges such as unsatisfactory selectivity and detection limits (LODs) still hinder their performance.

Wearable HS Sensors with Enhanced Humidity Tolerance: Microcrumpled SnO Quantum-Wire Films for Real-Time Room-Temperature Gas Monitoring.

The detection of hydrogen sulfide (HS) in humid environments remains a significant challenge, particularly in wearable gas sensors where humidity, mechanical flexibility, and power consumption are critical constraints. In this study, we introduce a stretchable, humidity-resistant HS sensor based on microcrumpled SnO quantum-wire films, designed for efficient gas detection at room temperature with low-power consumption. The sensor's architecture enhances gas adsorption by increasing the active surface area while minimizing water accumulation through surface energy modulation.

Tuning Gas Sensing Properties through Metal-Nanocluster Functionalization of 3D SnO Nanotube Arrays for Selective Gas Detection.

The demand for highly sensitive and selective gas sensors for the detection of target gases in complex environments is rapidly increasing. In this study, we present a novel approach utilizing atomic layer deposition (ALD) technology to fabricate gas sensors based on metal-nanocluster functionalized 3D SnO nanotube arrays. Pd/Au-nanocluster-sensitized SnO sensors exhibit high sensitivity to formaldehyde, toluene, and acetone at room temperature, with detection limits of 1.

Development of a Three-Dimensional Nanostructure SnO-Based Gas Sensor for Room-Temperature Hydrogen Detection.

The development of gas sensors with high sensitivity and low operating temperatures is essential for practical applications in environmental monitoring and industrial safety. SnO-based gas sensors, despite their widespread use, often suffer from high working temperatures and limited sensitivity to H gas, which presents significant challenges for their performance and application. This study addresses these issues by introducing a novel SnO-based sensor featuring a three-dimensional (3D) nanostructure, designed to enhance sensitivity and allow for room-temperature operation.

Fingertip-chip sensor based on Pd nanocluster sensitized 3D NiO nanotube arrays for real-time, selective methane detection.

The selective detection of methane (CH) at trace levels is essential for applications such as mining safety and natural gas leak detection. However, achieving high selectivity and sensitivity remains a significant challenge due to interference from gases like hydrogen sulfide (HS) and carbon monoxide (CO). In this study, we present a novel fingertip-chip sensor that combines palladium (Pd) nanoclusters with three-dimensional (3D) nickel oxide (NiO) nanotube arrays for highly selective and sensitive CH detection.

Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models.

Accessing the synthesizability of crystal structures is crucial for transforming theoretical materials into real-world applications. Nevertheless, there is a significant gap between actual synthesizability and thermodynamic or kinetic stability commonly used to screen synthesizable structures. Herein, we develop the Crystal Synthesis Large Language Models (CSLLM) framework, which utilizes three specialized LLMs to predict the synthesizability of arbitrary 3D crystal structures, possible synthetic methods, and suitable precursors, respectively.

Cobalt-modified crystalline carbon nitride homojunction with enhanced interfacial charge separation for photocatalytic pure water splitting.

Photocatalytic hydrogen production in pure water without oxygen precipitation is highly effective owing to the minimal efficiency of water oxidation for oxygen generation and the complexity of the reaction, yet it presents a significant hurdle. Here, we report the preparation of crystalline carbon nitride (CCN) homojunction-anchored Co atoms using the molten salt and reflux method. Our findings indicate that elevated temperature during ionothermal synthesis promotes the phase transition of poly(heptazine) imides (PHI) to poly(triazine) imides (PTI), and the homogeneous junction formed in this process promotes exciton dissociation as well as carrier migration through the built-in electric field formed by the semi-coherent interface.

Inverse design of promising electrocatalysts for CO reduction via generative models and bird swarm algorithm.

Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation with Efficient Global Chemical Space Search (MAGECS) that addresses this challenge by integrating the bird swarm algorithm and supervised graph neural networks, enabling effective navigation of generative models in the immense chemical space towards materials with target properties.

Facet-engineered NaNbO cubes with exposed (101) plane for enhanced lithium-ion storage.

Density functional theory (DFT) predicts superior lithium-ion diffusion the (101) plane of NaNbO. Experimental results confirm that despite a reduction in surface area, 6 μm NaNbO cubes with more (101) plane exposure exhibit enhanced Li storage, underscoring the significance of crystal facet engineering.

How the Spacer Influences the Stability of 2D Perovskites?

Two-dimensional lead halide perovskites (2D HPs) represent as an emerging class of materials given their tunable optoelectronic properties and long-term stability in perovskite solar cells. However, the ever-growing field of optoelectronic devices using 2D HPs requires fundamental understanding of the influence of the spacer on the physiochemical properties and stability of perovskites as well as establish which cation properties are closely related to suppress the halogen ion mobility. This study focuses on investigating the influence of organic spacers with intrinsic properties (e.

Nano-Schottky-junction-engineered Pd/SnO nanotube array for ultrasensitive hydrogen sensing at room temperature.

Detecting H at low concentrations is important due to it being a major safety concern in practical applications. However, semiconductor chemiresistive gas sensors always suffer from high operating temperatures and power consumption, as well as a limited concentration detection range, which restricts their widespread use. Herein, we developed a 3D nanostructured gas sensor employing a Pd-nanocluster-decorated SnO nanotube array as the sensing layer.

Research on the coupling measurement of medical education and health resource allocation under the background of healthy China.

Objective: To analyze the coupling and coordination level of medical education and health resource allocation in China, and to provide scientific basis for promoting the high-quality development of medical education and the efficient allocation of health resources.

Methods: Based on the panel data from 2011 to 2021, the coupling coordination degree model was used to measure the coupling coordination index of medical education and health resources in China. The spatial auto-correlation model was used to analyze the development status and distribution characteristics of the coupling coordination degree of the two systems.

Ivermectin for treatment of COVID-19: A systematic review and meta-analysis.

The effect of ivermectin (IVM) in treating coronavirus disease 2019 (COVID-19) is still controversial, yet the drug has been widely used in the world. The aim of this review was to systematically evaluate the clinical outcomes of IVM in patients with COVID-19. From inception to June 22, 2023, the PubMed, EMBASE, Web of Science (WOS), and scopus databases were searched for relevant observational studies on the risk of RA in migraineurs.

Ultra-Sensitive and Stable Multiplexed Biosensors Array in Fully Printed and Integrated Platforms for Reliable Perspiration Analysis.

Electrochemical biosensors have emerged as one of the promising tools for tracking human body physiological dynamics via non-invasive perspiration analysis. However, it remains a key challenge to integrate multiplexed sensors in a highly controllable and reproducible manner to achieve long-term reliable biosensing, especially on flexible platforms. Herein, a fully inkjet printed and integrated multiplexed biosensing patch with remarkably high stability and sensitivity is reported for the first time.

Adaptive Design of Alloys for CO Activation and Methanation via Reinforcement Learning Monte Carlo Tree Search Algorithm.

Data-driven machine learning (ML) has earned remarkable achievements in accelerating materials design, while it heavily relies on high-quality data acquisition. In this work, we develop an adaptive design framework for searching for optimal materials starting from zero data and with as few DFT calculations as possible. This framework integrates automatic density functional theory (DFT) calculations with an improved Monte Carlo tree search via reinforcement learning algorithm (MCTS-PG).

Distilling universal activity descriptors for perovskite catalysts from multiple data sources multi-task symbolic regression.

Developing activity descriptors data-driven machine learning (ML) methods can speed up the design of highly active and low-cost electrocatalysts. Despite the fact that a large amount of activity data for electrocatalysts is stored in the literature, data from different publications are not comparable due to different experimental or computational conditions. In this work, an interpretable ML method, multi-task symbolic regression, was adopted to learn from data in multiple experiments.

Inverse design with deep generative models: next step in materials discovery.

Data-driven inverse design for inorganic functional materials is a rapidly emerging field, which aims to automatically design innovative materials with target properties and to enable property-to-structure material discovery.

Microheater Integrated Nanotube Array Gas Sensor for Parts-Per-Trillion Level Gas Detection and Single Sensor-Based Gas Discrimination.

Real-time monitoring of health threatening gases for chemical safety and human health protection requires detection and discrimination of trace gases with proper gas sensors. In many applications, costly, bulky, and power-hungry devices, normally employing optical gas sensors and electrochemical gas sensors, are used for this purpose. Using a single miniature low-power semiconductor gas sensor to achieve this goal is hardly possible, mostly due to its selectivity issue.

Impairment of μ-calpain activation by rhTNFR:Fc reduces severe burn-induced membrane disruption in the heart.

Stress cardiomyopathy is a major clinical complication after severe burn. Multiple upstream initiators have been identified; however, the downstream targets are not fully understood. This study assessed the role of the plasma membrane in this process and its relationship with the protease μ-calpain and tumor necrosis factor-alpha (TNF-α).

Wireless Self-Powered High-Performance Integrated Nanostructured-Gas-Sensor Network for Future Smart Homes.

The accelerated evolution of communication platforms including Internet of Things (IoT) and the fifth generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks for real-time monitoring chemical safety and personal health. However, this application scenario requires a challenging combination of characteristics of gas sensors including small formfactor, low cost, ultralow power consumption, superior sensitivity, and high intelligence. Herein, self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network are demonstrated here.

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts.

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity.

Porous defective carbon nitride obtained by a universal method for photocatalytic hydrogen production from water splitting.

For the first time, herein this work, we have developed an effective and adaptable method to introduce defects onto the polymeric carbon nitride by simply grinding urea with urea nitrate which resulting new carbon nitride composite (UNU-CN) and melamine with urea nitrate which resulting new carbon nitride composite (UNM-CN). The UNU-CN reveals high performance towards photocatalytic hydrogen production and as well as photocatalytic removal of contaminants. The results confirm that the defects enhanced the specific surface area, and improved performance of adsorbed oxygen which beneficial to generate more active radicals and more conducive sties to improve d the overall photocatalytic performance.