Owing to their distinct chemical and physical properties, mesoporous metal oxide semiconductors have shown great application potential in catalysis, electrochemistry, energy conversion, and energy storage. In this study, mesoporous crystalline SnO materials have been synthesized through an evaporation-induced co-assembly (EICA) method using poly(ethylene oxide)-b-polystyrene diblock copolymers as the template, tin chlorides as the tin sources, and tetrahydrofuran as the solvent. By controlling conditions of the co-assembly process and employing a carbon-supported thermal treatment strategy, highly ordered mesoporous SnO materials with a hexagonal mesostructure (space group P6/mmc) and crystalline pore walls can be obtained. The mesoporous SnO is employed for fabricating gas sensor nanodevices which exhibit an excellent sensing performance toward HS with high sensitivity (170, 50 ppm) and superior stability, owing to its high surface area (98 m/g), well-connected mesopores of ca. 18.0 nm, and high density of active sites in the crystalline pore walls. The chemical mechanism study reveals that both SO and SnS are generated during the gas sensing process on the SnO-based sensors.

The advantages of existing ordered mesoporous materials have not yet been fully realized, due to their limited accessibility of in‐pore surface and long mass‐diffusion length. A general, controllable, and scalable synthesis of a family of two‐dimensional (2D) single‐layer ordered mesoporous materials (SOMMs) with completely exposed mesopore channels, significantly improved mass diffusion, and diverse framework composition is reported here. The SOMMs are synthesized via a surface‐limited cooperative assembly (SLCA) on water‐removable substrates of inorganic salts (e.g., NaCl), combined with vacuum filtration. As a proof of concept, the obtained CeO2‐based SOMMs show superior catalytic performance in CO oxidation with high conversion efficiency, ≈33 times higher than that of conventional bulk mesoporous CeO2. This SLCA is a promising approach for developing next‐generation porous materials for various applications.

Semiconducting metal oxides have attracted increasing attention in various fields due to their intrinsic properties. In this study, a facile solvent evaporation-induced multicomponent co-assembly approach coupled with a carbon-supported crystallization strategy is employed to controllably synthesize crystalline mesoporous nickel oxide-doped tungsten oxides in an acidic THF/H2O solution with poly­(ethylene oxide)-b-polystyrene diblock copolymers (PEO-b-PS) as the structure-directing agent, tungsten­(VI) chlorides as WO3 precursors, and Ni­(AcAc)2 as the NiO precursor. The obtained materials possess a face-centered cubic mesoporous structure, large pore size (∼30 nm), high surface area (30–50 m2 g–1), large pore volume (0.15–0.19 cm3 g–1), and ultralarge pore windows (12–16 nm) connecting adjacent mesopores, and the mesoporous WO3 framework was decorated by ultrafine NiO nanocrystals. Due to their well-connected porous structure and high surface areas with rich WO3–NiO interfaces, the composite materials exhibit superior gas sensing performance with an ultrafast response (∼4 s), high sensitivity (R a/R g = 58 ± 5.1), and selectivity to 50 ppm H2S at a relatively low working temperature (250 °C). The chemical mechanism study reveals complicated surface reactions of WO3/NiO-based gas sensors, and SO2, WS2, and NiS intermediates were found to be generated during the gas sensing process.

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/smll.201904240. the gas sensor based on Au/WO 3 materials possess enhanced ethanol sensing performance with a good response (R air /R gas = 36-50 ppm of ethanol), high selectivity, and excellent low-concentration detection capability (down to 50 ppb) at low working temperature (200 °C). www.advancedsciencenews.com

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Nuclear energy plays an important role in global energy supply, especially as a key low-carbon source of power. However, safe operation is very critical in nuclear power plants (NPPs). Given the significant impact of human-caused errors on three serious nuclear accidents in history, artificial intelligence (AI) has increasingly been used in assisting operators with regard to making various decisions. In particular, data-driven AI algorithms have been used to identify the presence of accidents and their root causes. However, there is a lack of an open NPP accident dataset for measuring the performance of various algorithms, which is very challenging. This paper presents a first-of-its-kind open dataset created using PCTRAN, a pre-developed and widely used simulator for NPPs. The dataset, namely nuclear power plant accident data (NPPAD), basically covers the common types of accidents in typical pressurised water reactor NPPs, and it contains time-series data on the status or actions of various subsystems, accident types, and severity information. Moreover, the dataset incorporates other simulation data (e.g., radionuclide data) for conducting research beyond accident diagnosis.

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