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Environmental pollutants threaten millions of lives and state-of-the-art strategies, mostly based on surface catalytic activities to remediate environmental issues, have emerged. Despite their active capabilities, traditional schemes are only capable of a single function, either sensing hazardous chemicals or their reduction, limiting the identification of clear solutions to environmental problems. This study proposes a material engineering method that adopts both the detection and neutralization of environmental pollutants for remediation. This strategy exploits ultrafast flash lamp-driven thermal engineering to realize ultra-small (<5 nm) polyelemental nanoparticles with a uniform size distribution on a three-dimensional (3D) metal oxide nanostructure. Specifically, an intense pulse light treatment on highly periodic 3D thin-shell TiO2 triggers an intensive photothermal effect, enabling instant reduction of various surface-decorated metal ion precursors into an atomically mixed heterostructure. Experimental and computational studies were conducted to investigate the physicochemical reactions occurring on the heterometal catalysts. As a proof-of-concept, the universal photocatalytic utility of dual-mode photoactivated quaternary phase (PtPdNiCo) NPs incorporated into 3D TiO2 was demonstrated for gaseous chemical sensing and degradation of environmental pollutants in water. [ABSTRACT FROM AUTHOR]

2D transition metal dichalcogenides (TMDs) have significant research interests in various novel applications due to their intriguing physicochemical properties. Notably, one of the 2D TMDs, SnS2, has superior chemiresistive sensing properties, including a planar crystal structure, a large surface‐to‐volume ratio, and a low electronic noise. However, the long‐term stability of SnS2 in humid conditions remains a critical shortcoming towards a significant degradation of sensitivity. Herein, it is demonstrated that the subsequent self‐assembly of zeolite imidazolate framework (ZIF‐8) can be achieved in situ growing on SnS2 nanoflakes as the homogeneous porous materials. ZIF‐8 layer on SnS2 allows the selective diffusion of target gas species, while effectively preventing the SnS2 from severe oxidative degradation. Molecular modeling such as molecular dynamic simulation and DFT calculation, further supports the mechanism of sensing stability and selectivity. From the results, the in situ grown ZIF‐8 porous membrane on 2D materials corroborates the generalizable strategy for durable and reliable high‐performance electronic applications of 2D materials. [ABSTRACT FROM AUTHOR]

Atomic layer deposition (ALD) is considered a promising growth technique for transition metal dichalcogenides (TMDCs) because it ensures uniformity and homogeneity of the TMDC grains. However, the poor crystallinity of ALD-grown TMDCs remains a critical challenge. Although crystallinity depends on the growth mechanism, the growth behavior of crystalline TMDCs in ALD is unclear. We investigated the growth behavior of highly crystallized molybdenum disulfide (MoS2) by ALD at 650 °C with an extra pulse of remote H2 plasma. Growth at high temperatures using the activated species aided surface diffusion of the adsorbates. The ALD process facilitates repeated growth and saturation of MoS2, unlike the normal ALD of 3D bulk materials, where the film thickness monotonically increases with the number of ALD cycles. This unique behavior resulted from the evolution of the basal plane without dangling bonds. On the basal plane, MoS2 lateral growth dominates vertical growth, and prolonged incubation is required for nucleation on the basal plane. The grain size is small (up to two monolayers) because of the limited mobility on SiO2, and the grains of the third layer grow to a few hundred nanometers. These findings provide insights into the development of ALD technology for application to high-quality TMDCs. [ABSTRACT FROM AUTHOR]

Morphological engineering of two-dimensional chalcogenides has led to significant advances in terms of high responses and low power consumption for chemiresistive gas sensors. Nevertheless, the practical use of such nanostructured two-dimensional materials is still limited. The difficulties in patterning resulting from the morphological complexity of nanostructures and the absence of highly sensitive p-type semiconductor sensors are major obstacles. In this study, we report a highly sensitive NO2 gas sensor composed of cross-linked p-type SnS nanoplates on SiO2 nanorods. The area-selective growth of SnS by atomic layer deposition allows for the self-aligned formation of SnS nanoplates only on SiO2 nanorods without an additional patterning process. The cross-linked structure of the SnS nanoplates enabled the electrical connection of small and very thin SnS nanoplates, which increased the resistance difference between the hole accumulation layer across the entire surface and the less conductive core. Consequently, this cross-linked structure enhances the gas response of p-type semiconductor sensors. The gas response did not vary significantly when the relative humidity (RH) changed from 40% to 80%. Under ambient conditions of 60% RH at room temperature, the SnS sensor exhibited a high response of 116% to 5 ppm NO2, along with an extremely low detection limit of 21 ppt. The sensor showed excellent selectivity for NO2, with a minimal response to other gases. This approach provides possibilities for employing p-type semiconductors in practical room-temperature sensor applications. [ABSTRACT FROM AUTHOR]

The switching characteristics and performance of oxide-based memristors are predominately determined by oxygen- or oxygen-vacancy-mediated redox reactions and the consequent formation of conducting filaments (CFs). Devices using oxide thin films as the switching layer usually require an electroforming process for subsequent switching operations, which induces large device-to-device variations. In addition, the hard-to-control redox reaction during repeated switching causes random fluctuations or degradation of each resistance state, hindering reliable switching operations. In this study, an HfO 2 nanorod (NR)-based memristor is proposed for simultaneously achieving highly uniform, electroforming-free, fast, and reliable analogue switching properties. The well-controlled redox reaction due to the easy gas exchange with the environment at the surface of the NRs enhances the generation of oxygen or oxygen vacancies during the switching operation, resulting in electroforming-free and reliable switching behavior. In addition, the one-dimensional surface growth of CFs facilitates highly linear conductance modulation with smaller conductance changes compared with the two-dimensional volume growth in thin-film-based memristors, resulting in a high accuracy of >92% in the Modified National Institute of Standards and Technology pattern-recognition test and desirable spike-timing-dependent plasticity.

Modern artificial neural network technology using a deterministic computing framework is faced with a critical challenge in dealing with massive data that are largely unstructured and ambiguous. This challenge demands the advances of an elementary physical device for tackling these uncertainties. Here, we designed and fabricated a SiO x nanorod memristive device by employing the glancing angle deposition (GLAD) technique, suggesting a controllable stochastic artificial neuron that can mimic the fundamental integrate-and-fire signaling and stochastic dynamics of a biological neuron. The nanorod structure provides the random distribution of multiple nanopores all across the active area, capable of forming a multitude of Si filaments at many SiO x nanorod edges after the electromigration process, leading to a stochastic switching event with very high dynamic range (≈5.15 × 10 10 ) and low energy (≈4.06 pJ). Different probabilistic activation (ProbAct) functions in a sigmoid form are implemented, showing its controllability with low variation by manufacturing and electrical programming schemes. Furthermore, as an application prospect, based on the suggested memristive neuron, we demonstrated the self-resting neural operation with the local circuit configuration and revealed probabilistic Bayesian inferences for genetic regulatory networks with low normalized mean squared errors (≈2.41 × 10 -2 ) and its robustness to the ProbAct variation., (© 2021 Wiley-VCH GmbH.)