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Integrating real‐time electrical gas sensing and highly identifiable surface‐enhanced Raman spectroscopy (SERS) for volatile organic compounds (VOCs) monitoring holds significant potential for safeguarding public health and safety. However, this technique remains in the proof‐of‐concept stage because the performance and reproducibility of devices fall short of meeting the practical application requirements. This work addresses this challenge by employing vortex engineering on a bifunctional dual‐layer array of Ni‐doped SnO2 (Ni‐SnO2) bowl‐coated on Ni‐SnO2/Au/SiO2 and developing highly reproducible device fabrication. In the dual‐layer array, vortices generated in upper Ni‐SnO2 bowls slow down the VOC flow and direct its delivery to gaps between lower Ni‐SnO2/Au/SiO2 units that are critical to SERS and electrical sensing. Experimental results show that the vortex effect in the array enables a low limit of detection of 10 ppb with a response and recovery in seconds. The excellent practicality of the array has been demonstrated through a ca. 100 hours of quantitative dual‐monitoring of styrene in a spacious environment (~60 m3) with a 5‐meter distance between the leakage source and the array. This work advances dual‐monitoring sensors for practical applications and offers insights for designing gas‐sensing materials and devices. [ABSTRACT FROM AUTHOR]

A simple and flexible method of preparing an ultrathin semiconducting oxide layer-wrapped gold nanoparticles (NPs) is presented. The method is a single-step procedure based on laser ablation in a precursor solution. The spherical Au NPs (<20 nm in mean size) wrapped with a SnO 2 layer of approximately 2 nm in thickness are formed after the laser ablation of a gold target in SnCl 4 solutions with concentrations of 0.01–0.1 M. The thickness of such SnO 2 shell is nearly independent of Au particle sizes. Results reveal that the formation of Au@SnO 2 NPs involves a two-step process: the laser ablation-induced formation of Au NPs and subsequent Coulomb effect-based colloidal attachment and self-assembly on the Au NPs. Au@SnO 2 NPs-built film exhibits significantly stronger surface-enhanced Raman scattering effect to organic phosphor molecules (phenylphosphonic acid) and much better gas sensing performance to H 2 S at room temperature compared with the bare Au NPs and pure SnO 2 NPs films, respectively. This work presents a simple route to fabricating noble-metal NPs wrapped with symmetrical and ultrathin semiconducting oxide shells. [ABSTRACT FROM AUTHOR]

Surface Enhanced Raman Spectroscopy (SERS) could be a powerful technique for detecting trace gaseous sulfur-mustard, but it is still challenging due to the difficulty in efficiently capturing sulfur-mustard molecules by normal SERS substrates. Here, a chemically trapping strategy is presented for such detection via coating an ultrathin metal-oxide sensing layer on a SERS substrate. In the strategy, a SERS substrate Au-wrapped Si nanocone array is designed and fabricated by Si wafer-based organic template-etching and appropriate Au deposition, and coated with an ultrathin CuO for chemically capturing sulfur-mustard molecules. The validity of such strategy has been demonstrated via taking the gaseous 2-chloroethyl ethyl sulfide (a simulant of sulfur-mustard, or 2-CEES for short) as the target molecules. The response of the CuO-coated SERS substrate to the gaseous 2-CEES is detectable within 10 min, and the lowest detectable concentration is 10 ppb or less. Further experiments have shown that there exists an optimal CuO coating thickness which is about 6 nm. The CuO coating-based capture of 2-CEES molecules is attributed to the surface hydroxyl-induced specific adsorption, which is subject to the pseudo-second-order kinetics and Freundlich-typed model. This study presents the practical SERS chips and new route for the trace detection of gaseous sulfur-mustard. [Display omitted] • A chemically trapping strategy is presented based on the CuO-coated SERS substrate. • The CuO coating evidently increases SERS responses to gaseous sulfur-mustard simulant. • The SERS response is detectable within 10 min and obeys pseudo-second-order kinetics. • The detection of the gaseous simulant is quantifiable with a lower limit below 10 ppb. • Such CuO coating-enhanced SERS response comes from the hydroxyl-induced adsorption. [ABSTRACT FROM AUTHOR]

• Au@D-SnO 2 NPs are prepared creatively by alloying and selective-oxidation. • The NPs show strong sunlight harvesting and efficient charge separation. • The NPs show efficient photocatalytic-removal of NH 3. Strong sunlight harvesting and efficient charge separation are of great significances for solar energy utilization of metal oxides, but it is a challenge to realize these two aspects simultaneously. Here, we strategize oxygen-vacancy-rich (or defective)-SnO 2 wrapped gold nanoparticles (Au@D-SnO 2 NPs) for this. The designed NPs are fabricated just by adding precursors into boiling pre-prepared Au colloidal solution and then re-dispersing the reacted NPs in water, based on surface alloying and selective oxidation. Typically, the fabricated Au@D-SnO 2 NPs (with 12 nm thick shell) exhibit strong absorption in full solar spectrum (200–2000 nm) with an energy absorption efficiency as high as 84%. Meanwhile, the photocurrent response of the typical NPs is 2.3 and 13.6 times as high as that of Au@SnO 2 NPs (with poor oxygen vacancies) and commercial SnO 2 NPs, respectively, showing efficient charge separation. Such excellent performance is attributed to the synergistic effect of surface plasmon resonance (SPR) of the Au core and the band gap narrowing induced by the abundant oxygen vacancies. Further, the excellent photocatalytic performance of the Au@D-SnO 2 NP catalyst has been demonstrated via exemplary photocatalytic removal of gaseous ammonia. The catalytic rate is about 4.2 and 25 times as high as that of the Au@SnO 2 NP and the commercial SnO 2 NP catalysts, respectively. It is expected that such excellent photocatalytic performance will also be obtained on other defective oxides wrapped metal structures. This work provides a preparation method for Au@D-SnO 2 NPs, which can be used for efficient solar energy utilization. [ABSTRACT FROM AUTHOR]

Rapid urban development in China has aggravated land subsidence, which poses a potential threat to sustainable urban development. It is imperative to monitor and predict land subsidence over large areas. To address these issues, we chose Henan Province as the study area and applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain land deformation information for monitoring land subsidence from November 2019 to February 2022 with 364 multitrack Sentinel-1A satellite images. The current traditional time-series deep learning models suffer from the problems of (1) poor results in extracting a sequence of information that is too long and (2) the inability to extract the feature information between the influence factor and the land subsidence well. Therefore, a long short-term memory-temporal convolutional network (LSTM-TCN) deep learning model was proposed in order to predict land subsidence and explore the influence of environmental factors, such as the volumetric soil water layer and monthly precipitation, on land subsidence in this study. We used leveling data to verify the effectiveness of SBAS-InSAR in land subsidence monitoring. The results of SBAS-InSAR showed that the land subsidence in Henan Province was obvious and uneven in spatial distribution. The maximum subsidence velocity was −94.54 mm/a, and the uplift velocity was 41.23 mm/a during the monitoring period. Simultaneously, the land subsidence in the study area presented seasonal changes. The rate of land subsidence in spring and summer was greater than that in autumn and winter. The prediction accuracy of the LSTM-TCN model was significantly better than that of the individual LSTM and TCN models because it fully combined their advantages. In addition, the prediction accuracies, with the addition of environmental factors, were improved compared with those using only time-series subsidence information. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman scattering (SERS) provides an unprecedented opportunity for fingerprinting identification and trace-level detection in chemistry, biomedicine, materials, and so on. Although great efforts have been devoted to fabricating sensitive plasmonic nanomaterials, it is still challenging to batch-produce a SERS substrate with high sensitivity, good reproducibility, and perfect recyclability. Here, we describe a facile fabrication of three-dimensional (3D) hierarchical Au/CuS nanocomposites, in which high-density Au nanotips enable highly SERS-active sensing, and the well-defined microflower (MF) geometry produces perfect signal reproducibility (RSD < 5%) for large laser spot excitations (>50 μm2), which is particularly suitable for practical on-site detection with a handheld Raman spectrometer. In addition, a self-cleaning ability of this Au/CuS Schottky junction photocatalyst under sunlight irradiation allows complete removal of the adsorbed analytes, realizing perfect regeneration of the SERS substrates over many cycles. The mass-production, ultra-sensitive, high-reproducibility, and fast-recyclability features of hierarchical Au/CuS MFs greatly facilitate cost-effective and field SERS detection of trace analytes in practice. [ABSTRACT FROM AUTHOR]