Hydrogen Sensing Mechanism of WS2 Gas Sensors Analyzed with DFT and NAP-XPS.

Nanostructured tungsten disulfide (WS2) is one of the most promising candidates for being used as active nanomaterial in chemiresistive gas sensors, as it responds to hydrogen gas at room temperature. This study analyzes the hydrogen sensing mechanism of a nanostructured WS2 layer using near-ambient-pressure X-ray photoelectron spectroscopy (NAP-XPS) and density functional theory (DFT). The W 4f and S 2p NAP-XPS spectra suggest that hydrogen makes physisorption on the WS2 active surface at room temperature and chemisorption on tungsten atoms at temperatures above 150 °C. DFT calculations show that a hydrogen molecule physically adsorbs on the defect-free WS2 monolayer, while it splits and makes chemical bonds with the nearest tungsten atoms on the sulfur point defect. The hydrogen adsorption on the sulfur defect causes a large charge transfer from the WS2 monolayer to the adsorbed hydrogen. In addition, it decreases the intensity of the in-gap state, which is generated by the sulfur point defect. Furthermore, the calculations explain the increase in the resistance of the gas sensor when hydrogen interacts with the WS2 active layer.

Three-Dimensional Assemblies of Edge-Enriched WSe2 Nanoflowers for Selectively Detecting Ammonia or Nitrogen Dioxide.

Herein, we present, for the first time, a chemoresistive-type gas sensor composed of two-dimensional WSe2, fabricated by a simple selenization of tungsten trioxide (WO3) nanowires at atmospheric pressure. The morphological, structural, and chemical composition investigation shows the growth of vertically oriented three-dimensional (3D) assemblies of edge-enriched WSe2 nanoplatelets arrayed in a nanoflower shape. The gas sensing properties of flowered nanoplatelets (2H-WSe2) are investigated thoroughly toward specific gases (NH3 and NO2) at different operating temperatures. The integration of 3D WSe2 with unique structural arrangements resulted in exceptional gas sensing characteristics with dual selectivity toward NH3 and NO2 gases. Selectivity can be tuned by selecting its operating temperature (150 °C for NH3 and 100 °C for NO2). For instance, the sensor has shown stable and reproducible responses (24.5%) toward 40 ppm NH3 vapor detection with an experimental LoD < 2 ppm at moderate temperatures. The gas detecting capabilities for CO, H2, C6H6, and NO2 were also investigated to better comprehend the selectivity of the nanoflower sensor. Sensors showed repeatable responses with high sensitivity to NO2 molecules at a substantially lower operating temperature (100 °C) (even at room temperature) and LoD < 0.1 ppm. However, the gas sensing properties reveal high selectivity toward NH3 gas at moderate operating temperatures. Moreover, the sensor demonstrated high resilience against ambient humidity (Rh = 50%), demonstrating its remarkable stability toward NH3 gas detection. Considering the detection of NO2 in a humid ambient atmosphere, there was a modest increase in the sensor response (5.5%). Furthermore, four-month long-term stability assessments were also taken toward NH3 gas detection, and sensors showed excellent response stability. Therefore, this study highlights the practical application of the 2H variant of WSe2 nanoflower gas sensors for NH3 vapor detection.

On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials.

This paper presents a methodology to quantify oxidizing and reducing gases using n-type and p-type chemiresistive sensors, respectively. Low temperature sensor heating with pulsed UV or visible light modulation is used together with the application of the fast Fourier transform (FFT) to extract sensor response features. These features are further processed via principal component analysis (PCA) and principal component regression (PCR) for achieving gas discrimination and building concentration prediction models with R2 values up to 98% and RMSE values as low as 5% for the total gas concentration range studied. UV and visible light were used to study the influence of the light wavelength in the prediction model performance. We demonstrate that n-type and p-type sensors need to be used together for achieving good quantification of oxidizing and reducing species, respectively, since the semiconductor type defines the prediction model's effectiveness towards an oxidizing or reducing gas. The presented method reduces considerably the total time needed to quantify the gas concentration compared with the results obtained in a previous work. The use of visible light LEDs for performing pulsed light modulation enhances system performance and considerably reduces cost in comparison to previously reported UV light-based approaches.

Spontaneous in-flight assembly of magnetic nanoparticles into macroscopic chains.

Knowing the interactions controlling aggregation processes in magnetic nanoparticles is of strong interest in preventing or promoting nanoparticles' aggregation at wish for different applications. Dipolar magnetic interactions, proportional to the particle volume, are identified as the key driving force behind the formation of macroscopic aggregates for particle sizes above about 20 nm. However, aggregates' shape and size are also strongly influenced by topological ordering. 1-D macroscopic chains of several micrometer lengths are obtained with cube-shaped magnetic nanoparticles prepared by the gas-aggregation technique. Using an analytical model and molecular dynamics simulations, the energy landscape of interacting cube-shaped magnetic nanoparticles is analysed revealing unintuitive dependence of the force acting on particles with the displacement and explaining pathways leading to their assembly into long linear chains. The mechanical behaviour and magnetic structure of the chains are studied by a combination of atomic and magnetic force measurements, and computer simulation. The results demonstrate that [111] magnetic anisotropy of the cube-shaped nanoparticles strongly influences chain assembly features.