We report on studies of new gas sensing devices to be used in high humidity environments. Highly thermal-stable, super hydrophobic 2-dimensional (2D) boron nitride nanosheets (BNNSs) functionalized with Pt nanoparticles were prepared and used as an active layer for the prototype. The morphologic surface, crystallographic structures and chemical compositions of the synthesized 2D materials were characterized by using optical microscope, scanning electron microscope (SEM), transmission electron microscope (TEM) and atomic force microscope (AFM) and Raman scattering, respectively. The experimental data reveals that high-quality BNNSs were prepared. A pair of Au electrodes were combined with a basic electrical circuit and the 2D sensing material to form high-performance gas sensors for the detection of pollution gases. The present structure is simple and the fabrication is easy and fast, which ensures the creation of a low-cost prototype with harsh (high humidity, high temperature) environment resistance and potential for miniaturization. The responses of the prototype to different target gases with different concentrations were characterized. The influences of the operating temperature and bias voltage effect on sensing performances were also investigated. The fabricated sensors appear to have high selectivity, high sensitivity and fast response to target gases. The sensing mechanism in the present case is attributed to the electron donation from the target gas molecules to the active layer, leading to the change of electrical properties on the surface of BNNS layer.
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Quantitative gas-phase transmission electron microscopy: Where are we now and what comes next?
Based on historical developments and the current state of the art in gas-phase transmission electron microscopy (GP-TEM), we provide a perspective covering exciting new technologies and methodologies of relevance for chemical and surface sciences. Considering thermal and photochemical reaction environments, we emphasize the benefit of implementing gas cells, quantitative TEM approaches using sensitive detection for structured electron illumination (in space and time) and data denoising, optical excitation, and data mining using autonomous machine learning techniques. These emerging advances open new ways to accelerate discoveries in chemical and surface sciences.
- NSF-PAR ID:
- 10489564
- Publisher / Repository:
- Cambridge University Press (CUP)
- Date Published:
- Journal Name:
- MRS Bulletin
- Volume:
- 49
- Issue:
- 2
- ISSN:
- 0883-7694
- Format(s):
- Medium: X Size: p. 174-183
- Size(s):
- ["p. 174-183"]
- Sponsoring Org:
- National Science Foundation
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2020 ,58 , 673–682
