The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Dissolved gas analysis in transformer oil using Pd catalyst decorated MoSe2 monolayer: A first-principles theory
Abstract
Abstract Dissolved gas analysis (DGA) by high-sensitive gas sensors is an effective manner to estimate the operation status of electrical transformers. In this paper, based on first-principles calculations, a novel 2D material, Pd-doped MoSe2 (Pd-MoSe2) monolayer, is explored as gas sensor or scavenger for detection or removal of typical gases in transformer oil, including H2, CO and C2H2, to guarantee the safety operation for the transformers. Adsorption configuration, density of state (DOS), recovery property, band structure and frontier molecular orbital theory are used to fully investigate the adsorption and desorption behaviors for Pd-MoSe2/gas systems. It is found that Pd-MoSe2 monolayer possesses strong adsorption performance towards CO molecule, thus allowing the removal of such pollutant to guarantee the safety operation of transformers. Besides, Pd-MoSe2 monolayer would be a promising candidate for C2H2 sensing due to the desirable adsorption and desorption behaviors. However, the poor adsorption behavior of Pd-MoSe2 monolayer upon H2 molecule even at ambient temperature reveals its unsuitability for H2 detector. Our results not only expound the transition metal doping effect on pure MoSe2 monolayer, but also provide a first insight into the potential application of Pd-MoSe2 monolayer in the field of electrical engineering.
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Cites
From molecules to solids with the DMol3 approach
Delley, B. (2000) · The Journal of Chemical Physics · doi:10.1063/1.1316015 · Cited by 11,026
The chemistry of two-dimensional layered transition metal dichalcogenide nanosheets
Chhowalla, M., Shin, H. S., Eda, G., Li, L.-J., Loh, K. P., & Zhang, H. (2013) · Nature Chemistry · doi:10.1038/nchem.1589 · Cited by 10,034
Molecular Single‐Bond Covalent Radii for Elements 1–118
Pyykkö, P., & Atsumi, M. (2009) · Chemistry – A European Journal · doi:10.1002/chem.200800987 · Cited by 2,318
Hardness conserving semilocal pseudopotentials
Delley, B. (2002) · Physical Review B · doi:10.1103/physrevb.66.155125 · Cited by 1,847
Tongay, S., Zhou, J., Ataca, C., Lo, K., Matthews, T. S., Li, J., Grossman, J. C., & Wu, J. (2012) · Nano Letters · doi:10.1021/nl302584w · Cited by 1,428
Sensing Behavior of Atomically Thin-Layered MoS2 Transistors
Late, D. J., Huang, Y.-K., Liu, B., Acharya, J., Shirodkar, S. N., Luo, J., Yan, A., Charles, D., Waghmare, U. V., Dravid, V. P., et al. (2013) · ACS Nano · doi:10.1021/nn400026u · Cited by 1,374
Zhang, Y.-H., Chen, Y.-B., Zhou, K.-G., Liu, C.-H., Zeng, J., Zhang, H.-L., & Peng, Y. (2009) · Nanotechnology · doi:10.1088/0957-4484/20/18/185504 · Cited by 1,331
Self-citationChemical Vapor Sensing with Monolayer MoS2
Perkins, F. K., Friedman, A. L., Cobas, E., Campbell, P. M., Jernigan, G. G., & Jonker, B. T. (2013) · Nano Letters · doi:10.1021/nl3043079 · Cited by 1,138
Phosphorene as a Superior Gas Sensor: Selective Adsorption and Distinct I–V Response
Kou, L., Frauenheim, T., & Chen, C. (2014) · The Journal of Physical Chemistry Letters · doi:10.1021/jz501188k · Cited by 1,045
Ab initio study of CNT NO2 gas sensor
Peng, S., Cho, K., Qi, P., & Dai, H. (2004) · Chemical Physics Letters · doi:10.1016/j.cplett.2004.02.026 · Cited by 782
Cited by
First-principles insight into Ni-doped InN monolayer as a noxious gases scavenger
Cui, H., Zhang, X., Li, Y., Chen, D., & Zhang, Y. (2019) · Applied Surface Science · doi:10.1016/j.apsusc.2019.07.218 · Cited by 325
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Metadata
- Authors
- Cui, H., Chen, D., Zhang, Y., & Zhang, X.
- Venue
- Sustainable Materials and Technologies
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 20
- Pages
- e00094
- ISSN
- 2214-9929 (ISSN-L)
- 2214-9937
- OpenAlex
- W2913967905
- Cited by
- 253
- References
- 30
- FWCI
- 11.90
- Type
- journal article
- Language
- en
- Open access
- closed
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