JOURNAL ARTICLE

An In-depth Analytical Approach to Unfold Material-level Insights in Pristine and Platinum-doped MoSe2 Nanosheet During Carbon Monoxide Detection.

  • Published In: Journal of Active & Passive Electronic Devices, 2025, v. 19, n. 3. P. 217 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: MAITY, INDRANIL; MAJUMDAR, AHELI; MAITY, SUVOJIT; MAITY, INDRAJIT 3 of 3

Abstract

This article aims to explore and compare the sensing capability of pristine MoSe2 nanosheet (NS) and platinum (Pt)-doped MoSe2 NS towards toxic carbon monoxide (CO) gas. First-principles-based calculations were performed to analyze the adsorption characteristics of CO on pristine MoSe2 (System I) and Pt-doped MoSe2 (System II), utilizing the Gaussian 09W and GaussView 6.0 software packages. Various crucial electrochemical properties, such as binding distance, adsorption energy, Mulliken charge profile, band structure, density of states (DOS), projected DOS (PDOS), electron density difference (EDD), X-Ray Diffraction (XRD) spectra, and several global sensing parameters, were evaluated. System I exhibited better adsorption ability (with an adsorption energy value of -1.981 eV), compared to System II (-0.536 eV). Moreover, System I also demonstrated slightly enhanced sensitivity (up to 99.79%) in comparison to System II (i.e., 99.56%). These results indicate that pristine MoSe2 NS would be a better choice for preparing high-performance CO gas sensor devices in future instead of incorporating foreign Pt dopant. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Active & Passive Electronic Devices. 2025/11, Vol. 19, Issue 3, p217
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2025
  • ISSN:15550281
  • Accession Number:190242880
  • Copyright Statement:Copyright of Journal of Active & Passive Electronic Devices is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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