Hiring in life sciences? Share your open positions with our professional community. Read more Close

Advertisement

Rational design of ZrSeTe-based two-dimensional sensors for lung cancer biomarker detection.

Created on 31 Jul 2026

Authors

Suresh V Chaudhary, Nikhil M Solanki, Sanjeev K Gupta, P N Gajjar

Published in

Journal of materials chemistry. B. Jul 31, 2026. Epub Jul 31, 2026.

Abstract

The exponential growth in the population and drastic changes in the environment and living standards can have adverse effects on metabolism, which can lead to serious diseases like lung cancer. Early detection of lung cancer plays a remarkable role in its diagnosis with 68% survival rates. To tackle this problem, application of some currently used diagnosis methods is required, which can save patients from unnecessary exposure to radiation. The chemiresistive gas sensor plays crucial role as part of the e-nose sensor by detecting certain VOCs exhaled by the patients. Herein, benzene, acetone and ethanol were adsorbed on Ni-, Pd- and Pt-decorated ZrSeTe monolayers. The location of the d-centre of Ni and Pd lies near the Fermi level, making it interact strongly with the MLs compared to Pt. There is a noteworthy charge transfer from the adsorbates to the adsorbent, and work function differences are observed in all the cases. As a consequence of the flow, the charges, resistance and hence the conductivity are all changed. These variations lead to the strong electrical response. The calculated desorption times are much lower in the cases of Pt and Pd (a fraction of second and a few tens of seconds, respectively), leading to reversible gas sensors. In contrast, Ni-substitution results in a very high desorption time, making it a non-reversible sensor (however, it can be used for gas entrapment). These features collectively infer that the X@ZrSeTe (where X = Ni, Pd and Pt) monolayers may be a potential candidate for sensing benzene, ethanol and acetone, leading towards its possible use in lung cancer detection.

PMID:
42533643
Bibliographic data and abstract were imported from PubMed on 31 Jul 2026.

Read full publication at:
Please sign in to see all details.

Advertisement

Stats

  • Community rating n/a 0 votes
  • Reviewers' rating n/a 0 votes
  • Your rating

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

  • Recommendations n/a n/a positive of 0 vote(s)
  • Views 1
  • Comments 0

Recommended by

  • No recommendations yet.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.

Advertisement