A1 Refereed original research article in a scientific journal

First-Principles Structure Search Study of 17-β-Estradiol Adsorption on Graphene




AuthorsSippola, Saara; Todorović, Milica; Peltola, Emilia

PublisherAmerican Chemical Society

Publication year2024

JournalACS Omega

Journal name in sourceACS Omega

Volume9

Issue32

First page 34684

Last page34691

DOIhttps://doi.org/10.1021/acsomega.4c03485

Web address https://doi.org/10.1021/acsomega.4c03485

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/457462293


Abstract
17-Beta-estradiol (E2), a steroid hormone synthesized from cholesterol, has various impacts on health and the environment. Currently, the gold standard for its measurement in the body is a conventional blood test (mass spectrometry), but carbon-based electrochemical sensors have been proposed as an alternative due to their advantages, such as rapid analysis time and sensitivity. To improve the atomic-level understanding of the interactions at the substrate surface, we performed density functional theory (DFT) simulations to study the nature of the adsorption of E2 on pristine graphene. Bayesian Optimization Structure Search (BOSS) was employed to reduce human bias in the determination of the most favorable adsorption configurations. Two stable adsorption minimum configurations were found. Analysis of their electronic properties indicates that E2 physisorbs on graphene. Embarking upon complex carbonaceous materials, the importance of finding all possible minimum candidates with automated structure search tools is highlighted. Computational investigations facilitate tailoring substrate materials with outstanding performance and applications in neuroscientific research, fertility monitoring, and clinical trials. Combining them with experimental research carries significant potential to advance sensor design beyond the current state-of-the-art.

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Funding information in the publication
This researchhas been supported by the Research Council of Finland (grant#347021). The work was conducted under the #SUSMATumbrella.


Last updated on 2025-23-04 at 11:33