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.