Data-driven Sabatier-inspired screening of antiviral adsorption on N-doped ZnO nanoparticles
Surfaces and Interfaces, cilt.97, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 97
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.surfin.2026.110112
- Dergi Adı: Surfaces and Interfaces
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, INSPEC
- Anahtar Kelimeler: discriminant distance, median absolute deviation, Mermin-derived effective adsorption descriptor, N-doped ZnO nanoparticles, Sabatier-inspired screening
- Bursa Uludağ Üniversitesi Adresli: Evet
Özet
Computational adsorption screening often ranks the most negative adsorption energy as the most favorable state, although excessively strong binding may be incompatible with balanced interfacial behavior. Here, we present a data-driven, Sabatier-inspired screening strategy for identifying balanced adsorption states of oseltamivir, remdesivir, and zanamivir on N-doped ZnO nanoparticles. DFTB+/COSMO/D3(BJ) calculations were performed for two adsorption configurations across the N0-N9 dopant series, and the Mermin-derived effective adsorption descriptor (ΔFadseff) was used as the primary descriptor. The workflow combines a feasibility gate to remove globally over-binding configurations, an over-binding dispersion analysis to assess within-configuration energetic imbalance, a robust balanced interaction window derived from median–MAD statistics, and a Sabatier discriminant distance to rank feasible states by proximity to the balanced regime. Using the representative feasible pooled dataset, the framework yields a common balanced interaction window of -3.94 to -2.71 eV. Within this window, REM-P2-N2 and REM-P2-N6 show the smallest discriminant distances, followed by OSE-P2-N2 and ZAN-P2-N4, whereas OSE-P1 is excluded as a persistent over-binding configuration. These results show that the strongest adsorption state is not necessarily the most relevant one and that dopant engineering can shift adsorption toward balanced adsorption-descriptor regimes. The proposed framework provides a reproducible, dataset-derived approach for screening adsorption states on nanostructured oxide surfaces.