Atomistic Analysis of the Microbial Influence on the Adsorption Characteristic of Sulfur, Hydrogen, and SO<sub>4</sub>on Iron Surfaces
Bibliographic record
Abstract
The corrosion of metals is enhanced by the presence of microbes on the surface; however, the mechanisms governing this impact are poorly understood. This study used a molecular modeling approach to understand microbial activity on a metal surface. As microbes can remove protons from the biofilm or consume electrons from the iron surface, this electron transfer activity is modeled in density functional theory as an external electric field. Three corrosion-causing components, (i) sulfur, (ii) hydrogen, and (iii) SO 4, are studied over iron surfaces. It is observed that the adsorption energy of SO 4, sulfur, and hydrogen on Fe (100), Fe (110), and Fe (310) surfaces increased with the applied electric field, irrespective of the direction (positive/negative) of the electric field. The Fe (110) surface is more stable than the Fe (100) and Fe (310) surfaces for the adsorption of sulfur and SO 4, even at higher electric fields and concentrations of the components. This confirms the common understanding that Fe (110) is a higher corrosion resistive compared to Fe (100) and Fe (310). Further investigation is performed using population analysis, electronic density of states, and density difference. This analysis confirms Fe (110) microbial corrosion resistance characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".