A Preliminary Molecular Simulation Study on the Use of HS− as a Parameter to Assess the Effect of Surface Deposits on the SRB-initiated Pitting on Metal Surfaces
Bibliographic record
Abstract
Abstract Studies in microbiologically influenced corrosion (MIC) have reported on the effects of pre-corrosion surface deposits on the localized pitting which occurs on metals. But due to the complexity and heterogeneity of these deposits, which include biofilms, it is necessary to investigate how the components of these deposits and the conditions therein influence the formation of pits on the metal surfaces. To gain a better understanding of the occurrence and growth of pits under these deposits, it is imperative to consider their interactions with the metal surface at the atomistic level. In this work, molecular modelling is used to study these interactions, with the focus being on parameterizing the role of HS− in microbiologically influenced pitting. The bond length of HS− is used as a predictive parameter in the molecular model to study the MIC interface. It is observed that changes in the HS− bond length denote HS− reactivity and the subsequent production of sulfides which are the main by-products of MIC. This study also shows how changes in temperature impact HS− reactivity and thus MIC activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".