<scp>d</scp>-Phenylalanine Alleviates the Corrosion by <i>Desulfovibrio vulgaris</i> in Saline Water
Bibliographic record
Abstract
A biofilm is a major contributor to microbiologically influenced corrosion (MIC) in cooling water systems, resulting in severe economical and environmental impacts. d -Amino acids offer a potential alternative for preventing biofilm formation in these systems, where salinity levels vary due to diverse water sources, such as freshwater and diluted seawater. However, the impact of d -amino acids on corrosion inhibition under saline conditions remains unexplored. In this study, we evaluated the effect of d -phenylalanine ( d -Phe) on corrosion by Desulfovibrio vulgaris at three salinity levels. d -Phe (10 mg/L) played little role in corrosion inhibition at low salinity (5 g/L) but obviously decreased the corrosion by 40.6% and 59.6% at moderate salinity (15 g/L) and high salinity (20 g/L), respectively. It was attributed to that d -Phe reduced the secretion of extracellular protein from 292.5 μg/mg to 245.6 μg/mg and decreased the biofilm thickness from 25.46 μm to 20.87 μm on the coupon surface. Besides, d -Phe decreased the sessile cells from 15.1 × 10 7 cells/cm 2 to 12.8 × 10 7 cells/cm 2 at high salinity. Furthermore, transcriptome analysis found that indole, the signal molecule negatively regulating the biofilm formation, was increased by adding d -Phe at high salinity. Moreover, peptidoglycan reorganization was strengthened at high osmotic pressure via absorbing additional d -Phe, leading to weak bacterial adhesion. The work provides mechanistic insights into the application of d -Phe for biofilm inhibition and MIC mitigation in industries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".