Hexagonal Boron Nitride for Sulfur Corrosion Inhibition
Bibliographic record
Abstract
Abstract Corrosion by sulfur compounds is a long-standing challenge in many engineering applications. Specifically, designing a coating that protects metals from both abiotic and biotic forms of sulfur corrosion remains an elusive goal. Here we report that atomically thin layers (∼4) of hexagonal boron nitride (hBN) act as a protective coating to inhibit corrosion of the underlying copper (Cu) surfaces (∼6–7-fold lower corrosion than bare Cu) in abiotic (sulfuric acid and sodium sulfide) and biotic (sulfate-reducing bacteria medium) environments. The corrosion resistance of hBN is attributed to its outstanding barrier properties to the corrosive species in diverse environments of sulfur compounds. Increasing the number of atomic layers did not necessarily improve the corrosion protection mechanisms. Instead, multilayers of hBN were found to upregulate the adhesion genes in Desulfovibrio alaskensis G20 cells, promote cell adhesion and biofilm growth, and lower the protection against biogenic sulfide attack when compared to the few layers of hBN. Our findings confirm hBN as the thinnest coating to resist diverse forms of sulfur corrosion.
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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".