Characterization of Carbon Anode Protected by Low Boron Level: An Attempt To Understand Carbon–Boron Inhibitor Mechanism
Bibliographic record
Abstract
Several chemical reactions occur during the electrolysis of alumina in the Hall–Héroult process resulting in a significant overconsumption of the carbon anode. Carbon oxidation with oxygen is one of these reactions. The inhibition of this reaction by the application of protective layers on the anode can be an effective technique to reduce carbon consumption. Boron impregnation was shown to suppress this reaction. In this study, very low boron content has been impregnated on the anode, and several characterization methods have been performed to understand the protection mechanism of such a low boron concentration during the oxidation reaction of anodes. An air reactivity test of boron-impregnated anodes has been performed at temperatures between 400 and 600 °C. The samples were characterized using XPS, Raman spectroscopy, XRD, XRF, porosimetry, and thermogravimetric analysis (TGA). TGA revealed that the total number of interactions between oxygen atoms and carbon active sites was reduced, decreasing the pre-exponential factor. Time-of-flight secondary ion mass spectroscopy (ToF-SIMS) has been employed as a highly sensitive surface characterization method to identify chemical forms of boron on the anode. It has been confirmed that boron blocks active sites of carbon by creating boron–carbon bonds, thus reducing the interaction of carbon active sites with oxygen.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".