Surface Activity and Chemistry of Thermal Carbon Blacks
Bibliographic record
Abstract
Abstract The surface energy of thermal and furnace carbon blacks was determined by inverse gas chromatography (IGC) at infinite dilution. In general, the specific surface energy decreases with decreasing carbon black specific surface area. However, there is also an influence of the concentration of impurities during the carbon black production. The surface energy decreases with decreasing concentration of impurities. The carbon black surface and bulk chemistry was studied by electron spectroscopy for chemical analysis (ESCA), secondary ion mass spectroscopy (SIMS) and Raman spectroscopy. Scanning tunnelling microscopy (STM) was used for characterization of the surface morphology. Thermal grades of carbon black produced from high purity natural gas feedstock do not contain fewer surface functional groups than the other grades. No correlation between the concentration and nature of the oxygen and sulphur surface groups and the carbon black surface energy was found. Instead, a correlation between the surface energy and the polyaromatic character of the carbon black surface exists. Both increased in the order: thermal blacks from high purity natural gas feedstock < thermal black from oil feedstock < furnace blacks. The increase of the surface energy might be related to the formation of active sites which are formed upon removal of non-carbon elements during the carbon black formation. There was no principal difference in the surface morphology of thermal blacks from high purity gas feedstock and other blacks.
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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 | 0.000 |
| 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 | 0.000 |
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".