Effect of nickel promoter on solvent‐free sulphated zirconia catalyst for the esterification of acetic acid with <i>n</i>‐butanol
Bibliographic record
Abstract
Abstract A simple solvent‐free preparation method was used to synthesize sulphated zirconia catalyst with various amounts of nickel promoter. This research investigated the effect of nickel addition to sulphated zirconia on the catalyst performance in esterification of acetic acid (a bio‐oil model) with n‐butanol. The results show that the addition of nickel contributed to the increase of sulphur content that bonded with nickel, the increase of thermal stability of the catalysts, and the formation of additional sulphate groups, and thus resulted in better catalytic performance compared to sulphated zirconia without nickel. More nickel being added resulted in a significant increase of acetic acid conversion; with 0.05 mol/mol nickel amount, the conversion was 97.93 %. This result represents a dramatic increase of about 2 and 3 times compared to the conversions of non‐nickel sulphated zirconia catalyst and non‐catalyst reactions, respectively.
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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 | 0.000 |
| 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".