Effect of the Co( <scp>II</scp> )@ <scp>AC</scp> catalyst with the activated carbon modified by zinc chloride as a carrier on the synthesis of 1‐(4‐chlorophenyl)‐ <scp>1H</scp> ‐pyrazol‐3‐ol from the oxidation of 1‐(4‐chlorophenyl)‐pyrazolidin‐3‐one by oxygen
Bibliographic record
Abstract
Abstract The oxidation of 1‐(4‐chlorophenyl)‐pyrazolidin‐3‐one to 1‐(4‐chlorophenyl)‐1H‐ pyrazol‐3‐ol by air can be accelerated by the Co(II)@AC catalyst prepared by loading Co(II) on activated carbon with wet impregnation method. ZnCl 2 solution has been used to treat activated carbon to ameliorate the catalytic ability of Co(II)@AC catalyst. The experiments indicate that the optimal catalyst is made by immersing activated carbon in 0.7 mol/L ZnCl 2 solution at 50°C for 6 h with a liquid/solid ratio of 30/1 (mL/g) followed being calcined at 700°C for 5 h at a heating rate of 5°C/min under N 2 protection. The 1‐(4‐chlorophenyl)‐1H‐pyrazol‐3‐ol concentration catalyzed by the catalyst loaded on the best carbon reaches 0.01953 kg/L, which is 27.56% higher than that catalyzed by the catalyst loaded on the original carbon. The characterization results manifest that the modification with ZnCl 2 enriches the porous structure and increases the acidic groups on the surface of activated carbon. The conversion of 1‐(4‐chlorophenyl)‐pyrazolidin‐3‐one is mainly determined by the physical structure of the activated carbon while the selectivity to 1‐(4‐chlorophenyl)‐1H‐ pyrazol‐3‐ol is largely affected by the chemical characteristics of the activated carbon.
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) | 0.001 | 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".