One pot peroxidation of oleic acid rich <i>Azadirachta indica</i> oil over bio‐waste derived heterogeneous catalyst
Bibliographic record
Abstract
Abstract In this work, a heterogeneous catalyst was developed from waste fish bone. ZnO was deposited onto the waste fish bone to enhance the surface properties along with ion exchangeability. After characterization, the developed catalyst was found to have a much higher surface area (217 m2 · g−1) than that of raw fish bone (10 m2 · g−1). This catalyst was used during peroxidation of oleic acid rich Azadirachta indica oil (neem oil) to observe its suitability and efficiency. A quadratic model was developed with five input variables (temperature (T), time (t), g/g of catalyst (HZn), H2O2:oil ratio (γH:FA), formic acid:oil ratio (γF:FA)) and 2 output variables (Iodine value, Oxirane Oxygen Conversion). The optimized parametric values for T, t, HZn, γH:FA, and γF:FA were found to be 60 °C, 3.88 h, 20 g/g, 19.05:1, and 19.85:1 respectively. The final epoxidized oil was characterized using FTIR and 1HNMR. The reusability of the catalyst was studied both quantitatively and qualitatively.
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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) | 0.000 | 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".