The Renewable of Low Toxicity Gelcasting Porous Ceramic as Fe2O3 Catalyst Support on Phenol Photodegradation
Bibliographic record
Abstract
Low toxicity gelcasting water-based method was conducted using a non-acrylamide system, with cassava starch and natural clay as poregenic agent and raw material, respectively, and the resulting porous ceramic was used as Fe2O3 catalyst support. The concentrations of cassava starch used were 1%, 3%, 5%, 7%, and 9%, the sintering process was performed based on the results of TGA/DTA thermal analysis. In addition, the sol-gel coating method was used to impregnate the Fe2O3 catalyst into the porous ceramic. BET results show that the pores formed in the ceramic body are micro pores with a size range of 21.41-23.27 Å, hence the concentration of cassava starch does not affect the pore characteristics. The morphology of SEM results also indicated the presence of pore formation in the ceramic body. According to the quantitative XRD analysis, the cassava concentration affects the percentage of catalyst impregnated. The highest percentage of Fe2O3 catalyst on 7% cassava starch was 41.15% and the phase of the catalyst successfully impregnated was α-Fe2O3with a rhombohedral structure. In addition, the highest percentage of phenol degradation was 59.15% with good performance after 8 times of recycling. In this study, we provide for the first time, utilization of Fe2O3-porous ceramics with a wonderful performance of recycling ability in the process of phenol photodegradation.
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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".