A Facile Green Synthetic Route for the Preparation of Highly Active γ-Al2O3 from Aluminum Foil Waste
Bibliographic record
Abstract
Abstract A novel green preparation route to prepare nano-mesoporous γ-Al2O3 from AlCl3.6H2O derived from aluminum foil waste and designated as ACFL550 is demonstrated, which showed higher surface area, larger pore volume, stronger acidity and higher surface area compared to γ-Al2O3 that is produced from the commercial AlCl3 precursor, AC550. The produced crystalline AlCl3.6H2O and Al(NO3)3.9H2O in the first stage of the preparation method were characterized by single-crystal XRD, giving two crystal structures, a trigonal (R-3c) and monoclinic (P21/c) structure, respectively. EDX analysis showed that ACFL550 had half the chlorine content (Cl%) relative to AC550, which makes ACFL550 a promising catalyst in acid-catalysed reactions. Pure and modified ACFL550 and AC550 were applied in acid-catalysed reactions, the dehydration of methanol to dimethyl ether and the total methane oxidation reactions, respectively. It was found that ACFL550 showed higher catalytic activity than AC550. This work opens doors for the preparation of highly active and well-structured nano-mesoporous alumina catalysts/supports from aluminum foil waste and demonstrates its application in acid-catalysed reactions.
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) | 0.000 | 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".