Activated Clay Prepared by Waste Acid Recycling: Technology and Mechanism
Bibliographic record
Abstract
We determined the optimum activation conditions and formation mechanism of activated clay prepared using a waste acid recycling method. The aim of this study was to determine the effect of the processes used on the quality of the activated clay. Key objectives were to optimize operating conditions and to determine the mechanism of acid treatment. Results of single-factor experiments showed the following optimum conditions: sulfuric acid concentration of 22%, liquid–solid ratio of 3.5, and 4 h reaction time at 90°C. Under these conditions, the performance of activated clay appears to be much better than that prepared using the traditional method. Compared with the traditional method, the waste acid recycling process consumed less sulfuric acid (up to 21%) and the amount of aluminum released during the activation processes was also lower. Improvement in clay quality was due to the presence of several types of sulfate residue in the waste acid, preventing further dissolution of the crystal structure of montmorillonite. This improved structure uniformity resulted in an activated clay with better performance. Moreover, the waste acid recycle method reduces the environmental impact and pollution due to acid reuse.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".