Red mud (aluminum industrial waste): An eco‐friendly treatment of electroplating effluent
Bibliographic record
Abstract
Abstract In this work, aluminum industrial waste, red mud (RM), was activated to verify its potential in the management of electroplating wastewater containing hexavalent chromium (Cr(VI)). A comparison between the adsorption capabilities of RM and activated red mud (ARM) towards Cr(VI) from aqueous solutions was made. The effects of several parameters were evaluated. The adsorbents were characterized by field emission scanning electron microscopy (FESEM), Fourier transmission infrared spectroscopy (FTIR), x‐ray diffraction (XRD), zeta potential, and thermogravimetric analysis (TGA). The particle size was observed as 23.59 nm. The ARM demonstrated an acceptable adsorption capacity of 25.641 mg/g at a pH of 2, adsorbent dosage of 2 g/L, initial Cr(VI) concentration of 100 mg/L, at 25°C. The experimental data is in good agreement with Langmuir adsorption isotherm. The kinetic study was performed to verify that the adsorption follows pseudo‐second‐order kinetics. In addition, the ARM showed decent recyclability for adsorbing Cr(VI) as even after three adsorption cycles, and the adsorption capacity was reduced by ~30%. The results recommend ARM to be an efficient and cost‐effective adsorbent for Cr(VI) removal from industrial wastewater.
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".