Removal of direct Red‐31 and direct Orange‐26 by low cost rice husk: Influence of immobilisation and pretreatments
Bibliographic record
Abstract
Abstract The aim of the present study is to investigate the influence of free, carboxymethyl cellulose (CMC) immobilised, PVA–alginate immobilised, and HCl treated rice husk on the removal of Direct Red‐31 and Direct Orange‐26 dyes. The biosorption capacity of the rice husk increased with HCl treatment (67.39 and 45.34 mg/g) and decreased with PVA–alginate immobilisation (9.73 and 10.03 mg/g) as compared to the free biomass (65.56 and 45.58 mg/g) at 200 mg/L dye concentration for Direct Red‐31 and Direct Orange‐26, respectively. Equilibrium data were best described by Langmuir Type 1 for Direct Red‐31 and Direct Orange‐26 (free, CMC immobilised, PVA–alginate immobilised, and HCl treated). Best correlation coefficients for Direct Red‐31 and Direct Orange‐26 using free, CMC immobilised, PVA–alginate immobilised, and HCl treated rice husk were obtained for pseudo‐second order and Elovich kinetic models. Values of Gibbs free energy (ΔG°) and enthalpy change (ΔH°) indicated that reaction was spontaneous and endothermic in nature at the studied temperatures. FT‐IR studies showed the involvement of carbonyl, carboxyl, and amide groups in the biosorption process. SEM exhibited the morphological changes on the biosorbent surface and BET analysis to determine the surface area is also carried out.
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".