Adsorption mechanism modeling using lead (Pb) sorption data on modified rice bran-insoluble fiber as universal approach to assess other metals toxicity
Bibliographic record
Abstract
The binding capacity of Pb on modified rice bran-insoluble fibers RBIF-0.2, RBIF-1.25, and RBIF-2.0 (0.2%, 1.25%, and 2.0% H2SO4 with 1.25% KOH) having the variable specific surface area and cation-exchange capacity was evaluated. The Pb binding in terms of maximum binding capacity (BCmax) with RBIF increased with pH (2–7). However, the presence of sodium and calcium reduced the binding of Pb to RBIFs. Further, adsorption kinetics followed pseudo-second-order kinetic model when fitted to real Pb sorption data. However, sorption mechanism data (scanning electron microscopy images) were well fitted to Langmuir model instead of Freundlich and Dubinin–Radushkevich that suggests monomolecular adsorption of Pb to RBIF (<8 kJ/mol). Moreover, negative ΔG and positive ΔH of Pb2+ sorption confirmed the occurrence of spontaneous and endothermic process. The modeling of single-metal (Pb) data will be advantageous to use as a universal model for other heavy metals that will save the experimental costs. The data on all other heavy metals could be used with chosen models to elucidate their sorption mechanism to control the metallic-induced toxicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".