Application of neural network in metal adsorption using biomaterials (BMs): a review
Bibliographic record
Abstract
With growing environmental consciousness, biomaterials (BMs) have garnered attention as sustainable materials for the adsorption of hazardous water contaminants. These BMs are engineered using surface treatments or physical alterations to enhance their adsorptive properties. The lab-scale methods generally employ a One Variable at a Time (OVAT) approach to analyze the impact of biomaterial modifications, their characteristics and other process variables such as pH, temperature, dosage, etc., on the removal of metals via adsorption. Although implementing the adsorption procedure using BMs seems simple, the conjugate effects of adsorbent properties and process attributes implicate complex nonlinear interactions. As a result, artificial neural networks (ANN) have gained traction in the quest to understand the complex metal adsorption processes on biomaterials, with applications in environmental remediation and water reuse. This review discusses recent progress using ANN frameworks for metal adsorption using modified biomaterials. Subsequently, the paper comprehensively evaluates the development of a hybrid-ANN system to estimate isothermal, kinetic and thermodynamic parameters in multicomponent adsorption systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".