Feather keratin-based hybrid adsorbents: Reproducibility and efficacy in metal removal from synthetic wastewater
Bibliographic record
Abstract
This study investigates the reproducibility of two effective keratin-based biopolymers (KBPs), modified through specific chemical processes, and their subsequent combination for the simultaneous removal of multiple metals from synthetic wastewater. The KBPs modified with an ionic liquid and propylamine are designated as KBP-IV and KBP-V, respectively. Under simulated industrial conditions, both KBP-IV and KBP-V exhibited high adsorption capacities, effectively removing oxyanions (e.g., V 5+ , Cr 6+ , As 3+ ) and divalent cations (e.g., Ni 2+ , Co 2+ , Cd 2+ ), respectively. The surface characteristics of the triplicate modifications of each KBP-IV or KBP-V in this study demonstrated consistent thermal properties, crystallinity, and functional groups. These results confirm the reproducibility of these materials, as reflected in the consistent metal adsorption outcomes across the various modifications. A hybrid adsorbent (KBP-IX) was produced by combining KBP-IV and KBP-V in a 1:1 ratio to evaluate their combined effectiveness in multi-metal removal. KBP-IX adsorbed all divalent cations and oxyanions, with better divalent cation removal than KBP-V but lower oxyanion removal than KBP-IV. Isotherm studies were performed using several two and three-parametric nonlinear isotherm models for the adsorption of V 5+ and Cr 6+ on KBP-IX. Cr 6+ adsorption was aligned more closely with the Redlich-Peterson isotherm model (χ 2 = 1.94E-5, R 2 = 0.9986), which incorporates elements of both the Langmuir (monolayer) and Freundlich (multilayer) models, while V 5+ was better represented by the Langmuir (monolayer) isotherm model (χ 2 = 1.75E-4, R 2 = 0.9838). The findings of this work underscore the promising potential of these KBPs for valorization and their reproducibility in effectively removing divalent cations and oxyanions from synthetic industrial wastewater.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".