Green Nanoengineered Keratin Derived Bio‐Adsorbent for Heavy Metals Removal from Aqueous Media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Exploiting poultry chicken feathers, a keratin‐rich by‐product offers a sustainable raw material for bio‐adsorbents in water remediation. This study developed a bio‐adsorbent from chicken feathers keratin (CFK), functionalized with surface‐modified graphene oxide (SMGO). The bio‐adsorbent was tested for adsorbing metal cations (Pb, Cd, Ni, Zn, Co) and oxyanions (As, Se, Cr) from water contaminated with 600 µg/L of each metal at pH 5.5, 7.5, and 10.5. Results showed optimal removal efficiencies at pH 7.5, with anions achieving ≥91.10% for As (III), ≥89.55% for Cr (VI), and ≥74.33% for Se (IV). Cations removal reached 96.34% for Co (II), 97.36% for Ni (II), 99.03% for Cd (II), 99.21% for Pb (II), and 59.06% for Zn (II). Kinetic studies indicated rapid initial uptake within the first 6 hours, reaching equilibrium at 24 hours. The bio‐adsorbent maintained high adsorption capacities over four regeneration cycles with minimal efficiency loss, showing strong stability and reusability. Removal efficiency followed the order: Pb (II) 〉 Cd (II) 〉 Ni (II) 〉 Co (II) 〉 Zn (II), correlating with their ionic radii. Ni 2+ adsorbed more effectively than Co 2+ due to a smaller ionic radius and stronger electrostatic attraction. These findings highlight CFK‐SMGO's efficacy in wastewater treatment, promoting bio‐based sustainable adsorbents.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it