Green Nanoengineered Keratin Derived Bio‐Adsorbent for Heavy Metals Removal from Aqueous Media
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.
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) | 0.000 | 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, 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".