Insights into simultaneous adsorption and oxidation of antimonite [Sb(III)] by crawfish shell-derived biochar: spectroscopic investigation and theoretical calculations
Bibliographic record
Abstract
Abstract Removal of antimonite [Sb(III)] from the aquatic environment and reducing its biotoxicity is urgently needed to safeguard environmental and human health. Herein, crawfish shell-derived biochars (CSB), pyrolyzed at 350, 500, and 650 ° C, were used to remediate Sb(III) in aqueous solutions. The adsorption data best fitted to the pseudo-second-order kinetic and Langmuir isotherm models. Biochar produced at 350 ° C (CSB350) showed the highest adsorption capacity (27.7 mg g − 1 ), and the maximum 78% oxidative conversion of Sb(III) to Sb(V). The adsorption results complemented with infrared (FTIR), X-ray photoelectron (XPS), and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy analyses indicated that the adsorption of Sb(III) on CSB involved electrostatic interaction, surface complexation with oxygen-containing functional groups (C = O, O = C–O), π–π coordination with aromatic C = C and C–H groups, and H-bonding with –OH group. Density functional theory calculations verified that surface complexation was the most dominant adsorption mechanism, whilst π–π coordination and H-bonding played a secondary role. Furthermore, electron spin resonance (ESR) and mediated electrochemical reduction/oxidation (MER/MEO) analyses confirmed that Sb(III) oxidation at the biochar surface was governed by persistent free radicals (PFRs) (•O 2 − and •OH) and the electron donating/accepting capacity (EDC/EAC) of biochar. The abundance of preferable surface functional groups, high concentration of PFRs, and high EDC conferred CSB350 the property of an optimal adsorbent/oxidant for Sb(III) removal from water. The encouraging results of this study call for future trials to apply suitable biochar for removing Sb(III) from wastewater at pilot scale and optimize the process. Graphical abstract
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".