Reusable magnetite nanoparticles–biochar composites for the efficient removal of chromate from water
Bibliographic record
Abstract
Abstract Biochar (BC) and magnetite (Fe3O4) nanoparticles (MNP) have both received considerable recent attention in part due to their potential use in water treatment. While both are effective independently in the removal of a range of anionic metals from aqueous solution, the efficacy of these materials is reduced considerably at neutral pH due to decreased metal adsorption and MNP aggregation. In addition to synthetic metal oxide–biochar composites for use in treatment and remediation technologies, aggregates may also occur in nature when pyrolytic carbon is deposited in soils. In this study, we tested whether magnetite synthesized in the presence of biochar leads to increased removal efficiency of hexavalent chromium, Cr(VI), at the mildly acidic to neutral pH values characteristic of most natural and contaminated aqueous environments. To do so, magnetite nanoparticles and biochar produced from ground willow were synthesized to form composites (MNP–BC). Batch studies showed that MNP–BC markedly enhanced both adsorption and reduction of Cr(VI) from aqueous solution at acidic to neutral pH as compared to MNP and BC separately, suggesting a strong synergetic effect of hybridizing Fe3O4 with BC. Mechanistically, the Cr(VI) removal processes occurred through both adsorption and intraparticle diffusion followed by reduction to Cr(III). Synchrotron-based X-ray absorption spectroscopy analyses confirmed that Cr(VI) was reduced at the surface of MNP–BC, with electrons derived directly from both biochar and magnetite at low pH, while at near-neutral pH, biochar increased Cr(VI) reduction by inhibiting MNP aggregation. Extended X-ray absorption fine structure fitting results confirmed that the Cr(III) precipitates consist of Cr(OH)3 and chromite (Cr2FeO4) nanoparticles. Our results demonstrate that MNP–BC composites have great potential as a material for the treatment of chromate-containing aqueous solutions across a wide range of pH values, and provide information valuable broadly relevant to soils and sediments that contain biochar.
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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) | 0.000 | 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".