Removal efficiency and mechanisms of dissolved Cr(VI) using oak wood biochar
Bibliographic record
Abstract
Environmental chromium (Cr) contamination has led to serious problems in the ecosystem owning to the carcinogenicity, toxicity, and teratogenicity of Cr. In this study, the latent application of oak wood biochar for dissolved Cr(VI) removal was investigated. Optimal treatment of Cr(VI) was achieved with a removal efficiency of 99.9% at a pH 2.0. Another critical factor influencing removal efficiency was the initial concentration of Cr(VI). The removal efficiency of Cr(VI) was >99.9% at 1-50 mg L -1 ; nevertheless, Cr(VI) removal rate decreased at Cr(VI) concentrations (50-600 mg L -1 ). Five kinetic equations were used to describe the Cr(VI) removal kinetics and the best fit was the pseudo-secondorder model. Fourier transform infrared spectroscopy results showed that C-O groups of alcohol and C=O bonds may participate in the reaction. X-ray photoelectron spectroscopy analysis demonstrated that Cr is predominantly excited as Cr(III) species (~82.96%). The increase in pH values and concentrations of the cations Ca 2+ , Na + , and K + in the aqueous solution after the reaction indicated that ion exchange was likely responsible for Cr(III) binding with the biochar. Results indicated that the electrostatic force between Cr 2 O 7 2-and biochar, Cr(VI) reduction by C-O groups in alcohol, and ion exchange and complexation between Cr(III) and C=O were the reaction mechanism.
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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 | 0.001 |
| 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".