Enhanced Transformation of Cr(VI) by Heterocyclic-N within Nitrogen-Doped Biochar: Impact of Surface Modulatory Persistent Free Radicals (PFRs)
Bibliographic record
Abstract
Redox processes mediated by biochar(BC) enhanced the transformation of Cr(VI), which is largely dependent on the presence of PFRs as electron donors. Natural or artificial dopants in BC's could regulate inherent carbon configuration and PFRs. Until recently, the modulation of PFRs and transformation of Cr(VI) in BC by nonmetal-heterocyclic dopants was barely studied. In this study, changes in PFRs introduced by various nitrogen-dopants within BC are presented and the capacity for Cr(VI) transformation without light was investigated. It was found N-dopants were effectively embedded in carbon lattices through activated-Maillard reaction thus altering their charge and PFRs. Transformation of Cr(VI) in N doped biochar relied on mediated direct reduction by surface modulatory PFRs. The kinetic rate of transformation of Cr(VI) was increased 1.4-5 fold in N-BCs compared to nondoped BCs. Theortical calculation suggested a deficiency in surface electrons induced Lewis acid-base bonding which could acted as a bridge for electron transfer. Results of PCA and orbital energy indicated a colinear relationship between PFRs and pyrrolic N, as well as its dual-mode transformation of Cr(VI). This study provides an improved understanding of how N-doped BC contributes to the evolution of PFRs and their corresponding impacts on the transformation of Cr(VI) in environments.
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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) | 0.001 | 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 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".