Arsenic (III) and (V) remediation in water using a particulate photocatalytic carbon nitride (CNx) system
Bibliographic record
Abstract
• Modified carbon nitride (CNx) for arsenic water remediation. • Dual function of photooxidation of As(III) and adsorption of As species. • Up to 97 % remediation of As(III) under photooxidation conditions. • Use of colorimetric and electrochemical techniques for As speciation. Carbon nitride (CN x ) has recently gained widespread attention as a greener material for water remediation. The inherent biocompatibility of CN x coupled with its adsorption and photocatalytic abilities underscore its potential utilization to overcome the major challenge of arsenic (As) contamination in water. Nonetheless, CN x typically exhibits poor dispersibility in aqueous media, suppressing its effectiveness as both adsorbent and photocatalyst. Herein, we present the use of CN x and its modified counterpart as effective agents for remediating As(III) and As(V) in water. Our results reveal that both benchmark CN x (CN x0 ) and the modified CN x (CN x50 ) exhibit promising As remediation effectiveness driven by photooxidation and adsorption. CN x0 exhibits strong affinity for As(III) with an adsorption effectiveness of 89 %, whereas CN x50 demonstrates an adsorption effectiveness of 76 % for As(V). Notably, CN x50 outperforms CN x0 in the photoconversion of As(III) into As(V) under simulated solar irradiation and quasi-monochromatic irradiation due to its lower amount of trapped charges and improved water dispersibility. In fact, the more toxic As(III) species is remediated by CN x50 with an efficiency of ≥ 95 %, through the combination of photooxidation and adsorption. Overall, this study emphasizes, for the first time, the potential of CN x to integrate both photocatalysis and adsorption processes for As remediation, providing a simplistic approach to addressing As contamination in water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".