Strengthening surface oxygen vacancies with triblock copolymers to balance nitrate and water adsorption for efficient electrochemical denitrification
Bibliographic record
Abstract
Abstract Reducing nitrate (NO 3 − ) to ammonia (NH 3 ) through electrochemical methods and recovering or post‐treatment ammonia is an environmentally friendly approach for denitrification of high‐salt wastewater. Compounds based on copper (Cu) and cobalt (Co) have been proven to effectively electrochemically reduce NO 3 − , but modification is still needed during the preparation process to improve the adsorption performance of NO 3 − on their surfaces. In this study, P123 was used as a structure‐directing agent to synthesize a Cu 2+ doped flower‐like Co 3 O 4 composite through the hydrothermal method. The addition of P123 is beneficial for increasing the proportion of Cu(0) and can expose more oxygen vacancies on the surface. Compared to the sample without P123 modification, not only does the electron transfer resistance decrease, but the electrochemical surface area also increases by 2.4 times. The results of in‐situ infrared spectroscopy indicate that the increased oxygen vacancies facilitate the adsorption of NO 3 − , which improves the NO 3 − removal rate constant by 2.5 times at a current density of 10 mA cm −2 . The application of P123‐Cu x Co 3‐x O 4 /NF in the treatment of actual coking wastewater results in a removal of NO 3 − ‐N from 489 to 5 mg L −1 , in compliance with WHO emission standards. Moreover, benefiting from the high concentration of Cl − in the wastewater mediated by the anode, the total nitrogen removal can reach 99%, accompanied by 86% chemical oxygen demand removal. The P123‐Cu x Co 3‐x O 4 /NF cathode has shown great application prospects in denitrification of high salt wastewater.
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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".