Development of Pollutant-Targeted Recognition Slow-Release Materials and Research on Petrochemical Wastewater Treatment
Bibliographic record
Abstract
In response to the trailing and rebounding problems caused by conventional remediation technologies for NAPL pollutants in groundwater at petrochemical contaminated sites, this study developed a modified cellulose as a safe, non-toxic and biodegradable embedding matrix, and combined it with advanced oxidants such as potassium permanganate, persulfate, and nZVI-activated persulfate as active remediation agents.The effective embedding of advanced oxidants was achieved by using organic phase separation method to achieve sustained release of oxidants, and targeted recognition and directional enrichment of organic pollutants.A solvent recycling process for the organic phase separation assembly system was constructed to significantly reduce the production cost of pollutant-targeted recognition slow-release materials.The surface properties of the modified cellulose matrix in the slow-release material and its mechanism of action in targeted recognition of pollutants were studied, and the law of the surface modification process and targeted recognition of pollutants was characterized.The dissolution mechanism and law of typical pollutants in petrochemical contaminated sites were studied to elucidate the release mechanism of targeted recognition slow-release materials for pollutants, and to establish a quantitative relationship between the slow-release rate, slow-release time, material ratio assembly process, and material ratio, to achieve quantitative control of the slow-release performance of pollutant-targeted recognition slow-release materials.This study has significant implications for the development of effective and sustainable remediation technologies for NAPL pollutants in groundwater at petrochemical contaminated sites, and provides a theoretical basis and technical support for the practical application of pollutant-targeted recognition slow-release materials.
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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 | 0.001 |
| 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".