Combined Treatment of Coking Wastewater with N-Ce-TiO2 and Modified Inferior Coal Char
Bibliographic record
Abstract
Treatment of coking wastewater simulated by phenol with coconut shell modified Shanxi coal and N-Ce-TiO2.The SEM of modified char showed that the particles were smaller and rougher than Shanxi coal.But the specific surface area larger.The SEM of TiO2 showed that the particles of N-Ce-TiO2 was smaller than others, the particles of Ce-TiO2 between two parties, and the conclusion is the same to the BET.NH3-TPD showed that the N and Ce can made the adsorption performance improved.UV-VIS N and Ce reduced the band gap and made the catalytic performance enhanced of visible light.potency:300mg/L,char:4g/L,pH=1, the efficiency of modified char is 62.5 %; potency: 20 mg/L, N-Ce-TiO2:4 g/L, pH=2,the efficiency of N-Ce-TiO2 is 69 %.The efficiency of co-processing is better than that of single treatment and the BOD/COD of phenol wastewater about 600mg/L was more than 0.3.The application experiment of simulated wastewater shows that modified char and N-Ce-TiO2 can treat phenol wastewater more efficiently and improve biodegradability, to lay a foundation for subsequent treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".