Heterogeneous catalytic ozonation of organic wastewater by using bimetallic catalysts supported on honeycomb‐structured activated carbon
Bibliographic record
Abstract
Abstract Honeycomb‐structured activated carbon (HAC) is one of the most studied chemicals with large specific surface area, high porosity, unique adsorption properties, and other characteristics, which has been widely used in the fields of exhaust gas adsorption, wastewater adsorption, and catalyst support. However, the study and application of HAC in the field of wastewater treatment is not extensive. Herein, a novel HAC‐based bimetallic catalyst was prepared by incipient wetness co‐impregnation, which exhibited high catalytic activity in heterogeneous catalytic ozonation of methylene blue (MB) organic wastewater. A high MB removal of 95.43% and a chemical oxygen demand (COD) removal of 85.19% were achieved by using Cu‐La/HAC under optimized conditions. The inductively coupled plasma‐mass spectrometry (ICP‐MS) results showed that the metal dissolution concentration of Cu‐La/HAC was the lowest. Moreover, X‐ray photoelectron spectroscopy (XPS) characterization of copper‐containing catalysts indicated that the strong interaction between copper and lanthanum resulted in the low metal dissolution concentration.
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 | 0.001 | 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 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".