Using Solar-TiO2 and Biocarbon to Decompose and Adsorb Amoxicillin from Polluted Waters
Bibliographic record
Abstract
Surface water discharge of domestic sewage poses a treat mostly due to antibiotics content as amoxicillin. Its environmental presence provides the bacterial resistance enhancement and disturbance in aquatic life. The biocarbon is an organic carbon compound obtained by biomass pyrolisis at 300oC to 750oC under low oxygen environment. It is an effective adsorbent derived from agricultural and industrial solid biomass also frequently used to remove various pollutants, including dyes, pesticides, organic compounds and heavy metals from aqueous solutions. The importance of this natural material rises as low cost abundant and renewable alternative to activated carbon used on wastewater treatment application. Several technologies are employed to modify crude precursors on biocarbon preparation including chemical, physical and biological treatments with the addition of functional groups. The raw biomass material also provides some radicals and humic acids with promising water adsorbent results. The integrated process of the efficient Solar-TiO2photodecomposition followed by biocarbon adsorption resulted on 94% of amoxicillin removal percentage and avoids the toxic treatment sludge production.
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".