Bibliographic record
Abstract
The presence of microconstituents (MCs) in the environment has become an emerging concern to scientists and engineers. Sorption is one of the important removal mechanisms for MCs in wastewater treatment plants (WWTPs) since there is significant sludge production. The purpose of this study is to explore sorption kinetics and isotherms of MCs onto primary sludge. Three MCs, bisphenol-A (BPA), 17-α-ethinylestradiol (EE2) and triclosan (TCS), were chosen for this study. They are hydrophobic and have low vapor pressure, which makes sorption a potential removal mechanism. Both sorption kinetics and isotherm experiments were conducted using primary sludge collected from a local municipal WWTP. The time to equilibrium was around 7 h for all chosen MCs. A pseudo second-order rate model was better at describing the sorption rate than a pseudo first-order rate model. Linear sorption isotherm models were found to fit the experimental data, and the solid-liquid partitioning coefficients for BPA, EE2 and TCS were 81, 728 and 6,407 L/kg.
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".