Modified competitive Langmuir model for prediction of multispecies PFAS competitive adsorption equilibria on colloidal activated carbon
Bibliographic record
Abstract
Competitive adsorption of four perfluoroalkyl substances (PFAS), i.e., perfluorobutyl sulfonate (PFBS), perfluorohexane sulfonate (PFHxS), perfluorooctanoate (PFOA), and perfluorooctane sulfonate (PFOS), on colloidal activated carbon (CAC) was studied and a new predictive model, modified competitive Langmuir model (MCLM), was proposed to predict the competitive adsorption equilibria. The new model is a modification of the competitive Langmuir model (CLM) that gives additional weighting to the molecular weight of the PFAS molecules. A comparative study was done to test the capability of the new model by comparing its predictions to the widely used CLM and ideal adsorbed solution theory (IAST), as well as the experimental data from the batch adsorption experiments on seven different mixtures of the abovementioned PFAS. The models were tested on various combinations of two and three PFAS analytes. The analysis showed better predictions by MCLM over the existing models in most of the cases. Additionally, an error analysis was performed to fit the single-solute isotherms that indicated a better fit of the Langmuir isotherm over Freundlich isotherm. Moreover, the study also showed approximately equal impact of PFOA and PFHxS on PFOS adsorption and a higher impact of PFHxS, compared to PFOA, on PFBS adsorption which hints at equal hydrophobicity but higher adsorption of PFHxS compared to PFOA due to its smaller size and higher pore accessibility.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".