Development of a Novel Competitive Adsorption Model and a Contaminant Transport Model to Predict Competitive Adsorption of PFAS in a CAC Barrier
Bibliographic record
Abstract
Groundwater contamination by per-and polyfluoroalkyl substances (PFAS) is a global problem, and current treatment methods are expensive and/or inefficient.Attenuation of PFAS plume by subsurface Colloidal Activated Carbon (CAC) sorptive barrier is a potential interim alternative.However, competitive adsorption can impact the longevity of CAC barriers.Competitive adsorption is predicted using models such as ideal adsorbed solution theory (IAST) and competitive Langmuir model (CLM).This study tested the existing competitive adsorption models and developed a new model, Modified CLM (MCLM), to predict the competitive adsorption equilibria of PFAS on CAC.The findings indicate that neither IAST nor CLM accurately predicts the competitive adsorption equilibria, but MCLM performs best and suggests a relationship between PFAS competitive adsorption and their molecular weights.A 1D transport model with CLM was also developed which was able to simulate the chromatographic effect and the gradual breakthrough curve of long-chained PFAS observed in the literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".