Long-chain perfluoroalkyl carboxylic acids removal by biochar: Experimental study and uncertainty based data-driven predictive model
Bibliographic record
Abstract
Given the persistence and toxicity of long-chain perfluoroalkyl carboxylic acids (PFCAs) and their rising concentrations, there is an urgent need for effective removal strategies. This study investigated the adsorptive removal of PFCAs, specifically perfluorononanoic acid (PFNA) and perfluorodecanoic acid (PFDA), using biochar derived from wood and compost. Factors such as biochar size, weight, and initial PFCA concentrations were analyzed to assess their impact on adsorption efficiency over time. The adsorption of PFDA and PFNA reached 90.13% and 85.8%, respectively, at an initial concentration of 500 μg/L. Advanced machine learning techniques, specifically deep neural networks, were employed to model adsorption behavior, incorporating noise injection to account for data uncertainties and preventing overfitting. Results demonstrated the superior performance of compost-derived biochar due to its higher aromaticity and functional group availability. The longer chain length of PFDA contributed to its higher adsorption efficiency compared to PFNA.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".