Adsorption of emerging pollutants on activated carbon
Bibliographic record
Abstract
Abstract Many emerging pollutants (also known as micro-pollutants) including pesticides, pharmaceutical and personal care products (PPCPs), and endocrine disrupting chemicals (EDCs) have frequently been detected in surface, ground, and drinking water at alarming concentrations. The emission and accumulation of these anthropogenic chemicals in nature is a potential threat to human health and aquatic environment. Therefore, it is essential to devise an effective and feasible technology to remove the micro-pollutants from water. Activated carbon adsorption has been introduced and utilized as a promising treatment to reduce the concentration of the emerging pollutants in water. A summary of research on the removal of pesticides, PPCPs, and EDCs by activated carbon adsorption process is presented in this report. The effects of carbon characteristics, adsorptive properties, and environmental factors on the adsorption capacity of activated carbon are reviewed. In addition, the mechanisms of the adsorption including hydrophobicity and the nature of the functional groups of activated carbon and organic compounds are discussed. Furthermore, the applied equilibrium adsorption isotherms (Langmuir, Freundlich, BET, Sips, Dubinin-Astakhov, Dubinin-Radushkevich, and Toth) and the most common kinetic models (pseudo-first- and second-order models, film and intra-particle diffusion models, and adsorption-desorption model) are also included for further investigation. This comprehensive review report aims to identify the knowledge deficiencies regarding emerging pollutant treatment via activated carbon adsorption process and open new horizons for the future research on the adsorption of emerging pollutants on activated carbon.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".