Adsorption of emerging pollutants on activated carbon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it