Influencing Factors in the Adsorption of Chlorpyrifos on Various Substrates: Insights into Adsorbents, Mechanisms and Efficiency
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Bibliographic record
Abstract
This narrative review examines recent advances in the adsorption of chlorpyrifos (CPF) from aqueous solutions, focusing on studies from the past decade. Significant progress has been made in developing high-performance adsorbents and optimising batch systems for single-contaminant removal. Solution pH consistently emerged as a critical factor, with optimal CPF adsorption typically occurring below neutral to just above the pKa of CPF pyridine ring (~3–4). Most studies used CPF concentrations relevant to industrial discharge, while nanogram-level concentrations typical of environmental contamination remain underexplored. Notable adsorption performance among carbon-based adsorbents was 132.0 mg/g in 10 min by a cellulose-derived carbon fibre. Among metal-organic frameworks, pAAm-g-XG/HKUST-1@Fe₃O₄ biopolymer reached 1708.7 mg/g in 15 min. The highest reported capacity was 1814.0 mg/g in 20 min using an amine-modified mesoporous silica SBA-15 hybrid composite. A promising emerging approach involved ultrasonic-assisted adsorption, which reduced equilibrium time from 50 to 10 min, highlighting the opportunity for further work into its scalability and performance in complex wastewaters. Detailed mechanistic studies reveal an interplay of hydrophobic interactions, electrostatic attraction, π-π stacking and hydrogen bonding. However, the depth of the study varied markedly among researchers. Desorption studies reported promising reusability (up to 10 cycles with <5% efficiency loss), but long-term impacts and the effects of real-world wastewater remain underexplored. Key gaps persist in thermodynamic analyses, detailed mechanistic elucidation, and the integration of statistical tools (e.g., response surface methodology) to enhance optimisation. Scalability is a significant challenge, with targeted research needed to address particle enlargement and structural modifications for industrial applications.
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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 | 0.001 | 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) | 0.000 | 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