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Record W4411222684 · doi:10.5539/jsd.v18n4p47

Influencing Factors in the Adsorption of Chlorpyrifos on Various Substrates: Insights into Adsorbents, Mechanisms and Efficiency

2025· article· en· W4411222684 on OpenAlexvenueno aff
Clint Sutherland

Bibliographic record

VenueJournal of Sustainable Development · 2025
Typearticle
Languageen
FieldChemistry
TopicDye analysis and toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsAdsorptionChlorpyrifosChemistryEnvironmental chemistryBiochemical engineeringChemical engineeringOrganic chemistryEcologyBiologyPesticide

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.226
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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