Superhydrophilic graphene oxide/electrospun cellulose nanofibre for efficient adsorption of organophosphorus pesticides from environmental samples
Bibliographic record
Abstract
Superhydrophilic graphene oxide/electrospun cellulose nanofibre (GO/CNF) was synthesized, characterized and successfully used in a solid-phase membrane tip adsorption (SPMTA) as an adsorbent towards a simultaneous analysis of polar organophosphorus pesticides (OPPs) in several food and water samples. Separation, determination and quantification were achieved prior to ultra-performance liquid chromatography coupled with ultraviolet detector. The influence of several parameters such as sample pH, adsorption time, adsorbent dosage and initial concentration were investigated. SPMTA was linear in the range of 0.05 and 10 mg l −1 under the optimum adsorption conditions (sample pH 12; 5 mg of adsorbent dosage; 15 min of adsorption time) for methyl parathion, ethoprophos, sulfotepp and chlorpyrifos with excellent correlation coefficients of 0.994–0.999. Acceptable precision (RSDs) as achieved for intraday (0.06–5.44%, n = 3) and interday (0.17–7.76%, n = 3) analyses. Low limits of detection (0.01–0.05 mg l −1 ) and satisfactory consistency in adsorption (71.14–99.95%) were obtained for the spiked OPPs from Sungai Pahang, Tasik Cheras, cabbages and rice samples. The adsorption data were well followed the second-order kinetic model and fits the Freundlich adsorption model. The newly synthesized GO/CNF showed a great adsorbent potential for OPPs analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".