Evaluation of thin film microextraction based on graphene oxide/ polymer composite: Experimental and theoretical insights
Bibliographic record
Abstract
Experimental and theoretical assessments of a graphene oxide-based polymer as adsorbent for thin film microextraction (TFME) were conducted as part of this research. Graphene oxide (GO) was embedded in the organic polymer poly(styrene-co-divinylbenzene) (PS-DVB) to prepare a sorbent suitable for direct-immersion TFME. A TFME membrane coating prepared with the GO/PS-DVB sorbent and polydimethylsiloxane (PDMS) as binder was then applied for extraction of organic pollutants from aqueous and gaseous samples. The surface morphology of the TFME coating was examined by scanning electron microscopy (SEM). Various TFME parameters influencing extraction efficiency, such as extraction time and temperature, desorption temperature, and ionic strength, were investigated and optimized. In a comparison of TFME membranes, the GO/PS-DVB/PDMS TFME membrane was shown to yield higher extraction efficiencies for the targeted analytes than the pure PDMS and DVB/PDMS TFME membranes. The calibration graphs of the organic pollutants displayed linearity for most of the target analytes within the 10–2000 ng L−1 concentration range. The repeatability (RSD %, n = 5) and reproducibility (RSD %, n = 3) of the method were in the ranges of 2.2–5.9 %, and 3.2–8.5 %, respectively, at a concentration level of 500 ng L−1, whereas accuracy (%) ranged between 79.8 and 119 %. The developed method was successfully applied for determinations of organic pollutants in tap water, lake water, and wastewater samples. Furthermore, the impact of mass transfer kinetics on extractions by the GO/PS-DVB/PDMS TFME membrane from gaseous samples was theoretically discussed and experimentally verified. The results of this work demonstrate that the GO/PS-DVB/PDMS TFME method is a simple, efficient, and environmentally friendly method for pre-treatment of organic pollutants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".