Performance Evaluation of Extraction Coatings with Different Sorbent Particles and Binder Composition
Bibliographic record
Abstract
Binders are critical components used in the preparation of a range of extraction devices, including solid-phase microextraction (SPME) devices. While the main role of a binder is to affix the sorbent particles to the selected support, it is critical to select the optimal binder to ensure that it does not negatively impact the coating’s particle sorption capability. This work presents the first comprehensive investigation of the interactions between binders and solid sorbent particles as these interactions can significantly impact the performance of the coating. Specifically, the findings presented herein provide a better understanding of the extraction mechanisms of composite coatings and new rules for predicting the particle adhesion forces and binder distribution in the coating. The influence of binder chemistry on coating performance is investigated by examining a selection of the most used binders, namely, polydimethylsiloxane (PDMS), polyacrylonitrile (PAN), poly(vinylidene difluoride) (PVDF), polytetrafluoroethylene amorphous fluoroplastics (PTFE AF 2400), and polybenzimidazole (PBI). The solid particles (e.g., hydrophilic–lipophilic balanced (HLB) and C 18 ) used in this work were selected for their ability to provide optimal extraction coverage for a broad range of analytes. The results show that PDMS does not change the properties of the solid particles and that the binder occupies a negligible volume due to shrinking after polymerization, resulting in the solid particles making up most of the coating volume. Hence, the coating sorption characteristics correspond closely to the properties of the selected solid particles. On the other hand, the results also showed that PTFE AF 2400 can interact with the active surface of the sorbent, leading to the deactivation of the sorbent particles. Therefore, the extraction performance and permeability coefficients decrease as the size of the penetrant increases, indicating a rigid porous structure. The results of this study can aid in the optimization of SPME devices as they provide reference values that can be used to determine the optimal binder and the sorbent affinity for the targeted compounds. Finally, the present work also provides the broader scientific community with a strategy for investigating the properties of sorbent particle/binder structures and defines the characteristics of a good coating/membrane by analyzing all parameters such as kinetics, thermodynamic equilibria, and morphology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".