Systematic Evaluation of Different Coating Chemistries Used in Thin-Film Microextraction
Bibliographic record
Abstract
A systematic evaluation of eight different coatings made of solid phase extraction (SPE) and carbon-based sorbents immobilized with polyacrylonitrile in the thin-film microextraction (TFME) format using LC-MS/MS was described. The investigated coatings included graphene, graphene oxide, multi-walled carbon nanotubes (MWCNTs), carboxylated MWCNTs, as carbon-based coatings, and polystyrene-divinylbenzene (PS-DVB), octadecyl-silica particles (C18), hydrophilic-hydrophobic balance particles (HLB) and phenyl-boronic acid modified particles (PBA), as SPE-based coatings. A total of 24 compounds of diverse moieties and of a wide range of polarities (log P from -2.99 to 6.98) were selected as probes. The investigated coatings were characterized based on their extraction performance toward the selected probes at different pH values and at optimized desorption conditions. In the case of SPE-based coatings, PS-DVB and HLB exhibited a balanced extraction for compounds within a wide range of polarities, and C18 showed superior extraction recoveries for non-polar analytes. Carbon-based coatings showed high affinity for non-polar compounds given that their main driving force for extraction is hydrophobic interactions. Interestingly, among the studied carbon-based coatings, graphene oxide showed the best extraction capabilities toward polar compounds owing to its oxygen-containing groups. Overall, this work provided important insights about the extraction mechanisms and properties of the investigated coatings, facilitating the coating selection when developing new TFME 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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".