The development of selective and biocompatible coatings for solid phase microextraction
Bibliographic record
Abstract
Abstract The development of solid phase microextraction (SPME) has seen huge growth since its conception as a new approach to sample preparation in the early 1990s. In comparison to existing technologies such as liquid‐liquid or solid phase extraction (LLE and SPE, respectively), the technique offers many advantages, including simplicity, speed, solventless extraction, and a convenient format. However, an important aspect in the future application and growth of SPME is the development of new extraction coatings. The objective of this review is to present an overview of the advances in coating development for solid phase microextraction (SPME). More specifically, this review will focus on the use of molecular recognition elements in SPME coatings to provide enhanced extraction selectivity and their applicability to the direct analysis of complex samples, such as biological fluids. The work will also include short overviews of emerging extraction phase technologies such as molecularly imprinted polymers (MIPs) and restricted access materials (RAM). The biocompatibility of RAMs has been combined with SPME and highlighted in several examples for more targeted and direct extraction possibilities in complex samples. The ability to perform direct and selective extraction of analytes from complex samples will extend SPME into the field of bioanalysis. Lastly, the future outlook for SPME and potential new applications for these fibers will be discussed.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".