Nanoporous Conducting Polymer–Based Coatings in Microextraction Techniques for Environmental and Biomedical Applications
Bibliographic record
Abstract
Biologically active compounds constitute a wide group of chemicals, therefore it is a big challenge to create sorbents that are sensitive as well as selective. Development of nanoporous sorbents based on conducting polymers has expanded the boundaries of detection and quantification. Additionally, electrochemical synthesis used to deposit polymeric coatings directly on solid supports makes it possible to control physico-chemical properties of such sorbents. Besides the development of new polymeric nanoporous materials, the question of selectivity needs to be addressed. One possibility, successfully adapted to solid-phase microextraction, is molecular imprinting. Coatings created with this technology allow obtaining higher selectivity, with sensitivity at a constantly high level. The main aim of this review is to present comprehensively the concept of nanoporous sorbents based on conducting polymers, possible coating methods with their characteristics, and their various applications. This article focuses on applications in environmental and biomedical analyses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".