Paper Electronics and Paper-Based Biosensors
Bibliographic record
Abstract
Traditionally, and still today, analytical procedures are usually performed at high-quality laboratories with bench-top analyzers. However, researchers have been studying the possibility of producing portable and easy-to-use analytical devices that satisfy the needs of an analytical test such as sensitivity, selectivity, and reproducibility. A few decades ago, biosensors appeared as a new, easy-to-use analytical device that can detect a target analyte with high sensitivity and selectivity in a few minutes. As a result, several strategies and designs were developed for biosensor construction and applications, with the aim to improve their analytical characteristics. About a decade ago, paper emerged as one of the most-used materials for easy and disposable microfluidic devices. The combination of paper microfluidics and biosensors technologies has produced many analytical devices that comprise the advantages of an ideal point-of-care and point-of-need device. In this chapter, we explore paper as a support material for single-use, disposable biosensing devices. Its relatively recent introduction as a biosensing platform has been stimulated by its cost-effective and environmentally compatible properties. The rapidly growing bibliography of published research in this area has not been comprehensively reviewed in this chapter. Instead, we have emphasized significant recent advances and achievements in paper fabrication (chemical, physical, and biological modifications of fibers) and paper-based biosensors developed to be used in clinical, environmental, and food fields. Further, we offer a perspective on new challenges and prospects in this field.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".