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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".