Novel Lab-on-a-Chip Sensing Systems: Applications of Optical, Electrochemical, and Piezoelectric Transduction in Bioanalysis
Bibliographic record
Abstract
The integration of novel transduction strategies into lab-on-a-chip (LOC) platforms allows for sensors that are cost effective, field portable, easy to use, and amenable to automation. Alongside the unabated interest in functional nanoparticles (NPs), countless new sensors have been designed for bioanalyses ranging from small molecule toxins to complex biomolecular targets such as proteins and nucleic acids that are markers of pathogens or disease. Furthermore, the unique properties of NPs have provided for new detection modalities that are well suited to miniaturisation. Herein we describe recent developments and application of novel LOC biosensors that operate using optical, electrochemical, and piezoelectric techniques. Focus is given to examples of sensors that incorporate NPs into their detection strategy. A brief theoretical description of the many transduction methods is provided and examples from recent literature are used to highlight the application of each detection method in the field. The chapter concludes with a prospective outlook on future directions for LOC biosensing.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".