Electrolyte-Gated Field Effect Transistors in Biological Sensing: A Survey of Electrolytes
Bibliographic record
Abstract
Low operating voltages, rapid response, and high-throughput fabrication compatibility are key advantages for the development of electrolyte-gated field effect transistors (EGFETs) for biological sensing. Among the key components in EGFET biosensors, electrolyte materials are relatively less investigated, especially alternatives to water-based liquid electrolytes such as ionic liquids, ion gels, polyelectrolytes, and solid polymer electrolytes. These electrolytes enable portable devices and environmental stability superior to their water-based liquid alternatives. In this review, we offer an up-to-date evaluation of the state of EGFET research and gauge the strengths and limitations of high-performance electrolytes for use in EGFET biosensor applications as well as the potential for computer-aided design of such sensing platforms. The recent progress of EGFET biosensors for some popular analytes are reviewed and the performance of these alternative electrolytes in transistor biosensing is assessed. The challenges and opportunities for electrolytes in EGFETs are discussed for future research directions 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".