Emerging CMOS Capacitive Sensors for Biomedical Applications: A multidisciplinary approach
Bibliographic record
Abstract
CMOS-based sensors offer significant advantages to life science applications, such as non-invasive long-term recordings, fast responses and label-free processes. They have been widely applied in many biological and medical fields for the study of living cell samples such as neural cell recording and stimulation, monitoring metabolic activity, cell manipulation, and extracellular pH monitoring. Compared to other sensing techniques, capacitive sensors are low-complexity, high-precision, label-free sensing methods for monitoring cellular activities such as cell viability, proliferation and morphology. The development of capacitive sensors for use in life sciences requires thorough knowledge of both the intended biological applications and CMOS circuitry. This book addresses the principles, design, implementation and testing, and packaging of CMOS circuits for these applications. Existing applications, markets, and potential future developments are also covered, plus the relevant biological protocols. Emerging CMOS Capacitive Sensors for Biomedical Applications provides information and guidance for researchers and advanced students in the field of microelectronics who are looking to specialise in biological applications. It is also relevant to academic and industrial researchers already working in the biosensors field, who wish to expand their knowledge and keep abreast of new developments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".