Initial experiments with flexible conductive electrodes for potential applications in cancer tissue screening
Bibliographic record
Abstract
We present initial results on the fabrication and testing of micropatternable conductive nanocomposite polymer (C-NCP) electrodes for tissue impedance measurements. We present these proof-of-concept results as a first step toward the realization of our goal: an improved Electrical Impedance Scanning (EIS) system, whereby tissue can be scanned for cancerous tissue and other anomalies using large arrays of highly flexible microfabricated electrodes. Previous limitations of existing EIS system are addressed by applying polymer based microelectromechanical system (MEMS) technology. In particular, we attempt to minimize mechanical skin contact issues through the use of highly compliant elastomeric polymers, and increase the spatial resolution of measurements through the development of microelectrodes that can be micropatterned into large, highly dense arrays. We accomplish these improvements through the development of C-NCP electrodes that employ silver nanoparticle fillers in an elastomer polymer base that can be easily patterned using conventional soft lithography techniques. These new electrodes are tested on conventional tissue phantoms that mimic the electrical characteristics of human tissue. We characterize the conductivity of the electrodes (average resistivity of 7x10-5 ohm-m +/- 14.3% at 60 wt-% of silver nanoparticles), and further employ the electrodes for impedance characterization via Cole-Cole plots to show that measurements employing C-NCP electrodes are comparable to those obtained with normal macroscopic metal electrodes. We also demonstrate anomaly detection using our highly flexible Ag/AgCl C-NCP electrodes on a tissue phantom.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".