Bio Phantoms Mimicking the Dielectric and Mechanical Properties of Human Skin Tissue at Low-Frequency Ranges
Bibliographic record
Abstract
Tissue phantoms are widely used as substitute materials for real tissue validation of various newly emerging biomedical technologies such as ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). However, there is no specific recipe for fabricating skin-mimicking phantoms which can mimic both the mechanical and dielectric properties of human skin at lower frequency ranges. The objective of this paper is to present a variety of tissue-mimicking materials for filling this research gap in the lower frequency range from 20 Hz to 300 kHz. The starting point of our experiments is based on the oil-in-gelatin based tissue-mimicking materials (TMMs) that have shown to mimic the dielectric properties of human skin in higher frequency ranges. This paper examines the mechanical and dielectric performance of five major classes of tissue-mimicking materials (1) Oil-in-gelatin, (2) lignin and graphene nanopowder in gelatin, (3) gelatin and distilled water, (4) mixed oil in gelatin and distilled water, and (5) lignin in gelatin and distilled water. Mechanical and electrical testing was performed using compression testing and parallel plate method respectively. The effect of electrode polarization was considered in the measured data and the intrinsic impedance values were found to be following the Cole-Cole equation. The Young's modulus range of all tissue-mimicking materials was within the range of skin.
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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 | 0.001 |
| 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) | 0.000 | 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".