Experimental Methods of Actuation, Characterization and Prototyping of Hydrogels for BioMEMS/NEMS Applications
Bibliographic record
Abstract
As a member of the smart polymer material group, stimuli responsive hydrogels have achieved a wide range of applications in microfluidic devices, micro/nano bio and environmental sensors, biomechanics and drug delivery systems. To optimize the utilization of a hydrogel in various micro and nano applications it is essential to have a better understanding of its mechanical and electrical properties. This paper presents a review of the different techniques used to determine a hydrogel's mechanical properties, including tensile strength, compressive strength and shear modulus and the electrical properties including electrical conductivity and dielectric permittivity. Also explored the effect of various prototyping factors and the mechanisms by which these factors are used to alter the mechanical and electrical properties of a hydrogel. Finally, this review discusses a wide range of hydrogel fabrication techniques and methods used, to date, to actuate this family of smart polymer material.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".