A cost-effective microindentation system for soft material characterization
Bibliographic record
Abstract
Microindentation is a useful experimental technique for characterizing mechanical properties of soft materials for research in biomechanics, biomaterials, tissue engineering. Despite its powerful capabilities, the access to microindentation techniques is hampered by the low performance-to-cost ratio of current commercial microindentation systems. This paper describes a new approach for constructing microindentation systems from readily available laboratory resources, and reports a force-controlled, cost-effective microindentation system capable of elastic and viscoelastic characterization of soft materials. A micro-electro-mechanical systems (MEMS) based piezoresistive force sensor and a motorized micromanipulator are employed to indent a sample and collect the force-deformation data for extraction of elastic and viscoelastic parameters. To overcome the shortcomings of previously reported customized systems, closed-loop position and force controllers are designed and implemented to accurately regulate the indentation depth and force. Tests on elastomeric and hydrogel materials prove the effectiveness of the system for elastic, relaxation, and creep tests, providing comparable measurement results with commercial microindentation systems at a much lower cost.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".