Experimental measurement of base tilting effects in magnetically actuated FeC-PDMS micropillar structures
Bibliographic record
Abstract
Abstract Polymer micropillar structures have been used as microsensors and microactuators in scientific studies. In these studies, the deformation of the micropillar is used to estimate the forces applied on the micropillars from the external environment, or the forces generated by the micropillars. The accuracy of such force calculations depends on the accurate estimation of the structure’s spring constant which requires a correct understanding of micropillar deformation under loading. For a micropillar sitting on top of a flexible polymer base, base deformation, or tilting, is a major contributor to the total pillar deformation and the total spring stiffness of the structure. In this work, we visualize and quantify the base tilting effects for magnetic FeC-PDMS micropillar structures. We compare and verify our experimental observations against those predicted by an analytical model developed accounting for the base tilting effects. In our experiments, for pillars with smaller aspect ratios of L / D ≈ 3, pillar bending and base tilting account for 87% and 13% of the total angular deformation, whereas for pillars with larger aspect ratios of L / D ≈ 7, pillar bending and base tilting account for 94% and 6% of the total angular deformation, respectively. As predicted by analytical models, the contribution of the base tilting effect, largely ignored in many studies, becomes more significant for micropillars with smaller aspect ratios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".