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Record W3120127100 · doi:10.1088/1361-6439/abd8df

Experimental measurement of base tilting effects in magnetically actuated FeC-PDMS micropillar structures

2021· article· en· W3120127100 on OpenAlexafffund
Farzad Khademolhosseini, Mu Chiao

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2021
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsKillam TrustsCMC Microsystems
KeywordsBase (topology)Materials scienceOptoelectronicsNanotechnology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.184
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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