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Record W3024005836 · doi:10.1115/1.4047178

Analytical and Experimental Study of Magnetomechanical Properties of Magnetic Porous PDMS Sponge Under Non-Uniform Magnetic Fields

2020· article· en· W3024005836 on OpenAlexafffund
Ali Shademani, Mu Chiao

Bibliographic record

VenueJournal of Applied Mechanics · 2020
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsMaterials sciencePolydimethylsiloxaneMagnetic fieldComposite materialPorosityCoupling (piping)Porous mediumCompression (physics)ElastomerPhysics

Abstract

fetched live from OpenAlex

Abstract Magnetic elastomers (MEs) respond to an applied magnetic field through magnetomechanical coupling, where the mechanical properties of the MEs change with magnetic field strength. These phenomena have been mostly studied under homogenous magnetic fields due to the simplicity. In this work, the effects of the magnetic field gradient on the mechanical properties and the response of the MEs was examined. MEs are made by embedding carbonyl iron microparticles (CI) into a polydimethylsiloxane (PDMS) matrix, which is later rendered porous. The influence of the CI concentration was investigated by manipulating four different samples with CI/PDMS weight ratios of 0.2, 0.6, 1.0, and 1.4. An analytical method was proposed to further understand the interactions of the magnetic field gradient and the material’s response. The proposed theory was later verified with experimental results from compression tests in the presence of different magnetic fields. The proposed theoretical framework and experimental methods can be used to improve the design of MEs in the future.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.210
Teacher spread0.192 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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