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Record W2900681419 · doi:10.25071/10315/35429

High Strain-Rate Behavior Of Mechanoluminescent Material Dispersed In A Soft Polymer Matrix

2018· article· en· W2900681419 on OpenAlexafffund
Chukwubuikem Johnson Omeziri, Aleksander Czekanski

Bibliographic record

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsYork University
FundersYork University
KeywordsMaterials scienceComposite materialPolymerMatrix (chemical analysis)Strain rate

Abstract

fetched live from OpenAlex

is the emission of light from a solid material in response to mechanical stimuli Mechanoluminescent materials are classified based on the types of stress activation required for luminescence behaviour. In this paper, we propose the study of light emission characteristics of an elastico-mechanoluminescent (EML) material dispersed in a soft polymer matrix, under high-strain compressive loading for a qualitative and quantitative testing of onsite strain. We developed a strain sensor by creating a composite material through the combination of a polydimethylsiloxane (PDMS) matrix structure and strontium aluminate, europium, and dysprosium doped (SAOED) crystals. SAOED crystals possess a strong luminescence behavior, which has led to their proliferation as a viable optical sensor. Composite materials were tested using a modified Kolsky bar and high-speed camera. The light-emitting characteristics of the SAOED crystals in response to mechanical stimuli allows for the quantification of stress-strain experienced by the composite material. The light intensity, which is measured by a complementary metal-oxide-semiconductor (CMOS) sensor in a high-speed digital camera, provides a quantitative measure of the strain-rate. Light-emitting characteristics of the EML material and the strain behavior of the material were studied.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score0.999

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)0.0020.000

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 head0.234
Teacher spread0.228 · 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.

Study designTheoretical or conceptual
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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueProgress in Canadian Mechanical EngineeringSame topicMechanical and Optical ResonatorsFrench-language works237,207