High Strain-Rate Behavior Of Mechanoluminescent Material Dispersed In A Soft Polymer Matrix
Bibliographic record
Abstract
Mechanoluminescence (ML) is the emission of light from a solid material in response to mechanical stimuli [1].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.
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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 | 0.000 |
| 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".