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Record W4308970530 · doi:10.1177/25165984221131400

The effect of laser cutting on the Young’s modulus of Polydimethylsiloxane

2022· article· en· W4308970530 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Micromanufacturing · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsQueen's University
Fundersnot available
KeywordsPolydimethylsiloxaneMaterials scienceModulusThermosetting polymerComposite materialCuring (chemistry)FabricationLaserYoung's modulusStiffnessPolymerOptics

Abstract

fetched live from OpenAlex

Laser cutting is often used in the fabrication of Polydimethylsiloxane (PDMS) substrates for novel microdevices such as wearable sensors and microfluidic devices. PDMS is a thermosetting polymer whose material properties are affected by the thermal conditions during the curing process. Since laser cutting exposes the cutting material to high temperatures, this might affect the heat-sensitive material properties. In this work, we examine how laser cutting affects the stiffness of PDMS by measuring the Young’s modulus of PDMS and comparing that to the Young’s modulus of laser-cut PDMS. We find an increase in the Young’s modulus from 0.34 to 0.37 MPa (9%) for PDMS mixed at a ratio of 20:1 base to curing agent. For 10:1 ratio PDMS, we find the increase in Young’s modulus is 31%, from 1.02 to 1.34 MPa.

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.

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)0.001
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 score0.373
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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