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Record W2019487006 · doi:10.1063/1.4898128

Design and characterization of ultra-stretchable monolithic silicon fabric

2014· article· en· W2019487006 on OpenAlexaff
Jhonathan P. Rojas, Arpys Arevalo, Ian G. Foulds, Muhammad M. Hussain

Bibliographic record

VenueApplied Physics Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsElectronicsMicrofabricationFabricationMaterials scienceSiliconStretchable electronicsCharacterization (materials science)NanotechnologyWearable technologyWearable computerComputer scienceMechanical engineeringOptoelectronicsElectrical engineeringEngineeringEmbedded system

Abstract

fetched live from OpenAlex

Stretchable electronic systems can play instrumental role for reconfigurable macro-electronics such as distributed sensor networks for wearable and bio-integrated electronics. Typically, polymer composite based materials and its deterministic design as interconnects are used to achieve such systems. Nonetheless, non-polymeric inorganic silicon is the predominant material for 90% of electronics. Therefore, we report the design and fabrication of an all silicon based network of hexagonal islands connected through spiral springs to form an ultra-stretchable arrangement for complete compliance to highly asymmetric shapes. Several design parameters are considered and their validation is carried out through finite element analysis. The fabrication process is based on conventional microfabrication techniques and the measured stretchability is more than 1000% for single spirals and area expansions as high as 30 folds in arrays. The reported method can provide ultra-stretchable and adaptable electronic systems for distributed network of high-performance macro-electronics especially useful for wearable electronics and bio-integrated devices.

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 score0.272
Threshold uncertainty score0.499

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 head0.010
GPT teacher head0.182
Teacher spread0.172 · 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

Citations66
Published2014
Admission routes1
Has abstractyes

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