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A Uniform Electro-Thermal Film For Electronic Clothing

2024· article· en· W4408442559 on OpenAlexaff
Wilson Hou-Sheng Huang, Tsai Shu-Chu, Gaozhi Xiao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsClothingMaterials scienceThermalThermal management of electronic devices and systemsEngineering physicsOptoelectronicsElectrical engineeringComposite materialMechanical engineeringEngineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Traditional printed elastic heating films are notable for providing active and controllable heating effects. But wires, cables, or large batteries lead to electron concentration at the power input and output ends, resulting in hot spots or poor signal quality. Using electronic clothing (E-textile) over extended periods can cause burns to the user's skin. In severe cases, excessive noise and impedance generated by the circuit may cause circuit backtracking, leading to an overload of the temperature sensing system in the controller. This can result in controller burnout and additional safety concerns. To address these issues, this study employs two types of elastic conductive slurries with various resistance characteristics (carbon slurry and silver slurry) in conjunction with a screen-printing process for precise alignment and presents a new printed electric heating module with uniform heating effects. Specifically, a new layer-by-layer stacking technology is utilized in this study to reduce the interface resistance between various materials, resulting in a more stable and efficient heating module. The temperature difference between the environment and the electrode decreased from 13.34°C to 3.12°C, the temperature difference in heating areas decreased from 24.36% to 19.26%, and the average temperature increased from 41.10°C to 46.20°C.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.152
Threshold uncertainty score1.000

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)0.0060.001

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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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