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A Novel One-Step Process to Fabricate Polymeric Thermal Ground Planes

2022· article· en· W4313137056 on OpenAlexaff
Doriane Ibtissam Hassaine Daoudji, Samaneh Karami, Etienne Léveillé, Amrid Amnache, Anthony Ouellet, Mahmood R. S. Shirazy, Luc G. Fréchette

Bibliographic record

Venue2022 21st IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (iTherm) · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsInstitut interdisciplinaire d'innovation technologique
Fundersnot available
KeywordsFabricationMaterials scienceFlexibility (engineering)ThermalThermal conductivityElectronicsProcess (computing)Mechanical engineeringWearable technologyMoldNanotechnologyComputer scienceWearable computerComposite materialElectrical engineeringEngineeringEmbedded system

Abstract

fetched live from OpenAlex

This paper introduces a novel approach to make polymeric thermal ground planes (TGPs), which consist of flat heat pipes covering large surfaces. TGP are typically made of copper, which is not adapted to recent applications such as in-mold electronics, wearable technologies or embedded systems. We introduce the polymeric TGP as a thermal management solution for these emerging technologies, where the flexibility and compactness are major advantages. On one hand, polymers bring flexibility and compatibility with 3D geometries. On the other hand, it gives access to high volume manufacturing processes such as molding that reduce the cost. Our novel fabrication process for polymeric TGPs allows assembly and sealing in a single step, with in-situ filling. The fabricated TGP has an effective thermal conductivity of 150 W/m K which is within the range of literature for polymeric TGPs and heat pipes as it varies from 100 to 800 W/m K. Thus, the work that we are presenting shows that we can make a polymeric TGP with a one-step fabrication process that results in a low thermal budget and low-cost process.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.228
Teacher spread0.205 · 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 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
Published2022
Admission routes1
Has abstractyes

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Same venue2022 21st IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (iTherm)Same topicHeat Transfer and Boiling StudiesFrench-language works237,207