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Record W1923748046 · doi:10.1109/stherm.2005.1412159

In-situ thickness method of measuring thermo-physical properties of polymer-like thermal interface materials

2005· article· en· W1923748046 on OpenAlexaff
Rose Smith, Richard Culham

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceThermal conductivityThermalThermal resistanceComposite materialIn situDeflection (physics)Thermal contact conductanceThermal conductivity measurementHeat fluxCharacterization (materials science)Material propertiesMechanical engineeringHeat transferMechanicsThermodynamicsOpticsNanotechnologyEngineeringPhysics

Abstract

fetched live from OpenAlex

A critical property in understanding and accurately predicting the thermal resistance of polymer-like thermal interface joints in micro-electronic cooling applications is the bulk thermal conductivity of thermal interface materials (TIMs). A unique experimental test stand was developed and validated which accurately measures the in-situ thickness of a TIM sample in a vacuum during thermal resistance testing. The system has a resolution capability of /spl plusmn/ 1.0 /spl mu/m and is designed in such a manner as to continuously measure the true relative deflection of a TIM sample taking into account any mechanical and/or thermal deflections of the entire test stand. The data and analysis demonstrate that applying the current American standard test method (ASTM) ASTM D 5470 without accounting for in-situ thickness deviations can result in over estimating the bulk thermal conductivities for these types of materials by as much as 40%. These types of errors in fundamental material properties can cause the over-prediction of thermal heat flux in a system and an under-prediction of the temperatures of the system.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.274
Teacher spread0.236 · 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

Citations4
Published2005
Admission routes1
Has abstractyes

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