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Record W4366446345 · doi:10.1002/mame.202300001

High Thermal Conductivity Polymer Composites Fabrication through Conventional and 3D Printing Processes: State‐of‐the‐Art and Future Trends

2023· article· en· W4366446345 on OpenAlexaff
T.R. Vijaybabu, T. Ramesh, Suman Pandipati, Sujit Mishra, G. Sridevi, C. Pradeep Raja, Rhoda Afriyie Mensah, Oisik Das, Manjusri Misra, Amar K. Mohanty, Karthik Babu

Bibliographic record

VenueMacromolecular Materials and Engineering · 2023
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMaterials scienceThermal conductivityComposite materialFabricationCompression moldingMolding (decorative)PolymerComposite numberNanocompositeElectrical conductorPolymer nanocompositeThermalParticle (ecology)ConductivityMold

Abstract

fetched live from OpenAlex

Abstract The lifespan and the performance of flexible electronic devices and components are affected by the large accumulation of heat, and this problem must be addressed by thermally conductive polymer composite films. Therefore, the need for the development of high thermal conductivity nanocomposites has a strong role in various applications. In this article, the effect of different particle reinforcements such as single and hybrid form, coated and uncoated particles, and chemically treated particles on the thermal conductivity of various polymers are reviewed and the mechanism behind the improvement of the required properties are discussed. Furthermore, the role of manufacturing processes such as injection molding, compression molding, and 3D printing techniques in the production of high thermal conductivity polymer composites is detailed. Finally, the potential for future research is discussed, which can help researchers to work on the thermal properties enhancement for polymeric materials.

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 score0.015
Threshold uncertainty score0.559

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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

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