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Record W2041124025 · doi:10.1115/1.4003988

Oxidized Graphite Nanoplatelets as an Improved Filler for Thermally Conducting Epoxy-Matrix Composites

2011· article· en· W2041124025 on OpenAlex
Xiaobo Sun, Aiping Yu, Palanisamy Ramesh, Elena Bekyarova, Mikhail E. Itkis, Robert C. Haddon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Electronic Packaging · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGraphiteMaterials scienceComposite materialEpoxyExfoliation jointCarbon nanotubePolymerRaman spectroscopySurface modificationFourier transform infrared spectroscopyNanocompositeFiller (materials)GrapheneChemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

We report a 40% improvement of the thermal conductivity of graphite nanoplatelets–epoxy composites by chemical functionalization of graphite nanoplatelets utilizing nitric acid treatment, which also serves to enhance the spreadability of the material. FTIR and Raman spectroscopy confirmed the presence of a variety of oxygen functional groups at the edges and basal plane of the functionalized graphite nanoplatelets, which contributed to improved interaction with the polymer matrix. A comparative statistical analysis of the particle size distributions in pristine and functionalized graphite nanoplatelets based on scanning electron microscopy showed an increasing degree of exfoliation of the functionalized material. We compare the performance of the functionalized graphite nanoplatelets and carbon nanotubes as fillers in the polymer matrix and discuss the prospects for utilization of graphite nanoplatelets-based thermal interface materials in electronic packaging.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

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)0.0000.000

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 head0.031
GPT teacher head0.291
Teacher spread0.260 · 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