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Record W4401593881 · doi:10.1002/cjce.25448

Impact of raw material on thermo‐physical properties of carbon foam

2024· article· en· W4401593881 on OpenAlexvenueno aff
Mohammad Almajali, Mingcong Zhang, Yahya Al-Majali, Jason Trembly

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon nanofoamRaw materialMaterials scienceComposite materialCarbon fibersEnvironmental sciencePorosityComposite numberChemistry

Abstract

fetched live from OpenAlex

Abstract Carbon foam materials are currently used in several industrial and engineering applications due to their outstanding properties. The properties of carbon foam can be altered through the manufacturing processes applied in specific applications. In this paper, we collected and analyzed four samples manufactured by CFOAM and one sample developed by Ohio University (OU) to understand the behaviour of this material and determine its properties. We utilized advanced techniques to experimentally measure and determine the following properties: pore size and volume, porosity, specific surface area, mass, density, and thermal conductivity. Among the samples, the low‐porosity CFOAM (CF35) and the OU sample exhibited higher specific surface areas and densities compared to the others. However, CF35 demonstrated the highest thermal conductivity, while OU displayed the lowest. As a result, CF35 emerges as the optimal choice for applications requiring high‐rate heat transfer, while the remaining CFOAM samples are well‐suited for lightweight applications. Thus, OU foam proves to be a highly suitable candidate for insulation applications such as building sidewalls.

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 score0.002
Threshold uncertainty score0.275

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 head0.198
Teacher spread0.190 · 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

Citations18
Published2024
Admission routes1
Has abstractyes

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