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Record W4385431921 · doi:10.18280/rcma.330302

Superior Long-Term Performance of Composite Phase Change Material with High-Density Polyethylene under Thermal Aging Process

2023· article· fr· W4385431921 on OpenAlexvenueno aff
Budhi Muliawan Suyitno, Dwi Rahmalina, Ismail Ismail, Reza Abdu Rahman

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2023
Typearticle
Languagefr
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsnot available
FundersKementerian Pendidikan, Kebudayaan, Riset, dan Teknologi
KeywordsMaterials scienceComposite numberComposite materialTerm (time)ThermalPolyethyleneProcess (computing)Phase (matter)ThermodynamicsComputer scienceChemistryPhysics

Abstract

fetched live from OpenAlex

Protecting the phase change material (PCM) from thermal aging is done by using polyethylene (high-density/HDPE) as supporting matrix.The HDPE content is set at 5 wt%, 10 wt% and 15 wt%.The composite PCM (cPCM) is treated through repeating melting/solidification process up to 10,000 cycles which is conducted between temperature 55℃-65℃.The thermal capacity of the paraffin wax (PW) is reduced notably, where enthalpy of fusion is decreased from 145.92 J/g to 138.46 J/g.The cPCM with 10 wt% and 15 wt% HDPE maintain a notable performance with only reduces slightly from 130.7 J/g to 129.7 J/g and 127.7 J/g to 127.2 J/g, respectively.The supercooling degree of PW increases around 1.64℃, which accelerates the cooling rate to 4.81℃/min.The HDPE in the cPCM maintain the physical behavior of PW, which minimizes the shrinkage effect during solidification.The highest heating rate is found at 2.29℃/min with minor decrement (0.06℃/min) for cPCM 15 wt% HDPE after treatment.The finding from this work can be used for fundamental basis to develop a high cycle PCM which able to operate at extended cycle with minor supercooling degree that is suitable for temperature sensitive operation.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.333
Teacher spread0.228 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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