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Record W3211255013 · doi:10.32920/ryerson.14661522.v1

Hygrothermal Analysis and Prediction of Long-Term Thermal Performance of Aerogel-Enhanced Superinsulation Products

2021· preprint· en· W3211255013 on OpenAlexaff
Hamideh Nosrati

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsToronto Metropolitan UniversitySciencetech (Canada)
Fundersnot available
KeywordsAerogelMaterials scienceComposite materialDurabilityThermal conductivityThermal resistanceThermalHumidityMoistureRelative humidityMeteorology

Abstract

fetched live from OpenAlex

Aerogel-enhanced insulating materials provide significantly higher thermal resistance per unit of thickness compared to conventional insulating materials. These superinsulation materials are relatively new in the construction industry, and their thermal properties under different hygric conditions and their durability are still unknown. In this study, the main characteristics of a variety of aerogel-based materials under different climatic conditions were studied. Furthermore, the samples underwent accelerated aging tests, and the impact of aging on the thermal performance of aerogel-based products was investigated. The results showed that the thermal properties of aerogel-based products are affected by moisture content and their thermal resistance degraded under extreme humidity conditions. The analysis of the aging effects showed that aerogel-based products would maintain their superior thermal performance over time. The average increase in the thermal conductivity (compared to the pristine condition) was below 10% after the equivalent of twenty years of aging under various climatic factors.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.094
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.230
Teacher spread0.213 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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