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Record W4200552788 · doi:10.1002/pen.25871

High‐performance thermal insulator based on polymer foam and silica xerogel

2021· article· en· W4200552788 on OpenAlexaff
Shinsuke Nagamine, Masahiro Ohshima, Denis Rodrigue

Bibliographic record

VenuePolymer Engineering and Science · 2021
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsUniversité Laval
FundersAdvanced Low Carbon Technology Research and Development Program
KeywordsMaterials scienceMethyltrimethoxysilaneNanoporousChemical engineeringSol-gelThermal insulationThermal conductivityComposite materialPolymerScanning electron microscopeNanotechnology

Abstract

fetched live from OpenAlex

Abstract In this work, methyltrimethoxysilane (MTMS) was used as a precursor, n‐hexadecyltrimethylammonium bromide (CTAB) as a surfactant, and EtOH/H 2 O as a co‐solvent, while 10 mM HCl and urea were used as acid and base catalysts to prepare silica xerogels and xerogel inside the cells of a polyethylene (PE) foam via a sol–gel process combined with ambient pressure drying. The xerogels and PE‐silica xerogel foams were characterized by scanning electron microscope (SEM), Brunauer–Emmett–Teller method (BET), and thermal conductivity. The results indicate that the xerogels have a three‐dimensional nanoporous structure, and the specific surface areas of the SiO 2 xerogel and PE‐xerogel foam are 536 and 377 m 2 /g, respectively. The PE‐xerogel foam exhibited very low thermal conductivity (22–24 mW/m.K) compared with conventional insulation materials on the market (30–50 mW/m.K). The thermal insulation properties of PE‐silica xerogels were increased by 45% and 49% compared with neat PE foam. Additionally, the compressive modulus and density of the PE‐xerogel foam was increased by the presence of the silica xerogel. These PE‐xerogel foams are believed to be excellent candidates for thermal insulation applications in terms of cost/performance.

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 score0.204
Threshold uncertainty score0.488

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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