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Record W4213187137 · doi:10.3390/nano12040609

Cost-Effective Fabrication of Modified Palygorskite-Reinforced Rigid Polyurethane Foam Nanocomposites

2022· article· en· W4213187137 on OpenAlexaff
Yulei Wang, Kaibin Cui, Baizeng Fang, Fei Wang

Bibliographic record

VenueNanomaterials · 2022
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsUniversity of British Columbia
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMaterials scienceThermal stabilityPolyurethaneNanocompositeThermogravimetric analysisComposite materialThermogravimetryPalygorskiteThermal insulationCompressive strengthChemical engineeringMineralogyChemistry

Abstract

fetched live from OpenAlex

Integration of nanoclay minerals into rigid polyurethane foams (RPUFs) is a cost-effective solution to enhance foam's performance via environmental protection technology. In this work, palygorskite/RPUFs nanocomposites (Pal/RPUFNs) with excellent mechanical properties and thermal stability were prepared via a one-step method, using 4,4'-diphenylmethane diisocyanate and polyether polyol as the starting materials, coupled with Pal modified by silane coupling agent KH570. The effects of the modified Pal on the mechanics, morphology, and thermal properties of the nanocomposites were studied systematically. When the content of the modified Pal was 8 wt% of polyether polyol, the elastic modulus and compressive strength of the Pal/RPUFNs were increased by ca. 131% and 97%, respectively. The scanning electron microscopy images indicated that the addition of the modified Pal significantly decreased the cell diameter of the Pal/RPUFNs. The results of thermogravimetric and derivative thermogravimetry analyses revealed that the addition of the modified Pal increased the thermal weight loss central temperature of the Pal/RPUFNs, showing better thermal stability in comparison with the pure RPUFs. A self-made evaluation device was used to estimate the thermal insulation ability of the Pal/RPUFNs. It was found that the small cell size and uniform cellular structure were keys to improving the thermal insulation performance of the RPUFs. The prepared Pal/RPUFNs are expected to have great potential in the field of building insulation.

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 categoriesInsufficient 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 score0.004
Threshold uncertainty score0.998

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)0.0030.000

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 head0.023
GPT teacher head0.266
Teacher spread0.243 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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