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Macroporous geopolymers designed for facile polymers post-infusion

2020· article· en· W3011002150 on OpenAlexafffund
Jacques Fiset, Maëlle Cellier, Pascal Y. Vuillaume

Bibliographic record

VenueCement and Concrete Composites · 2020
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsCegep de Thetford
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Éducation et de l'Enseignement supérieur
KeywordsMaterials scienceGeopolymerPorosityCompressive strengthComposite materialToughnessBrittlenessPolymerChemical engineering

Abstract

fetched live from OpenAlex

Highly porous geopolymers suffers from poor mechanical properties; typically intrinsic brittleness, low compression strength and very weak toughness. Geopolymers were designed so that they can be reinforced by incorporating a low viscosity polymeric resin through a facile post-infusion process. Porosity in the range of 100–4700 μm was generated by decomposition of hydrogen peroxide and large expanses of open porosity was obtained by the addition of small amounts of saponified canola oil as a surfactant. Geopolymers with high open porosity (~60 vol%) and low compression strength (<1.5 MPa) were infilled with an unsaturated polyester resin. High amounts of resin (>75 vol% with respect to the open porosity) were incorporated and polymerized within the porous geopolymer framework. As a result, compressive strength of geopolymers composites can be increased as much as 40 times despite poor interfacial adhesion prevailing between inorganic and organic phases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.225
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations37
Published2020
Admission routes2
Has abstractyes

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