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Effect of Water Glass on Compressive Strength of Aluminosilicate-Based Geopolymer

2010· article· en· W2021969055 on OpenAlexaff
Jin Tae Kim, Dong Seok Seo, Gab Joong Kim, Jong Kook Lee

Bibliographic record

VenueAdvanced materials research · 2010
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsEcoMetrix
FundersKorea Institute for Advancement of Technology
KeywordsGeopolymerCompressive strengthMaterials scienceAluminosilicateFly ashDistilled waterComposite materialPolymerizationMicrostructureGlass microsphereChemical engineeringPolymerMicrosphereChemistry

Abstract

fetched live from OpenAlex

The inorganic binders, fly ash and meta kaolin were used to prepare geopolymer. Water glass was added to the recycled inorganic binders to improve compressive strength of geopolymers. The ratio of the solid materials (inorganic binder and alkali activators) and liquid materials (distilled water, water glass) for the polymerization was optimized as 3:1. Compressive strength of the geopolymers increased because water glass improved the extent of polymerization of the inorganic binder and resulted in dense microstructure. It was found that geopolymers using fly ash showed the higher value of compressive strength, compared with meta kaolin- based geopolymers.

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 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 score0.005
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.015
GPT teacher head0.321
Teacher spread0.306 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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