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Record W4379052502 · doi:10.5267/j.esm.2023.4.002

Influence of chemical composition of soda ash activated fly ash and copper slag geopolymer pastes on compressive strength

2023· article· en· W4379052502 on OpenAlexvenueno aff
I.O. Erunkulu, Goitseone Malumbela, Oluseyi Philip Oladijo

Bibliographic record

VenueEngineering Solid Mechanics · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersBotswana International University of Science and Technology
KeywordsCompressive strengthMaterials scienceFly ashGeopolymerCopper slagComposite materialCopperMetallurgy

Abstract

fetched live from OpenAlex

The effect of the chemical composition of geopolymer pastes on compressive strength was investigated in high-calcium fly ash and copper slag blends. In synthesizing the pastes, soda ash at activator to binder ratio of 0.4 was used. The characterization of material samples and the hardened fly ash-copper slag pastes was conducted through X-ray fluorescence (XRF), and X-ray diffraction (XRD) for the major oxide and phase composition. The hardened paste cubes which were cured at 80 °C were tested for compressive strength at ages 3, 7, and 28 days to obtain the mechanical performance property of each respective mix. The findings establish the impact of variation in the individual material and paste composition on the compressive strength of fly ash-copper slag geopolymer. The result shows that increase in the SiO2/Al2O3 and Na2O/Al2O3 ratios of paste products of samples corresponded to an increase in compressive strength. Whilst Fe2O3 wt.% increase in products from slag addition and positively influenced the compressive strength as fillers. However, CaO had no positive influence on the matrix of the activated product. The optimal blend mix design was 40 wt.% of copper slag which achieved a 28-day compressive strength of 24.66 MPa.

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 score0.035
Threshold uncertainty score0.686

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 head0.229
Teacher spread0.222 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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