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Record W3135733432 · doi:10.1016/j.jobab.2021.02.008

Experimental evaluation of rice husk ash for applications in geopolymer mortars

2021· article· en· W3135733432 on OpenAlexvenueno aff
G. Ogwang, Peter Wilberforce Olupot, Hillary Kasedde, Emmanuel Menya, Henning Storz, Yohannes Kiros

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersVolkswagen Foundation
KeywordsHuskMetakaolinMaterials scienceGeopolymerMortarPozzolanCrystallinityAmorphous solidCompressive strengthSilica fumeComposite materialFlexural strengthFly ashCementChemistryPortland cementBotanyOrganic chemistry

Abstract

fetched live from OpenAlex

Rice husks obtained from upland and lowland rice varieties were characterized for composition and content of ash. Each of the rice husk varieties was fired at temperatures of 600, 800 and 900 °C for a soaking period of 3 h. The resultant rice husk ash was analyzed for oxide composition and crystallinity using X-ray fluorescence and diffraction techniques, respectively. The generated amorphous ash with the highest silica content, together with metakaolin, aggregate, water, and an alkaline activator was employed to formulate geo-polymer mortar prisms according to the standard EN 196–1. Results showed that the content of ash in the varieties ranged from 18.3% to 28.6% dry basis. Out of this, 89 wt%–96 wt% was silica, with amorphous and crystalline forms of silica obtained at 600 °C and 900 °C, respectively, regardless of the rice variety. However, at 800 °C, the silica in the generated ash exhibited both amorphous and crystalline forms. The amorphous ash generated at 600 °C was used in formulation of geopolymer mortars. Compressive and flexural strength of the formulated mortar after 7 days of curing was 1.5 and 1.3 MPa, respectively. These results reveal the firing protocol to form pozzolanic ash, with potential applications in mortar production.

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 score0.010
Threshold uncertainty score0.186

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 head0.028
GPT teacher head0.295
Teacher spread0.267 · 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

Citations51
Published2021
Admission routes1
Has abstractyes

Explore more

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