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Record W4415663474 · doi:10.11648/j.ajche.20251305.13

Effect of Rice Hull Ash on the Geopolymerization of a Kaolinite Clay from Togo

2025· article· en· W4415663474 on OpenAlexaff
Anove Mawoulikplim, Kpetemey Amen, Tchanate N’Djoibini, Koffi Agbégnigan Degbe, Babakoua Diana, Sanonka Tchegueni, Douty Damdjoin, Fiaty Koffi, Patrick Drogui, Gado Tchangbédji

Bibliographic record

VenueAmerican Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsInstitut National de la Recherche ScientifiqueEnvironment and Climate Change Canada
Fundersnot available
KeywordsHuskGeopolymerSodium silicateCompressive strengthCalcinationAluminosilicateKaolinite

Abstract

fetched live from OpenAlex

Geopolymers have recently emerged as a promising class of inorganic aluminosilicate polymer materials. They present a viable alternative to Portland cement, which is well known for its significant contribution to greenhouse gas emissions. To support the reduction of these emissions, this study aims to develop geopolymer cements by investigating the impact of rice husk ash on the geopolymerization of local kaolinite clay using an alkaline solution. Rice husk ash (with a SiO<sub>2</sub> content of 91.6%), obtained by calcining rice husks at 600°C, serves as a source of amorphous silica. The GP<sub>0</sub> geopolymer material, derived from clay calcined at 750°C and activated with a 12N sodium hydroxide solution, exhibits a compressive strength of 9.9 MPa. This mechanical strength was enhanced by incorporating rice husk ash, which produces sodium silicate solutions as activators. The addition of 10% rice husk ash increased the compressive strength from 9.9 MPa to 15.9 MPa. The sodium silicate solution derived from the ash proved to be an effective alkaline activator in the geopolymer synthesis. Consequently, rice husk ash can potentially replace commercial sodium silicate solutions, contributing to the formulation of more eco-friendly materials. However, further research is needed to optimize the mechanical properties of these 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)0.000
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 score0.006
Threshold uncertainty score0.349

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 head0.003
GPT teacher head0.223
Teacher spread0.220 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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