MétaCan
Menu
Back to cohort
Record W4413797046 · doi:10.1111/ijac.70055

The effect of basalt glass microspheres and microfibers on the wear resistance of metakaolin‐based geopolymer composites

2025· article· en· W4413797046 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Applied Ceramic Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsTransAlta (Canada)
FundersConstruction Engineering Research Laboratory
KeywordsMaterials scienceComposite materialGeopolymerMetakaolinAbrasion (mechanical)BasaltMicrofiberComposite numberGrogCompressive strength

Abstract

fetched live from OpenAlex

Abstract This study investigates the effect of various microfillers on the abrasion resistance of metakaolin‐based, geopolymer composites. Comparative tests were conducted on a baseline geopolymer and composites reinforced with chamotte powder, basalt microfiber, prestressed, solid, basalt glass microspheres (PSBGM), and their combinations. Abrasion resistance was evaluated according to ASTM C501‐21 using the Taber Abraser. The results demonstrate that prestressed, solid, basalt glass microspheres significantly enhance wear resistance, both individually and in combination with chamotte powder or basalt microfiber. Quantitative analysis indicates that basalt‐based microfillers enhance abrasion resistance by a factor of 3 to 4. This is due to the high surface microhardness of prestressed, solid, basalt glass microspheres, produced using the superheated melt method, which is approximately 7.1 GPa on the Vickers scale. These results confirm the potential of basalt microfillers and geopolymer matrices for developing highly wear‐resistant, fully inorganic, composite materials, coatings, and repair mixtures.

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.

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 score0.017
Threshold uncertainty score0.222

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 science0.0010.000
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 head0.229
Teacher spread0.227 · 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