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Record W4285798272 · doi:10.3390/buildings12071041

Transforming Conventional Construction Binders and Grouts into High-Performance Nanocarbon Binders and Grouts for Today’s Constructions

2022· article· en· W4285798272 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

VenueBuildings · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsMcMaster University
FundersUniversiti Tenaga NasionalTenaga Nasional Berhad
KeywordsGroutMaterials scienceCompressive strengthComposite materialCementMortarGypsumLimeMetallurgy

Abstract

fetched live from OpenAlex

The transformation of conventional binder and grout into high-performance nanocarbon binder and grout was evaluated in this investigation. The high-performance nanocarbon grout consisted of grey cement, white cement, lime, gypsum, sand, water, and graphite nanoplatelet (GNP), while conventional mortar is prepared with water, binder, and fine aggregate. The investigated properties included unconfined compressive strength (UCS), bending strength, ultrasound pulse analysis (UPA), and Schmidt surface hardness. The results indicated that the inclusion of nanocarbon led to an increase in the initial and long-term strengths by 14% and 23%, respectively. The same trend was observed in the nanocarbon binder mortars with white cement, lime, and gypsum in terms of the UCS, bending strength, UPA, and Schmidt surface hardness. The incorporation of nanocarbon into ordinary cement produced a high-performance nanocarbon binder mortar, which increased the strength to 42.5 N, in comparison to the 32.5 N of the ordinary cement, at 28 days.

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 score0.144
Threshold uncertainty score0.672

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 head0.009
GPT teacher head0.213
Teacher spread0.205 · 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