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Record W3130251750 · doi:10.3390/ma14040938

Performance of Mortars with Commercially-Available Reactive Magnesium Oxide as Alternative Binder

2021· article· en· W3130251750 on OpenAlexaboutno aff
Miguel Bravo, Javier A. Forero, José Nobre, Jorge de Brito, Luís Evangelista

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Oxide Properties and Applications
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaCivil Engineering Research and Innovation for Sustainability
KeywordsMortarMaterials scienceShrinkageCompressive strengthFlexural strengthDurabilityComposite materialAbsorption of waterCementMagnesiumYoung's modulusPorosityMetallurgy

Abstract

fetched live from OpenAlex

This paper intends to analyze the performance of mortars with reactive MgO, as a sustainable alternative to cement. Six different MgOs from Australia, Canada, and Spain were used in the production of mortars as partial substitutes for cement, namely 5%, 10%, 15%, 20%, and 25% (by weight). MgOs with different levels of reactivity were used to analyze its influence on the performance of MgO mortars. In order to evaluate the mechanical performance of these mortars, compressive strength, flexural strength, dynamic modulus of elasticity, and ultrasonic pulse velocity tests were performed. Compressive strength tests showed that the use of 25% reactive MgO can cause a decrease of this property of between 28% and 49%. The use of reactive MgO affected the other mechanical properties less. This paper also intends to analyze the durability performance of mortars with reactive MgO. To that effect, water absorption by capillarity was assessed. In this research, the effect of using MgO on the shrinkage was also analyzed. It was found that shrinkage may decrease by more than a half in some cases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2021
Admission routes1
Has abstractyes

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