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Record W4391428873 · doi:10.1080/21650373.2024.2310508

Synthesis, performance and mechanism of novel polymer-type shrinkage reducing agents for cement-based materials

2024· article· en· W4391428873 on OpenAlexaff
Beibei Zhou, Yihan Ma, Shengnan Sha, Yifei Wang, Yi Liu, Yuchong Xiao, Caijun Shi

Bibliographic record

VenueJournal of Sustainable Cement-Based Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicConcrete Properties and Behavior
Canadian institutionsUniversity of British Columbia
FundersMinistry of Science and Technology, IsraelNational Natural Science Foundation of China
KeywordsShrinkageCementMaterials sciencePorositySurface tensionPolymerComposite materialMortar

Abstract

fetched live from OpenAlex

Simplifying the synthesis process and preparing a new type of shrinkage reducing agent (SRA) with a low dosage and high efficiency are beneficial for promoting the development of SRA and its application in cement-based materials. This study aims to synthesize three novel polymer-type SRAs with diverse structures through free radical copolymerization and to investigate their performance and working mechanisms in cement-based materials. Results clearly showed that the three synthesized SRAs reduced the surface tension of pore solution, and increased the flowability of cement paste. The SRAs-1 with butyl resulted in higher flowability of cement paste due to lower surface tension. Their addition at a low dosage of 0.5% by cement mass effectively mitigated the shrinkage of cement mortar. Among the three, SRAs-1 with butyl exhibited a higher capability of reducing drying shrinkage due to lower surface tension, lower porosity in the range of 2.5–50 nm, and lower total porosity.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.020
GPT teacher head0.238
Teacher spread0.218 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

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