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Record W4406850996 · doi:10.1080/21650373.2025.2452302

Effects of polycarboxylate superplasticizers with different functional groups on the adsorption behavior and rheology of cement paste containing montmorillonite

2025· article· en· W4406850996 on OpenAlexaff
Shengnan Sha, Lei Lei, Yihan Ma, Dengwu Jiao, Xiao Zhiqiang, Caijun Shi

Bibliographic record

VenueJournal of Sustainable Cement-Based Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of British Columbia
FundersMinistry of Science and Technology, IsraelChina Scholarship Council
KeywordsSuperplasticizerRheologyMontmorilloniteAdsorptionCementMaterials scienceChemical engineeringComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The dispersing effectiveness of polycarboxylate superplasticizer (PCE) in cement paste is dramatically reduced as a result of the sorption of PCE by montmorillonite (MMT) clay. To improve the dispersing effectiveness of PCE in MMT-containing cement paste, three PCEswere synthesized by copolymerizing isopentenyl polyoxyethylene ether (TPEG) with trans-2-butenedioic acid (FA) (FA-TPEG), FA and methacryloxyethyltrimethyl ammonium chloride (FA-DMC-TPEG), FA and sodium p-styrene sulfonate (FA-SSS-TPEG), respectively. Their molecular structures were characterized by gel permeation chromatography, specific charge density , Fourier transform infrared spectrum, and 1H nuclear magnetic resonance spectroscopy. X-ray diffraction and adsorption analysis were used to reveal the interactions between PCEs and MMT, and their dispersing performance in cement-MMT paste was evaluated using mini-cone and rheology measurements.. Results indicate that the difference in flowability caused by PCEs is primarily attributed to their different affinity for MMT in cement paste. Among them,, FA-SSS-TPEG showed excellent flowability due to its high affinity for cement particles and strong steric hindrance effect.

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)0.001
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 score0.014
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable Cement-Based MaterialsSame topicConcrete and Cement Materials ResearchFrench-language works237,207