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Insights on chemical and physical chloride binding in blended cement pastes

2022· article· en· W4214861710 on OpenAlex
William Wilson, Julien Gonthier, Fabien Georget, Karen Scrivener

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCement and Concrete Research · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologies
KeywordsEttringiteChlorideSulfateSalt (chemistry)AdsorptionChemistryHydrotalciteInorganic chemistryBinding energyChemical engineeringMagnesiumPortland cementCementNuclear chemistryMaterials scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

This study investigates chloride binding in blended cement pastes exposed to 0.5 M NaCl solutions (with and without pH adjustment) using X-ray diffraction and energy-dispersive X-ray spectroscopy image analysis (edxia). The aim is to better understand the effects of the binder type, the water-to-binder ratio and the pH on the chemical binding in AFm phases and the physical binding on C-A-S-H. Results show that the binding cannot be predicted from AFm and C-A-S-H contents alone because competing ions in the system affect both the Friedel's salt solid solution chemistry and the C-A-S-H binding capacity. Notably, the high content of aluminous hydrates in LC3 systems leads to a high chemical binding even if Friedel's salt solid solutions have relatively low chloride contents (particularly at a higher pH). On the contrary, the CEMIII/A paste showed low binding because of relatively high sulfate and magnesium contents which compete for incorporation/adsorption in aluminous hydrates (AFm, ettringite and hydrotalcite).

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)0.001
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 score0.077
Threshold uncertainty score0.633

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 science0.0000.001
Research integrity0.0000.001
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 head0.043
GPT teacher head0.301
Teacher spread0.258 · 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