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Record W4220726042 · doi:10.3390/cmd3010009

The Influence of CaCl2-Blended Acrylic Polymer on Steel Rebar Corrosion and Acid Attack Resistance of Mortar

2022· article· en· W4220726042 on OpenAlexaff
Obinna Onuaguluchi, Nemkumar Banthia

Bibliographic record

VenueCorrosion and Materials Degradation · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRebarCorrosionMaterials scienceMortarLepidocrociteComposite materialCementChlorideConcrete coverAkaganéiteMetallurgyGoethiteChemistry

Abstract

fetched live from OpenAlex

A realistic forecast of the impact of CaCl2-blended acrylic polymer (CP) emulsions on steel reinforcement corrosion is difficult without a detailed study. This is traceable to the conflicting effects of cement additives on the chloride threshold value, pore solution chemistry, and matrix resistivity. Hence, in the study reported herein, the actual influence of 0.5–1.5 wt% CP on rebar corrosion was assessed via an accelerated corrosion test. The macrocell current, half-cell potential measurements (HCP), reinforcement cover crack propagation, and rebar deterioration were monitored. The resistance of mortar specimens to acid-induced degradation was also evaluated over time. The corrosion test results indicate that steel rebar corrosion initiation in the mortar expedited as the CP dosage in mixtures increased. Consequently, the time required for the CP-modified specimens to crack shortened. Moreover, non-uniform rebar section loss and surface crack width widening were also observed in CP-modified mortar specimens. However, X-ray diffraction (XRD) analyses of the plain Ref and 1.5% CP corrosion byproduct residues indicated that key phases such as akaganeite, goethite, lepidocrocite, hematite, and magnetite were quantitatively similar. Interestingly, the addition of 1.0–1.5% CP to mixtures enhanced the acid attack resistance of mortars. For now, these results indicate that CP should only be used as a chemical admixture in unreinforced cement composites.

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)0.000
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 score0.020
Threshold uncertainty score0.509

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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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