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Influence of Content and Source of Calcium Sulfate on Supersulfated Cement Exposed to Sodium and Magnesium Sulfate Attack at Later Ages

2022· article· en· W4306920020 on OpenAlexaff
Priscila Ongaratto Trentin, Isabel Cristina Magro, Laura Rorato Moraes Neubern Souza, Janaína Sartori Bonini, Caroline Angulski da Luz, Ronaldo A. Medeiros-Junior, R.D. Hooton

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSulfateEttringiteGypsumMagnesiumCalciumPhosphogypsumSodium sulfateCementChemistrySodiumRaw materialPortland cementMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Supersulfated cement (SSC) consists essentially of blast-furnace slag and calcium sulfate. It can be considered an alternative to Portland cement because its production minimizes the environmental impacts caused by the exploration of raw materials and CO2 emissions. However, the long-term durability of SSC still needs to be studied. In this article, the influence of the composition of the SSC in the attack by sodium and magnesium sulfates was analyzed, varying the source (gypsum and phosphogypsum) and content (10% and 20%) of calcium sulfate. The source of calcium sulfate did not significantly influence the resistance to sulfate attack, but it did change the mechanical strength of the SSC. The results showed that at the end of two years, all SSC samples were resistant to sodium sulfate attack; SSC pastes naturally formed a large amount of ettringite at early ages, preventing expansion at later ages. The SSC containing more calcium sulfate was the only mixture resistant under magnesium sulfate attack. A higher content of calcium sulfate in the composition of SSC can reduce the rate of diffusion of sulfate ions and improve their resistance to attack by magnesium sulfate. The lower formation of C─ S─ H, together with a higher content of calcium sulfate, provided an SSC with the best performance under magnesium sulfate attack.

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 score0.240
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.033
GPT teacher head0.243
Teacher spread0.210 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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