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Record W2089422439 · doi:10.1680/adcr.12.00055

Solvent exchange in sulfoaluminate phases. Part II: monosulfate

2013· article· en· W2089422439 on OpenAlexaff
Rahil Khoshnazar, J.J. Beaudoin, Laïla Raki, Rouhollah Alizadeh

Bibliographic record

VenueAdvances in Cement Research · 2013
Typearticle
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsGiatec Scientific (Canada)National Research Council Canada
Fundersnot available
KeywordsMethanolSolventChemistryThermogravimetric analysisMicrostructureEthanolGravimetric analysisMoleculeReactivity (psychology)Infrared spectroscopyAlcoholInorganic chemistryNuclear chemistryChemical engineeringOrganic chemistryCrystallography

Abstract

fetched live from OpenAlex

The influence of organic solvent exchange techniques on the microstructure and the dimensional stability of monosulfate (3CaO.Al 2 O 3 .CaSO 4 .12H 2 O) was critically investigated. Monosulfate samples were treated with methanol, ethanol and isopropanol, and examined by different analytical techniques including X-ray diffraction, Fourier transform infrared spectroscopy, thermal gravimetric analysis and scanning electrom microscopy. Nitrogen surface area measurements (Brunauer–Emmett–Teller (BET) method) and length-change of the monosulfate samples in the solvents were also recorded. Evidence was obtained that indicates monosulfate was readily dehydrated from 12 water molecules to ten water molecules once it was treated by the investigated alcohols. The alcohol molecules, however, likely intercalated into the monosulfate structure, limiting an expected decrease in the interlayer distance of the monosulfate structure. Methanol had the greatest damaging effect on the monosulfate microstructure. Ethanol, however, resulted in higher long-term expansion owing to its intermediate molecular size and reactivity with monosulfate compared to methanol and isopropanol.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.067
GPT teacher head0.360
Teacher spread0.292 · 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.

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

Citations15
Published2013
Admission routes1
Has abstractyes

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