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Record W1850590082 · doi:10.3151/jact.13.415

Characteristics and Engineering Performance of C-S-H/Aminobenzoic Acid Composite Systems

2015· article· en· W1850590082 on OpenAlexaff
Rahil Khoshnazar, J.J. Beaudoin, Laïla Raki, Rouhollah Alizadeh

Bibliographic record

VenueJournal of Advanced Concrete Technology · 2015
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsGiatec Scientific (Canada)National Research Council Canada
Fundersnot available
KeywordsComposite numberDurabilityCalcium silicate hydrateFourier transform infrared spectroscopyMaterials scienceAminobenzoic acidAdsorptionHydrateNuclear chemistryInfrared spectroscopyPhase (matter)Chemical engineeringAnalytical Chemistry (journal)ChemistryComposite materialChromatographyOrganic chemistryCement

Abstract

fetched live from OpenAlex

Calcium-silicate-hydrate/aminobenzoic acid (C-S-H/ABA) composite systems were synthetized and characterized for the first time. Each of 3- or 4-aminobenzoic acid with a concentration of 0.01 mol. per mol. of Ca was added to the C-S-H preparations during their hydration. The C-S-H/ABA systems were filtered and dried after three weeks. These were, then, characterized by X-ray diffraction, Fourier transform infrared spectroscopy and nitrogen adsorption analysis. Porous bodies were also prepared from C-S-H/ABA compacted powders and used for the length-change and mass-change measurements in different test solutions. In addition, the microindentation technique was used to determine the creep modulus and hardness of the compacted samples. It is suggested that the C-S-H/ABA systems had improved durability and enhanced mechanical properties compared to the phase pure C-S-H reference materials. The influence of the 3- and 4-aminobenzoic acid on durability factors was similar. The C-S-H/3-ABA, however, had superior mechanical performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2015
Admission routes1
Has abstractyes

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