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Resistance of Alkali-Activated Binders to Organic Acids Found in Agri-Food Effluents

2021· article· en· W3097272797 on OpenAlexfundno aff
Timothy A. Aiken, Jacek Kwasny, Wei Sha

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsPortland cementFly ashCementitiousCalcium silicate hydrateCementCompressive strengthCalcium hydroxideMaterials scienceEttringiteLactic acidSlag (welding)Acetic acidChemistryWaste managementMetallurgyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Organic acids, such as acetic and lactic acids, are prevalent in agricultural and food effluents. They pose a considerable pollution threat and must be collected and stored safely before treatment and release. They cause significant damage to cementitious materials, reducing the service life of structures. In this study, the resistance of alkali-activated fly ash and slag-blended binders to organic acids was studied, and a comparison with ordinary portland cement binders was carried out. The findings demonstrate that alkali-activated binders with increased fly ash content have marginally better resistance to acetic acid, but mixes with increased slag content have better resistance to lactic acid. This is due to the solubility of the calcium and aluminum salts of acetic and lactic acids. Overall, the performance of the alkali-activated binders was better than that of the ordinary portland cement binder, with a lower mass and strength losses observed. This was attributed to their lower calcium content with less vulnerable phases, such as calcium hydroxide and ettringite. Instead, the calcium-silicate-hydrate (C-S-H) type gels in alkali-activated binders suffered decalcification and dealumination but left behind a silicon-rich gel, which helped to resist further acid 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 score0.040
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.231
Teacher spread0.218 · 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

Citations17
Published2021
Admission routes1
Has abstractyes

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