Assessment of Oat Husk Ash from Cold Climates as a Supplementary Cementitious Material
Bibliographic record
Abstract
To support the decarbonization efforts within the cement industry, it is imperative to explore novel sources of supplementary cementitious materials (SCMs). Agricultural ashes have emerged as promising candidates owing to their advantageous properties. However, their availability is predominantly influenced by geographical factors, with greater prevalence observed in tropical and subtropical regions. Hence, this study aimed to comprehensively evaluate oat husk ashes (OHA) obtained from a cold climate region (Manitoba, Canada) as a potential SCM. The physicochemical properties of OHA were examined utilizing analytical techniques including laser-scattering particle size analysis, X-ray fluorescence (XRF), X-ray diffraction (XRD), and environmental scanning electron microscopy (ESEM) equipped with energy dispersive X-ray (EDX). From nine distinct combustion protocols, the optimal OHA was identified by maximizing silica content, determined via XRF oxide analysis, and achieving higher reactivity, evaluated through the R3 (rapid, relevant, and reliable) test. Subsequently, the strength activity index of cement-OHA mortar formulations, incorporating the optimized OHA, was determined, and augmented thermal and microstructural analyses were carried out. The outcomes showed the potential of integrating OHA as a SCM in concrete to achieve satisfactory pozzolanic performance. Notably, the pozzolanic efficacy of optimized OHA may surpass that of Class F fly ash, particularly when subjected to combustion at 600°C for 4 h. Indeed, optimized OHA presents a promising alternative SCM, pivotal not only for advancing sustainability efforts within the cement and concrete industry, especially in cold climate regions, but also for mitigating the adverse impacts of uncontrolled combustion and landfill accumulation of agricultural residues.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".