Study of biochar in cementitious materials for developing green concrete composites
Bibliographic record
Abstract
Biochar is a waste biomass derived carbon enriched solid material that is capable for carbon sequestration. This study illustrates a revolutionary study on the waste biomass pyrolyzed biochar-based concrete with 0-5 wt% cement replacement by the biochar as a potential binder material without using any petroleum-derived superplasticizer. The mechanical strength properties along with the water absorption of the proposed hybrid composite are evaluated for a fixed concrete composition. Moreover, the volumetric property of the formed concrete is determined using the synchrotron-based micro-computed tomography. This study revealed that biochar incorporation up to optimum concentration helps in enhancing the mechanical strength properties of concrete. The optimum dosage of biochar which is 2 wt% increases the compressive strength, splitting tensile strength, and flexural load at fracture by 18.95%, 19.64%, and 12% respectively. Moreover, the optimum sample has shown lowest water absorption among all other samples which indicated reduced porosity in the concrete with biochar introduction. X-ray diffraction analysis confirmed the more production of calcium-silicate-hydrate hydration products for the optimum biochar-augmented concrete composites which resulted in high strength concrete formation. However, the higher concentration of biochar seemed to have negative influence on the strength and durability properties of the concrete which can be seen in terms of physical strength properties and water absorption data. Nonetheless, our novel design of biochar-based concrete can open up a new field of biochar application in construction materials without sacrificing its mechanical strength and water absorption properties to develop green concrete composites.
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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 | 0.000 | 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) | 0.001 | 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".