Potential for carbon sequestration in modern cementitious materials
Bibliographic record
Abstract
Abstract Carbon sequestration in modern cementitious materials has emerged as a critical study domain in the attempt to reduce global carbon emissions. This review analyzes the capacity of diverse and innovative concrete types, such as engineered cementitious composites (ECC), ultra-high-performance concrete (UHPC), limestone calcined clay cement (LC 3 )-based concrete, geopolymer concrete, 3D-printed concrete (3DCP), and recycled aggregate concrete, to sequester and store carbon during the curing stage. With a focused effort on understanding carbon sequestration during curing, this study offers a comprehensive overview and examines the challenges associated with optimizing curing processes, highlighting their impact on the mechanical performance and durability of carbon-sequestered matrices. The effects of different curing methods and materials are examined concerning their influence on concrete performance and the optimization for carbon storage. The study emphasizes perpetual issues and outlines critical research gaps that must be addressed to enhance the efficacy of carbon sequestration in modern concrete systems. Recommendations for future research areas are offered to facilitate the advancement of more sustainable and carbon-efficient cementitious materials.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".