Electrospun MXene–Sericin nanofibers for carbonated recycled aggregates: Toward intelligent, durable, and low-carbon cementitious composites
Bibliographic record
Abstract
The environmental impacts of extracting raw materials for construction are an emerging concern. The incorporation of recycled fine aggregates (RFAs) in the production of cement-based composites is gaining significance. RFAs offer environmental advantages in construction materials; yet, they may compromise mechanical properties. This work presents an innovative approach to enhance the performance and sustainability of mortars by using electrospun MXene/sericin (MXS) nanofibers with recycled fine aggregate (RFA). The multifunctional nanofibers were designed to improve carbonation efficiency, mechanical strength , durability, and CO₂ sequestration. The results demonstrate a 15 % increase in compressive strength and a 26 % rise in flexural strength after 28 days with 0.3 wt% MXS, as well as a 40 % reduction in the chloride migration coefficient. The porosity analysis revealed a 28 % reduction in cumulative porosity and a 29 % decrease in effective porosity, while capillary absorption decreased by 18 %. Moreover, CO₂ absorption to a peak of 9.3 kg/ton with 0.3 wt% MXS, indicating enhanced carbonation efficiency. Furthermore, the composites exhibited remarkable electrical conductivity and self-sensing capacities due to the percolating MXene networks, enabling real-time structural monitoring. The results confirm that MXS nanofibers are a feasible enhancement for developing high-performance, low-carbon cementitious systems that offer mechanical strength , durability, environmental benefits, and integrated intelligence.
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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".