Application of Graphene in Fiber-Reinforced Cementitious Composites: A Review
Bibliographic record
Abstract
Graphene with fascinating properties has been deemed as an excellent reinforcement for cementitious composites, enabling construction materials to be smarter, stronger, and more durable. However, some challenges such as dispersion issues and high costs, hinder the direct incorporation of graphene-based reinforcement fillers into cementitious composites for industrial production. The combination of graphene with conventional fibers to reinforce cement hence appears as a more promising pathway especially towards the commercialization of graphene for cementitious materials. In this review paper, a critical and synthetical overview on recent research findings of the implementation of graphene in fiber-reinforced cementitious composites was conducted. The preparation and characterization methods of hybrid graphene-fiber fillers are first introduced. Mechanical reinforcing mechanisms are subsequently summarized, highlighting the main contribution of nucleation effect, filling effect, interfacial bonding effect, and toughening effect. The review further presents in detail the enhancements of multifunctional properties of graphene-fiber reinforced cementitious composites, involving the interfacial properties, mechanical properties, durability, electrical conductivity, and electromagnetic interference shielding. The main challenges and future prospects are finally discussed to provide constructive ideas and guidance to assist with relevant studies in future.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".