Strong Graphene 3D Assemblies with High Elastic Recovery and Hardness
Bibliographic record
Abstract
The rational design and construction of 3D graphene assemblies is a crucial step to extend the graphene properties for practical applications. Here, a novel interfacially reactive self-assembling process is reported to prepare well-organized 3D honeycomb-like graphene assemblies with unique polygonal nanopores interconnected by silicon-oxygen chemical bonds. The newly developed silicate-bridged graphene assembly (SGA) exhibits an exceptionally high hardness of 13.09 GPa, outperforming all existing 3D graphene materials, while maintains high Young's modulus (162.96 GPa), elastic recovery (75.27%), and superb thermal stability (600 °C in air). The observed unusual merits are resulted from unique pore structure combining the mechanical stability of the trihedral-nanopore structure and the deformability of the other polygonal nanopores. As a filling material, a merely 0.05% (w/w) addition of SGA could double the impact resistance of unsaturated resins (e.g., polyester). While SGA is attractive for various applications, including body armors, wearable electronics, space elevators, and multifunctional reinforcement fibers for automobiles, and aerospace vehicles, the novel liquid sodium-water interfacial reactive self-assembling developed in this study could open avenues for further development of various well-defined 3D assemblies from graphene and many other 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 | 0.000 | 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 | 0.001 |
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".