Hierarchical kirigami-inspired graphene and carbon nanotube metamaterials: Tunability of thermo-mechanic properties
Bibliographic record
Abstract
Tuning and programming the multiphysical properties of advanced materials are of critical importance for developing the next generation of adaptable multifunctional metamaterials. This study demonstrates the tunability of thermo-mechanical properties of graphene sheets and carbon nanotubes by inspiring from hierarchical kirigami mechanical metamaterials. The theoretical investigation, multiscale simulation, and experimentation show that the thermo-mechanical properties of nano-architected kirigami metamaterials can be tuned by altering geometrical parameters and introducing the hierarchical cutting patterns. Additionally, the thermal conductivity of kirigami-inspired graphene and carbon nanotube metamaterials can be regulated by an external mechanical tension. We develop closed-form formulations for predicting the mechanical behavior of kirigami graphene sheets and carbon nanotubes. Molecular dynamics and finite element simulations are conducted to evaluate theoretical predictions. By analyzing and comparing the results from atomistic and continuum-based simulations, the effect of length scale on the thermo-mechanical properties is explored. We realize that the stress–strain response, thermal conductivity, and buckling-induced 3D patterns of nano-architected graphenes can be programmed by utilizing kirigami building blocks, nano-architectural hierarchy, and heterogeneous material design.
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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) | 0.000 | 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".