Engineering the Mechanical Properties of Monolayer Graphene Oxide at the Atomic Level
Bibliographic record
Abstract
The mechanical properties of graphene oxide (GO) are of great importance for applications in materials engineering. Previous mechanochemical studies of GO typically focused on the influence of the degree of oxidation on the mechanical behavior. In this study, using density functional-based tight binding simulations, validated using density functional theory simulations, we reveal that the deformation and failure of GO are strongly dependent on the relative concentrations of epoxide (-O-) and hydroxyl (-OH) functional groups. Hydroxyl groups cause GO to behave as a brittle material; by contrast, epoxide groups enhance material ductility through a mechanically driven epoxide-to-ether functional group transformation. Moreover, with increasing epoxide group concentration, the strain to failure and toughness of GO significantly increases without sacrificing material strength and stiffness. These findings demonstrate that GO should be treated as a versatile, tunable material that may be engineered by controlling chemical composition, rather than as a single, archetypical material.
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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".