The Role of Water in Mediating Interfacial Adhesion and Shear Strength in Graphene Oxide
Bibliographic record
Abstract
Graphene oxide (GO), whose highly tunable surface chemistry enables the formation of strong interfacial hydrogen-bond networks, has garnered increasing interest in the design of devices that operate in the presence of water. For instance, previous studies have suggested that controlling GO's surface chemistry leads to enhancements in interfacial shear strength, allowing engineers to manage deformation pathways and control failure mechanisms. However, these previous reports have not explored the role of ambient humidity and only offer extensive chemical modifications to GO's surface as the main pathway to control GO's interfacial properties. Herein, through atomic force microscopy experiments on GO-GO interfaces, the adhesion energy and interfacial shear strength of GO were measured as a function of ambient humidity. Experimental evidence shows that adhesion energy and interfacial shear strength can be improved by a factor of 2-3 when GO is exposed to moderate (∼30% water weight) water content. Furthermore, complementary molecular dynamics simulations uncovered the mechanisms by which these nanomaterial interfaces achieve their properties. They reveal that the strengthening mechanism arises from the formation of strongly interacting hydrogen-bond networks, driven by the chemistry of the GO basal plane and intercalated water molecules between two GO surfaces. In summary, the methodology and findings here reported provide pathways to simultaneously optimize GO's interfacial and in-plane mechanical properties, by tailoring the chemistry of GO and accounting for water content, in engineering applications such as sensors, filtration membranes, wearable electronics, and structural materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".