Graphene Nanoscrolls via Electric-Field-Induced Transformation of Water-Submerged Graphene Nanoribbons for Energy Storage, Nanofluidic, and Nanoelectronic Applications
Bibliographic record
Abstract
Nanoscroll is a rolled-up sheet of nanoribbon resembling a spiral papyrus-like multilayer structure, having a broad range of applications from gas and energy storage to nanofluidic and nanoelectronic devices. However, the existing methods of fabrication suffer from complex processing, high energy consumption, abundant impurities, and/or hybrid nanostructures, rendering them insufficient to fabricate scalable and high-quality nanoscrolls. Here, we predict that a graphene nanoribbon self-assembles into a nanoscroll under the influence of an external rotating electric field. Using molecular dynamics simulation, we show that electric-field-induced alignment of water dipoles originates rotation in a water-submerged graphene nanoribbon. On the basis of this principle, we propose a setup for nanoscroll formation from water-submerged graphene nanoribbon where one end of the nanoribbon is kept fixed, while the other end orients itself with the rotating electric field and, eventually, self-assembles into a nanoscroll. The nanoscroll is found to be energetically more stable than the initial configuration and retains its stability on removal of the external field as well as the aqueous environment. Findings from concentration profiles of the nanoscroll further confirm the stability as well as uniformity of its morphology. The formation mechanism is found to be minimally dependent on the applied field’s strength and frequency. The proposed method can be used to induce self-assembly of any nanoribbon structure independent of its dimensions and chirality and multilayer nanoribbons as well as to form nanotemplate encapsulated core/shell composites. The proposed method would enable large-scale realization of high-quality nanoscrolls from nanoribbons, facilitating fundamental and applied research on nanomaterials.
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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".