Automated and parallel transfer of arrays of oriented graphene ribbons
Bibliographic record
Abstract
Abstract The transfer of two-dimensional materials from their growth substrate onto application wafers is a critical bottleneck in scaling-up devices based on such nanomaterials. Here, we present an innovative approach to achieve the automated and simultaneous transfer of arrays of graphene ribbons, with precise control over their orientation and alignment onto patterned wafers. The transfer is performed in a simple, yet efficient apparatus consisting of an array of glass columns, strategically shaped to control ribbon orientation and arranged to match the destination wafer, coupled to a dual inflow/outflow pumping system. This apparatus enables the transfer of a custom array of parallel graphene ribbons in a fraction of the time required with traditional methods. The quality of the transferred graphene was evaluated using optical imaging, scanning electron microscopy, hyperspectral Raman imaging, and electrical transport: all consistently indicating that the transferred graphene exhibits excellent quality, comparable to a manual transfer by an expert user. The proposed apparatus offers several competitive advantages, including ease of use, high transfer throughput, and reduced nanomaterial consumption. Moreover, it can be used repeatedly on the same wafer to assemble arrays of overlayed materials with controlled relative orientations. This approach thus opens promising opportunities for the large-scale fabrication of various heterostructures and devices based on vertical assemblies of 2D 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".