Thin-suspended 2D materials: facile, versatile, and deterministic transfer assembly
Bibliographic record
Abstract
Abstract We report a deterministic 2D material (2DM) transfer method to assemble any-stacking-order heterostructures incorporating suspended ultra-thin 2DMs, such as single-layer graphene (SLG) and bilayer graphene (BLG). The transfer procedure relies on a single-step preparation nitrocellulose micro-stamp, which combines both outstanding adhesion and softness. It permits the dry pick-up of naked 2D crystals (graphene, MoS 2 , and hBN) directly from a SiO 2 substrate, and to precisely transfer them on substrates or trenches. Optical and Raman data show that no significant defect is introduced upon transfer, even in suspended SLG and BLG. The areas transferred range up ∼1000 µ m 2 on substrate. High-yield transfer of suspended ultra-thin 2DMs does not require critical point drying for areas up to 15 µ m 2 or suspension heights down to 160 nm. To demonstrate the method’s capabilities, we assembled on-substrate and suspended optical cavities tuning BLG’s Raman scattering intensity by factors of 19 and 4, respectively. This resilient and rapid 2DM transfer procedure will accelerate the fabrication of many heterostructures and permit versatile suspension of 2DMs for research in twistronics, straintronics, and nano-opto-electro-mechanical systems.
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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 | 0.000 | 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".