Universal Transfer Printing of Micelle-Templated Nanoparticles Using Plasma-Functionalized Graphene
Bibliographic record
Abstract
Abstract Nanostructure incorporation into devices plays a key role in improving performance, yet processes for preparing two-dimensional (2D) arrays of colloidal nanoparticles tend not to be universally applicable, particularly for soft and oxygen-sensitive substrates for organic and perovskite-based electronics. Here, we show a method of transferring reverse micelle-deposited (RMD) nanoparticles (perovskite and metal oxide) on top of an organic layer, using a functionalized graphene carrier layer for transfer printing. As the technique can be applied universally to RMD nanoparticles, we used magnetic (γ-Fe2O3) and luminescent (methylammonium lead bromide (MAPbBr3)) nanoparticles to validate the transfer-printing methodology. The strong photoluminescence from the MAPbBr3 under UV illumination and high intrinsic field of the γ-Fe2O3 as measured by magnetic force microscopy (MFM), coupled with Raman measurements of the graphene layer, confirm that all components survive the transfer-printing process with little loss of properties. Such an approach to introducing uniform 2D arrays of nanoparticles onto sensitive substrates opens up new avenues to tune the device interfacial properties.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".