Tunable Etching of CVD Graphene for Transfer Printing of Nanoparticles Driven by Desorption of Contaminants with Low Temperature Annealing
Bibliographic record
Abstract
Due to its exceptional mechanical properties, graphene can be an ideal support for nanotransfer printing. However, in its as-received state, it is incompatible with some processes for preparing 2D arrays of colloidal nanoparticles from reverse micelle templating. By treating CVD graphene with low temperature annealing, we have created a universal carrier to transfer such nanoparticles onto organic surfaces, taking advantage of the activation of the graphene surface via oxygen plasma etching. Desorption of hydrocarbon contaminant species by low temperature annealing is essential to ensure that exposure of the CVD graphene to the plasma oxidizes the film rather than etching it, as confirmed by Raman, Attenuated Total Reflectance- Fourier Transform Infrared (ATR-FTIR), and X-ray photoelectron spectroscopy measurements. Upon transfer printing to an organic surface, the nanoparticles are sandwiched between the reduced graphene oxide-like layer and the organic surface as shown by scanning near-field optical microscopy (SNOM), making them ideal as an interlayer in organic devices. The combination of exposure to plasma and annealing gives two vectors for controlling the oxygen doping profile in the activated graphene on Cu, and suggests new avenues for patterning nanostructures in devices with processing sensitive active layers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".