Laser heat treatment of aerosol-jet additive manufactured graphene patterns
Bibliographic record
Abstract
Abstract In this article, a laser processing protocol for heat treatment of micro-scale printed graphene patterns is developed, and the results are compared with the counterpart results obtained by the conventional heat treatment process carried out in a furnace. A continuous-wave Erbium fiber laser is used to enhance electrical properties of the aerosol-jet printed graphene patterns through removing solvents and a stabilizer polymer. The laser power and the process speed are optimized to effectively treat the printed patterns without compromising the quality of the graphene flakes. Furthermore, a heat transfer model is developed and its results are utilized to optimize the laser treatment process. It is found that the laser heat treatment process with a laser speed of 0.03 mm s−1, a laser beam diameter ~50 μm, and a laser power of 10 W results in pure graphene patterns with no excessive components. The ratio of D to G bands ( in Raman graph of the laser treated pure graphene, which is an indicator of the level of the active defects in graphene structures, is 0.52. The laser treated pure graphene structures also have a C/O ratio and an electrical resistivity of ~4.5 and 0.022 Ω cm, respectively. These values are fairly comparable with the results of samples treated in a furnace. The results suggest that the laser processing has the capability of removing stabilizer polymers and solvents through a localized moving heat source, which is preferable for flexible electronics with low working temperature substrates.
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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".