Laser-induced graphene synthesis from the ultraviolet irradiation of polyimide using varied optical fluence
Bibliographic record
Abstract
Laser-induced graphene (LIG) has drawn immense interest among researchers worldwide since its development in 2015. The laser writing strategy used to synthesize LIG is particularly advantageous, as it enables the direct patterning of graphene with micron-sized features. There have been many attempts to reduce the feature size of LIG in recent years, however, the studies have shown wide variations in the methods and findings. As such, this work presents a rigorous study on the irradiation of polyimide via an ultraviolet (355-nm) laser to realize micron-scale, high-quality LIG. Our work shows that there is often a tradeoff between micron-scale features and high-quality material, as the tightly focused beams that are demanded for small features are predisposed to ablation of the material. This work investigates such LIG synthesis by correlating the characteristics of the material, via scanning electron microscopy and Raman spectroscopy, to the optical fluence incident on the polyimide substrate, providing a measure of applied optical energy per unit area. The findings reveal that—given suitable attention to the optical fluence—high-quality LIG with Raman 2D-to-G peak height ratios approaching 0.7 can be synthesized with feature sizes down to 18 ± 2 μm. Furthermore, optical fluences between 40 to 50 J/cm<sup>2</sup> produced the optimal LIG characteristics, as such optical fluences promote graphenization while minimizing ablation. The authors hope the findings of this study provide a foundation for the use of LIG in future integrated technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".