Laser micromachining of oxygen reduced graphene-oxide films
Bibliographic record
Abstract
Non-conductive graphene-oxide (GO) inks can be synthesized from inexpensive graphite powders and deposited on functionalized flexible substrates using inkjet printing technology. Once deposited, the electrical conductivity of the GO film can be restored through laser assisted thermal reduction. Unfortunately, the inkjet nozzle diameter (~40μm) places a limit on the printed feature size. In contrast, a tightly focused femtosecond pulsed laser can create precise micro features with dimensions in the order of 2 to 3 μm. The smallest feature size produced by laser microfabrication is a function of the laser beam diameter, power level, feed rate, material characteristics and spatial resolution of the micropositioning system. Laser micromachining can also remove excess GO film material adjacent to the electrode traces and passive electronic components. Excess material removal is essential for creating stable oxygen-reduced graphene-oxide (rGO) printed circuits because electron buildup along the feature edges will alter the conductivity of the non-functional film. A study on the impact of laser ablation on the GO film and the substrate are performed using a 775nm, 120fs pulsed laser. The average laser power was 25mW at a spot size of ~ 5μm, and the feed rate was 1000-1500mm/min. Several simple microtraces were fabricated and characterized in terms of electrical resistance and surface topology.
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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) | 0.002 | 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".