A Novel Femtosecond Laser‐Assisted Method for the Synthesis of Reduced Graphene Oxide Gels and Thin Films with Tunable Properties
Bibliographic record
Abstract
Graphene and its functionalized derivatives are unique and multifaceted novel materials with a wide range of applications in chemistry, healthcare, and optoelectronic engineering. 3D graphene materials exhibit several advantages over 2D (monolayer) graphene for a variety of devices applications. Here a novel and effective room temperature technique is introduced to convert an aqueous graphene oxide solution into a reduced graphene oxide gel with tunable physical and chemical properties comparable to a monolayer graphene sheet, without the need for any additives or chemical agents. The femtogel is synthesized by exposing an ultrahigh concentration graphene oxide solution with single‐layer flakes to high intensity femtosecond laser pulses. The femtosecond laser beam is focused on the air/aqueous solution interface to enhance the vaporization of functional groups and water, enabling femtogel formation. By controlling the pulsed laser intensity, beam focal parameters, and pulse duration, it is possible to produce several milliliters of femtogel in as little as 8 min. Through initial optimization of the irradiation parameters, a thin film is produced from a femtogel that demonstrates a surface roughness less than 6 nm, and more than 95% reduction in OH absorbance, as compared to a thin film produced from the unexposed graphene oxide solution.
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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 | 0.001 |
| 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".