A Universal Photochemical Approach to Ultra‐Small, Well‐Dispersed Nanoparticle/Reduced Graphene Oxide Hybrids with Enhanced Nonlinear Optical Properties
Bibliographic record
Abstract
Integrating multiple functionalities into individual nanoscale hybrids with strong nonlinear optical (NLO) response to ultrafast laser pulses is of tremendous importance. Here, a series of ultra‐small and well‐dispersed nanoparticles (NPs) supported on the undoped and doped reduced graphene oxide (rGO) are obtained via a general and versatile photochemical technique. Extremely fast heterogeneous nucleation rate and cooling rate having negligible thermal effect induced by a femtosecond laser are proposed to play a determined role in the formation of ultra‐small NPs. No surfactants, reduction reagents, or toxic materials are needed. The prepared rGO hybrids exhibit significantly enhanced ultrafast NLO response with very low optical limiting threshold, which originates from the fast and efficient electron and/or energy transfer from ultra‐small NPs to rGO. This study may represent an important universal step toward the generation of graphene hybrid nanostructures and even complex 3D functional systems consisting of multiple functional ultra‐small subunits with new horizons for numerous applications, especially in ultrafast nonlinear optics.
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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".