Stable graphene oxide hydrophobic photonic liquids
Bibliographic record
Abstract
Graphene oxide (GO) is an important nanomaterial for producing photonic liquids due to its ability to display full-color reflections in water. However, the poor stability of GO photonic liquids and unsatisfactory dispersibility of GO nanosheets in hydrophobic liquid media have been significant drawbacks to developing photonic materials based on GO. Here, stable GO hydrophobic photonic liquids are demonstrated for the first time. GO nanosheets are directed into different hydrophobic liquid media, including reactive liquid precursors like tetraethoxysilane and ethyl acrylate, in the presence of phase transfer additives. These liquids exhibit tunable reflection wavelength up to ∼1300 nm with improved stability relative to aqueous GO photonic suspensions at elevated temperatures or under ambient conditions. Supported by an entropy-driven depletion mechanism, hydrophobic additives can effectively mediate the self-assembly of GO to produce tunable photonic liquids without the need to adjust GO concentrations. Furthermore, simultaneous infrared and visible light reflection can be achieved, enabling infrared photonic GO liquids to display visible colors. The improved stability and tunable photonic properties of hydrophobic GO liquids will open a way for developing GO-based optical materials and devices.
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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".