Tuning the photonic properties of graphene oxide suspensions with nanostructured additives
Bibliographic record
Abstract
Photonic materials that can selectively reflect light across the visible spectrum are valuable for applications in optical devices, sensors, and decoration. Although two-dimensional (2D) colloids that stack into layers with spacing of hundreds of nanometers are able to selectively diffract light, controlling their separation in solution has proven challenging. In this work, we investigate the role of additives to control the photonic properties of hybrid colloidal suspensions of graphene oxide (GO). We discovered that low concentrations of colloidal additives like cellulose nanocrystals (CNCs) and clay nanoparticles (hectorite) added to GO suspensions lead to dramatic color changes. These hybrid colloidal suspensions demonstrate tunable structural colors and temperature-sensitive properties that likely originate from the entropically driven ejection of guests between the sheets, and from the interactions between colloidal electrical double layers and additional counterions. On the other hand, blending polymeric or molecular additives with GO suspensions either deteriorates or does not impact the photonic properties. These results are helpful to understand the interaction between GO suspensions and additives over different length scales, and open a path to advancing photonic materials based on hybrid colloidal suspensions.
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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) | 0.000 | 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".