Fast Self‐Assembly of Scalable Photonic Cellulose Nanocrystals and Hybrid Films via Electrophoresis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nano-enabled, bio-based, functional materials are key for the transition to a sustainable society as they can be used, owing to both their performance and nontoxicity, to gradually replace existing nonrenewable engineering materials. Cellulose nanocrystals (CNCs), produced by acid hydrolysis of cellulosic biomass, have been shown to possess distinct self-assembly, optical, and electromechanical properties, and are anticipated to play an important role in the fabrication of photonic, optoelectronic, and functional hybrid materials. To facilitate CNCs' technological viability, a method suitable for industrial exploitation is developed to produce photonic films possessing long-range chirality on conductive, rigid, or flexible, substrates within a few minutes. The approach is based on electrophoretic deposition (EPD)-induced self-assembly of CNCs, where photonic films of any size can be produced by controlling CNC surface properties and EPD parameters. CNC film coloration can be determined by the CNC aqueous suspension characteristics, while their reflected intensity can be tuned by changing the duration and number of electrodeposition cycles. EPD-induced self-assembly of CNCs is compatible with in situ reduction of gold precursors without the need to use additional reducing agents (some of which are considered toxic), thereby allowing the preparation of hybrid photonic films with tunable plasmonic response in a one-pot process.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it