Fast Self‐Assembly of Scalable Photonic Cellulose Nanocrystals and Hybrid Films via Electrophoresis
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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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".