Abstract 1628 Energy-Efficient Color Manipulation
Bibliographic record
Abstract
Chromatophores, the light-manipulating optical filters that enable rapid camouflage and signaling in cephalopod skin, operate through the expansion and contraction of pigment-containing sacs, resulting in vivid color changes. Chromatophore-inspired materials have long relied on non-mechanical processes or demanded substantial power inputs if they are mechanically-driven. Drawing inspiration from biological systems, we have developed double network (DN) hydrogels that utilize mechanical changes to modulate optical properties, presenting a promising avenue for achieving energy-efficient color manipulation. These materials have a covalently crosslinked, rigid network intricately entwined with an ionically crosslinked, ductile network, mitigating the fragility observed in single network hydrogels, and can be embedded with color-changing molecular absorbers. These conformational changes, driven by pH variations due to the presence of pH-sensitive acrylic acid/acrylamide components, enable these DN gels to be mechanically compliant, allowing substantial and reversible mechanical actuation without breakage or dissolution. The biomimetic approach enables achieving energy-efficient and dynamically responsive bio-inspired color displays. I would like to express my gratitude to Northeastern's Project-Based Exploration for the Advancement of Knowledge (PEAK) award for funding this project.
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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) | 0.017 | 0.003 |
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".