Electrically fueled active supramolecular materials
Bibliographic record
Abstract
Fuel-driven dissipative supramolecular assemblies in biology, such as actin filaments and microtubules contribute to the formation of complex, dynamic structures in living organisms and give rise to emergent functions such as motility, homeostasis, self-healing, and camouflage. Several synthetic dissipative supramolecular materials have been created using chemicals or light as fuel, with the goal of furthering our understanding of biological systems and creating synthetic materials that have life-like dynamic properties. However, electrical energy, one of the most common energy sources, has remained mostly unexplored for such purposes. Here we demonstrate the use of electrically fueled dissipative assembly as a new platform for creating active supramolecular materials. Through an electrochemical redox reaction network operating in mild aqueous buffers, a transient and highly active supramolecular assembly based on a redox-sensitive cysteine derivative is achieved by applying an electric potential. The dissipative self-assembly as well as its emergent properties can be spatiotemporally controlled by modulation of electrical signals on patterned microelectrodes. Using electrical energy as a readily available and clean fuel, we are able to create dissipative supramolecular materials rapidly (in seconds to minutes) and repetitively under mild conditions with directional and spatiotemporal control. As electronic signals are the default information carriers in modern technology, the described approach offers a promising opportunity to integrate active materials into electronic devices for bioelectronics applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".