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Re-programming Hydrogel Properties Using a Fuel-Driven Reaction Cycle

Open access
Singh, Nishant, Lainer, Bruno, Formon, Georges J. M., De Piccoli, Serena, Hermans, Thomas M.·2020·Journal of the American Chemical Society

Abstract

Nature uses catalysis as an indispensable tool to control assembly and reaction cycles in vital non-equilibrium supramolecular processes. For instance, enzymatic methionine oxidation regulates actin (dis-)assembly, and catalytic guanosine triphosphate hydrolysis is found in tubulin (dis-)assembly. Here we present a completely artificial reaction cycle which is driven by a chemical fuel that is catalytically obtained from a "pre-fuel". The reaction cycle controls the dis-assembly and re-assembly of a hydrogel, where the rate of pre-fuel turnover dictates the morphology as well as the mechanical properties. By addition of additional fresh aliquots of fuel and removal of waste, the hydrogels can be re-programmed time after time. Overall, we show how catalytic fuel generation can control reaction/assembly kinetics and materials' properties in life-like non-equilibrium systems.

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Metadata

Authors
Singh, N., Lainer, B., Formon, G. J. M., De Piccoli, S., & Hermans, T. M.
Venue
Journal of the American Chemical Society
Published
2020-02-17
Volume
142
Issue
9
Pages
4083-4087
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W3007374364
Cited by
159
References
0
FWCI
6.23
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Re-programming Hydrogel Properties Using a Fuel-Driven Reaction Cycle. https://app.cochat.ai/papers/10.1021/jacs.9b11503
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Singh, N., Lainer, B., Formon, G. J. M., De Piccoli, S., & Hermans, T. M. (2020). Re-programming Hydrogel Properties Using a Fuel-Driven Reaction Cycle. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.9b11503

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.