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M3ALI

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Metabolic Mechanical Materials Adaptation Learning Interactivity

The central objective of M3ALI is to introduce concepts for adaptation, simplistic learning by training (physical exercise, not teaching), and interactivity in mechanically stimulated polymer materials by developing metabolic modu... ver más

31/10/2026

JOHANNES GUTENBERG...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

JOHANNES GUTENBERGUNIVERSITAT MAINZ No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-02-27

ERC-2020-COG: ERC CONSOLIDATOR GRANTS

I+D

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2 Participantes

JOHANNES GUTENBERGUNIVERSITA...

2.00M€ | Lider

ELDU

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Duración del proyecto: 68 meses Fecha Inicio: 2021-02-27
Fecha Fin: 2026-10-31

Líder del proyecto

JOHANNES GUTENBERGUNIVERSITAT MAINZ No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The central objective of M3ALI is to introduce concepts for adaptation, simplistic learning by training (physical exercise, not teaching), and interactivity in mechanically stimulated polymer materials by developing metabolic modules for mechanical memories (that can also be forgotten), for down-stream chemical processes and for active communication. The key experimental methodology is based on two classes of molecularly engineered mechanoprobes (MPs) that are capable of defined downstream reactivity up to the level of chemical reaction networks (CRNs). We build on our recent concept of DNA-based mechanofluorescent folding motifs in hydrogels, and extend it to cyclic disulfide MPs, and embed them into hydrogels and elastomers of controlled topology. DNA-based MPs will engage in DNA-based downstream reactions, while disulfide MPs will engage in complementary radical chemistry. The key concept is to code mechanical deformation into chemical signals that can be processed ultimately in CRNs to enable a behavioral evolution of the materials systems by installing memories, as well as by signal amplification, processing, translation and transport, and where the processed chemical information is fed back into the material to develop a full mechano-chemo-mechano signal processing language. We will break new ground in proof-of-concept applications in mechanical training and forgetting (physical exercise similar to muscle training), adaptive and interactive soft robotics, adaptive mechanical metamaterials, as well as interactive mechanical synchronization and interactive cell/material systems. Our approach to metabolic mechanical materials that use systems chemistry concepts to empower mechanical materials with the capacity to adapt, learn and interact profoundly contrast present research on responsive materials. In long term such concepts will provide the basis for more life-like materials systems capable of true adaptivity, interactivity and co-evolution in open systems.

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