Metabolic Mechanical Materials: Adaptation, Learning & Interactivity

Project Information

M3ALI

Grant agreement ID: 101001638

DOI

10.3030/101001638

EC signature date 27 February 2021

Start date 1 November 2021

End date 31 October 2026

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 998 750,00

EU contribution

€ 1 998 750,00

Coordinated by

JOHANNES GUTENBERG-UNIVERSITAT MAINZ
Germany

Periodic Reporting for period 2 - M3ALI (Metabolic Mechanical Materials: Adaptation, Learning & Interactivity)

Reporting period: 2023-05-01 to 2024-10-31

Summary of the context and overall objectives of the project

The M3ALI project addresses the critical issue of developing polymer materials that can adapt, learn, and interact in ways that current materials cannot. Traditional materials lack the ability to evolve or respond dynamically to their environment, limiting their applications. M3ALI aims to overcome these limitations by introducing adaptation, learning through physical exercise, and interactivity into polymer materials. The project focuses on creating metabolic modules that allow these materials to have mechanical memories, drive downstream chemical processes, and communicate via mechanical signals.

This research is crucial as materials with these capabilities could transform multiple industries. In robotics, such materials could lead to more advanced, responsive, and adaptable robots. In healthcare, they could improve prosthetics and medical devices, making them more attuned to patient needs. The ability of materials to evolve and interact with their environment could lead to groundbreaking technologies enhancing quality of life and promoting sustainability.

The M3ALI project has several objectives. First, it aims to introduce adaptation and learning into polymer materials, enabling them to build mechanical memories through physical exercise, similar to muscle training. Second, it seeks to enable these materials to interact and communicate through mechanical signals, fostering the development of more responsive and intelligent systems. To achieve these goals, the project will develop experimental methodologies using molecularly engineered mechanoprobes (MPs) capable of defined downstream reactivity, extending to complex chemical reaction networks (CRNs) and using DNA nanoscience approaches.

A key concept is encoding mechanical deformations into chemical signals processed in CRNs, allowing the materials to evolve by installing memories and amplifying, processing, translating, and transporting signals. This approach will be tested through proof-of-concept applications in adaptive and interactive soft robotics, mechanical metamaterials, and cell/material communication.

Ultimately, M3ALI aims to lay the foundation for future innovations in materials science. By establishing the basis for materials systems that exhibit true adaptivity, interactivity, and co-evolution, the project seeks to push the boundaries of current responsive materials research, leading to more life-like material systems and significant advancements across various fields.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

In mechano-to-chemo coupling in polymer materials, we developed a system where mechanoactivation forms radicals that amplified by signal transducers to self-destruct gels. This chemistry was applied in soft robotic bilayers, where bending leads to self-immolation and degradation, resulting in self-regulated unbending. Additionally, we demonstrated pressure-induced debonding in on-demand adhesives.
We also integrated light-driven molecular machines into polymeric materials to create light-driven soft robotics. This study demonstrated the scalability of molecular motor-polymer conjugates for artificial muscles.
In signal transport and adaptive responses, we developed hydrogels with urea/urease reaction networks and an autocatalytic feedback mechanism, enabling pH front transduction from a local signal trigger. We developed metamaterial strain gates that activate front propagations from global mechanical strain with precise gates, combining chemical and physical intelligence. Coupling the pH wave with a self-reinforcing fibrillization reaction led to self-strengthening, and combining it with bilayers triggered soft robot actuation. Using simpler concepts, we showed how pH reaction circuits control autonomous soft robots and incorporate chemo-mechanical feedback for self-regulation. In mechano-to-chemo coupling in DNA materials, we identified suitable amplification reactions with low background parasitic reactions and high amplification levels. Significant efforts showed catalytic strand displacement and rolling circle amplification as promising. We established a diverse toolbox for creating photopolymerizable hydrogels with DNA mechanoprobes, focusing on highly entangled and classical hydrogels. Significant efforts were directed at establishing conditions for precise optical detection and minimizing degradation during photopolymerization. Using catalytic amplification reactions, we explored DNA gel particles (artificial cells) that transduce tiny signals, presenting an opportunity for rapid implementation with lower material needs. Building on our understanding of DNA circuits, we assembled DNA gel/artificial cell particles using DNAzyme mediated strand displacement reactions, showcasing advances in metabolic DNA protocells.
In building the mechano-interface to cells, we pursued two main directions. First, we activated DNA mechanoprobes in a cell-selective fashion using DNA mechanoprobe functionalized aptamers. Second, we developed highly stable DNA mechanoprobes using L-DNA, the stereo-isomer of D-DNA, which degrades less in cell culture media.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

M3ALI has significantly advanced beyond the current state of the art in several key areas. We have successfully integrated mechanoactivation and chemo-responsive elements into polymer materials, creating innovative self-regulating devices for applications such as soft robotics. Our work on urea/urease reaction networks has pioneered new methods for global actuation in soft robotics and mechanoactivation using signal transduction mechanisms. This work has merged chemical and physical intelligence in unprecedented ways, demonstrating precise strain gates for device activation, which was previously unattainable.

In DNA materials, we have pushed the boundaries by identifying optimal amplification reactions and creating robust photopolymerizable hydrogels with DNA mechanoprobes. This has paved the way for advanced signal transduction mechanisms and adaptive responses in DNA gel particles, or artificial cells. Our innovations in L-DNA mechanoprobes have opened new avenues for long-term biological studies, significantly extending the stability and usability of these probes in cellular environments.

Expected Results Until the End of the Project
We have built a solid foundation for tackling more challenging objectives in training and learning, utilizing cyclic disulfide mechanoprobes as well as DNA mechanoprobes. Significant progress is also expected in cell-triggered material adaptation processes, where important groundwork has already been laid. Most system components are now operational, and the next challenge is to leverage them in system integration tasks. We will also focus on the conceptual integration of chemical and physical intelligence, combining chemical circuits with metamaterial design. This integration opens new and unforeseen opportunities, pushing the boundaries of current technologies and enabling new applications in soft robotics, adaptive materials, and biointeractive systems.

DNA gels with downstream reactions