Predictive design of stretchable electrodes with strain-insensitive performance via robotics- and machine...

Area of Science:

• Materials Science
• Robotics
• Electronics

Background:

• Wearable electronics and soft robotics require stretchable electrodes.
• Achieving high stretchability, performance, and strain insensitivity simultaneously is challenging.
• Traditional experimental methods are time-consuming and inefficient for complex parameter spaces.

Purpose of the Study:

• To develop a predictive design workflow for stretchable electrodes with strain-insensitive properties.
• To overcome limitations of conventional experimental approaches in materials discovery.
• To enable rapid optimization of stretchable electrode parameters using machine intelligence.

Main Methods:

• Integrated workflow combining robot-automated experimentation, machine learning (ML) predictions, and finite element simulations.
• Ensemble of artificial neural networks constructed via a two-stage workflow with active learning.
• Microtextured stretchable nanocomposite platform developed using ML predictions and simulations.
• Conformal deposition of gold and electrodeposition of Zn and MnO2 for battery applications.

Main Results:

• Discovery of a microtextured stretchable nanocomposite as a strain-stable platform.
• Achieved metal-like conductivity and high resistance-insensitive stretchability with a gold layer.
• Demonstrated a stretchable Zn||MnO2 battery with large elongation and strain-insensitive electrochemical performance.
• Validated the efficacy of the machine intelligence-driven approach for multi-parameter optimization.

Conclusions:

• The integrated workflow significantly accelerates the design and optimization of stretchable electrodes.
• Machine intelligence enables the creation of strain-insensitive materials for advanced electronic applications.
• This approach facilitates the development of high-performance, durable, and stretchable devices.