Hygromnemics: Programmable Material Memory Matter Actuators via Wet Pre‐Constraining

Charles de Kergariou, Findlay S. G. Smith, Richard S. Trask, Adam W. Perriman, Fabrizio Scarpa, David Correa · Wiley · 2025

Hygromnemic actuators utilize humidity to store and program shape memory, enabling innovative soft robotic applications.

High AI ConfidenceStrong SourceConceptEarly Research

Plain English summary

This research introduces hygromnemic actuators that can store shape memory by being pre-constrained in dry conditions and activated by humidity. The actuators can achieve different actuation patterns based on how long they are constrained and can operate effectively in low humidity environments. They demonstrate significant strength and stiffness in dry conditions compared to wet conditions.

Why this matters

Hygromnemic actuators represent a low-cost solution for creating programmable materials that can adapt their shape based on environmental humidity. This technology could enhance the functionality of soft robotics, making them more versatile and efficient in various conditions.

Key findings

  • Hygromnemic actuators store shape memory activated by humidity.
  • Actuators can achieve sinusoidal actuation patterns.
  • They operate effectively in low humidity (12% relative humidity).
  • Strength is 253% higher in dry conditions compared to wet.
  • Shape storage capability allows control during operational service.

What's new

The introduction of hygromnemic actuators that utilize humidity for shape memory programming is a new approach compared to traditional thermally-induced methods.

Limitations

The abstract does not provide details on the long-term durability of the actuators or their performance in varying humidity levels beyond the tested conditions.

Commercial context

The technology is still in the conceptual stage and has not been demonstrated in commercial applications.

Publication

Publisher
Wiley
Publication date
August 23, 2025
Research type
Paper
License
http://creativecommons.org/licenses/by/4.0/

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Method note: Summaries and ratings on this page are generated by AI from the abstract only. Read the original paper for full context. · Model: gpt-4o-mini-2024-07-18