Highly Responsive Self-Healing and Degradable Piezoelectric Soft Machines.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date: Jul 14, 2025

Abstract

Piezoelectric materials that are simultaneously healable, stretchable, and degradable have remained an unmet challenge, limiting advancements in wearable and implantable electronics, where devices face multidimensional mechanical deformation, causing a risk of damage. To address this critical gap, a biocompatible piezoelectric material is developed for ultrahigh piezoelectric effects with DL-alanine amino acid crystals, which is stretchable, healable, and degradable. The in situ grown DL-alanine piezoelectric crystals within an ionically cross-linked gelatin hydrogel matrix strengthen the piezoelectric properties with an ultrahigh voltage coefficient of 1.6 Vm N. The combination of the piezo-ionic property and crystal alignment results in a record-breaking energy harvesting figure-of-merit value at 57.6 pm N to deliver outstanding mili-watt level power outputs in proof-of-concept devices which can power up even several electric light bulbs. An elastically stretchable, damage resistant strain sensor is further optimized for real-time healthcare monitoring and biomechanical motion tracking. By integrating machine learning algorithms, the sensing system intelligently classifies biomechanical activities with high accuracy, enabling advanced applications in healthcare, rehabilitation, and sports monitoring.

Authors

  • Sujoy Kumar Ghosh
    Berkeley Sensor and Actuator Center, Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
  • Subhajit Pal
    Department of Bioengineering, University of California, Berkeley, CA, 94720, USA.
  • Krittish Roy
    Department of Physics and Bernal Institute, University of Limerick, Limerick, V94 T9PX, Ireland.
  • Wei Yue
    School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China.
  • Yuan Gao
    Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou Zhejiang Province, China.
  • Fan Xia
  • Peisheng He
    Berkeley Sensor and Actuator Center, Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
  • Sabyasachi Sarkar
    The California Institute for Quantitative Biosciences, University of California, Berkeley, CA, 94720, USA.
  • Megan Teng
    Berkeley Sensor and Actuator Center, Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
  • Jongha Park
    Berkeley Sensor and Actuator Center, Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
  • Peggy Tsao
    Berkeley Sensor and Actuator Center, Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
  • Xiaosa Li
    Berkeley Sensor and Actuator Center, Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
  • Syed A M Tofail
    Department of Physics and Bernal Institute, University of Limerick, Limerick, V94 T9PX, Ireland.
  • Phillip B Messersmith
    Department of Bioengineering, University of California, Berkeley, CA, 94720, USA.
  • Liwei Lin
    Berkeley Sensor and Actuator Center, Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.

Keywords

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