Chemically functionalized cellulose triboelectret nanogenerator for machine-learning-enabled tactile sensing.

Area of Science:

• Materials Science
• Nanotechnology
• Energy Harvesting

Background:

• Self-powered energy harvesting is crucial for biomedical devices, sensors, and wearables.
• Triboelectric nanogenerators (TENGs) offer high efficiency but suffer from poor long-term stability due to charge annihilation.

Purpose of the Study:

• To introduce a new triboelectret nanogenerator (E-TENG) that combines triboelectric and electret properties.
• To address the charge retention and long-term stability issues in traditional TENGs.
• To develop a stable, high-performance energy harvesting device using sustainable cellulosic materials.

Main Methods:

• Developed an E-TENG using cellulosic materials for both triboelectric active layers.
• Functionalized cellulose nanofibers with nitro groups (tribo-negative) and stearoyl groups (tribo-positive).
• Created a hydrophobic aerogel structure in the tribo-positive layer to enhance charge retention.

Main Results:

• The E-TENG demonstrated superior performance over traditional cellulose-based TENGs, achieving a maximum power density of 6.8 W m⁻².
• Long-term durability testing confirmed stable electrical output over 90 days.
• The device successfully monitored biomechanical signals and performed tactile sensing with 98.6% accuracy in finger touch prediction via machine learning.

Conclusions:

• The developed E-TENG effectively overcomes charge annihilation issues, offering enhanced stability and charge retention.
• Cellulose-based E-TENGs represent a promising platform for scalable, sustainable electronics.
• The technology has significant potential for applications in gesture recognition, human-machine interfaces, robotics, healthcare, and consumer electronics.