Digital Fibers: Materials, Processing, and Information

Ubiquitous computation has influenced a broad array of domains from manufacturing to drug discovery and from communications to machine learning. While the capabilities of computing platforms have progressed dramatically, one can argue that materials have not been tailored or designed to capture the spectra of digital capabilities out there. In this thesis, I seek to synergize digital tools with fiber materials towards constructing devices of new form factors and fibers with digital features. First, digital additive manufacturing of devices has been limited by the lack of materials suitable for printing. Overcoming this limitation, I have harnessed multimaterial fibers as the ’ink’ in 3D-printers to print objects not only with digitally designed shapes, but also with user defined device functions. A new print approach, termed as fiber surface heating, is introduced where the print nozzle is modified so that these fibers can be heated and fused to each other during printing, while ensuring that their device functions are well-retained when forming the 3D structure. This approach was later validated by printing fibers of different functions, including light-detection, light-emission, and energy storage. Several 3D objects of tailored shapes and spatially-defined device functions were showcased. This print technique is also capable of printing custom porous scaffolds from engineered porous fibers, enabling a means for accelerated nerve regeneration for patients with nerve injuries. Finally, I describe the fabrication of fibers with digital capabilities, including memory storage and analog-to-digital sensing. These polymeric fibers contain an engineered material setup that allows for the connections of multiple addressable discrete digital microchips along their length, enabling independent operation of different functions within a single fiber. This fiber, when woven into a shirt, senses the body temperature, stores its values, and through a trained neural network stored within the fiber, provides inference on the wearer’s activity. This approach sets a foundation for future applications in fabric-based computing and on-body machine learning inference.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Materials Science and Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Loke Zi Jie, Gabriel

Advisor dc:contributor.advisor

• Fink, Yoel

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139549

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139549