Available at: https://digitalcommons.calpoly.edu/theses/3287
Date of Award
6-2026
Degree Name
MS in Engineering - Biochemical Engineering
College
College of Engineering
Advisor
Long Wang
Advisor Department
Civil and Environmental Engineering
Advisor College
College of Engineering
Abstract
Mechanoluminescent (ML) materials offer a self-emissive approach to force sensing by converting mechanical loading into visible light. However, many flexible ML composites rely on homogeneous mixing of phosphor particles throughout an elastomer matrix, which limits optical efficiency as emitted photons must travel through the bulk material before exiting. This thesis investigates whether electric-field-assisted fabrication can create a ML sensing platform that maintains the compliance needed for flexible sensing applications and how that fabrication method alters the performance mechanically and optically.
An ultra-sensitive mechanoluminescent composite (USMLC) was fabricated by depositing ZnS:Cu,Cl particles into uncured polydimethylsiloxane (PDMS) using electric field-assisted particle deposition. Optical characterization revealed the formation of a uniform and dense ML layer approximately 300 µm thick when using this fabrication method. Mechanical testing showed that the USMLC exhibited increased low-strain stiffness relative to homogeneous controls and pristine PDMS but retained high overall stretchability (over 85%). Mechano-optical characterization demonstrated substantially improved sensing performance, including a nearly 4000% increase in measured light-emission relative to homogeneous composites fabricated at the same particle concentration (30%). A clear and linear sensitivity to loading rate and stability of the light emission during 100 loading cycles was revealed by additional testing.
The USMLC was integrated with compact photodiodes (PDs) to form integrated sensing devices. A two-PD prototype confirmed that the device could capture the ML response reproducibly during cyclic loading. An eight-PD circular device then demonstrated spatial sensing under indentation. COMSOL simulations were used as a validation framework to interpret the ML responses captured and showed that direct interpolation from sparse PD measurements was qualitatively useful but physically limited. Additional prosthetic-interface and electronic-skin demonstrations showed early translational potential for capturing dynamic loading conditions.
To improve spatial interpretation, a simulation-informed reconstruction workflow was developed using COMSOL-generated training data, principal component analysis (PCA), Gaussian process (GP) regression, and experimental calibration. This framework reconstructed ML intensity fields from eight PD measurements while also revealing limitations imposed by sparse sensor geometry, forward-model fidelity, and calibration mismatch.
Overall, this thesis demonstrates that electric-field-assisted fabrication can significantly improve the sensing performance of flexible ML composites and that the resulting material platform can be extended into compact sensing and combined with machine learning to enhance interpretability. These results establish a strong foundation for future soft ML sensing technologies in biomechanical monitoring and spatial force sensing.