Research

Our research group (D2M Data to Manufacturing Laboratory) is situated at the intersection between advanced manufacturing and material informatics. We are developing state-of-the-art hybridized additive manufacturing platforms to construct 3D functional materials with controlled architectures across length-scales. These 3D-printed hierarchical materials are strategically designed by tuning compositions and geometries to enhance their performance in energy storage and biomedical sensing applications. Using machine learning (ML), a tool within the artificial intelligence (AI) space, we can leverage the sizable dataset generated through automated manufacturing processes and further improve material functional performance. Hidden fundamental relations between the device performance and fabrication parameters can potentially be revealed and further predictions on product quality and performance can be made.

Electrochemical capacitors (ECs) represent an emerging technology that plays a vital role as an energy supply for its high energy and power densities. Our group is utilizing the Direct Ink Writing (DIW) strategy to directly print self-standing EC electrodes that offer controlled electrical conductivity, high specific surface area, and enhanced transport properties that contribute to electrochemical performance enhancement. With many control parameters to consider during experimentation and multiple objectives for optimized outcomes in EC electrochemical testing, it is challenging to use existing modeling tools to make accurate predictions. Therefore, by using tailored ML algorithms, it becomes possible to make meaningful connections between the performance outcomes and the processing parameters, ultimately creating the next generation of optimized energy storage devices. 

Electronic skin mimics the functions of actual human skin. The construction of electronic skin involves the creation of materials that combine sensing capabilities (strain, tactile, temperature, etc.) with appropriate mechanical properties (stretchability, flexibility, J-shaped behaviours, etc.). Our group works on the design of tuneable additive manufacturing strategies that can create sustainable, non-toxic, and biocompatible elastomeric/polymeric materials with desired functionalities.

Human exhaled breath contains a variety of gaseous constituents and biomarkers that offer valuable insights into the health conditions of a person. The research field of breath sensors has been advancing quickly, but there are only a handful of devices that are available on the market. By understanding the relationship between fabrication techniques and the optimized output of the functional sensors, insights on the future clinical relevance of personalized breath sensors can be revealed.

Neural implants are engineering devices directly interfaced with the body's nervous systems, acting as cell scaffolds, to restore motor and sensory functions after brain injury or degenerative diseases. However, their benefits remain limited, mainly because of the diversity of injuries or diseases for different individuals, their limited functional regeneration efficacy, and their long-term biocompatibility concerns. Therefore, biocompatible, electro-active and mechanically soft neural implants that can be personalized for an individual's injury and implantation site coupled with minimally invasive delivery have the potential to improve the effectiveness in restoring degenerated neural functions. We are developing extrusion-based additive manufacturing techniques that can create personalized biocompatible implants with a variety of different functional material compositions.