| dc.description.abstract |
<p>Our lab designs hydrogel microparticles (HMPs) that are interlinked to form microporous
annealed particle (MAP) scaffolds for wound healing applications. The therapeutic
effects of MAP are attributed, in part, to the void space between particles that creates
inherent micro-porosity through which cells can infiltrate and migrate unhindered.
Cell behavior is influenced by local geometry, and our goal is to design scaffolds
that influence cells toward pro-healing behaviors. To accomplish this, we need a methodology
for quantitatively characterizing the void space of MAP scaffolds in order to study
the relationships between internal microarchitecture and therapeutic outcomes. The
work presented here is a visually-rich dissertation that covers our approach for analyzing
the void space of packed particles. We use techniques from computational geometry
and graph theory to develop a robust methodology for segmenting the void space into
natural pockets of open space and outputting a set of descriptors that characterize
the space. Our methods are developed using simulated MAP scaffolds covering a range
of particle compositions, including mixed particle sizes, stiffnesses, and shapes.
Our software, called LOcal Void Analysis of MAP scaffolds (LOVAMAP), has allowed us
to study many aspects of void space, including global descriptors like void volume
fraction, local ‘pore’ measurements of size and shape, and additional features like
ligand availability, paths, isotropy/anisotropy, and available regions for unhindered
migration based on size. LOVAMAP is an enabling technology that can be used for analyzing
real scaffolds or studying simulated scaffolds to inform material design. It serves
as a platform for void space analysis that can easily be built upon to encompass ever-growing
innovations in scaffold characterization.</p>
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