ENGINEERING NANOMATERIALS FOR IMAGING AND THERAPY OF BACTERIA AND BIOFILM-ASSOCIATED INFECTIONS

Infections caused by multidrug-resistant (MDR) bacteria pose a serious global burden of mortality, causing thousands of deaths each year. The “superbug” risk is further exacerbated by chronic infections generated from antibiotic-resistant biofilms that are highly resistant to available treatments. Synthetic macromolecules such as polymers and nanoparticles have emerged as promising antimicrobials. Moreover, ability to modulate nanomaterial interaction with bacterial cellular systems plays a pivotal role in improving the efficacy of the strategy. In the initial studies on engineering nanoparticle surface chemistry, I investigated the role played by surface ligands in determining the antimicrobial activity of the nanoparticles. In further study, I determined that surface monolayer of hydrophobic ligands facilitated the nanoparticles to block bacterial efflux pumps, yielding reduction in antibiotic dosage to treat pathogenic bacteria including methicillin-resistant S. aureus (MRSA). Moreover, functionalization of nanoparticle surface with pH-responsive ligand was used to develop a general strategy to target and image bacterial biofilms for a broad-range of species. In a subsequent study, I have utilized a unique approach of integrating synthetic nanomaterials on the surface of natural super carrier-Red Blood Cells for selective delivery of nanoparticles to the site of bacterial infection for antimicrobial therapy. This strategy shows potential to combat bacterial infections without harming the ecology of human microbiome, as well as circumvent the issues associated with non-specific uptake of nanoparticles by the reticuloendothelial system. In another study, systematic investigation of antimicrobial activity of oxanorbornene-polymer derivatives generated polymer nanoparticles with unprecedented therapeutic selectivity towards MDR bacteria. Additionally, polymeric nanoparticles prevented onset of resistance development in bacteria for ~1300 generations and eradicate biofilms on infected mammalian cells, a feat unachieved by previous antimicrobial polymers. Amphiphilic polymer derivates increased the influx of antibiotics in Gram-negative bacteria and biofilms, resulting in synergistic antimicrobial therapy. Subsequently, we utilized engineered polymers to generate nanosponges through self-assembly of polymers around essential-oil based cores for topical treatment of wound biofilms. Overall, our results show strong potential as an infectious disease therapeutic while simultaneously provide a rational approach to design novel antimicrobials for sustainably combating bacterial infections

SPECTRAL DATABASE AND FRAMEWORK FOR COMPUTATIONALLY EFFICIENT CRYSTAL PLASTICITY SIMULATIONS

Most of the commercially used metals and alloys exhibit polycrystalline microstructures that are composed of numerous grains (individual crystals). In these metals and alloys, plastic deformation occurs mainly through the movement of dislocations. Crystal plasticity models have been developed and used over the past several decades to describe physically the behavior of metals. They not only provide better predictions of the anisotropic material response but can also capture the texture evolution in a polycrystalline sample. However, crystal plasticity models are extremely computationally expensive, limiting their adoption. In this work, this limitation is addressed by using a recently developed spectral database approach based on discrete Fourier transforms (DFTs). This approach has demonstrated impressive computational advantages over the conventional approaches. Despite their wide applicability, in some applications the DFT database approach has encountered significant hurdles such as for prediction of crystal plasticity based forming limit diagram (CP-FLD) and for carrying out simulations of crystal plasticity finite element method (CPFEM). Forming limit diagram (FLD) is the most commonly used indicator of localized necking in automotive industry. In this work, significant improvements were made to the prior approach and a new DFT database was developed to address these challenges. New database was integrated with M-K approach to develop spectral crystal plasticity forming limit diagram (SCP-FLD) numerical tool for very fast CP-FLD predictions. The new DFT database was also implemented with finite elements (FE) package ABAQUS through a user materials subroutine, UMAT to develop an improved spectral crystal plasticity finite element method (SCPFEM) framework, for computationally efficient CPFEM predictions of deformation processing. Proper utilization of these toolsets can lead to accelerated insertion of new and improved materials into practice.Ph.D

A protocol for automated a posteriori adaptive meshing with SimVascular: a test case

Objective Operational details regarding the use of the adaptive meshing (AM) algorithm available in the SimVascular package are scarce despite its application in several studies. Lacking these details, novice users of the AM algorithm may experience undesirable outcomes post-adaptation such as increases in mesh error metrics, unpredictable increases in mesh size, and losses in geometric fidelity. Here we present a test case using our proposed iterative protocol that will help prevent these undesirable outcomes and enhance the utility of the AM algorithm. We present three trials (conservative, moderate, and aggressive settings) applied to a scenario modelling a Fontan junction with a patient-specific geometry and physiologically realistic boundary conditions. Results In all three trials, an overall reduction in mesh error metrics is observed (range 47%–86%). The increase in the number of elements through each adaptation never exceeded the mesh size of the pre-adaptation mesh by one order of magnitude. In all three trials, the protocol resulted in consistent, repeatable improvements in mesh error metrics, no losses of geometric fidelity and steady increments in the number of elements in the mesh. Our proposed protocol prevented the aforementioned undesirable outcomes and can potentially save new users considerable effort and computing resources