In recent years, the field of cell therapy has grown vastly. Currently, there are over 2,000 active cell therapy clinical trials worldwide. While scientists are interested in discovering targets in various disease areas, oncology is the area where greatest focus for future discoveries remains. CAR-T cell therapies are types of immunotherapies, and they are the latest promising treatments with potentials for curing different types of cancer. After the 2017 approval of both Novartis’ Kymriah® for the treatment of children and young adults with leukemia and Kite’s Yescarta® for adults with certain types of non-Hodgkin’s lymphoma, the field has become increasingly promising and competitive. The majority of large pharmaceutical companies have placed the development of CAR-T cell therapies targeting different types of cancer as part of their main portfolios. While all current FDA approvals are autologous therapies that do show a tremendous response from terminally ill patients, the cost is still a big factor and hindrance for the future of such treatments. And while the allogeneic approach would offer off-the shelf, affordable cell-based therapeutics, the field needs to further develop before that becomes feasible. From an engineering standpoint, how is the process development for such therapies going to look like? What are the differences between allogeneic and autologous cell therapies? What challenges exist with both and how should they be addressed?

Predictive models are needed for design, and are also very helpful in interpretation of experimental observations, and for understanding the behavior of complex systems. Historically, it has been challenging to predict most technologically-important reacting systems, because they typically involve a large number of reactions: it is hard to know exactly which reactions are important, and invariably some of the reactions are inconvenient to study experimentally. Recently, it has become possible to accurately compute the equilibrium constants Keq and rate coefficients k of some types of reactions from first principles, based on high-level quantum chemistry calculations. Machine learning methods have made it easier to use all of the limited data available to develop better estimates of rate and thermochemical parameters, and also to make qualitative predictions (e.g. “which molecule will be the major product at this condition?”). Methods for constructing reaction networks, either to design synthetic routes to new pharmaceuticals, or to model system kinetics including byproduct formation, have also advanced. Here we present the current state of the art in predictive modeling of complex reacting systems, and highlight some of the challenges for the future.

Growing demand for energy and energy-intensive materials in the developing countries is still being primarily met with fossil resources, manifesting in increased CO₂ emissions.  Significant reductions in solar and wind electricity production costs have led to increased penetration of renewable energy. At the same time, advent of vast resources of relatively cheap shale oil and shale gas in US has fundamentally altered electricity and chemical production domstically. Concentration of CO₂ in the air continues to increase and now has crossed 400 ppm mark, which has generated a robust political debate to deal with increasing carbon emissions worldwide.

To address this grand challenge, US and foreign governments, business sector, venture community, and philanthropic organizations are pooling together resources to invest in the research, development, and deployment of low-carbon technologies.  Some of key technologies include CO₂ capture (from point sources, direct air capture, etc.), utilization, and sequestration; distributed hydrogen production for fuel cell cars and other devices (e.g., forklifts); integration of renewable energy with chemical sector and CO₂ utilization, waste and biomass utilization, etc.  This presentation will highlight current and emerging R&D opportunities and available funding from the Government and private sector.

Nanotechnology deals with the science and engineering of functional materials and devices at the nanometer length scales. This rapidly growing field harnesses the unique properties and behavior of matter that emerge when it is confined to nanoscale dimensions. The potential applications of nanotechnology are enormous, impacting almost all aspects of our daily life. In our laboratory, we use statistical physics-based simulation methods to provide a fundamental, molecular-level understanding of nanoscale systems with the aim of discovering new phenomena and developing new materials and technologies. In this talk, I will show how we are using such computational tools to advance two different areas in soft nanotechnology: assembling polymer-embedded plasmonic nanoparticles into unique spatial arrangements relevant for optical applications and introducing dynamic mechanisms into rigid DNA origami nanostructures for applications in sensing and actuation.

Polymers are ubiquitous in our modern society, and the fact that polymer properties can be widely manipulated by altering the constituent repeat units, their arrangement, and macromolecular size drives their use across a spectrum of technologies. Polymer-based additive manufacturing, or 3D printing, has gained traction as a rapid and efficient mode of manufacturing. However, 3D printing using polymeric materials is often challenged by a limited scope of feedstock materials and weak interfaces in printed parts. Motivated by this problem, we have focused on using polymer- and copolymer-grafted nanoparticles as way to affect organization and improve macroscopic properties of 3D printed nanocomposites created by fused deposition modeling (FDM). Specifically, we have examined the utility of ternary interactions that promote miscibility of dissimilar materials or hydrogen bonding interactions that create thermoresponsive networks, both of which enhance mechanical properties of FDM-printed materials. Inferences based on macroscopic performance characteristics are supported by insights derived from X-ray scattering and broadband dielectric spectroscopy measurements. The design-structure-property relationships built from the central idea of encoding instructions in tethered chains arrayed at nanoparticle surfaces to control organization and properties are conceptually important and expected to inform efforts to produce hybrid systems with enhanced performance for polymer additive manufacturing.

Currently, there are more than 1.1 billion vehicles in the world. Internal combustion engines power most of these vehicles. If a low-cost electric car with no greenhouse gas emissions could be produced and made broadly available, this would have a significant impact on our global carbon footprint. Currently, the only zero-emission vehicles are electric vehicles powered by rechargeable lithium-ion batteries (e.g., Tesla Model S) or hydrogen-fueled proton exchange membrane (PEM) fuel cells (e.g., Toyota Mirai). Fuel cell electric vehicles have several advantages over battery electric vehicles for driving ranges greater than 300 miles, such as significantly lower vehicle weight, six-times higher specific energy density, and instant re-fueling. Although automakers have engineered solutions to many of the major hurdles of bringing fuel cell electric vehicles to the market place, the high cost of the required precious metal platinum (Pt) electrodes remains one of the few major factors limiting the mass commercialization of low-cost fuel cell electric vehicles. In our laboratory, we have investigated two routes to overcome this limitation. In our first approach, we have developed a new process to fabricate high surface area fuel cell electrodes based on super proton conductive nanofibers, which results in high fuel cell power densities at ultra-low Pt loadings. These results were motivated by our exploration into the fabrication of Nafion nanofibers via electrospinning, where we observed super high proton conductivity of a single Nafion nanofiber (as high as 1.5 S/cm) relative to Nafion bulk film conductivity (~0.1 S/cm). The discovery, fabrication, properties, and fuel cell performance of super proton conductive nanofibers electrodes will be discussed. In our second approach, we have pursued solid-state anion exchange membrane (AEM) alkaline fuel cells (AFCs), which do not require the expensive components (Pt) of their PEM fuel cell counterpart, operating with much less expensive non-noble metal catalysts (e.g., Ni). We have developed a new AEM chemistry (hydroxide-conducting poly(ionic liquid) block polymers), which addresses critical issues impeding AEM-AFC technology and provides a platform to investigate improving fuel cell performance. AEM synthesis, morphology, transport properties, and fuel cell performance will be discussed.

Worldwide energy demand is projected to grow strongly in the coming decades, with most of the growth in developing countries that have rapidly growing populations.  Even with unprecedented growth rates in the development of renewable energy technologies such as solar, wind and bioenergy, the world will continue to rely on fossil fuels as a predominant energy source for at least the next several decades.  This likelihood, coupled with the observation that most climate models now suggest that limiting global warming to < 1.5 or 2 °C is impossible in the absence of carbon dioxide removal from the atmosphere, requires that we develop negative emissions technologies (NETs).¹ Our group has played a leading role in developing scalable “direct air capture” (DAC) technologies for CO₂ removal from the atmosphere.²

In this lecture, I will describe the design and synthesis, characterization and application of new porous amine/oxide composite materials that we have developed as cornerstones of new technologies for the removal of CO₂ from dilute gas streams.³ These chemisorbents efficiently remove CO₂ from simulated flue gas streams, and the CO₂ capacities are actually enhanced by the presence of water, unlike in the case of physisorbents such as zeolites.  Interestingly, the heat of adsorption for these sorbents is sufficiently high that the sorbents are also capable of capturing CO₂ from extremely dilute gas streams, such as the ambient air.  Indeed, our oxide-supported amine adsorbents are quite efficient at the “direct air capture” of CO₂ and an introduction to DAC technologies will be presented.  Air capture systems offer one of the few scalable options that could be deployed as a NET, actually reducing the amount of CO₂ in the atmosphere, potentially allowing for the slow reversal of climate change.  Simultaneous management of the global energy supply and CO₂ emissions presents society with the most scientifically pressing problem of our generation, one that chemists and chemical engineers are well-positioned to address.

Future Leaders in Chemical Engineering is a one and one-half day, all-expense paid research symposium at NC State University recognizing the top undergraduate researchers in chemical engineering in the United States. Chemical engineering researchers (and those in related fields) who are within two years of graduating are eligible to apply.

Awardees will receive a plaque and present their research at a research symposium held October 28, 2019 on Centennial Campus at NC State University in Raleigh, NC. The symposium will also include information about applying to graduate school and fellowships. Students must be present to be eligible.

Multiple directions in battery research are now being pursued with the goal of advancing beyond the specific energy limits imposed by current Li-ion batteries.  When considering the design of new high-energy storage systems, new materials, processes, or chemistries are introduced that are inherently more unstable than conventional Li-ion battery materials, resulting in limited battery cycle life and safety.  Two such examples of high energy battery chemistries, high voltage operation of Ni-rich Li[Ni, Mn, Co]O₂ Li⁺ insertion electrodes (Ni-rich NMC) and Li-O₂ electrochemistry, will be discussed in this presentation. Previous observations of high-voltage instabilities include NMC surface reconstruction, transition metal dissolution, electrolyte decomposition, and formation of solid surface species. However, the picture of these processes is still incomplete, with the dependence on electrolyte and NMC composition not yet fully understood.  I will present results in which isotopic labeling of ¹⁸O in Ni-rich NMCs is combined with quantitative gas evolution analysis to identify key contributions to these high voltage instabilities, including instabilities related to solid-state anionic (oxygen) redox and the surprising impact of residual solid lithium carbonate (Li₂CO₃) on electrolyte and electrode degradation. These results are reminiscent of similar issues with Li₂CO₃ formation during Li-O₂ battery operation, where large overpotentials are observed during battery charging as a result of parasitic interfacial carbonate formation.  This presentation will emphasize the need to accurately quantify these minor parasitic side reactions to fully understand their large influence on battery performance.

Immune sentinel cells such as macrophages survey the tissue microenvironment and must initiate the appropriate immune response upon sensing the presence of diverse pathogens or immune threats. Indeed, macrophages are capable of stimulus-specific responses, but a very large number of receptors utilize only a handful of signaling pathways.  Interestingly, the downstream transcription factors exhibit complex dynamics, which in turn determine which genes are activated.  I will discuss our recent studies to determine whether these dynamics represent a signaling code or language that may specify the immune responses of immune sentinel cells.  I will present recent work in which we were able to identify the “signaling codons” or code words of this language, and characterize their reliability and points of confusion. Further, I will discuss to what extent such a code may be harnessed to achieve greater pharmacological specificity when therapeutically targeting pleiotropic signaling hubs.

Millions of people worldwide are afflicted with neurological diseases such as Parkinson’s disease, Alzheimer’s disease, brain cancer, and cerebral AIDS. Although many new drugs are being developed to combat these and other brain diseases, few new treatments have made it to the clinic.  The impermeable nature of the brain vasculature, also known as the blood-brain barrier (BBB), is at least partially responsible for the paucity of new brain therapeutics.  As examples, approximately 98% of small molecule pharmaceuticals do not enter the brain after intravenous administration, and the BBB prevents nearly all protein and gene medicines from entering the brain.  Our research group is therefore focused on developing tools for the analysis of the brain drug delivery process and identifying novel strategies for circumventing this transport barrier.  This presentation will detail our recent work regarding the development of stem cell-based in vitro experimental models that accurately mimic the BBB characteristics observed in vivo.  Such models are amenable to drug permeability screening and human disease modeling.  In addition, I will discuss our efforts to overcome BBB restrictions on brain drug delivery. To this end, we are mining large antibody libraries to identify antibodies that can target and act as artificial substrates for endogenous receptor-mediated BBB nutrient transport systems.   After conjugation to drug payloads that can include small molecules, proteins, or DNA therapeutics, these antibodies could have the potential to deliver medicines across the BBB noninvasively.

TBD