Organizational Unit:

School of Chemical and Biomolecular Engineering

Permanent Link

https://hdl.handle.net/1853/70759

Description

School established in 1901 as the School of Chemical Engineering; in 2003, renamed School of Chemical and Biomolecular Engineering

Parent Organization

Organizational Unit

College of Engineering

ArchiveSpace Name Record

https://finding-aids.library.gatech.edu/agents/corporate_entities/407

Full item page

Publication Search Results

Now showing 1 - 10 of 1978

Promoting And Deactivating Effects of Carbonaceous Deposits During Skeletal 1-Butene Isomerization Over Ferrierite

(Georgia Institute of Technology, 2024-07-30) Hebisch, Karoline L.

Most microporous solid acid catalysts deactivate during hydrocarbon conversion because of carbon depositions (“coke”) blocking pores and poisoning active centers. However, several cases of reaction enhancement have been reported. One reaction in which coke has a promoting effect is the skeletal isomerization of linear butene to iso-butene over the zeolite ZSM-35 (framework type FER, possessing perpendicular intersecting 8-R and 10-R channels). While consensus exists about the reaction mechanism during reaction startup, the reaction location and the reaction pathway at peak catalyst performance are still contested. Time-resolved catalyst characterization data are collected to (1) understand the effects of high temperature (T=420 °C) and carbon deposition on the zeolite micropore structure and Brønsted acid site accessibility and (2) the location and chemical nature of carbon deposits. Three distinct reaction stages are identified: catalyst startup (0-24 h), optimal performance (50-300 h) and catalyst deactivation (>300 h). Most of the deposits (~5 wt%) form within the initial 24 h and are located inside the micropores, rendering them effectively inaccessible to probe molecules (e.g., N2 and Ar) and leading to an expansion of the crystal unit cell. Adsorption isotherms of several hydrocarbons revealed that only small molecules with a kinetic diameter of <4.7 Å can diffuse into the pores before they become obstructed by carbon deposits. Calculation of diffusion coefficients from transient adsorption data at reaction temperature shows that even small molecules are severely hindered in their diffusion, concluding that the reaction occurs at the pore entrances. Operando reactivity quenching with basic probe molecules with different steric constraints shows that acid centers exist under reaction conditions for tens of hours, are located in the pore mouths, and can be reversibly poisoned. Because a combination of steric confinement, acidity, and carbonaceous deposits is needed to successfully facilitate skeletal isomerization, the active sites are concluded to be monoaromatic species, which form during catalyst startup in the pore mouths and are protonated by internal Brønsted acid sites. The positive charge is delocalized and communicated via a methyl group to the catalyst exterior, where skeletal butene isomerization is facilitated. The outstanding activity of ferrierite for this reaction is explained by FER’s ability to anchor the catalytically active deposits in the pores while preventing their premature deactivation by hindering side-chain growth and condensation reactions. Catalyst deactivation is explained by the formation of external, polyaromatic condensates preventing the reactants from accessing the active sites in the pore mouths. With this information, the catalyst performance is optimized by selective oxidation of residual organic structure directing agent (OSDA). Three promoting effects of residual OSDA are identified. Residual OSDA selectively poisons the strongest Brønsted acid sites, thereby substantially suppressing side product formation while also improving catalyst lifetime. Carbonaceous fragments further act as a precursor for the active site, thereby shortening the unselective startup phase. Lastly, two catalyst regeneration strategies are explored – oxidation and supercritical fluid extraction. Characterization data of oxidatively regenerated samples shows that coke combusts in a manner similar to a shrinking-core model. If done under milder conditions (500 °C in air flow) for 30 min – 1 h, a substantial amount of deactivating species can be removed while the internal deposits remain largely intact. In contrast, due to higher solubility and increased mobility of small aliphatic, olefinic, and monoaromatic species, internal deposits are preferentially removed during solvent extraction with supercritical CO2. Based on preliminary performance and catalyst characterization data, recommendations for future regeneration approaches are derived. Understanding the activating and deactivating effects on catalyst activity is crucial for prolonging catalyst lifetimes and increasing the efficiency of regeneration processes, which will help the transition from a fossil-resource-based economy to a bioeconomy.
Engineering Metabolically Weaponized T Cell Therapies for Solid Tumor Translation

(Georgia Institute of Technology, 2024-07-26) Cox, John

Chimeric Antigen Receptors (CAR) T cell therapies are revolutionizing treatment of cancer in hematological malignancies, eclipsing 50-90% response rates for refractory, or previously non-responding cancers. If we could recapitulate even a fraction of this efficacy for solid tumor indications, it would provide enormous hope for patients who have no recourse. However, the translation of CAR T cells to the solid tumor has come with several stumbling blocks, broadly in the realm of safety and efficacy due to the immunosuppressive tumor microenvironment. The metabolite adenosine accumulates in tumors at 1000x concentrations compared to healthy tissue and inhibits most essential T cell antitumor functions (tumor killing, proliferation, and cytokine release). Fortunately, using CAR T cells as shuttles, or “living drugs” to unload immunostimulatory proteins to tumors has begun to move the needle on efficacy. Additionally, many researchers have developed sophisticated ways to synthesize cellular logic in CAR T cell therapies to augment their safety profiles. This thesis details two technologies that address both efficacy and safety considerations of CAR T cells (Figure 1). To improve CAR T cell function in solid tumors, we developed a novel ADA2 enzyme that can slow tumor growth when delivered from murine CAR T cells in syngeneic mouse models of triple negative breast cancer. In the realm of safety, we developed and characterized adenosine-activated synthetic gene switches that can provide local activity when expressed by murine CAR T cells in tumor environments. The following introduction explains the current challenge of adenosine in tumor microenvironments as well as developments in therapeutic T cell engineering, providing context for our technological advancements.
Advanced optimization in nonconvex stochastic programming and integrated carbon capture systems

(Georgia Institute of Technology, 2024-07-24) Cheng, Pengfei

This work explores advanced optimization techniques, focusing on both theoretical developments in nonconvex stochastic programming and practical applications in integrated energy systems with carbon capture technologies. The first part of this work delves into the theoretical analysis of decomposition-based global optimization algorithms for two-stage stochastic programming problems. We rigorously examine the convergence behavior of state-of-the-art algorithms, highlighting the critical role of value function regularity that are highly problem specific. Our findings indicate that while these methods can offer promising convergence rates under specific conditions, the general case remains challenging due to the inherent irregularities in value functions. This theoretical insight lays the groundwork for future algorithmic enhancements aimed at improving efficiency and robustness in solving nonconvex stochastic problems. The second part of the work applies optimization techniques to the practical challenge of integrating carbon capture technologies with natural gas combined cycle (NGCC) plants. This integration aims to enhance the operational flexibility and economic viability of NGCC plants in response to volatile electricity demands and evolving carbon market conditions. We develop comprehensive optimization frameworks for both single-stage and multi-stage retrofit integration of NGCC, post-combustion carbon capture (PCC), and direct air capture (DAC) units. The models incorporate detailed operational constraints and detailed energy market scenarios to evaluate the economic and environmental impacts of the proposed retrofits. Key contributions include the development of novel optimization formulations that account for sorbent dynamics in DAC operations, the development of a dynamic programming algorithm tailored for large-scale multi-stage retrofitting planning problems, and the construction of long-term carbon markets with high-resolution electricity demands. The results demonstrate significant potential for enhanced profitability and extended dispatching capabilities of NGCC plants through strategic integration with PCC and DAC technologies, especially under favorable carbon pricing and policy incentives.
Advancing the Application of Neural Networks for Atomistic Systems in Surface Science and Catalysis

(Georgia Institute of Technology, 2024-05-10) Hu, Yuge (Nicole)

This thesis explores the integration of neural networks into studying surface science and catalysis, focusing on the development and application of neural network force fields (NNFFs). The NNFFs, once trained on quantum mechanical calculations, enable accurate prediction of energy and forces with significantly reduced computational costs, which would facilitate rapid catalyst design and optimization. A major contribution of this thesis is the development of a novel uncertainty quantification (UQ) method using the conformal prediction framework, enhancing prediction reliability across different ML models, including feed-forward and graph neural networks. Furthermore, the Python module \texttt{AmpTorch} is introduced, which integrates UQ into training NNFFs, capable of handling over 1 million configurations and diverse chemical elements. The thesis also investigates the use of NNFFs in studying platinum-graphene interactions under strain, revealing critical insights into the bonding environments, which are corroborated by Density Functional Theory calculations. Additionally, an unsupervised neural network approach is presented for selecting model compounds in catalysis, demonstrating the power of neural networks to streamline complex chemical analyses. Collectively, the thesis underscores the transformative impact of neural networks in advancing the methodological, software, and practical applications in surface science and catalysis.
Enhancing the Efficiency of Solving Global Dynamic Optimization Problems by Improving the Calculation of State Relaxations

(Georgia Institute of Technology, 2024-04-28) Ye, Jason

The ability to solve global dynamic optimization (GDO) problems computationally is crucial in modeling and optimizing processes that change over time. For instance, in chemical manufacturing, one might be interested in determining how much raw materials to inject into a batch reactor to maximize the product that can sell for the most money, subject to constraints in the form of such physical laws as transient mass and energy balances. In air navigation, it may be of interest to find the thrust of an aircraft to minimize its traveling distance as it moves over time to its destination, subject to the constraint that it does not collide with an obstacle. Spatial branch-and-bound (B&B) is a technique used to solve GDO problems. Currently, B&B can only solve, within realistic time, GDO problems with a limited number of input variables, namely up to around 10 state and 3 decision variables in roughly 3 hours. However, this falls far short of the capability needed to solve GDO problems computationally at the real scale, which contains far more such variables. Yet, even if we can solve GDO problems containing tens of state and decision variables in a matter of hours, we will have already been able to solve many more GDO problems using branch-and-bound than we are currently able to, including problems dealing with reaction kinetics. Thus, there is a critical need to improve B&B’s efficiency in solving GDO problems. A useful technique for enhancing the efficiency of B&B is improving the accuracy or bounding tightness of a convex program whose optimal objective value underestimates that of the original optimization problem. The calculation of such convex programs, known as convex relaxations, is a major step in B&B. By tightening these relaxations around their real functions, their bounding accuracy improves, which leads to a shorter computational time taken by B&B in solving the GDO problem at hand. To that end, this thesis presents multiple methods for improving the calculation of relaxations in a GDO problem, in which enhancing their bounding tightness is a key goal. By improving the efficiency of solving xviiiGDO problems, we can come closer to the goal of solving these problems at the real scale of interest, thus serving as an alternative to hands-on experimentation for answering the kinds of questions targeted by GDO problem solving.
Design And Evaluation Of Lunar Regolith Simulant Composites For UV-Assisted Direct Ink Write In Extreme Cold

(Georgia Institute of Technology, 2024-04-27) Marnot, Alexandra

With the anticipated return of humans to the lunar surface, a growing area of interest is infrastructure development on the Moon facilitated through additive manufacturing. While the Moon presents challenges to manufacturing, including a harsh environment and limited resources, direct ink write (DIW) 3D printing and its versatile ink formulations can be carried out in extreme cold temperatures and allows for high amounts of regolith particles native to the lunar surface to be utilized within the inks. However, ink flow complications arise with solid loadings over 60 vol%, and ink extrusion and solidification mechanisms are not well understood at sub-zero temperatures. In this thesis, I first present parameters that bridge ink rheology and printing extrusion quality to predict and mitigate composition change in high solid DIW inks. Then I discuss considerations to modify commercial DIW printers for optimal operation at -30°C following proof-of-concept printing of inks containing glass microspheres. The curing kinetics of various UV cure inks are also investigated when printing at -30°C with crosslinked microstructures compared to those obtained in ambient printing. Lastly, the effect of both printing at -30°C and subsequent lunar thermal weathering on the degradation of lunar regolith composite prints are examined. Throughout this work, several ink design parameters, including the bimodal ratio of particle size, the solid loading, and the thermal behavior of the UV cure binders, prove to be critical to ensure printing success and to produce desired mechanical properties. The work described in this thesis will contribute to facilitating progress in space exploration by enabling manufacturing methods in environments significantly different from Earth.
Large-Scale Computational Screening of Aluminosilicate Zeolites for Molecular Capture, Storage and Separation

(Georgia Institute of Technology, 2024-04-27) Daou, Alan S. S.

Industrial alkane separations traditionally rely on energy-intensive distillation processes. To mitigate this, adsorption-based separations using porous materials, specifically zeolites, offer a non-thermal alternative. However, large combinations of exchanged cations, framework topologies, and aluminum compositions exist, making it challenging to identify the optimal candidates. Large scale computational studies present an efficient approach to screen candidates and design processes before lab experimentation. These studies rely on accurate results from classical simulation techniques such as Grand Canonical Monte Carlo or Molecular Dynamics and an efficient workflow. The accuracy and/or transferability of the available forcefields often limit the scale of these studies. This thesis aimed to address these limitations by developing a suite of tools for the high-throughput screening of silica and aluminosilicate zeolites for separations. We first studied the impact of intrinsic flexibility on adsorption properties in zeolites and confirmed the viability of rigid frameworks, an essential step for the development of computationally efficient force fields. We then developed a fully transferable force field based on first-principles quantum mechanical methods that can accurately describe both the adsorption and diffusion properties of alkanes and some small adsorbates in siliceous and cationic zeolites. By fitting these force fields to DFT/CC energies, we retain the accuracy of QM methods. To streamline screening, we developed an algorithm based on DFT methods to efficiently generate computationally ready cationic zeolite structures with accurate Si/Al ratio dependent lattice constants. These allowed us to obtain simulation results that are quantitatively accurate to experimental measurements. This led to the creation of a computational screening workflow for adsorption-based methane/butane separation in zeolites using the algorithmically generated structures and DFT-parametrized force fields. We then developed machine learning tools for the screening of real and hypothetical zeolites to facilitate high throughput screening. These machine learning models, trained on accurate data from our force fields, is a valuable complement to classical zeolite screening simulations. Overall, the work presented here serves to present more accurate, faster methods to screen zeolites for separation purposes.
Beta-Sheet Peptide Assembly Beyond Sequence Patterning: Incorporation of Functional Domains, Computational Energy Minimization, and Assembly Pathway Selection

(Georgia Institute of Technology, 2024-04-25) Robang, Alicia Sze

We are interested in β-sheet peptide assembly for two reasons: 1) β-sheets are central to protein aggregation diseases, such as Alzheimer’s Disease, and 2) they are a design platform for therapeutic nanostructures. These reasons are connected – designing amino acid sequences to form specific structures reveals insights into possible β-sheet structures in diseases. Previous work has established that biological effects (e.g., therapeutic cellular stimulation or pathological toxicity) depend on structure (e.g., β-sheets can contain parallel or antiparallel β-strands). Furthermore, peptide aggregates can be polymorphic (inhomogeneous in molecular structure) and assemble into various nanostructures (e.g., nanofibers or oligomeric “nanoparticles,” which are aggregates of less than ~50 molecules). We seek to determine assembled β-sheet structures as a basis for predicting structures based on amino acid sequences, design amino acid sequences for assembly into specific structures, and understand relationships between structures and biological effects. My thesis contributes to this field in two main ways: First, we applied structural biology to understand possible structures. Second, we implemented new methods for designing and characterizing β-sheet assemblies. In our structural biology efforts, we revealed the unexpected βsheet structures within Q11 peptide biomaterials, discovered a novel type of peptide coassembly, and identified challenges in incorporating functional domains into β-sheet self-assembling peptides, which can be detrimental to therapeutic applications. In designing new β-sheet peptides, we established a computational-experimental approach to form parallel or antiparallel β-sheets selectively. We also proposed an assembly pathway selection-based model inspired by structural measurements on a size-limited Aβ42 oligomer. Altogether, our work expands our understanding of potential structures in disease-related protein aggregation and inspires new routes for peptide-based biomaterial designs.
Facilitating an Integrated Data-Centric Approach to Optimize Donor-Acceptor Copolymer Based Organic Field Effect Transistors

(Georgia Institute of Technology, 2024-04-17) Venkatesh, Rahul

The ever-growing demands of consumers in a rapidly expanding global population underscore the need for innovative materials to develop efficient and affordable electronic devices. One such area grappling with this surge in demand is the realm of conjugated polymer (CP)--based electronic materials. These semiconducting polymers have emerged as promising substitutes for traditional silicon, paving the way for flexible, lightweight, and cost-effective electronic devices such as organic field-effect transistors (OFETs), light-emitting diodes, and solar cells. Unlike silicon, CPs can be processed as solutions, rendering them more suitable for developing electronic devices with room for optimization. Unfortunately, progress in organic electronics is hindered by the vast and intricate processing landscape of these polymers, which has been demonstrated to directly impact performance. Moreover, traditional research methodologies have relied heavily on trial-and-error approaches, which not only slow down progress but also hinder the acquisition of insights and impede advancements toward real-world applications. Recent advancements in high-throughput experimentation (HTE) and materials informatics present solutions to these challenges. Thus, this thesis aims to integrate existing knowledge of polymer design and processing with HTE and polymer informatics methods to accelerate the development of OFETs derived from donor-acceptor (D-A) copolymers. The chapters of this dissertation exemplify the benefits of integrating data-centric approaches into the established polymer electronics framework to streamline the advancement of these materials. Throughout this thesis, a consistent focus lies on carefully evaluating processing conditions to optimize the performance of CP-based OFETs. Initially, employing data science algorithms on meticulously curated process-property datasets unveils the key processing variables influencing device performance, with algorithm-derived insights guiding future experiments. Subsequently, these informatics insights are validated through relevant experiments investigating the manipulation of CP solution states. These experiments aim to elucidate how variations in solution state parameters, such as concentration, impact the morphology of the final film and the functionality of the device. Lastly, this study delves into the burgeoning domain of polymer semiconductor-insulator blends (PSIBs), highlighting the potential of HTE through the fabrication and characterization of gradient thin films. This approach complements traditional discrete experiments, facilitating rapid screening of processing spaces for these blend systems, especially concerning blend composition. Moreover, it also provides a pathway to more efficient and comprehensive insights, uncovering trends occurring within narrow windows that might otherwise go unnoticed. In essence, this thesis underscores the integration of HTE and materials informatics into the existing polymer electronics paradigm to expedite the discovery and development of D-A polymer-based-OFETs.
Superfast excretion of viscous particle-laden droplets in phloem feeding insects

(Georgia Institute of Technology, 2024-02-08) Ha, Nami ; Challita, Elio J. ; Harrison, Jacob S. ; Clark, Elizabeth G. ; Cooperband, Miriam ; Bhamla, Saad

Fluid ejection is a tricky problem in biological systems, especially depending on the size scale and rheological properties of the fluid. An example is sharpshooter insects that feed on xylem sap and expel water droplets using a stylus at an ejection velocity of 0.4 m/s. In contrast to xylem feeders, spotted lanternflies (Lycorma delicatula) are insect species that feed on phloem sap rich in sugars and excrete sticky particle-laden honeydew droplets. Here, we explore how spotted lanternflies (SLFs) can flick sticky honeydew microdroplets. We analyze the rheological properties of fresh honeydew samples excreted by SLFs to quantify the viscosity and surface tension coefficients of honeydew. Using high-speed imaging technique, we found that SLFs fling droplets at an ejection velocity of 1.5 m/s using a stylus to expel their viscous liquid waste, seemingly similar to the catapult system of sharpshooters. The scaling analysis, however, suggests that droplets expelled from SLFs are governed by inertia rather than surface tension (Weber number ~ O(101)) and viscous force (Capillary number ~ O(10-1)), implying the ejection concept differs from sharpshooters that exploit the surface tension-driven droplet superpropulsion. Our findings on the superfast excretion behaviors of SLFs will inform us about how insects at small scales can overcome surface tension and viscous forces. This research sheds light on biofluid dynamics and development of self-cleaning bioinspired fluid ejectors at the millimeter-scale.