Department of Chemical and Biomolecular Engineering

To elucidate interfacial dynamics during electrocatalytic reactions, it is crucial to understand the adsorption behavior of organic molecules on catalytic electrodes within the electric double layer (EDL). However, the EDL structure in aqueous environments remains intricate when it comes to the electrochemical amination of acetone, using methylamine as a nitrogen source. Specifically, the interactions of acetone and methylamine with the copper electrode in water remain unclear, posing challenges in the prediction and optimization of reaction outcomes. In this study, initial investigations employed impedance spectroscopy at the potential of zero charge to explore the surface preconfiguration. Here, the capacitance of the EDL was utilized as a primary descriptor to analyze the adsorption tendencies of both acetone and methylamine. Acetone shows an increase in the EDL capacitance, while methylamine shows a decrease. Experiments are interpreted using combined grand canonical density functional theory and ab initio molecular dynamics to delve into the microscopic configurations, focusing on their capacitance and polarizability. Methylamine and acetone have larger molecular polarizability than water. Acetone shows a partial hydrophobic character due to the methyl groups, forming a distinct adlayer at the interface and increasing the polarizability of the liquid interface component. In contrast, methylamine interacts more strongly with water due to its ability to both donate and accept hydrogen bonds, leading to a more significant disruption of the hydrogen bond network. This disruption of the hydrogen network decreases the local polarizability of the interface and decreases the effective capacitance. Our findings underscore the pivotal role of EDL capacitance and polarizability in determining the local reaction environment, shedding light on the fundamental processes important for electro-catalysis.

Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial settings as they provide additional information regarding the process outcome. Individually, these models can reduce the number of metrology steps in semiconductor factories, and when developed further, can empower the development of advanced process control strategies.

Timely and effective interventions after tracheal mucosal injury are lack in clinical practices, which elevate the risks of airway infection, tracheal cartilage deterioration, and even asphyxiated death. Herein, we proposed a biomaterial-based strategy for the repair of injured tracheal mucosal based on a copper hydrogen phosphate nanosheets (CuHP NSs) functionalized commercial hydrogel (polyethylene glycol disuccinimidyl succinate-human serum albumin, PH). Such CuHP/PH hydrogel achieved favorable injectability, stable gelation, and excellent adhesiveness within the tracheal lumen. Moreover, CuHP NSs within the CuHP/PH hydrogel effectively stimulate the proliferation and migration of endothelial/epithelial cells, enhancing angiogenesis and demonstrating excellent tissue regenerative potential. Additionally, it exhibited significant inhibitory effects on both bacteria and bacterial biofilms. More importantly, when injected injured site of tracheal mucosa under fiberoptic bronchoscopy guidance, our results demonstrated CuHP/PH hydrogel adhered tightly to the tracheal mucosa. The therapeutic effects of the CuHP/PH hydrogel were further confirmed, which significantly improved survival rates, vascular and mucosal regeneration, reduced occurrences of intraluminal infections, tracheal stenosis, and cartilage damage complications. This research presents an initial proposition outlining a strategy employing biomaterials to mitigate tracheal mucosal injury, offering novel perspectives on the treatment of mucosal injuries and other tracheal diseases.

Modern life is essentially homochiral, containing D-sugars in nucleic acid backbones and L-amino acids in proteins. Since coded proteins are theorized to have developed from a prebiotic RNA World, the homochirality of L-amino acids observed in all known life presumably resulted from chiral transfer from a homochiral D-RNA World. This transfer would have been mediated by aminoacyl-RNAs defining the genetic code. Previous work on aminoacyl transfer using tRNA mimics has suggested that aminoacylation using D-RNA may be inherently biased toward reactivity with L-amino acids, implying a deterministic path from a D-RNA World to L-proteins. Using a model system of self-aminoacylating D-ribozymes and epimerizable activated amino acid analogs, we test the chiral selectivity of 15 ribozymes derived from an exhaustive search of sequence space. All of the ribozymes exhibit detectable selectivity, and a substantial fraction react preferentially to produce the D-enantiomer of the product. Furthermore, chiral preference is conserved within sequence families. These results are consistent with the transfer of chiral information from RNA to proteins but do not support an intrinsic bias of D-RNA for L-amino acids. Different aminoacylation structures result in different directions of chiral selectivity, such that L-proteins need not emerge from a D-RNA World.

Bacterial nanocellulose (BNC) is a durable, flexible, and dynamic biomaterial capable of serving a wide variety of fields, sectors, and applications within biotechnology, healthcare, electronics, agriculture, fashion, and others. BNC is produced spontaneously in carbohydrate-rich bacterial culture media, forming a cellulosic pellicle via a nanonetwork of fibrils extruded from certain genera. Herein, we demonstrate engineering BNC-based scaffolds with tunable physical and mechanical properties through postprocessing. Human skeletal muscle myoblasts (HSMMs) were cultured on these scaffolds, and in vitro electrical stimulation was applied to promote cellular function for tissue engineering applications. We compared physiologic maturation markers of human skeletal muscle myoblast development using a 2.5-dimensional culture paradigm in fabricated BNC scaffolds, compared to two-dimensional (2D) controls. We demonstrate that the culture of human skeletal muscle myoblasts on BNC scaffolds developed under electrical stimulation produced highly aligned, physiologic morphology of human skeletal muscle myofibers compared to unstimulated BNC and standard 2D culture. Furthermore, we compared an array of metrics to assess the BNC scaffold in a rigorous head-to-head study with commercially available, clinically approved matrices, Kerecis Omega3 Wound Matrix (Marigen) and Phoenix as well as a gelatin methacryloyl (GelMA) hydrogel. The BNC scaffold outcompeted industry standard matrices as well as a 20% GelMA hydrogel in durability and sustained the support of human skeletal muscle myoblasts in vitro. This work offers a robust demonstration of BNC scaffold cytocompatibility with human skeletal muscle cells and sets the basis for future work in healthcare, bioengineering, and medical implant technological development.

Successful nerve repair using bioadhesive hydrogels demands minimizing tissue-material interfacial mechanical mismatch to reduce immune responses and scar tissue formation. Furthermore, it is crucial to maintain the bioelectrical stimulation-mediated cell-signaling mechanism to overcome communication barriers within injured nerve tissues. Therefore, engineering bioadhesives for neural tissue regeneration necessitates the integration of electroconductive properties with tissue-like biomechanics. In this study, we propose a stretchable bioadhesive based on a custom-designed chemically modified elastin-like polypeptides (ELPs) and a choline-based bioionic liquid (Bio-IL), providing an electroconductive microenvironment to reconnect damaged nerve tissue. The stretchability akin to native neural tissue was achieved by incorporating hydrophobic ELP pockets, and a robust tissue adhesion was obtained due to multi-mode tissue-material interactions through covalent and noncovalent bonding at the tissue interface. Adhesion tests revealed adhesive strength ~10 times higher than commercially available tissue adhesive, Evicel®. Furthermore, the engineered hydrogel supported in vitro viability and proliferation of human glial cells. We also evaluated the biodegradability and biocompatibility of the engineered bioadhesive in vivo using a rat subcutaneous implantation model, which demonstrated facile tissue infiltration and minimal immune response. The outlined functionalities empower the engineered elastic and electroconductive adhesive hydrogel to effectively enable sutureless surgical sealing of neural injuries and promote tissue regeneration.

Cu dissolution has been identified as the dominant process that causes cathode degradation and losses even under cathodic conditions involving methylamine. Despite extensive experimental research, our fundamental and theoretical understanding of the atomic-scale mechanism for Cu dissolution under electrochemical conditions, eventually coupled with surface restructuring processes, is limited. Here, driven by the observation that the working Cu electrode is corroded using mixtures of acetone and methylamine even under reductive potential conditions (-0.75 V vs. RHE), we employed Grand Canonical density functional theory to understand this dynamic process under potential from a microscopic perspective. We show that amine ligands in solution directly chemisorb on the electrode, coordinate with the metal center, and drive the rearrangement of the copper surface by extracting Cu as adatoms in low coordination positions, where other amine ligands can coordinate and stabilize a surface copper-ligand complex, finally forming a detached Cu-amine cationic complex in solution, even under negative potential conditions. Calculations predict that dissolution would occur for a potential of -1.1 V vs. RHE or above. Our work provides a fundamental understanding of Cu dissolution facilitated by surface restructuring in amine solutions under electroreduction conditions, which is required for the rational design of durable Cu-based cathodes for electrochemical amination or other amine involving reduction processes.

ConspectusCreating a living system from nonliving matter is a great challenge in chemistry and biophysics. The early history of life can provide inspiration from the idea of the prebiotic RNA World established by ribozymes, in which all genetic and catalytic activities were executed by RNA. Such a system could be much simpler than the interdependent central dogma characterizing life today. At the same time, cooperative systems require a mechanism such as cellular compartmentalization in order to survive and evolve. Minimal cells might therefore consist of simple vesicles enclosing a prebiotic RNA metabolism.The internal volume of a vesicle is a distinctive environment due to its closed boundary, which alters diffusion and available volume for macromolecules and changes effective molecular concentrations, among other considerations. These physical effects are mechanistically distinct from chemical interactions, such as electrostatic repulsion, that might also occur between the membrane boundary and encapsulated contents. Both indirect and direct interactions between the membrane and RNA can give rise to nonintuitive, emergent behaviors in the model protocell system. We have been examining how encapsulation inside membrane vesicles would affect the folding and activity of entrapped RNA.Using biophysical techniques such as FRET, we characterized ribozyme folding and activity inside vesicles. Encapsulation inside model protocells generally promoted RNA folding, consistent with an excluded volume effect, independently of chemical interactions. This energetic stabilization translated into increased ribozyme activity in two different systems that were studied (hairpin ribozyme and self-aminoacylating RNAs). A particularly intriguing finding was that encapsulation could rescue the activity of mutant ribozymes, suggesting that encapsulation could affect not only folding and activity but also evolution. To study this further, we developed a high-throughput sequencing assay to measure the aminoacylation kinetics of many thousands of ribozyme variants in parallel. The results revealed an unexpected tendency for encapsulation to improve the better ribozyme variants more than worse variants. During evolution, this effect would create a tilted playing field, so to speak, that would give additional fitness gains to already-high-activity variants. According to Fishers Fundamental Theorem of Natural Selection, the increased variance in fitness should manifest as faster evolutionary adaptation. This prediction was borne out experimentally during in vitro evolution, where we observed that the initially diverse ribozyme population converged more quickly to the most active sequences when they were encapsulated inside vesicles.The studies in this Account have expanded our understanding of emergent protocell behavior, by showing how simply entrapping an RNA inside a vesicle, which could occur spontaneously during vesicle formation, might profoundly affect the evolutionary landscape of the RNA. Because of the exponential dynamics of replication and selection, even small changes to activity and function could lead to major evolutionary consequences. By closely studying the details of minimal yet surprisingly complex protocells, we might one day trace a pathway from encapsulated RNA to a living system.

We asked researchers from a range of disciplines across biology, engineering, and medicine to describe a current technological need. The goal is to provide a sample of the various technological gaps that exist and inspire future research projects.