Department of Chemical and Biomolecular Engineering

The protonation of CO is recognized as the rate-determining step in the generation of C1 products during the electrochemical CO₂ reduction reaction (CO₂RR) on Cu surfaces. However, the detailed mechanism and the precise proton source remain elusive. While density functional theory (DFT) calculations at the GGA level have been widely used, they struggle to accurately describe adsorbate-metal interactions and surface stability. Here, we employed the Random Phase Approximation (RPA), a method based on many-body perturbation theory, to overcome these limitations. We coupled the RPA framework with the linearized Poisson-Boltzmann equation to model solvation effects and a surface charging method to account for the influence of the electrochemical potential. Our study reveals that in neutral or alkaline electrolytes, adsorbed surface water acts as the proton source for *CO reduction to *COH over a broad potential range via the Grotthuss mechanism. At highly negative potentials, solvent water becomes the primary proton donor, with multiple competing mechanisms observed. In contrast, DFT-GGA functionals significantly underestimate the reaction barriers for *COH formation and consistently predict solvent water as the proton source across all potentials of interest. Additionally, RPA offers distinct insights into H₂O adsorption and highlights the significant range of reducing potentials within which surface *OH can exist, which is crucial for accurate CO₂RR modeling. These insights illustrate a pronounced divergence between RPA and DFT-GGA results. Our findings offer a fresh perspective on proton transfer in CO₂RR and establish a framework for future theoretical studies on electrochemical processes.

Supported Pd-Au bimetallic nanoparticles make up a promising class of catalysts used for hydrogenation and oxidation reactions. Recently, the role of dynamic restructuring of Pd regions at and near the nanoparticle surface in response to modulating gas (H₂ and O₂) concentrations was highlighted for controlling the surface Pd oxide stoichiometry. Here, we investigate the mechanism of formation and decomposition of Pd hydride (PdH_x) at and near the bimetallic nanoparticle surfaces, a key species for controlling the activity, selectivity, and stability of Pd catalysts in many hydrogenation reactions. We employ modulation excitation X-ray absorption spectroscopy (ME-XAS) to directly observe the time scale of PdH_x formation and decomposition on the surface of Pd-Au nanoparticles. Density functional theory (DFT) calculations provide additional insights into the stability and energetics of PdH_x formation under varying H fractions and Pd substructures. Our results reveal a complex interplay between Pd ensemble size, surface structure, and hydrogen environment in determining the kinetics and thermodynamics of PdH_x formation. By elucidating the mechanisms underlying surface PdH_x formation and decomposition, the rational design of dynamic catalysts with controlled Pd hydride stoichiometries can become possible.

The nucleolus, once considered a mere ribosome factory, is now recognized as a dynamic hub influencing nearly every aspect of cellular life, from genome organization to stress response and ageing. Despite being a hallmark of eukaryotic cells, recent discoveries reveal that even prokaryotes exhibit nucleolus-like structures, hinting at ancient origins for nucleolar functions. This review explores the evolutionary journey of the nucleolus, tracing its roots back to early life and examining its structural and functional diversity across domains. We highlight key nucleolar proteins that play vital roles not only in ribosome production but also in regulating cell cycle, DNA repair and cellular stress, linking nucleolar activity directly to health and disease. Dysfunctions in nucleolar processes are implicated in cancer, ribosomopathies and neurodegenerative disorders, positioning the nucleolus as a critical target for innovative therapeutic strategies. As advanced imaging and molecular techniques unlock deeper insights into both canonical and mysterious non-canonical roles, the nucleolus stands as a model for how cellular microenvironments can evolve to meet complex biological demands. By addressing open questions surrounding the evolution of the nucleolus, its organization and diverse functions, the ideas presented here aim to contribute to the ongoing discussion, challenging traditional paradigms and suggesting new avenues for uncovering the fundamental principles that drive cellular life.

Apart from conventional redox chemistries, exploring high-voltage anionic redox processes, such as pure oxygen or high-valent transition metal ion redox, poses challenges due to the instability of O nonbonding or O-dominant energy states. These states are associated with destructive behaviors in layered oxide cathodes, including local structural distortion, cationic disordering, and oxygen gas evolution. In this study, we suppress first-cycle voltage hysteresis and irreversible O2 evolution in Li-rich oxide cathodes through covalency competition induced by the substitution of electropositive groups. We found that the nonequivalent electron distribution within an asymmetric MA-O-MB backbone (metal-to-metal charge transfer via oxygen ligands) increases electron density on electronegative transition metal ions, preventing them from reaching unstable oxidation states within an operating voltage range. This phenomenon is observed across diverse transition metal combinations, providing insights into controlling unnecessary oxygen redox activity. Our findings open new avenues for controlling intrinsic redox chemistry and enabling the rational design of high-energy density Li-rich oxide cathodes.

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.

Facing the global superbug crisis due to the emergence and selection for antibiotic resistance, phages are among the most promising solutions. Fighting multidrug-resistant bacteria requires precise diagnosis of bacterial pathogens and specific cell-killing. Phages have several potential advantages over conventional antibacterial agents such as host specificity, self-amplification, easy production, low toxicity as well as biofilm degradation. However, the narrow host range, uncharacterized properties, as well as potential risks from exponential replication and evolution of natural phages, currently limit their applications. Engineering phages can not only enhance the host bacteria range and improve phage efficacy, but also confer new functions. This review first summarizes major phage engineering techniques including both chemical modification and genetic engineering. Subsequent sections discuss the applications of engineered phages for bacterial pathogen detection and ablation through interdisciplinary approaches of synthetic biology and nanotechnology. We discuss future directions and persistent challenges in the ongoing exploration of phage engineering for pathogen control.

The electrochemical hydrogenation (e-hydrogenation) of unsaturated compounds like imines or carbonyls presents a benign reduction method. It enables direct use of electrons as reducing agent, water as proton source, while bypassing the need for elevated temperatures or pressures. In this contribution, we discuss the active species in electrocatalytic reductive amination with the transformation of acetone and methylamine as model reaction. Surprisingly, lead impurities in the ppm-range proved to possess a significant effect in e-hydrogenation. Accordingly, the influence of applied potential and cathode material in presence of 1 ppm Pb was investigated. Finally, we transferred the insights to the reduction of acetone manifesting comparable observations as for imine reduction. The results suggest that previous studies on electrochemical reduction in the presence of lead electrodes should be re-evaluated.

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.