Bourns College of Engineering

Rhodium supported on titania (Rh/TiO 2 ) is an active catalyst for the reverse water gas shift reaction, yet the nature of the active sites for this reaction and others remain under debate due to the dynamic nature of the Rh coordination. Rhodium supported on titania (Rh/TiO 2 ) is an active catalyst for the reverse water gas shift reaction, yet the nature of the active sites for this reaction and others remain under debate. Single atom Rh sites have frequently been proposed as key sites, making it essential to monitor size changes of Rh species under in situ conditions to establish the structure–function relationship. However, surface-sensitive in situ measurements of nanosized particles remain experimentally challenging and have focused on metal oxide single crystal model systems. Here, we apply ambient pressure X-ray photoelectron spectroscopy (APXPS) to Rh/TiO 2 powdered catalysts under oxidizing and reducing environments. We find size-dependent binding energy shifts in both oxidized and reduced Rh species. By deconvoluting this size effect from oxidation state core level shifts, APXPS can provide qualitative evidence of Rh cluster size changes. Potential electronic effects responsible for these shifts are explored with density functional theory calculations. Ex situ transmission electron microscopy gives insight into particle size while Raman spectroscopy identifies Rh oxide phases. Correlative X-ray absorption spectroscopy and pair distribution function (PDF) measurements confirm the structural changes in the APXPS results. These findings offer direct insight into the dynamic behavior of Rh catalyst sintering and fragmentation based on core-level shifts.

Heterogeneous catalysis involves a complex interplay of adsorption, charge transfer, and catalyst restructuring at solid–gas or solid–liquid interfaces. While first-principles methods such as KS-DFT and AIMD accurately describe chemisorbed species, they struggle to capture weakly bound or dynamic molecules subject to thermal fluctuations. Continuum models provide macroscopic insight into electrostatics and transport but often neglect the interfacial molecular structure, especially within the Stern layer. The challenge is even greater at gas–solid interfaces, where the gas phase is typically ignored, giving rise to a long-standing pressure gap between theory and experiment. This Perspective advocates a statistical-mechanical description of interfacial species using classical density functional theory (cDFT), in which physisorption and gas/liquid-phase inhomogeneity near catalytic surfaces are represented by molecular density distributions rather than fixed atomic configurations. More importantly, we emphasize the necessity of integrating KS-DFT with such microenvironmental models and propose several potential strategies for coupling electronic-structure calculations with continuum and statistical-mechanical approaches. By merging first-principles, continuum, and statistical-mechanical approaches within open-system, physics-informed frameworks, it becomes possible to bridge electrochemical and thermocatalytic regimesfrom localized chemisorption to diffuse physisorptionand reveal the true complexity of catalytic interfaces.

New federal drinking water standards on per- and polyfluoroalkyl substances (PFAS) were recently established in the United States; however, the occurrence and speciation of PFAS in public drinking water systems nationwide is poorly understood. New federal drinking water standards on per- and polyfluoroalkyl substances (PFAS) were recently established in the United States; however, the occurrence and speciation of PFAS in public drinking water systems nationwide is poorly understood. This letter provides an urgently needed comprehensive analysis on PFAS occurrence in American public drinking water systems, via data mining on the most updated US EPA 5th round of Unregulated Contaminant Monitoring Rule database as of May 2026, representing 95% of eventual data entries and more than 1.9 million water samples from >10 000 public water systems. Data analysis showed that the most widely occurring PFAS compounds were PFPeA, PFHxA, PFBS, PFBA, PFOS, PFOA, PFHxS and PFHpA, detected by 3.3–13% of public water facilities nationwide. 90th percentile concentrations of these compounds ranged from ca. 7 to 17 ng L −1 , with PFBA, PFOS and PFOA exhibiting the highest concentrations. 6.7–6.8% of all drinking water samples had either PFOA or PFOS concentration exceeding 4 ng L −1 . Eastern U.S. and Pacific Southwestern regions have the highest percentages (4.6–19%) of public water systems with PFOA or PFOS level exceeding 4 ng L −1 . The occurrence of PFAS was also modestly associated with the dependence of groundwater as the drinking water source, with higher chances of PFOA or PFOS at more elevated levels in drinking water when associated with a groundwater source. This study provides new insights into PFAS occurrence nationwide, and can assist public water systems proactively prepare for PFAS treatment prioritization and future regulatory compliance.

High-temperature, multi-phase reactions in energetic composites present significant challenges in understanding their chemical processes and underlying mechanisms. These systems often involve rapid, heterogeneous reactions which require temporally and spatially resolved diagnostic tools. To address this need, this work develops a dual-camera system that integrates hyperspectral emission spectroscopy and three-color pyrometry, enabling simultaneous, high-speed measurements of gas-phase and condensed-phase temperatures. One camera captures RGB video for three-color pyrometry, while the other captures emission spectra using a slit-array mask and diffraction grating system. The slit-array mask is designed to achieve spatial coverage, allowing for 2D gas-phase temperature profiling. Gas-phase temperatures are derived using emission spectra based on Boltzmann equation, utilizing the intensity ratios of potassium emission lines near 580 nm and 693 nm. Demonstration experiments were conducted using 3D-printed Al-KClO4 thermites with varying equivalence ratios, revealing maximum gas-phase temperatures of 3300–3700 K, and condensed-phase temperatures about 2800 K. The gas-phase temperatures closely align with the material’s adiabatic flame temperatures, and the condensed phase temperatures of aluminum droplets are close to aluminum boiling point (2743 K). This diagnostic system offers significant advancements in the study of energetic materials by enabling synchronous, high-speed measurements of gas-phase and condensed-phase temperature distributions. Its capability for 2D profiling and adaptability to other high-temperature reaction systems represents a critical step forward in advancing fundamental research on energetic materials and combustion. The outcome of this work lays the foundation for exploring thermal dynamics and chemical kinetics in energetic composites, facilitating the design of nanostructured energetic materials.

Biological materials developed over millennia consist of simple biogenic materials, yet exhibit exceptional functional properties. Leveraging design features from these structures with engineered nanomaterial components can lead to bio-inspired structures that demonstrate superior performance over traditional engineering materials. We describe nanoscale based architectures in biological systems, their role in enhancement of structural, optical, thermal and sensing properties, and their subsequent translation to bio-inspired structures. In structurally robust biological materials, we highlight nanoscale design features that enhance strength and stiffness, while retaining toughness. In optically active biological materials, we show how periodic nanostructures manipulate electromagnetic waves resulting in structural coloration as well as antireflective and camouflaging properties. Thermally regulating biological materials utilize nanopores and other nanostructural features to statically or dynamically control temperature. In addition, biological materials that are used in sensing utilize various nanostructures that enhance sensitivity by decreasing activation thresholds for signal transduction. We discuss challenges and opportunities including understanding control mechanisms in the formation of biological materials and leveraging advancements in self-assembly with new additive manufacturing techniques. The continued evaluation of organisms, including those that exhibit multifunctionality, provides not only new design features and pathways, but significant prospects for innovation in this ever-emerging field.

Titanium dioxide (TiO₂) is one of the most extensively studied oxides as an active catalyst or catalyst support, particularly in energy and environmental applications, but the atomistic mechanisms governing its dynamic response to reactive environments and their correlation to reactivity remain largely elusive. Using in situ environmental transmission electron microscopy (ETEM), synchrotron X-ray diffraction (XRD), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), temperature-programmed reduction (TPR), reactivity measurements, and theoretical modeling, we reveal the dynamic interplay between oxygen loss and replenishment of anatase TiO₂ under varying reactive conditions. Under H₂ exposure, anatase TiO₂ undergoes surface reduction via lattice oxygen loss, forming Ti₃O₅. In contrast, CO₂ exposure induces oxygen replenishment, reversing stoichiometry. In mixed H₂/CO₂ environments, the reverse water-gas shift (RWGS) reaction proceeds selectively on stepped and high-indexed TiO₂ surfaces, whereas the thermodynamically stable TiO₂(101) surface remains inactive and intact. Critically, H₂ pretreatment generates oxygen vacancies on TiO₂(101), transforming it into an active Ti₃O₅ or defect-rich surface that catalyzes RWGS. By correlating surface structure, defect dynamics, and gas-phase interactions, this work deciphers the competition between H₂-driven reduction and CO₂-driven oxidation pathways at the atomic scale. These insights establish defect engineering as a strategic lever to activate inert TiO₂ facets, advancing the design of adaptive catalysts for sustainable fuel synthesis technologies.

Most of the industrially used heterogeneous catalysts have been discovered by trial and error, and despite decades of experience, the discovery of new catalysts continues to be extremely challenging. The drive to uncover guiding principles in catalyst design is more present than ever. We share a series of observations indicating that optimal catalysts typically function at characteristic phase boundaries (e.g., abrupt changes in adsorbate coverage, catalyst structure, etc.) accessed in the reaction conditions. The catalyst exploits the associated instability—the desire to exist in multiple states simultaneously—as a driving force for chemical transformations. In other words, phase boundaries are good places to start the catalyst search, and indeed, we should focus on at least two phases at once rather than just one. We substantiate this claim with several studies that combine statistical operando modeling and experiments. Transpiring from these observations is a hitherto unrecognized vector in catalyst discovery.

Designing CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) single guide RNA (sgRNA) libraries targeting entire kingdoms of life will significantly advance genetic research in diverse and underexplored taxa. Current sgRNA design tools are often species-specific and fail to scale to large, phylogenetically diverse datasets, limiting their applicability to comparative genomics, evolutionary studies, and biotechnology. Here, we introduce ALLEGRO, a combinatorial optimization algorithm designed to compose minimal, yet highly effective sgRNA libraries targeting thousands of species at the same time. Leveraging integer linear programming, ALLEGRO identified compact sgRNA sets simultaneously targeting multiple genes of interest for over 2000 species across the fungal kingdom. We experimentally validated sgRNAs designed by ALLEGRO in Kluyveromyces marxianus, Komagataella phaffii, Yarrowia lipolytica, and Saccharomyces cerevisiae, confirming successful genome edits. Additionally, we employed a generalized Cas9-ribonucleoprotein delivery system to apply ALLEGRO's sgRNA libraries to untested fungal genomes, such as Rhodotorula araucariae. Our experimental findings, together with cross-validation, demonstrate that ALLEGRO facilitates efficient CRISPR genome editing, enabling the development of universal sgRNA libraries applicable to entire taxonomic groups.

The hydrogenation of CO₂ to CH₃OH over Cu-based catalysts holds significant potential for advancing carbon sequestration and sustainable chemical processes. While numerous studies have focused on catalyst development, the environmental effects on underlying reaction mechanisms have yet to be fully understood. In this work, we develop a grand potential theory for a comprehensive analysis of CO₂ hydrogenation to CH₃OH over Cu (111) and Cu (211) surfaces. By integrating electronic and classical density functional calculations to bridge the "pressure gap", the theoretical results revealed that the HCOO* formation rate may vary by several orders of magnitude depending on reaction conditions. The grand potential theory enables us to elucidate the molecular mechanisms underlying the need for high H₂ pressure, the prevalence of saturated CO₂ adsorption, and the important roles of CO and H₂O in hydrogenation. Moreover, this study addressed and clarified controversies over CO₂ versus CO adsorption and hydrogenation, the formate versus carboxy pathways, and the difference in HCOO* hydrogenation activity between Cu (111) and Cu (211) surfaces. The theoretical analysis offers a new perspective for optimizing reaction conditions and catalyst performance in methanol synthesis and can be generalized to enhance our understanding of heterogeneous catalysis under industrially relevant conditions.

The Community Multiscale Air Quality (CMAQ) model simulates atmospheric phenomena, including advection, diffusion, gas-phase chemistry, aerosol physics and chemistry, and cloud processes. Gas-phase chemistry is often a major computational bottleneck due to its representation as large systems of coupled nonlinear stiff differential equations. We leverage the parallel computational performance of graphics processing unit (GPU) hardware to accelerate the numerical integration of these systems in CMAQ's CHEM module. Our implementation, dubbed CMAQ-CUDA, in reference to its use in the Compute Unified Device Architecture (CUDA) general purpose GPU (GPGPU) computing solution, migrates CMAQ's Rosenbrock solver from Fortran to CUDA Fortran. CMAQ-CUDA accelerates the Rosenbrock solver such that simulations using the chemical mechanisms RACM2, CB6R5, and SAPRC07 require only 51%, 50%, or 35% as much time, respectively, as CMAQv5.4 to complete a chemistry time step. Our results demonstrate that CMAQ is amenable to GPU acceleration and highlight a novel Rosenbrock solver implementation for reducing the computational burden imposed by the CHEM module.