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At least 577 records · Page 32

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu] 2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu] 2+ -catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.

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Prioritizing Mentorship as Scientific Leaders

Scientific careers are rarely straight paths. This article emphasizes the crucial role of mentorship in navigating scientific careers and sustaining innovation in STEMM fields. Effective mentorship can have a positive impact on graduate students' research productivity, research self-efficacy, degree completion, and program satisfaction. Despite its importance, mentorship is often an overlooked and underappreciated component of scientific training. As members of the 2022 CAS Future Leaders class, representing ten countries and various chemistry subdisciplines, we share our mentorship experiences to suggest actions to promote healthy and inclusive mentor-mentee relationships in chemistry. The article explores the importance of mentorship, outlines impactful strategies, and offers insights into how to create a scientific community that values and prioritizes effective mentorship.

99 GENERAL AND MISCELLANEOUS↗

Effect of Framework Composition and NH 3 on the Diffusion of Cu + in Cu-CHA Catalysts Predicted by Machine-Learning Accelerated Molecular Dynamics

Cu-exchanged zeolites rely on mobile solvated Cu + cations for their catalytic activity, but the role of the framework composition in transport is not fully understood. Ab initio molecular dynamics simulations can provide quantitative atomistic insight but are too computationally expensive to explore large length and time scales or diverse compositions. We report a machine-learning interatomic potential that accurately reproduces ab initio results and effectively generalizes to allow multinanosecond simulations of large supercells and diverse chemical compositions. Biased and unbiased simulations of [Cu(NH 3 ) 2 ] + mobility show that aluminum pairing in eight-membered rings accelerates local hopping and demonstrate that increased NH 3 concentration enhances long-range diffusion. The probability of finding two [Cu(NH 3 ) 2 ] + complexes in the same cage, which is key for SCR-NOx reaction, increases with Cu content and Al content but does not correlate with the long-range mobility of Cu + . Supporting experimental evidence was obtained from reactivity tests of Cu-CHA catalysts with a controlled chemical composition.

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Trap States in Reduced Colloidal Titanium Dioxide Nanoparticles Have Different Proton Stoichiometries

Added electrons and holes in semiconducting (nano)materials typically occupy “trap states,” which often determine their photophysical properties and chemical reactivity. However, trap states are usually ill-defined, with few insights into their stoichiometry or structure. Our laboratory previously reported that aqueous colloidal TiO 2 nanoparticles prepared from TiCl 4 + H 2 O have two classes of electron trap states, termed Blue and Red. Herein, we show that the formation of Red from oxidized TiO 2 requires 1e – + 1H + , while Blue requires 1e – + 2H + . The two states are in a protic equilibrium, Blue ⇌ Red + H + , with K eq = 2.65 mM. The Blue states in the TiO 2 NPs behave just like a soluble molecular acid with this K eq as their K a , as supported by solvent isotope studies. Because the trap states have different compositions, their population and depopulation occur with the making and breaking of chemical bonds and not (as commonly assumed) just by the movement of electrons. In addition, the direct observation of a 2H + /1e – trap state contradicts the emerging H atom transfer (1H + /1e – ) paradigm for oxide/solution interfaces. Finally, this work emphasizes the importance of chemical stoichiometries, not just electronic energies, in understanding and directing the reactivity at solid/solution interfaces.

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Unveiling a Novel, Cation-Rich Compound in a High-Pressure Pb–Te Binary System

Because of the common oxidation states of group IV elements (+2 or +4) and group VI elements (–2), 1:1 and 1:2 are two typical stoichiometries found in the IV–VI compounds. Particularly, in the Pb–Te binary system, the 1:1 stoichiometric PbTe is believed to be the only stable compound. Herein, using evolutionary algorithms, density functional theory, a laser-heated diamond anvil cell, and synchrotron X-ray diffraction experiments, we discovered a novel Pb–Te compound with an unexpected stoichiometry of 3:2 above 20 GPa. This tetragonal Pb 3 Te 2 is the one of the very few cation-rich compounds that has ever been discovered in the entire IV–VI binary system. Further analyses based on electron density distribution, electron localization function, and Bader charge have shown that this newly discovered compound has a mixed character of chemical bonding with a decreased ionicity. By further calculating the electron–phonon interaction, Pb 3 Te 2 is predicted to exhibit a superconducting transition at low temperatures. Lastly, the discovery of Pb 3 Te 2 paves the way for further explorations of other novel cation-rich IV–VI group compounds.

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Carbons with Regular Pore Geometry Yield Fundamental Insights into Supercapacitor Charge Storage

We conduct molecular dynamics simulations of electrical double-layer capacitors (EDLCs) using a library of ordered, porous carbon electrode materials called zeolite templated carbons (ZTCs). The well-defined pore shapes of the ZTCs enable us to determine the influence of pore geometry on both charging dynamics and charge storage mechanisms in EDLCs, also referred to as supercapacitors. We show that charging dynamics are negatively correlated with the pore-limiting diameter of the electrode material and display signatures of both progressive charging and ion trapping. However, the equilibrium capacitance, unlike charging dynamics, is not strongly correlated to commonly used, purely geometric descriptors such as pore size. Instead, we find a strong correlation of capacitance to the charge compensation per carbon (CCpC), a descriptor we define in this work as the average charge of the electrode atoms within the coordination shell of a counterion. A high CCpC indicates efficient charge storage, as the strong partial charges of the electrode are able to screen counterion charge, enabling higher ion loading and thus more charge storage within the electrode at a fixed applied voltage. We determine that adsorption sites with a high CCpC tend to be found within pockets with a smaller radius of curvature, where the counterions are able to minimize their distance with multiple points on the electrode surface, and therefore induce stronger local partial charges.

25 ENERGY STORAGE↗

Tunable Heteroassembly of a Plant Pseudoenzyme–Enzyme Complex

Pseudoenzymes have emerged as key regulatory elements in all kingdoms of life despite being catalytically nonactive. Yet many factors defining why one protein is active while its homologue is inactive remain uncertain. For pseudoenzyme–enzyme pairs, the similarity of both subunits can often hinder conventional characterization approaches. In plants, a pseudoenzyme, PDX1.2, positively regulates vitamin B 6 production by association with its active catalytic homologues such as PDX1.3 through an unknown assembly mechanism. Here we used an integrative experimental approach to learn that such pseudoenzyme–enzyme pair associations result in heterocomplexes of variable stoichiometry, which are unexpectedly tunable. We also present the atomic structure of the PDX1.2 pseudoenzyme as well as the population averaged PDX1.2–PDX1.3 pseudoenzyme–enzyme pair. Finally, we dissected hetero-dodecamers of each stoichiometry to understand the arrangement of monomers in the heterocomplexes and identified symmetry-imposed preferences in PDX1.2–PDX1.3 interactions. In this work, our results provide a new model of pseudoenzyme–enzyme interactions and their native heterogeneity.

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Biosynthesis of Haloterpenoids in Red Algae via Microbial-like Type I Terpene Synthases

Red algae or seaweeds produce highly distinctive halogenated terpenoid compounds, including the pentabromochlorinated monoterpene halomon that was once heralded as a promising anticancer agent. The first dedicated step in the biosynthesis of these natural product molecules is expected to be catalyzed by terpene synthase (TS) enzymes. Recent work has demonstrated an emerging class of type I TSs in red algal terpene biosynthesis. However, only one such enzyme from a notoriously haloterpenoid-producing red alga (Laurencia pacifica) has been functionally characterized and the product structure is not related to halogenated terpenoids. Herein, we report 10 new type I TSs from the red algae Portieria hornemannii, Plocamium pacificum, L. pacifica, and Laurencia subopposita that produce a diversity of halogenated mono- and sesquiterpenes. Here we used a combination of genome sequencing, terpenoid metabolomics, in vitro biochemistry, and bioinformatics to establish red algal TSs in all four species, including those associated with the selective production of key halogenated terpene precursors myrcene, trans-β-ocimene, and germacrene D-4-ol. These results expand on a small but growing number of characterized red algal TSs and offer insight into the biosynthesis of iconic halogenated algal compounds that are not without precedence elsewhere in biology.

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GAS41 Recognizes Diacetylated Histone H3 through a Bivalent Binding Mode

GAS41 is a chromatin-associated protein that belongs to the YEATS family and is involved in the recognition of acetyl-lysine in histone proteins. A unique feature of GAS41 is the presence of a C-terminal coiled-coil domain, which is responsible for protein dimerization. Here, we characterized the specificity of the GAS41 YEATS domain and found that it preferentially binds to acetylated H3K18 and H3K27 peptides. Interestingly, we found that full-length, dimeric GAS41 binds to diacetylated H3 peptides with an enhanced affinity when compared to those for monoacetylated peptides, through a bivalent binding mode. We determined the crystal structure of the GAS41 YEATS domain with H3K23acK27ac to visualize the molecular basis of diacetylated histone binding. Furthermore, our results suggest a unique binding mode in which full-length GAS41 is a reader of diacetylated histones.

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Binding Geometries of Silicate Species on Ferrihydrite Surfaces

Silicate sorption on ferrihydrite surfaces, as monomers, oligomers, and polymers, strongly affects ferrihydrite crystallinity, thermodynamic stability, and surface reactivity. How these silicate species bind on ferrihydrite surfaces is, however, not well understood. We have determined silicate binding geometries using a combination of X-ray absorption spectroscopy (XAS), differential atomic pair distribution function (d-PDF) analysis, and density functional theory (DFT) calculations. Silicon K-edge absorption pre-edges and DFT-predicted energies indicate that silicate forms monomeric monodentate–mononuclear (MM) complexes at low silicate sorption loadings. With increasing silicate loading, the pre-edge peak shifts to higher energies, suggesting changes in the silicate binding geometry toward multidentate complexation. The d-PDF analysis determines the Si–Fe interatomic distance to be ~3.25 Å for the high-loading samples. The DFT calculations indicate that such distance corresponds to an oligomer in the bidentate–binuclear (BB) binding geometry. The transition of the silicate sorption geometry accompanied by polymerization can affect stability of ferrihydrite and its adsorption and redox reactivity and increase the degree of Si isotopic fractionation upon silicate sorption on Fe oxides. MM monomeric complexes and BB oligomeric complexes should be used for surface complexation models predicting silicate sorption on Fe oxide surfaces.

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Reply to “Comment on ‘Roles of Hydration and Magnetism on the Structure of Ferrihydrite from First Principles’”

In the comments made by Manceau on the study by Sassi and Rosso, some concerns were raised regarding the potential for the structure theoretically found and identified as ferrihydrite (Michel model) to be hydromaghemite. In this reply, we address these concerns by showing that the structure theoretically generated cannot be unambiguously compared to hydromaghemite. Many of the concerns raised are, in fact, oblique to the given study. Notwithstanding, we reiterate clearly stated methodological details and caveats regarding the theoretical structural search performed, which disallow for the hydromaghemite prospect to manifest. We furthermore highlight the scholarly importance of examining experimental X-ray diffraction (XRD) and pair distribution function (PDF) of synthesized ferrihydrite samples from a variety of independent sources, as embodied in a broader set of relevant literature.

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Colloidal Quantum Shells: An Emerging 2D Semiconductor for Energy Applications

Low-dimensional semiconductors hold strong promise for future energy applications. These nanomaterials are inexpensive to process and offer a broad spectrum of attractive quantum-mechanical properties. The notorious problem of low-dimensional nanostructures, however, lies in their limited performance under high energetic loads, when more than one exciton per particle is created. Multiple excitons undergo fast annihilation, causing efficiently roll-off in energy-intensive applications, including high-brightness LEDs, X-ray scintillators, and solar cells. In this prospective, we will highlight an emerging type of low-dimensional semiconductors that allows avoiding such multi-exciton (MX) energy losses. Recently demonstrated colloidal quantum shells benefit from the spatial separation of multiple excitons, which leads to extraordinary improvements to MX lifetimes and MX quantum yield. This makes quantum shell morphology an attractive candidate for solution-processed optical and electrical devices. In this Focus Review, we compare the optoelectronic properties of quantum shells against other low-dimensional semiconductors and discuss their emerging opportunities in solid-state lighting and energy-harvesting applications.

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Intercomparison of Three Continuous Monitoring Systems on Operating Oil and Gas Sites

We compare continuous monitoring systems (CMS) from three different vendors on six operating oil and gas sites in the Appalachian Basin using several months of data. We highlight similarities and differences between the three CMS solutions when deployed in the field and compare their output to concurrent top-down aerial measurements and to site-level bottom-up inventories. Furthermore, we compare vendor-provided emission rate estimates to estimates from an open-source quantification algorithm applied to the raw CMS concentration data. This experimental setup allows us to separate the effect of the sensor platform (i.e., sensor type and arrangement) from the quantification algorithm. We find that 1) localization and quantification estimates rarely agree between the three CMS solutions on short time scales (i.e., 30 min), but temporally aggregated emission rate distributions are similar between solutions, 2) differences in emission rate distributions are generally driven by the quantification algorithm, rather than the sensor platform, 3) agreement between CMS and aerial rate estimates varies by CMS solution but is close to parity when CMS estimates are averaged across solutions, and 4) similar sites with similar bottom-up inventories do not necessarily have similar emission characteristics. These results have important implications for developing measurement-informed inventories and for incorporating CMS-inferred emission characteristics into emission mitigation efforts.

54 ENVIRONMENTAL SCIENCES↗

Hourly PM 2.5 Estimates across California from 2018 to 2023

This study presents a new data set of hourly PM 2.5 concentrations across California from 2018 to 2023 at a three-kilometer resolution. This data set was developed by assimilating observations from PurpleAir and the U.S. EPA Air Quality System monitors into wildfire smoke forecasts from the High-Resolution Rapid Refresh Smoke (HRRR-Smoke) model using the Gridpoint Statistical Interpolation (GSI) three-dimensional variational data assimilation framework. Archived forecasts of modeled wildfire smoke PM 2.5 from HRRR-Smoke create the background field for assimilation, which is then corrected using surface observations of total PM 2.5 . The resulting reanalysis from GSI provides an estimate of total PM 2.5 that minimizes error from both the observational and the model data. Validation results indicate strong performance, with monthly R 2 values ranging from 0.73 to 0.91 across the six-year data set, comparable to other PM 2.5 data sets. Case studies are presented for three major fire events, the 2018 Camp Fire, 2019 Kincade Fire, and 2020 Lightning Complex Fires to demonstrate the data set’s fidelity in resolving plume dynamics and local exposure patterns. Root-mean-squared error averaged over each month scales with average PM 2.5 concentrations, resulting in a low error under typical conditions but higher absolute errors during extreme smoke events. This is the first long-term, hourly PM 2.5 data set of its kind for California and enables the generation of subdaily exposure metrics, such as peak hourly concentrations, exceedance durations, and time-of-day exposure peaks. The novelty and strong validation of this data set make it a compelling resource for future studies on the impact and significance of subdaily PM 2.5 exposure.

PM2.5↗

Bayesian Learning of Adatom Interactions from Atomically Resolved Imaging Data

Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. In this work, we develop a workflow based on a machine-learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.

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Source of Bright Near-Infrared Luminescence in Gold Nanoclusters

Gold nanoclusters with near-infrared (NIR) photoluminescence (PL) have great potential as sensing and imaging materials in biomedical and bioimaging applications. In this work, Au 21 (S-Adm) 15 and Au 38 S 2 (S-Adm) 20 are used to unravel the underlying mechanisms for the improved quantum yields (QY), large Stokes shifts and long PL lifetimes in gold nanoclusters. Both nanoclusters show decent PL QY. In particular, the Au 38 S 2 (S-Adm) 20 nanocluster shows a bright NIR PL at 900 nm with QY up to 15% in normal solvents (such as toluene) at ambient conditions. The relatively lower QY for Au 21 (S-Adm) 15 (4%) compared to Au 38 S 2 (S-Adm) 20 is attributed to the lowest-lying excited state being symmetry-disallowed, as evidenced by the pressure-dependent anti-spectral shift of the absorption spectra compared to PL. Yet, Au 21 (S-Adm) 15 maintains some emissive properties due to a nearby symmetry-allowed excited state. Furthermore, our results show that suppression of non-radiative decay due to the surface “lock rings” which encircle the Au kernel and the surface “lock atoms” which bridge the fundamental Au-kernel units (e.g., tetrahedra, icosahedra, etc.) is the key to obtain high QYs in gold nanoclusters. Here, the complicated excited-state processes and the small absorption coefficient of the band-edge transition lead to the large Stokes shifts and the long PL lifetimes that are widely observed in gold nanoclusters.

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