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At least 73 records · Page 4

Surface facet dependence of competing alloying mechanisms

Metal alloys are ubiquitous in many branches of heterogeneous catalysis, and it is now fairly well established that the local atomic structure of an alloy can have a profound influence on its chemical reactivity. While these effects can be difficult to probe in nanoparticle catalysts, model studies using well defined single crystal surfaces alloyed with dopants enable these structure–function correlations to be drawn. The first step in this approach involves understanding the alloying mechanism and the type of ensembles formed. In this study, we examined the atomic structure of RhCu single-atom alloys formed on Cu(111), Cu(100), and Cu(110) surfaces. Our results show a striking difference between Rh atoms alloying in Cu(111) vs the more open Cu(100) and Cu(110) surface facets. Unlike Cu(111) on which Rh atoms preferentially place-exchange with Cu atoms in the local regions above step edges leaving the majority of the Cu surface free of Rh, highly dispersed, homogeneous alloys are formed on the Cu(100) and (110) surfaces. These dramatically different alloying mechanisms are understood by quantifying the energetic barriers for atomic hopping, exchange, swapping, and vacancy filling events for Rh atoms on different Cu surfaces through theoretical calculations. Density functional theory results indicate that the observed differences in the alloying mechanism can be attributed to a faster hopping rate, relatively high atomic exchange barriers, and stronger binding of Rh atoms in the vicinity of step edges on Cu(111) compared to Cu(110) and Cu(100). Furthermore, these model systems will serve as useful platforms for examining structure sensitive chemistry on single-atom alloys.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Effect of Ni particle size on the production of renewable methane from CO 2 over Ni/CeO 2 catalyst

Production of ‘renewable Methane’ has attracted renewed research interest as a fundamental probe reaction and process for CO 2 utilization through potential use in C1 fuel production and even for future space exploration technologies. CO 2 methanation is a structure sensitive reaction on Ni/CeO 2 catalysts. To precisely elucidate the size effect of the Ni metal center on the CO 2 methanation performance, we prepared 2%Ni/CeO 2 catalysts with pre-synthesized uniform Ni particles (2, 4 and 8 nm) on a high surface area CeO 2 support. Transmission electron microscopy (TEM) and ambient pressure X-ray photo spectroscopy (AP-XPS) characterization have confirmed that the catalyst structure and chemical state was uniform and stable under reaction conditions. The 8 nm sized catalyst showed superior methanation selectivity over the 4 and 2 nm counterparts, and the methanation activity in term of TOF is 10 times and 70 times higher than for the 4 and 2 nm counterparts, respectively. Here, the DRIFTS studies revealed that the larger Ni (8 nm particles) over CeO 2 efficiently facilitated the hydrogenation of the surface formate intermediates, which is proposed as the rate determining step accounting for the excellent CO 2 methanation performance.

36 MATERIALS SCIENCE↗

Size-Controlled Nanoparticles Embedded in a Mesoporous Architecture Leading to Efficient and Selective Hydrogenolysis of Polyolefins

A catalytic architecture, comprising a mesoporous silica shell surrounding platinum nanoparticles (NPs) supported on a solid silica sphere (mSiO2/Pt-X/SiO2; X is the mean NP diameter), catalyzes hydrogenolysis of melt-phase polyethylene (PE) into a narrow C23-centered distribution of hydrocarbons in high yield using very low Pt loadings (~10 -5 g Pt/g PE). During catalysis, a polymer chain enters a pore and contacts a Pt NP where the C–C bond cleavage occurs and then the smaller fragment exits the pore. mSiO 2 /Pt/SiO 2 resists sintering or leaching of Pt and provides high yields of liquids; however, many structural and chemical effects on catalysis are not yet resolved. Here, we report the effects of Pt NP size on activity and selectivity in PE hydrogenolysis. Time-dependent conversion and yields and a lumped kinetics model based on the competitive adsorption of long vs short chains reveal that the activity of catalytic material is highest with the smallest NPs, consistent with a structure-sensitive reaction. Remarkably, the three mSiO 2 /Pt-X/SiO 2 catalysts give equivalent selectivity. We propose that mesoscale pores in the catalytic architecture template the C 23 -centered distribution, whereas the active Pt sites influence the carbon–carbon bond cleavage rate. This conclusion provides a framework for catalyst design by separating the C–C bond cleavage activity at catalytic sites from selectivity for chain lengths of the products influenced by the structure of the catalytic architecture. The increased activity, selectivity, efficiency, and lifetime obtained using this architecture highlight the benefits of localized and confined environments for isolated catalytic particles under condensed-phase reaction conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning

The 2024 Nobel Prize in Chemistry was awarded in part for de novo protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and three-dimensional structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments. Herein, we provide an overview of the deterministic relationship between sequence and structure, which was hypothesized over half a century ago with profound implications for the biological sciences ever since. We postulate that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting protein conformational ensembles. Accordingly, we describe a conceptual model architecture, which may be trained on sequence data in combination with conformationally sensitive structural information, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Notwithstanding certain limitations in this context, NMR offers abundant structural heterogeneity conducive to conformational ensemble prediction. As NMR and other data continue to accumulate, sequence-informed prediction of protein structural dynamics with AI/ML has the potential to emerge as a transformative capability across the biological sciences.

Artificial intelligence↗

New perspectives on materials and device dynamics using time-resolved full-field diffraction X-ray imaging

Understanding how materials evolve during synthesis, processing, or device operation requires experimental access to structural dynamics across wide ranges of length and time scales, often in bulk samples or even within packaged devices. Time-resolved full-field diffraction X-ray microscopy has recently emerged as a powerful way to meet this need by combining the penetration and structural sensitivity of X-ray diffraction with objective-lens-based magnification, sensitive high-resolution X-ray imaging detectors, and pump-probe and real-time imaging strategies. Advances in full-field X-ray diffraction microscopy, often termed dark-field X-ray microscopy (DFXM), enable simultaneous imaging of extended fields of view while retaining crystallographic selectivity. Time-resolved DFXM promises to enable advances in materials processing and dynamics, electrochemical and photochemical processes, and the design of electronic devices. This Perspective summarizes the key instrumental concepts that define the performance of these methods, including the choice of imaging optics, detector considerations, and the impact of source time structure at synchrotron light sources and X-ray free-electron lasers (XFELs). We discuss the simultaneous use of complementary imaging modes that are increasingly used in practice. The early demonstrations of the potential of time-resolved DFXM include real-time defect and grain-boundary dynamics during metal annealing, stroboscopic imaging of functional devices with high strain sensitivity, and ultrafast pump-probe implementations at XFELs that directly visualize acoustic-wave propagation and energy dissipation in bulk crystals.

Dark-field X-ray microscopy↗

Overview of NETL’s Low Temperature CO2 Electrolysis Research

This keynote lecture will briefly overview diverse research areas of National Energy Technology Laboratory (NETL) to advance energy and environmental sustainability along with carbon management. Our electrochemistry efforts on carbon conversion directly support the US goal of achieving carbon-free power sector by 2035 and net zero emissions by 2050. Since CO2 electroreduction is highly structure-sensitive, NETL ongoing research has been focused on the rational design and engineering of electrocatalysts to facilitate the CO2 conversion to desirable products with good selectivity, activity, and durability. Different classes and types of electrocatalytic materials will be covered in this talk, from well-defined atomic-scale model catalysts to heterogenous, scalable powder systems at nano- and micro-scale for “real world” performance evaluation. Several spectroscopic, microscopic, and electrochemical characterization techniques along with computational findings will be additionally discussed to gain more insights into the structure-activity relation. The last part of this seminar will provide more detail on how NETL has transitioned from the most common aqueous H-type reactor for lab-scale validation to more realistic full electrolyzer cell in bench-scale prototype. The knowledge, electrocatalytic materials, and device validation achieved from NETL in-house research will be translated to industrial sector for large scale deployment and the anticipated outcome will help advance the development of low temperature CO2 electrolysis technologies.

Nguyen Phan, Thuy Duong↗

Evaluating Radiation Impact on Transmon Qubits in Above and Underground Facilities

Superconducting qubits can be sensitive to abrupt energy deposits caused by cosmic rays and ambient radioactivity. Previous studies have focused on understanding possible correlated effects over time and distance due to cosmic rays. In this study, for the first time, we directly compare the response of a transmon qubit measured initially at the Fermilab SQMS above-ground facilities and then at the deep underground Gran Sasso Laboratory (INFN-LNGS, Italy). We observe same average qubit lifetime T$_1$ of roughly 80 microseconds at above and underground facilities. We then apply a fast decay detection protocol and investigate the time structure, sensitivity and relative rates of triggered events due to radiation versus intrinsic noise, comparing above and underground performance of several high-coherence qubits. Using gamma sources of variable activity we calibrate the response of the qubit to different levels of radiation in an environment with minimal background radiation. Results indicate that qubits respond to a strong gamma source and it is possible to detect particle impacts. However, when comparing above and underground results, we do not observe a difference in radiation induced-like events for these sapphire and niobium-based transmon qubits. We conclude that the majority of these events are not radiation related and to be attributed to other noise sources which by far dominate single qubit errors in modern transmon qubits.

Roy, Tanay↗

Evaluating Radiation Impact on Superconducting Transmon Qubits in Above and Underground Facilities

Superconducting qubits can be sensitive to sudden energy deposits into the substrate from cosmic rays and ambient radioactivity. While previous studies have primarily focused on exploring correlated effects over time and distance due to cosmic rays, this study uniquely offers a direct comparison of a transmon qubit's response at Fermilab SQMS above-ground facilities and the deep-underground Gran Sasso Laboratory (INFN-LNGS, Italy). Despite the vastly different radiation environments, we observe a similar average qubit lifetime (T1) of approximately 80 microseconds at both sites. We then employ a fast decay detection protocol and investigate the time structure, sensitivity, and relative rates of triggered events due to radiation versus intrinsic noise. We compare the above and underground performance of several high-coherence qubits with similar designs. Our findings reveal no significant difference in events with radiation-like signatures between above-ground and underground conditions for these sapphire and niobium-based transmon qubits. This suggests that most such events are likely due to other noise sources, which predominantly contribute to single-qubit errors in contemporary transmon qubits. By exposing the qubits to gamma sources with varying activity levels, we further evaluate their response to radiation in a low-background environment. The results show that qubits can indeed detect particle impacts; however, this capability is only realized by substantially reducing other noise sources, which in turn affects detection efficiency [1].

Roy, Tanay↗

Size and Structure Effects of Carbon-Supported Ruthenium Nanoparticles on Waste Polypropylene Hydrogenolysis Activity, Selectivity, and Product Microstructure

Hydrogenolysis of plastic waste using Ru-based catalysts is promising for deconstructing polyolefins into lower molecular weight products. Yet, the effect of catalyst atomic structure and size on activity and product selectivity is poorly understood. Herein, we expose the effect of metal particle size and atomic structure on isotactic-polypropylene (i-PP) hydrogenolysis over Ru supported on carbon. Despite similar molecular weight distributions of solid and liquid products, their physical properties are distinct due to different chain regio-irregular CH3 sequences of steric pentads containing racemo configurations. We propose that i-PP hydrogenolysis entails an interplay of C–C bond scission and stereoisomerization. The active site’s local electronic environment and structure dictate the former, whereas polymer–catalyst surface interactions creating suitable polymer conformations control the latter. C–C scission and stereoisomerization are structure-sensitive. Small, disordered nanoclusters are effective in C–C bond scission, whereas larger metal nanoparticles promote stereoisomerization. Finally, we hypothesize that a heterogeneous distribution of metal active sites is essential for deconstruction and product (lubricant base oil) quality control.

36 MATERIALS SCIENCE↗

Toward Intelligent Multimodal Holography for Real-Time Chemical Imaging of Dynamic Ion Separation

Molecular-level visualization of ion transport and separation dynamics in complex environments is crucial for advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms that combine structural sensitivity, chemical specificity, and real-time operation. Digital off-axis holography (DOAH) provides high-throughput, label-free quantitative phase imaging but inherently lacks chemical selectivity. Integrating DOAH with complementary spectroscopic channels such as fluorescence or hyperspectral imaging introduces the needed molecular specificity, while also creating challenges in multimodal data fusion, synchronization, and computational throughput. Artificial intelligence offers a powerful route to address these limitations by uniting physics-based reconstruction with data-driven interpretation. In this Perspective, we outline a framework for intelligent multimodal holography and demonstrate its potential using a preliminary AI-driven test case. Raw DOAH holograms of lanthanide solutions subjected to magnetic field gradients were analyzed using multi-agent AI workflows that autonomously selected reconstruction tools, extracted NMF components, and generated scientific claims consistent with true paramagnetic and diamagnetic behavior. This demonstration shows how AI-enabled reasoning can deliver real-time chemical–structural interpretation directly from raw holograms. Together, these advances define a path toward adaptive, intelligent holography platforms capable of supporting in situ chemical separations, dynamic ion transport analysis, and next-generation interfacial science.

Ricchiuti, Giovanna↗

Regioselective Generation of Single‐Site Iridium Atoms and Their Evolution into Stabilized Subnanometric Iridium Clusters in MWW Zeolite

Abstract Preparation of supported metal catalysts with uniform particle size and coordination environment is a challenging and important topic in materials chemistry and catalysis. In this work, we report the regioselective generation of single‐site Ir atoms and their evolution into stabilized subnanometric Ir clusters in MWW zeolite, which are located at the 10MR window connecting the two neighboring 12MR supercages. The size of the subnanometric Ir clusters can be controlled by the post‐synthesis treatments and maintain below 1 nm even after being reduced at 650 °C, which cannot be readily achieved with samples prepared by conventional impregnation methods. The high structure sensitivity, size‐dependence, of catalytic performance in the alkane hydrogenolysis reaction of Ir clusters in the subnanometric regime is evidenced.

Liu, Lichen↗

Regioselective Generation of Single‐Site Iridium Atoms and Their Evolution into Stabilized Subnanometric Iridium Clusters in MWW Zeolite

Abstract Preparation of supported metal catalysts with uniform particle size and coordination environment is a challenging and important topic in materials chemistry and catalysis. In this work, we report the regioselective generation of single‐site Ir atoms and their evolution into stabilized subnanometric Ir clusters in MWW zeolite, which are located at the 10MR window connecting the two neighboring 12MR supercages. The size of the subnanometric Ir clusters can be controlled by the post‐synthesis treatments and maintain below 1 nm even after being reduced at 650 °C, which cannot be readily achieved with samples prepared by conventional impregnation methods. The high structure sensitivity, size‐dependence, of catalytic performance in the alkane hydrogenolysis reaction of Ir clusters in the subnanometric regime is evidenced.

Liu, Lichen↗

Regulating the Catalytic Activity of Pd Nanoparticles by Confinement in Ordered Mesoporous Supports

Studying structure-sensitive reactions requires the synthesis of catalytic nanoparticles with precisely controlled sizes. Here we demonstrate the facile production of size controlled Pd nanoparticles by confinement in the pores of mesoporous silica nanoparticles (MSN). We show that Pd particles 2.1 nm in size have a higher catalytic activity than larger nanoparticles for the Suzuki-Miyaura cross coupling between 4’-bromoacteophenone and phenyl boronic acid and for the hydrogenation of phenol in aqueous phase. The enhanced activity can be explained in terms of increased number of coordinatively unsaturated sites and higher back donation capacity of the small nanoparticles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Descriptor-Based Analysis of Atomic Layer Deposition Mechanisms on Spinel LiMn 2 O 4 Lithium-Ion Battery Cathodes

Protective coatings have been shown to effectively suppress Mn ion dissolution from the spinel LiMn 2 O 4 lithium-ion battery cathode by stabilizing the surface against undesired side reactions with the electrolyte. In spite of extensive study, however, there remains a lack of atomic-scale understanding of how such coatings are deposited, and no molecular-level descriptor to predict trends in deposition mechanisms has been identified. We have recently shown that Al 2 O 3 coatings grown by atomic layer deposition (ALD) with alternating trimethylaluminum (TMA) and water exposures exhibit submonolayer growth because of precursor decomposition on the lithium manganate spinel (LMO) surface during early ALD pulses. In the present work, we elucidate the underlying mechanisms of this Al 2 O 3 ALD process using density functional theory (DFT) calculations and X-ray photoelectron spectroscopy (XPS) experiments, and we introduce a generalized descriptor-based framework to understand the resulting trends across a spectrum of surface structures and functionalities. We demonstrate that all decomposition products, including CH 3 -aluminum adducts and dissociated CH 3 groups, are Lewis bases and are coordinated to oxygen atoms on the LMO surface, leading to charge transfer to Lewis acidic Mn 3d states. Inert-transfer XPS supports these theoretical predictions, showing an increase in near-surface Mn 3+ content following TMA exposure and shifts in C is spectra consistent with C-O bond formation. We extend the DFT studies to various low- and high-index LMO surface facets, as a proxy for tuning the Lewis acid-base interactions between surface-bound CH 3 * and near-surface Mn ions. The thermochemistry for TMA reactions on these chemically distinct LMO surfaces demonstrates that ALD is structure-sensitive and that there is higher reactivity for TMA decomposition and Al 2 O 3 nucleation near LMO steps and defects. Motivated by the Lewis basic character of the decomposition products, we introduce the oxygen vacancy formation energy as a descriptor for decomposition energetics, and we demonstrate that all energetics are correlated to this quantity through the number of electrons that are transferred along the reaction coordinate. Finally, based on these findings, we hypothesize that improved electrochemical cycling with only 1-2 ALD cycles may be due to selective passivation of defect sites on the LMO surface that are more susceptible to Mn dissolution, and we suggest that similar descriptor-based analyses could be useful for the study of other ALD coatings on oxide substrates.

25 ENERGY STORAGE↗

Bismuthene for highly efficient carbon dioxide electroreduction reaction

Bismuth (Bi) has been known as a highly efficient electrocatalyst for CO 2 reduction reaction. Stable free-standing two-dimensional Bi monolayer (Bismuthene) structures have been predicted theoretically, but never realized experimentally. Here, we show the first simple large-scale synthesis of free-standing Bismuthene, to our knowledge, and demonstrate its high electrocatalytic efficiency for formate (HCOO - ) formation from CO 2 reduction reaction. The catalytic performance is evident by the high Faradaic efficiency (99% at -580 mV vs. Reversible Hydrogen Electrode (RHE)), small onset overpotential (<90 mV) and high durability (no performance decay after 75 h and annealing at 400 °C). Density functional theory calculations show the structure-sensitivity of the CO2 reduction reaction over Bismuthene and thicker nanosheets, suggesting that selective formation of HCOO - indeed can proceed easily on Bismuthene (111) facet due to the unique compressive strain. This work paves the way for the extensive experimental investigation of Bismuthene in many different fields.

Science & Technology - Other Topics↗

A pronounced decline in northern vegetation resistance to flash droughts from 2001 to 2022

Climate change has led to the transition of droughts into rapid and intensified phenomena known as flash droughts, presenting considerable challenges for risk management, particularly concerning their impact on ecosystem productivity. Quantifying the ecosystem’s capacity to maintain productivity during flash droughts, referred to as ecosystem resistance, is crucial to assess drought impacts. However, it remains uncertain how the resistance of ecosystem productivity to flash drought changes over time. Here we show that vegetation resistance to flash droughts declines by up to 27% (±5%) over the Northern Hemisphere hotspots during 2001-2022, including eastern Asia, western North America, and northern Europe. The notable decline in vegetation resistance is mainly attributed to increased vapour pressure deficit and temperature, and enhanced vegetation structural sensitivity to water availability. Flash droughts pose higher ecological risks than slowly-developing droughts during the growing seasons, where ecosystem productivity experiences faster decline rates with a shorter response time. Our results underscore the limited ecosystem capacity to resist flash droughts under climate change.

54 ENVIRONMENTAL SCIENCES↗

PhaseT3M: 3D imaging at 1.6 Å resolution via electron cryo-tomography with nonlinear phase retrieval

Electron cryo-tomography (cryo-ET) enables 3D imaging of complex, radiation-sensitive structures with molecular detail. However, image contrast from the interference of scattered electrons is nonlinear with atomic density and multiple scattering further complicates interpretation. These effects degrade resolution, particularly in conventional reconstruction algorithms, which assume linearity. Particle averaging can reduce such issues but is unsuitable for heterogeneous or dynamic samples ubiquitous in biology, chemistry, and materials sciences. Here, we develop a phase retrieval-based cryo-ET method, PhaseT3M. We experimentally demonstrate its application to an approximately 7 nm Co3O4 nanoparticle on an approximately 30 nm carbon substrate, achieving a maximum resolution of 1.6 Å, surpassing conventional limits using standard cryo-TEM equipment. PhaseT3M uses a multislice model for multiple scattering and Bayesian optimization for alignment and computational aberration correction, with a positivity constraint to recover ‘missing wedge’ information. Applied directly to biological particles, it enhances reconstruction quality and reduces artifacts, establishing a standard for routine 3D imaging with phase contrast.

Biophysics↗