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1,087 records · Page 44

Operando microscopy for neuromorphic hardware

Microscopy techniques can uncover the physical properties and dynamic behaviours of materials, driving the discovery of emergent phenomena and guiding the design of next-generation computing hardware. As artificial intelligence becomes pervasive, the demand for high-performance materials to support sustainable information technologies is growing. Here, this Review highlights state-of-the-art imaging from electron and X-ray to optical techniques to probe the dynamics of neuromorphic materials, including operando characterization of devices. We examine design principles for neuromorphic materials, along with obstacles that hinder their development. Emphasis is placed on spatially and temporally resolved approaches that capture state changes including phase transitions, ferroic switching and spin-wave propagation that emulate biological components such as neurons, synapses and their connectivity. We discuss challenges in operando characterization and the integration of artificial intelligence-driven analysis for feedback-guided material discovery. Finally, we outline opportunities for real-time imaging of neuromorphic systems, paving the way towards adaptive, brain-inspired hardware.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND

Single‐photon emitters in PECVD‐grown silicon nitride films: from material growth to photophysical properties

Abstract Silicon nitride (SiN) is a key material for quantum photonics due to its wide transparency window, high refractive index, low optical losses, and semiconductor foundry compatibility. We study the formation of single‐photon emitters in SiN films grown by plasma‐enhanced chemical vapor deposition (PECVD), exploring their photophysical properties and dependence on growth conditions. Emitters were observed across the entire range of nitrogen‐to‐silicon precursor ratios, from silicon‐rich to nitrogen‐rich conditions, enabled by the low background fluorescence. We demonstrate single‐photon emitters in SiN films with a higher refractive index (1.8–1.9) compared to our previous reports (∼1.7). Notably, nitrogen‐rich, thinner films yield particularly bright emitters with shorter emission lifetimes, likely due to more efficient annealing. Silicon‐rich SiN films exhibit red‐shifted emission, suggesting that composition may provide a mechanism for wavelength tuning. These findings establish the feasibility of emitters formation in foundry standard PECVD tools, advancing the scalability and lab‐to‐fab transition of SiN‐based quantum photonic technologies.

Materials Science

Using scalable computer vision to automate high-throughput semiconductor characterization

Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Science & Technology - Other Topics

Three-chamber electrochemical reactor for selective lithium extraction from brine

Efficient lithium recovery from geothermal brines is crucial for the battery industry. Current electrochemical separation methods struggle with the simultaneous presence of Na + , K + , Mg 2+ , and Ca 2+ because these cations are similar to Li + , making it challenging to separate effectively. We address these challenges with a three-chamber reactor featuring a polymer porous solid electrolyte in the middle layer. This design improves the transference number of Li + (t Li+ ) by 2.1 times compared to the two-chamber reactor and also reduces the chlorine evolution reaction, a common side reaction in electrochemical lithium extraction, to only 6.4% in Faradaic Efficiency. Employing a lithium-ion conductive glass ceramic (LICGC) membrane, the reactor achieved high t Li+ of 97.5% in LiOH production from simulated brine, while the concentrations of Na + K + , Mg 2+ , and Ca 2+ are below the detection limit. Electrochemical experiments and surface analysis elucidated the cation transport mechanism, highlighting the impact of Na + on Li + migration at the LICGC interface.

Science & Technology - Other Topics

Magnetic structure of topological nodal-line semimetal EuSb 2

Here, we investigated the magnetic structure of the magnetic topological nodal-line semimetal EuSb 2 using x-ray resonant magnetic scattering at the Eu 𝐿 3 absorption edge. Forbidden magnetic reflections confirm antiferromagnetic ordering with propagation vector 𝐪 = (0.5,0,0) below 𝑇 N ≈ 27 K. Azimuthal-angle-dependent measurements reveal that the Eu moments are aligned along the monoclinic 𝑏 axis. Comparison between the measured 𝐐-dependent magnetic Bragg peak intensities and calculations based on representation analysis identifies the magnetic structure as the A-type antiferromagnetic 𝛤 4 representation with a (++−−) arrangement along the 𝑎 direction. These results establish the microscopic magnetic structure of EuSb 2 and provide a basis for understanding the interplay between antiferromagnetism and topological electronic states in this system.

36 MATERIALS SCIENCE

Netload Range Cost Curves for Coordinated Transmission-Distribution Planning Under DER Growth Uncertainty

The increasing penetration of distributed energy resources (DERs) requires better coordination between transmission and distribution (T&D) planning to ensure system security and cost efficiency. However, misaligned planning horizons, computational burdens, and privacy concerns hinder effective coordination, leading to either underutilized resources caused by overinvestments or reliability risks due to underinvestment. To address this challenge, we introduce netload range cost curves (NRCCs), a novel approach for managing long-term DER growth uncertainty through T&D coordination, while preserving existing data-sharing and regulatory structures. NRCCs provide pairs of (i) peak substation netload guarantees and (ii) corresponding distribution upgrade options and costs, enabling their seamless integration into transmission planning workflows. To compute NRCCs efficiently, we develop a transmission-aware distribution network planning (TADNP), which is subsequently integrated to an iterative computation procedure. These NRCCs are then embedded into an NRCC-informed transmission planning model to enable resource-efficient coordination. We illustrate our proposed approach with a case study based on realistic distribution and transmission systems in the San Francisco Bay Area, California. Our results indicate the possibility of dramatic savings in transmission investments by incorporating the proposed NRCC-integrated T&D coordination framework.

Li, Yujia

Dissipative Phase Transition in the Two-Photon Dicke Model

We explore the dissipative phase transition of the two-photon Dicke model, a topic that has garnered significant attention recently. Our analysis reveals that while single-photon loss does not stabilize the intrinsic instability in the model, the inclusion of two-photon loss restores stability, leading to the emergence of superradiant states, which coexist with the normal vacuum states. Using a second-order cumulant expansion for the photons, we derive an analytical description of the system in the thermodynamic limit, which agrees well with the exact calculation results. Additionally, we present the Wigner function for the system, shedding light on the breaking of the 𝑍4 symmetry inherent in the model. These findings offer valuable insights into stabilization mechanisms in open quantum systems and pave the way for exploring complex nonlinear dynamics in two-photon Dicke models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC

Surface science insight note: Optimizing XPS instrument performance for quantification of spectra

X-ray photoelectron spectroscopy (XPS) provides quantitative information from photoemission peaks and shapes observed within the background due to the inelastic scattering of photoelectrons. To quantify the signal, both photoemission peaks and background in spectra must be adjusted for instrumental transmission variations that are a consequence of changes in efficiency when recording electrons with different kinetic energy. While it is generally assumed that correcting spectroscopic data for transmission is a necessary part of quantification by XPS, there are consequences for the quantification of spectra measured using an instrument for which transmission has significant curvature. In this Insight, the implications of curvature in transmission characteristics are discussed and a method based on XPS microscopy is proposed that ensures the transmission response of an instrument is free from significant curvature. An example of an instrument for which a flat transmission response is presented is achieved through collecting spectra using lens modes designed to measure stigmatic images.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Highly textured metal anodes for stable aqueous batteries: Fabrication and characterization

We report a purely mechanical “cold-compression flow” method for fabricating Zn, Sn, and In substrates with tunable crystallographic textures. Using textured Zn as a model system, we investigate Zn electrocrystallization and demonstrate correlated growth of crystalline films with correlation lengths from tens to hundreds of micrometers. At 5 milliamperes per square centimeter (mA/cm 2 ), capacities between 20 and 82 milliampere hours per square centimeter (mA·hour/cm 2 ) are achieved depending on substrate texture level. At higher currents (40 mA/cm 2 ), capacities reach up to 604 mA·hour/cm 2 . Rotating disk electrode studies show that dominantly (002) textured Zn substrates exhibit enhanced corrosion resistance and reduced interphase passivation. We introduce an effective Damköhler number (Da*) to concisely describe morphological evolution during electrocrystallization across substrates with different textures. High-texture (002) Zn substrates substantially enhance performance in high-capacity (~20 mA·hour/cm 2 ) symmetric Zn||Zn cells and full cells (Zn||δ-MnO 2 and Zn||I 2 ), enabling fast-charging and prolonged energy storage in coin and pouch rechargeable Zn battery formats.

Science & Technology - Other Topics

Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br

Abstract We introduce the Vector-QM24 (VQM24) dataset comprehensively covering all possible neutral closed-shell small organic and inorganic molecules with up to five heavy (p-block) atoms: C, N, O, F, Si, P, S, Cl, Br. All valid stoichiometries, Lewis-rule-consistent graphs, and stable conformers (identified via GFN2-xTB) were enumerated combinatorially, yielding 577k conformational isomers spanning 258k constitutional isomers and 5,599 unique stoichiometries. DFT (ωB97X-D3/cc-pVDZ) optimizations were performed for all, and diffusion quantum Monte Carlo (DMC@PBE0(ccECP/cc-pVQZ)) energies are provided for 10,793 lowest-energy conformers with up to 4 heavy atoms. VQM24 includes structures, vibrational modes, rotational constants, thermodynamic properties (Gibbs free energies, enthalpies, ZPVEs, entropies, heat capacities), and electronic properties such as atomization, electron interaction, exchange-correlation, dispersion energies, multipole moments (dipole to hexadecapole), alchemical potentials, Mulliken charges, and wavefunctions. Machine learning models of atomization energies on this dataset reveal significantly higher complexity than QM9, with none achieving chemical accuracy. VQM24 offers a rigorous, high-fidelity benchmark for evaluating quantum machine learning models.

Science & Technology - Other Topics

The CP-PAW Code Package for First-Principles Calculations from a User’s Perspective

CP-PAW is a combined electronic structure and ab initio molecular dynamics code to perform mixed quantum and classical simulations of atomistic condensed phase systems, such as solids, liquids, and molecular systems. As the name suggests, the CP-PAW code unifies the all-electron projector augmented-wave (PAW) method with the Car–Parrinello (CP) approach to determine not only the electronic and nuclear ground states of condensed matter but also to study their properties and dynamics. In addition to briefly outlining the underlying theory, the focus will be on the unique aspects of CP-PAW and how to correctly employ them as a user. How to install CP-PAW using the new build system will also be briefly mentioned.

Blöchl, Peter E [Institute for Theoretical Physic

Entanglement Structure of Non-Gaussian States and How to Measure It

Rapidly growing capabilities of quantum simulators to probe quantum many-body phenomena require new methods to characterize increasingly complex states. Here, we present a protocol that constrains quantum states using experimentally measured correlation functions. This method enables measurement of a quantum state’s entanglement structure, opening a new route to study entanglement-related phenomena. Our approach extends Gaussian state parameterizations by systematically incorporating higher-order correlations. We show the protocol’s usefulness in conjunction with current and forthcoming experimental capabilities, focusing on weakly interacting fermions as a proof of concept. Here, the lowest nontrivial expansion quantitatively predicts early time thermalization dynamics, including signaling the onset of quantum chaos indicated by the entanglement Hamiltonian.

Fermi gases

Structure–Property Linkage in Alloys Using Graph Neural Network and Explainable Artificial Intelligence

Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure–property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information from 3D microstructure images, they often require a large network architecture and substantial training time. In this research, we trained a graph neural network (GNN) using phase field generated microstructures of Ni-Al alloys to predict the evolution of mechanical properties. We found that a single GNN is capable of accurately predicting the strengthening of Ni-Al alloys with microstructures of varying sizes and dimensions, which cannot otherwise be done with a CNN. Additionally, GNN requires significantly less GPU utilization than CNN and offers more interpretable explanation of predictions using saliency analysis as features are manually defined in the graph. We also utilize explainable artificial intelligence tool Bayesian Inference to determine the coefficients in the power law equation that governs coarsening of precipitates. Overall, our work demonstrates the ability of the GNN to accurately and efficiently extract relevant information from material microstructures without having restrictions on microstructure size or dimension and offers an interpretable explanation.

Chemistry

Shear and combustion characterization of printable ammonium perchlorate composite propellant formulations

Seven formulations of ammonium perchlorate composite propellant (APCP) are developed and their properties relevant for successful additive manufacture are characterized. Extrusion in a custom-built 3D printing system and spindle viscometry are used to collect viscosity measurements of curing primary and non-curing secondary versions of the formulations. The formulations that behave similarly to a Bingham plastic, with apparent viscosities between 4 and 8 thousand Pa*s at 30 minutes post-mix, are determined to be most suitable for printing applications. Measurements of one material show a yield stress of 1 kPa. Ambient pressure burn rates of 2.0–2.9 mm/s were measured for the compositions tested. All measured burn rates were comparable to the lower end of typical burn rates for APCP. In conclusion, the results of the characterization demonstrate a propellant suitable for use in a 3D printing system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks

We propose the artificial intelligence velocimetry-thermometry (AIVT) method to reconstruct a continuous and differentiable representation of the temperature and velocity in turbulent convection from measured three-dimensional (3D) velocity data. AIVT is based on physics-informed Kolmogorov-Arnold networks and trained by optimizing a loss function that minimizes residuals of the velocity data, boundary conditions, and governing equations. We apply AIVT to a set of simultaneously measured 3D temperature and velocity data of Rayleigh-Bénard convection, obtained by combining particle image thermometry and Lagrangian particle tracking. This enables us to directly compare machine learning results to true volumetric, simultaneous temperature and velocity measurements. We demonstrate that AIVT can reconstruct and infer continuous, instantaneous velocity and temperature fields and their gradients from sparse experimental data at a high resolution, providing an additional approach for understanding thermal turbulence.

Science & Technology - Other Topics

Elucidation of Design Criteria for V‐based Redox Mediators: Structure‐Function Relationships that Dictate Rates of Heterogeneous Electron Transfer

Redox mediators are attractive solutions for addressing the stringent kinetic stipulations required for efficient energy conversion processes. In this work, we compare the electrochemical properties of four vanadium complexes, namely [V(acac) 3 ], [V 6 O 7 (OMe) 12 ], [ n Bu 4 N] 3 [V 6 O 13 (TRIS NO2 ) 2 ], and [ n Bu 4 N] 5 [V 18 O 46 (NO 3 )] in non-aqueous solutions on glassy carbon electrodes. The goal of this study is to investigate the electron transfer kinetics and diffusivity of these compounds under identical experimental conditions to develop an understanding of structure-function relationships that dictate the physicochemical properties of vanadium oxide assemblies. Complex selection was dictated by two criteria – (1) nuclearity of the transition metal complexes (2) distribution of electron density in the native electronic configuration. In conclusion, our analyses establish that electronic communication between metal centers significantly impacts charge transfer kinetics of these vanadium-based compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Fast and Non‐equilibrium Uptake of Hydrogen by Pd Icosahedral Nanocrystals

Abstract We report for the first time that Pd nanocrystals can absorb H via a “single‐phase pathway” when particles with a proper combination of shape and size are used. Specifically, when Pd icosahedral nanocrystals of 7‐ and 12‐nm in size are exposed to H atoms, the H‐saturated twin boundaries can divide each particle into 20 smaller single‐crystal units in which the formation of phase boundaries is no longer favored. As such, absorption of H atoms is dominated by the single‐phase pathway and one can readily obtain PdH x with anyx in the range of 0–0.7. When switched to Pd octahedral nanocrystals, the single‐phase pathway is only observed for particles of 7 nm in size. We also establish that the H‐absorption kinetics will be accelerated if there is a tensile strain in the nanocrystals due to the increase in lattice spacing. Besides the unique H‐absorption behaviors, the PdH x (x=0–0.7) icosahedral nanocrystals show remarkable thermal and catalytic stability toward the formic acid oxidation due tothe decrease in chemical potential for H atoms in a Pd lattice under tensile strain.

Chemistry

Observational evidence for Criegee intermediate oligomerization reactions relevant to aerosol formation in the troposphere

Criegee intermediates are reactive intermediates that are implicated in transforming the composition of Earth’s troposphere and in the formation of secondary organic aerosol, impacting Earth’s radiation balance, air quality and human health. Yet, direct identification of their signatures in the field remains elusive. Here, from particulate and gas-phase mass-spectrometric measurements in the Amazon rainforest, we identify sequences of masses consistent with the expected signatures of oligomerization of the CH 2 OO Criegee intermediate, a process implicated in ozonolysis-driven aerosol formation. In this study, we assess the potential contributions of oligomerization through laboratory ozonolysis experiments, direct kinetic studies of Criegee intermediate reactions, and high-level theoretical calculations. Global atmospheric models built on these kinetics results indicate that Criegee intermediate chemistry may play a larger role in altering the composition of Earth’s troposphere than is captured in current atmospheric models, especially in areas of high humidity. However, the models still capture only a relatively small fraction of the observed signatures, suggesting considerable underestimates of Criegee intermediate concentrations and reactivity and/or the dominance of other, presently uncharacterized, oxidation mechanisms. Resolving the remaining uncertainties in emission inventories and the effects of atmospheric water vapour on key chemical reactions will be required to definitively assess the role of Criegee intermediate oligomerization reactions.

58 GEOSCIENCES