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At least 937 records · Page 52

A theoretical/computational framework to measure SiO2 and MgO viscosity at high pressure

The convection of the mantle of Earth and super-Earths is important for many terrestrial phenomena, from plate tectonics to outgassing. Rheological properties, such as viscosity, regulate the transport of thermal energy and mass. However, the viscosity of mantle-relevant materials, such as MgO, at relevant pressures (>120 GPa) are not well constrained. The objective of this work was to develop a computational platform to simulate novel experiments aiming to measure the viscosity of MgO at high pressures. Experiments performed by our collaborators at Johns Hopkins University and Lawrence Livermore National Laboratory use the OMEGA EP laser facility to shock a corrugated MgO interface to 170 GPa, with resulting velocity evolution governed by the viscous Richtmyer-Meshkov instability. We used an in-house hydrocode to simulate this process and thus provide a bound on viscosity by comparing our simulations results to these experiments, as well as to examine the physical processes at play. We simulated the experiments with different values of MgO viscosity (from inviscid to 10,000 Pa∙s) while taking into account the unsteady laser pulse, the material's equation of state, and the rate-dependent constitutive relation for MgO, and the materials' equations of state. Our results suggest that MgO at these conditions has a viscosity within an order of magnitude of 5000 Pa∙s.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

pyRMG: A framework for high-throughput, large-cell DFT calculations on supercomputers

Exascale computing delivers the raw power to simulate ever larger and more chemically realistic systems, but realizing this potential requires codes that can efficiently use thousands of processors. Our real-space multigrid (RMG) density functional theory (DFT) code’s grid-decomposition approach scales nearly linearly with the number of graphics processing units (GPUs), even for simulations exceeding thousands of atoms. This scalability makes RMG a compelling tool for high-throughput DFT studies of materials that would otherwise be bottlenecked in other codes (for example, by global fast Fourier transforms in plane-wave DFT). However, the limited workflow infrastructure for RMG has thus far constrained its adoption to a small user community. In this work, we present pyRMG, a Python package designed to streamline the setup and execution of RMG DFT calculations. Built on the pymatgen and ASE (Atomic Simulation Environment) computational materials science Python packages, pyRMG automates input generation and convergence checking, and it integrates with modern job schedulers (e.g., Flux) on leadership-class platforms such as Frontier and Perlmutter. Here, we demonstrate pyRMG for a high-throughput study of strain effects in 2D 2L-Bi 2 Se 3 /2L-NbSe 2 heterostructures, which offers chemical insights into this system and shows that RMG-based workflows can converge with limited user intervention.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Massively parallel and universal approximation of nonlinear functions using diffractive processors

Nonlinear computation is essential for a wide range of information processing tasks, yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical nonlinearities. Overcoming this limitation without relying on nonlinear optical materials could unlock unprecedented opportunities for ultrafast and parallel optical computing systems. Here, we demonstrate that large-scale nonlinear computation can be performed using linear optics through optimized diffractive processors composed of passive phase-only surfaces. In this framework, the input variables of nonlinear functions are encoded into the phase of an optical wavefront—e.g., via a spatial light modulator (SLM)—and transformed by an optimized diffractive structure with spatially varying point-spread functions to yield output intensities that approximate a large set of unique nonlinear functions–all in parallel. We provide proof establishing that this architecture serves as a universal function approximator for an arbitrary set of bandlimited nonlinear functions, also covering wavelength-multiplexed nonlinear functions as well as multi-variate and complex-valued functions that are all-optically cascadable. Our analysis also indicates the successful approximation of typical nonlinear activation functions commonly used in neural networks, including the sigmoid, tanh, ReLU (rectified linear unit), and softplus. We numerically demonstrate the parallel computation of one million distinct nonlinear functions, accurately executed at wavelength-scale spatial density at the output of a diffractive optical processor. Furthermore, we experimentally validated this framework using in situ optical learning and approximated 35 unique nonlinear functions in a single shot using a compact setup consisting of an SLM and an image sensor. These results establish diffractive optical processors as a scalable platform for massively parallel universal nonlinear function approximation, paving the way for new capabilities in analog optical computing based on linear materials.

Rahman, Md Sadman Sakib [University of California,↗

Functional, Hierarchical Colloidal Liquid Crystal Gels and Liquid Crystal Elastomers Nanocomposites. Final Report

Biological systems rely on a hierarchy of dynamic, complex and reconfigurable compartments to carry out functions essential for life. This hierarchical organization serves as an amplifier that allows highly localized, molecular events to propagate into the mesoscale, resulting in dynamic functional properties of biological systems that have not yet been fully realized in synthetic material designs. We pursued a program of research in which we recreated such principles using synthetic liquid crystals (LC) as a versatile platform. We elucidated new hierarchical design strategies that heavily leverage surface anchoring, coexisting phases and interfacial tension gradient to realize equilibrium and non-equilibrium, dynamic mesoscale phenomena in the context of micrometer-sized LC droplets containing multiple compartments. Specifically, we unmasked how equilibrium and dynamical phenomena emerged from hierarchical organizations in reconfigurable, thermotropic liquid crystalline assemblies. The long-term impact of the project is to advance new hierarchical designs of complex soft matter systems in which biomimetic principles, including the propagation of events or information over multiple temporal and spatial scales, are enacted in a facile manner.

36 MATERIALS SCIENCE↗

Glancing Angle Deposition in Gas Sensing: Bridging Morphological Innovations and Sensor Performances

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO2, CO, H2S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies.

Chemistry↗

Quantum Engineering With Hybrid Magnonic Systems and Materials (Invited Paper)

Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design and dynamic operation. In particular, the integration of different quantum modules has benefited from hybrid quantum systems, which provide an important pathway for harnessing the different natural advantages of complementary quantum systems and for engineering new functionalities. This review focuses on the current frontiers with respect to utilizing magnetic excitations or magnons for novel quantum functionality. Magnons are the fundamental excitations of magnetically ordered solid-state materials and provide great tunability and exibility for interacting with various quantum modules for integration in diverse quantum systems. The concomitant rich variety of physics and material selections enable exploration of novel quantum phenomena in materials science and engineering. In addition, the relative ease of generating strong coupling and forming hybrid dynamic systems with other excitations makes hybrid magnonics a unique platform for quantum engineering. We start our discussion with circuit-based hybrid magnonic systems, which are coupled with microwave photons and acoustic phonons. Subsequently, we are focusing on the recent progress of magnon-magnon coupling within confined magnetic systems. Next we highlight new opportunities for understanding the interactions between magnons and nitrogen-vacancy centers for quantum sensing and implementing quantum interconnects. Lastly, we focus on the spin excitations and magnon spectra of novel quantum materials investigated with advanced optical characterization.

42 ENGINEERING↗

One-directional polarization transport in electron/nuclear spin chains with loss and gain

Understanding the joint dynamics of electron and nuclear spins is central to core concepts in solid-state magnetic resonance such as spin-lattice relaxation and dynamic nuclear polarization, but a generalization that capitalizes on competing polarization loss and gain channels is still lacking. Here, we study theoretically the non-Hermitian dynamics of hybrid electron/nuclear spin systems in the simultaneous presence of electron spin pumping and spin-lattice relaxation. Focusing on periodic, one-dimensional chains, we find that, by adjusting the electron spin pumping to a critical level, it is possible to steer the flow of nuclear polarization to create site-dependent distributions where either end of the array polarizes in opposite ways, irrespective of the initial state. By contrast, we show that ring-like patterns—where the limit nuclear polarization is uniform—exhibit a non-decaying, externally driven nuclear spin current. Interestingly, cyclic magnetic field modulation can render these processes largely robust to defects in the chain, a response featuring some interesting similarities—and differences—with recent findings in other non-Hermitian physical platforms.

36 MATERIALS SCIENCE↗

Kinematics of a Cable-Driven Robotic Platform for Large-Scale Additive Manufacturing

Concrete additive manufacturing (AM) is a growing field of research. However, on-site, large-scale concrete additive manufacturing requires motion platforms that are difficult to implement with conventional rigid-link robotic systems. This article presents a new kinematic arrangement for a deployable cable-driven robot intended for on-site AM. The kinematics of this robot are examined to determine if they meet the requirements for this application, the wrench feasible workspace (WFW) is examined, and the physical implementation of a prototype is also presented. Data collected from the physical implementation of the proposed system are analyzed, and the results support its suitability for the intended application. The success of this system demonstrates that this kinematic arrangement is promising for future deployable AM systems.

36 MATERIALS SCIENCE↗

Nature-inspired methylated polyhydroxybutyrates from C1 and C4 feedstocks

Polyolefin plastics are widely used due to their low cost and outstanding properties, but their environmental persistence presents a major societal challenge. Polyhydroxyalkanoates (PHA) are biodegradable substitutes for polyolefins, but their high cost and thermal instability are impediments to their widespread application. Here we report a series of methylated polyhydroxybutyrates, poly(3-hydroxy-2-methylbutyrate)s, which are structurally inspired by natural PHAs. The cis homopolymers exhibit tacticity-independent crystallinity, which allows for the discovery of high-melting, thermally stable and mechanically tough copolymers, and a full range of polyolefin-like properties can be further achieved by tailoring the cis/trans ratio of the repeating units. Moreover, these materials can be synthesized from inexpensive carbon monoxide and 2-butene feedstocks, and they can be chemically recycled or upcycled at their end of life. As a result, the versatile properties, abundant feedstocks and end-of-life utility of this family of polyesters will enable a powerful platform for the discovery of sustainable alternatives to polyolefin plastics.

36 MATERIALS SCIENCE↗

Replicating Machine Learning Experiments in Materials Science

Transparency and reproducibility are important aspects of validation for Machine Learning (ML) models that are not fully understood and applies independently of the application domain.We offer a case study of reproducibility that highlights the challenges encountered when attempting to reproduce analyzes obtained with Machine Learning methods in materials informatics. Our study explores prediction results obtained with ML models and issues in training data serving as input. We discuss challenges related to theory-driven and numerical errors in training data, lack of reproducibility across platforms and versions, and effects of randomness when varying hyperparameters. In addition to model accuracy, a main metric of interest in the ML community, our results show that model sensitivity may be equally important for applying ML in domain applications such a materials science.

97 MATHEMATICS AND COMPUTING↗

An automatically curated first-principles database of ferroelectrics

Ferroelectric materials have technological applications in information storage and electronic devices. The ferroelectric polar phase can be controlled with external fields, chemical substitution and size-effects in bulk and ultrathin film form, providing a platform for future technologies and for exploratory research. In this work, we integrate spin-polarized density functional theory (DFT) calculations, crystal structure databases, symmetry tools, workflow software, and a custom analysis toolkit to build a library of known, previously-proposed, and newly-proposed ferroelectric materials. With our automated workflow, we screen over 67,000 candidate materials from the Materials Project database to generate a dataset of 255 ferroelectric candidates, and propose 126 new ferroelectric materials. We benchmark our results against experimental data and previous first-principles results. The data provided includes atomic structures, output files, and DFT values of band gaps, energies, and the spontaneous polarization for each ferroelectric candidate. Finally, we contribute our workflow and analysis code to the open-source python packages atomate and pymatgen so others can conduct analogous symmetry driven searches for ferroelectrics and related phenomena.

36 MATERIALS SCIENCE↗

Automated Redox Titrations via Interdigitated Electrode Arrays: Application to the Mediated Electron Transfer Interrogation of Charge and Rate on Electrodeposited Polymers

Mediated electron transfer (MET) plays a crucial role in energy storage and conversion technologies such as redox targeting flow batteries (RTFBs), yet its experimental investigation often requires labor-intensive and low-throughput setups. To address this, we developed a microfabricated interdigitated electrode array (IDA) platform that enables automated, high-throughput electrochemical redox titration measurement to be performed to study the MET process. Our redox titration method enables simultaneous measurement of the charge capacity and rate of MET processes on a material or surface. Automated redox titration (ART) facilitates systematic investigation of the MET process across a broad parameter space, exemplified through the study of polypyrrole (PPy) and a pyrene-4,5,9,10-tetrone azo group-based polymer (PTAP), both redox-active polymers relevant to various energy storage applications. Using PPy as a model material, 500 redox titration measurements were conducted within 50 h, varying the electrode gap widths, polymer charging potentials, voltammetric scan rates, and electrolyte concentrations. Finite-element simulations confirmed the electrochemical responses and elucidated the kinetics of the MET reactions. Our automated methodology was further tested with PTAP, revealing a surprising charging potential dependence on the rate of MET. The automation, flexibility, and scalability of our redox titration platform pave the way not only for advanced studies of MET processes relevant to RTFBs, but also with implications in the understanding of next-generation energy storage materials, molecular electrocatalysis, and biosensing.

electrochemical analysis↗

Sustainable recycling of cathode scraps via Cyrene-based separation

Separation of cathode materials from the current collector remains a challenging task for the recycling of both spent lithium-ion batteries and cathode scraps. Dissolving the organic binder polyvinylidene difluoride (PVDF) with an organic solvent to recover both cathode materials and Al foils is an efficient and promising method. However, the use of toxic solvents limits their practical application in recycling large amounts of cathode scraps generated during the manufacturing process. Cyrene, a bioderived green solvent, is proposed here for a solvent-based separation process. This study investigated a closed-loop recovery process to reclaim cathode materials, Al foils, and PVDF binder from cathode scraps. Furthermore, the reuse of the Cyrene solvent led to a circular recycling process. Additionally, the Cyrene-based separation process embraces a sustainable electrode recovery and reuse platform and paves the way for battery recycling.

25 ENERGY STORAGE↗

Resonant X-ray Diffraction Reveals the Location of Counterions in Doped Organic Mixed Ionic Conductors

Organic mixed ionic–electronic conductors (OMIECs) have the potential to enable diverse new technologies, ranging from novel in situ biosensors to flexible energy storage devices and neuromorphic computing platforms. However, their complex behavior in functional films involving electrolyte-induced swelling, ion ingress, and electrochemical doping inhibits rational material design. Of critical importance is an understanding of the specific location of the ions in the volumetrically doped material, yet this information is not readily available. Here, in this report, we present the use of grazing- incidence resonant X-ray diffraction (RXRD, also known as anomalous diffraction) at S and Cl K-edges to determine the structure of a doped, prototypical, semicrystalline polymer OMIEC based on oligo(ethylene glycol) substitution of regioregular polythiophene. The RXRD measurement provides two key insights. Quantitative analysis of the RXRD allows the determination of the position of the ion relative to the polymer backbone in the crystalline regions. We find that the anion is relatively distant from the backbone, nearer to the lamella mid-plane naively in conflict with expected Coulombic attraction between the ion and the doped polymer polaron. Comparison of RXRD to Cl – fluorescence (total Cl – ) allows determination of the relative order of doping between the crystalline and amorphous regions. We find preferential doping of the crystalline regions. Both insights, the preferential doping of crystals at low potential and the specific location of the counterion with respect to the polymer backbone, are critical to developing a microscopic understanding of transport in OMIECs.

36 MATERIALS SCIENCE↗

Bound Exciton Complexes in Near-Infrared Emitting Quantum Shells

Near-infrared (NIR) light sources based on colloidal semiconductor nanocrystals (NCs) represent a scalable, low-cost alternative to epitaxial semiconductor platforms. However, their performance remains hindered by rapid Auger recombination, a problem that is particularly pronounced in narrow-bandgap materials. Here, we report on CdS/HgS/CdS and CdS/HgCdSe/ ZnS quantum shells (QSs), a class of spherical quantum wells specifically engineered for a suppression of nonradiative Auger processes. Fabricated QSs exhibit tunable NIR emission with photoluminescence quantum yields reaching ∼60% below 1000 nm and up to 30% near 1300 nm. Optical gain and stimulated emission were observed in CdS/HgS/CdS QSs. In contrast, CdS/HgCdSe/ZnS QSs displayed a photoinduced absorption in lieu of optical gain despite demonstrating a comparatively stronger Auger suppression. Transient absorption spectroscopy revealed that this phenomenon arises from the formation of bound multiexciton complexes that induce long-lived sub-bandgap multiexciton states. The observation of such bound excitonic clusters at room temperature offers a pathway toward nonlinear NIR photonic phenomena, including biexciton−exciton cascade emission, optical modulation, and singleexciton gain.

Auger recombination↗

Peptoid-directed assembly of CdSe nanoparticles

The high information content of proteins drives their hierarchical assembly and complex function, including the organization of inorganic nanomaterials. Peptoids offer an organic scaffold very similar to proteins, but with a wider solubility range and easily tunable side chains and functional groups to create a variety of self-assembling architectures with atomic precision. If we could harness this paradigm and understand the factors that govern how they direct nucleation and assembly of inorganic materials to design order within such materials, new dimensions of function and fundamental science would emerge. In this work, peptoid tubes and sheets were explored as platforms to assemble colloidal quantum dots (QDs) and clusters. We have successfully synthesized CdSe QDs with difunctionalized capping ligands containing both carboxylic acid and thiol groups and mixed them with maleimide containing peptoids, to create an assembly of the QDs on the peptoid surface via a covalent linkage. This conjugation was seen to be successful with peptoid tubes, sheets and CdSe QDs and clusters. The particles were seen to have a high preference for the peptoid surface but non-specific interactions with carboxylic acid groups on the peptoids limited control over QD density via maleimide conjugation. Replacing the carboxylic acid groups with methoxy ethers, however, allowed for control over QD density as a function of maleimide concentration. 1 H NMR analysis demonstrated that binding of QDs to peptoids involved a subset of surface ligands bound through the carboxylate functional group, allowing the distal thiol to engage in a covalent linkage to the maleimide. Overall, we have shown the compatibility and control of CdSe-peptoid interactions via a covalent linkage with varying peptoid structures and CdSe particles to create complex hybrid structures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Thin film block copolymer self-assembly for nanophotonics

The nanophotonic engineering of light–matter interactions has profoundly changed research behind the design and fabrication of optical materials and devices. Metasurfaces—arrays of subwavelength nanostructures that interact resonantly with electromagnetic radiation—have emerged as an integral nanophotonic platform for a new generation of ultrathin lenses, displays, polarizers and other devices. Their success hinges on advances in lithography and nanofabrication in recent decades. While existing nanolithography techniques are suitable for basic research and prototyping, issues of cost, throughput, scalability, and substrate compatibility may preclude their use for many metasurface applications. Patterning via spontaneous self-assembly of block copolymer thin films offers an enticing alternative for nanophotonic manufacturing that is rapid, inexpensive, and applicable to large areas and diverse substrates. This review discusses the advantages and disadvantages of block copolymer-based nanopatterning and highlights recent progress in their use for broadband antireflection, surface enhanced Raman spectroscopy, and other nanophotonic applications. Here recent advances in diversification of self-assembled block copolymer nanopatterns and improved processes for enhanced scalability of self-assembled nanopatterning using block copolymers are also discussed, with a spotlight on directions for future research that would enable a wider array of nanophotonic applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Topological and nodal superconductor kagome magnesium triboride

Recently the kagome compounds have inspired enormous interest and made some great progress such as in the field of superconductivity and topology. Here we predict a different kagome magnesium triboride (Mg⁢B 3 ) superconductor with a calculated T c ~ 12.2K and T c ~ 15.4 K by external stress, the potentially highest among the reported diverse kagome-type superconductors. We reveal its various exotic physical properties including the van Hove singularity, flat-band, multiple Dirac points, and nontrivial topology. The system can be described by a two-band model with highly anisotropic superconducting gaps on Fermi surfaces. Its topological and nodal superconducting nature is unveiled by a recently developed symmetry indicators method. In conclusion, our results suggest that Mg⁢B 3 can be a new platform to study exotic physics in the kagome structure, and pave a way to seek for more superconductors and topological materials with XY 3 -type kagome lattice.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗