Material Measurement Laboratory

The solid electrolyte interphase (SEI) critically governs the reversibility of sodium metal batteries, through dynamically mediating ion transport and interfacial reactions. However, its kinetic evolution under operating conditions, and how it influences interfacial stability, remains poorly understood. Here, we reveal that the SEI undergoes coupled chemical and mechanical changes during sodium plating and stripping, leading to spatial and temporal heterogeneity that drives interfacial degradation. Synchrotron operando grazing-incidence wide-angle X-ray scattering and soft X-ray absorption spectroscopy capture the sequential formation and dissolution of inorganic SEI phases (NaF, NaH, NaOH, Na2PO3F), accompanied by depth-dependent alterations in organic SEI components. Mesoscale modeling connects this evolving SEI heterogeneity to localized current density fluctuations and stress accumulation at the Na interface, identifying pathways to electrically isolated sodium formation. These findings show that SEI instability fundamentally limits reversibility in sodium metal batteries, and that controlling SEI chemistry–mechanics coupling is essential to achieving its durability.

Determining lattice parameters in two-dimensional (2D) materials is essential for materials characterization and discovery. In this work, we propose a deep-learning-driven pipeline that addresses the regression task of estimating the lattice constants a and b and the angle γ directly from 2D images using computer vision. We evaluate our approach on three different 2D material data sets: JARVIS-2D (JV2D) and Computational 2D Materials Database (C2DB), and a newly created data set derived from the Alexandria database. Multiple architectures are compared, including DenseNet121, Vision Transformers (ViT-L/14) paired with Multi Layer Perceptron (MLP) heads, and GoogleNet. DenseNet121 achieves accurate performance, with mean absolute errors as low as 0.18 Å for the Alexandria-based data set and 0.17 Å for C2DB and 0.59 Å for JV2D, as well as up to 96% accuracy in classifying Bravais lattice types for the Alexandria-based data set.

Abstract The microscopic structure of water’s surface is crucial to many natural and industrial processes, but studying its hydrogen bond (H-bond) network directly remains challenging due to the required interfacial sensitivity of experimental techniques. By leveraging advances in flat liquid sheet microjets and terawatt-scale attosecond soft X-ray pulses from the LCLS X-ray free electron laser, we employed soft X-ray second harmonic generation (SXSHG) spectroscopy to examine the liquid water/vapor interface. SXSHG combines the elemental selectivity of X-ray spectroscopies with the surface selectivity of SHG and gives access to the electronic structure of interfacial species. Here, we show the SXSHG spectrum differs from bulk water’s X-ray absorption, with its peak shifted several eV, indicating a vastly different electronic environment at the interface as compared to the bulk. First-principles electronic structure calculations show the signal is highly sensitive to H-bond interactions, such as water molecules accepting a single H-bond, which are surface abundant.

BackgroundPer- and polyfluoroalkyl substances (PFAS) are highly stable chemical contaminants of emerging concern for human and environmental health due to their non-natural chemistry, widespread use, and environmental persistence. Despite conventional metrology, mitigation strategies, and removal technologies, the complexity of this growing problem necessitates alternative approaches to tackle the immense challenges associated with complex environmental PFAS contamination. Recently, biology has emerged as an alternative approach to detect and mitigate PFAS and understand the molecular-level responses of living organisms to these compounds. However, little is understood of the impacts of PFAS on the environment, particularly impacts on microorganisms that play pivotal roles in nearly every ecosystem. Therefore, further study is needed to understand how microorganisms respond to different PFAS across growth phases.ResultsIn this study, we performed RNA sequencing at mid-exponential, early stationary phase, and late stationary phase of bacterial growth to determine the global transcriptional response of a model chassis, Escherichia coli MG1655, induced by two PFAS, perfluorooctanoic acid (PFOA) and perfluorododecanoic acid (PFDoA), and equivalent non-fluorinated carboxylic acids (NFCA), octanoic acid and dodecanoic acid. Differential gene expression analysis revealed PFOA and PFDoA induced distinct changes in gene expression throughout cultivation. Specifically, we identified significant changes in expression of the formate regulon and sulfate assimilation at mid-exponential phase and ferrous iron transport, central metabolism, the molecular chaperone network, and motility processes during stationary phase. Importantly, many of these changes were distinct from changes induced by NFCAs across growth phases.ConclusionsIn summary, we find PFAS induce a system-level change in gene expression of metabolic, transport, and regulatory pathways, providing insight into how these non-natural chemicals interact with a model bacterium at various growth phases. In addition, the transcriptomic dataset presented could enable the development of future real-time environmental monitoring and mitigation technologies.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12864-025-12109-4.

SummaryLipoproteins (LPP) and extracellular vesicles are carriers of extracellular small RNA, with potential applications both in the areas of diagnostics and therapeutics. Lipid nanoparticles overlap across a range of densities and sizes in plasma, making them difficult to isolate intact and without contamination from other plasma components. Accurate characterization of their cargo through efficient isolation from other plasma components is required to understand their function. Here we describe the simultaneous separation of LPP classes using sequential flotation ultracentrifugation followed by size exclusion chromatography from 500μL of starting plasma. Using western blot, denaturing and non-denaturing gel electrophoresis, nuclear magnetic resonance, and electron microscopy, we demonstrate separation of the LPP classes with minimal contamination. We also show unique lipidomic, proteomic and small RNA signatures for each LPP class, including, for the first time, very high density high density lipoprotein particles in the density range of 1.21–1.25 g/mL.

In 2024, the AmericanChemical Society (ACS), Division of PolymericMaterials: Science and Engineering (PMSE), celebrated its centennial.This historic occasion was marked at the 2024 Spring ACS Meeting inNew Orleans with a Centennial Symposium entitled “PMSECentennial: Celebration of Success and New Frontiers in PolymericMaterials Science and Engineering”. The symposiumreflected on past scientific breakthroughs, technological advancements,and new frontiers in the field of polymeric materials science andengineering. Eight thematic areas comprised the symposium: Advanced Manufacturing, AI and Materials Discovery, Biomaterials, Electronic Materials, Energy, Entrepreneurship, Smart Materials, and Sustainability. The31 distinguished speakers, representing academia, industry, and nationallaboratories, shared their unique perspectives. Within this Viewpoint,distinguished speakers have expanded on the symposium’s themes,summarizing key takeaways, identifying critical challenges, and exploringopportunities for continued advancements in polymer science.

Owing to its unique mechanical properties, chemical resistance,and ion conductivity, Nafion is one of the most widely used polymerelectrolytes. In hydrogen fuel cells, it constitutes both the macroscopicmembrane separating the anode and the cathode, and as a thin film,Nafion appears as a binder in the catalyst layer where conductiveionic pathways must intimately interface with platinum catalyst particles,electrically conductive carbon particles, and porous surfaces thatfacilitate the transport of gases. Residing at the intersection ofthis diverse range of materials, the ionomer’s interfacialstructure influences interfacial impedance and thus device performance.This interface structure has been widely investigated on model surfaceswith neutron reflectometry and other techniques, resulting in thediscovery of a multilamellar structure at the interface with hydrophilicmaterials, or a single water-rich layer at the interface with, e.g.,metals, favoring tangential vs perpendicular ion transport, respectively.Here we demonstrate that self-assembled monolayers, SAMs, which cancoat various surfaces, can control whether single or multiple lamellaeoccur. These interfacial structures can be further modified throughacid–base interactions by protonating the terminal amine groupof a SAM at low pH. This establishes a methodology to control theinterfacial ionic transport pathways in Nafion and determine the interfacialimpedance.

X-ray stress analysis requires accurate X-ray elastic constants (XECs). One of the challenges for XEC calculation is that the modelling approach, preferred orientation and, to a lesser extent, the free surface affect the result. In this work, we investigate these factors. The surface effect is calculated under the assumption that within a surface layer the Poisson action along the normal direction caused by in-plane stresses must not be impeded by grain–matrix interaction in the normal direction due to the free surface. The model is evaluated for the pure surface effect for zero penetration and for a layer thickness equal to the grain size; the latter is calculated as the attenuation-weighted depth average of the surface grain layer and the bulk. Further comparisons are made with other XEC models and with measured XECs for different lattice planes (hkl), different X-ray wavelengths and varying penetration depths. The effect of preferred orientation is studied through variations of the grain orientation distribution function (ODF). It was found that the pure-surface-effect model performed best. ODF variations can significantly affect the XECs of individual (hkl), while the effect on the (hkl) average appears small.