Results for “spatial mapping” — Page 3

DOE OSTI2024

ARJUNA: An Electrochemical Interface Mapping Probe for Solid-State Batteries

Solid-state batteries (SSBs) are promising candidates for next-generation energy storage, although their performance can be compromised by interfacial heterogeneity within the electrolyte. Furthermore, ensuring the quality of large form-factors electrolyte film is crucial for establishing a robust manufacturing platform for solid-state batteries. Herein, we report on the use of ARJUNA, an electrochemical interface mapping system, to characterize heterogeneities at solid electrolyte interfaces and to serve as a quality control system for SSB manufacturing. In addition to spatial mapping, the proposed system can also probe the interface behavior as a function of pressure and temperature. We present the operating principle, design, instrumentation, and evaluation of the system alongside a typical hybrid solid electrolyte produced using two common manufacturing processes. This report showcases the capability of ARJUNA to probe the heterogeneity and quality of processed solid electrolyte films.

25 ENERGY STORAGE↗

DOE OSTI2026

Crystal Orientation and Defect Mapping in Electron-Beam-Sensitive Zeolites with Near-Axis Transmission Kikuchi Diffraction

Porous materials are vital in catalysis, energy conversion, and environmental remediation. Understanding structural heterogeneity in zeolites is key to linking synthesis, framework intergrowths, and catalytic performance, yet current methods for phase identification and spatial mapping lack sufficient resolution or throughput. We present a high-throughput approach using near-axis transmission Kikuchi diffraction in a scanning electron microscope, achieving high phase and spatial resolution for electron-beam-sensitive zeolites, including ZSM-5 and, for the first time, Zeolite A. Here, this method enables direct visualization of intergrowth features that critically affect catalytic and adsorption behavior, bridging the gap between ensemble-averaged X-ray diffraction and high-resolution but low-throughput transmission electron microscopy. Combining nanoscale mapping with statistical sampling is highly suited for machine-learning pipelines guiding new structure function understanding and could be extended to other beam-sensitive porous materials such as metal–organic or covalent–organic frameworks.

crystallography↗

DOE OSTI2020

Visualizing Charge Transport and Nanoscale Electrochemistry by Hyperspectral Kelvin Probe Force Microscopy

Charge-transport and electrochemical processes are heavily influenced by the local microstructure. Kelvin probe force microscopy (KPFM) is a widely used technique to map electrochemical potentials at the nanometer scale; however, it offers little information on local charge dynamics. In this study, we implement a hyperspectral KPFM approach for spatially mapping bias-dependent charge dynamics in timescales ranging from the sub-millisecond to the second regime. As a proof of principle, we investigate the role mobile surface charges play in a three-unit-cell LaAlO 3 /SrTiO 3 oxide heterostructure. We explore machine learning approaches to assist with visualization, pattern recognition, and interpretation of the information-rich data sets. Linear unmixing methods reveal hidden bias-dependent interfacial processes, most likely water splitting, which are essentially unnoticed by functional fitting of the dynamic response alone. Hyperspectral KPFM will be beneficial for investigating nanoscale charge transport and local reactivity in systems involving a possible combination of electronic, ionic, and electrochemical phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Stanford Thermal Earth Model for the Conterminous United States

Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States. The thermal earth model is made available as an application programming interface (API) and as feature layers on ArcGIS, which are both provided via links below. A data-driven spatial interpolation algorithm based on physics-informed graph neural networks was used to develop these national temperature-at-depth maps. The model satisfied the three-dimensional heat conduction law by predicting subsurface temperature, surface heat flow, and rock thermal conductivity. Many physical quantities, including bottomhole temperature, depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity were used as model inputs. Surface heat flow, temperature, and thermal conductivity predictions were constructed for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km2 per grid cell. The model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8C, 8.1 mW/m2 and 0.07 W/(C-m), respectively..

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Spatiotemporally Registered In-Situ and Ex-Situ Datasets for Laser-based Blown Powder Directed Energy Deposition

This dataset is comprised of in situ sensing data collected during laser-based, blown powder directed energy deposition (DED) of Inconel 718 representing eight different printing conditions: (1) nominal, (2) +15% scan speed, (3) +12% laser power, (4) +42% powder feed rate, (5) +100% jerk limit, (6) +10% layer height, (7) +20% hatch spacing, (8) +20% carrier gas flow. All eight DED builds constructed an identical test coupon geometry consisting of geometric features representative of industrial print requirements (e.g., bulk deposition, thin walls, overhangs). In situ data consists of xyz-coordinates (100 Hz) and on-axis melt pool camera video (60 Hz), both of which have been temporally synchronized to spatially map the melt pool camera data. In addition, post-build X-ray computed tomography (XCT) data for each of the eight test geometries have been spatially registered to the recorded xyz-coordinates, allowing for comparisons between melt pool camera data and flaws identified in the XCT data.

additive manufacturing↗

DOE OSTI2023

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform

Conventional proteomics measures the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of significant changes in subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot precisely infer the abundance changes of intact proteins, which are presented as proteoforms. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantification directly on thin tissue sections. The spatial TDP platform consisted of a nanoPOTS (nanodroplet Processing in One pot for Trace Samples)-based sample preparation system and an LCM (laser capture microdissection)-based cell isolation system. We improved the nanoPOTS sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ~200 cultured cells as model samples, the improved approach increased proteoform identifications from 493 to 700; newly identified proteoforms primarily corresponded to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed LCM-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 426 proteoforms by combining identifications from TopPIC and TDPortal with the quantitation from ProMex. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential PTM-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Determining Optimal Magnetometer Configuration on MAGIS-100

Long-baseline atom interferometers such as the Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) require stringent control and continuous characterization of background magnetic fields and spatial gradients to prevent systemic phase shifts that mimic ultralight dark matter or gravitational wave signatures. Because direct sensor placement within the ultra-high vacuum beam pipe is infeasible, in-situ magnetic field monitoring relies on external sensor arrays situated in the surrounding annular region. This work demonstrates a field reconstruction framework for a 5.3-meter MAGIS-100 modular section using finite-element Opera simulations. Transverse magnetic fields are expanded using a cylindrical multipole framework as informed by Fermilab’s Muon g-2 experiment, with magnetometer array configurations optimized via Fisher information matrix D-optimality. Inverting external sensor readings through a Gauss-Newton scheme recovers interior tube fields across distinct axial positions. In the discontinuity-averse uniform region (slice pair P4), the model achieves sub-noise-floor performance with a cross-validated root-mean-square error (RMSE) of $6.7227 \times 10^{-4}\text{ A/m}$ ($0.845\times$ sensor noise floor) and an interior field coefficient of variation of $1.71\%$. An elbow criterion in the Fisher bounds establishes $n_{\text{max}} = 2$ as the optimal multipole truncation order to prevent noise amplification from over-parameterization, with $n_{\text{max}} = 3$ (sextupole) order chosen for analysis to demonstrate further complexity and cross-pair comparison. Furthermore, analytical differentiation of the fitted multipole coefficients yields dense spatial maps of the transverse Jacobian gradient matrix $\nabla \mathbf{H}$ along with propagated $1\sigma$ uncertainty bounds across the beam region ($r \le 2.75\text{ in}$). This operational framework confirms that external magnetometer arrays can reliably monitor magnetic field uniformity and spatial gradients along the 100-meter flight path given appropriate sampling for any complexity order.

Appleby, Darwin [William Rainey Harper Coll.] (ORC↗

DOE OSTI2026

Determining Optimal Magnetometer Configuration on MAGIS-100

Long-baseline atom interferometers such as the Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) require stringent control and continuous characterization of background magnetic fields and spatial gradients to prevent systemic phase shifts that mimic ultralight dark matter or gravitational wave signatures. Because direct sensor placement within the ultra-high vacuum beam pipe is infeasible, in-situ magnetic field monitoring relies on external sensor arrays situated in the surrounding annular region. This work demonstrates a field reconstruction framework for a 5.3-meter MAGIS-100 modular section using finite-element Opera simulations. Transverse magnetic fields are expanded using a cylindrical multipole framework as informed by Fermilab’s Muon g-2 experiment, with magnetometer array configurations optimized via Fisher information matrix D-optimality. Inverting external sensor readings through a Gauss-Newton scheme recovers interior tube fields across distinct axial positions. In the discontinuity-averse uniform region (slice pair P4), the model achieves sub-noise-floor performance with a cross-validated root-mean-square error (RMSE) of $6.7227 \times 10^{-4}\text{ A/m}$ ($0.845\times$ sensor noise floor) and an interior field coefficient of variation of $1.71\%$. An elbow criterion in the Fisher bounds establishes $n_{\text{max}} = 2$ as the optimal multipole truncation order to prevent noise amplification from over-parameterization, with $n_{\text{max}} = 3$ (sextupole) order chosen for analysis to demonstrate further complexity and cross-pair comparison. Furthermore, analytical differentiation of the fitted multipole coefficients yields dense spatial maps of the transverse Jacobian gradient matrix $\nabla \mathbf{H}$ along with propagated $1\sigma$ uncertainty bounds across the beam region ($r \le 2.75\text{ in}$). This operational framework confirms that external magnetometer arrays can reliably monitor magnetic field uniformity and spatial gradients along the 100-meter flight path given appropriate sampling for any complexity order.

Appleby, Darwin [William Rainey Harper Coll.] (ORC↗

DOE OSTI2026

In-Situ Magnetic Field Reconstruction in the MAGIS-100 Experiment

Long-baseline atom interferometers such as the Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) require stringent control and continuous characterization of background magnetic fields and spatial gradients to prevent systemic phase shifts that mimic ultralight dark matter or gravitational wave signatures. Because direct sensor placement within the ultra-high vacuum beam pipe is infeasible, in-situ magnetic field monitoring relies on external sensor arrays situated in the surrounding annular region. This work demonstrates a field reconstruction framework for a 5.3-meter MAGIS-100 modular section using finite-element Opera simulations. Transverse magnetic fields are expanded using a cylindrical multipole framework as informed by Fermilab’s Muon g-2 experiment, with magnetometer array configurations optimized via Fisher information matrix D-optimality. Inverting external sensor readings through a Gauss-Newton scheme recovers interior tube fields across distinct axial positions. In the discontinuity-averse uniform region (slice pair P4), the model achieves sub-noise-floor performance with a cross-validated root-mean-square error (RMSE) of $6.7227 \times 10^{-4}\text{ A/m}$ ($0.845\times$ sensor noise floor) and an interior field coefficient of variation of $1.71\%$. An elbow criterion in the Fisher bounds establishes $n_{\text{max}} = 2$ as the optimal multipole truncation order to prevent noise amplification from over-parameterization, with $n_{\text{max}} = 3$ (sextupole) order chosen for analysis to demonstrate further complexity and cross-pair comparison. Furthermore, analytical differentiation of the fitted multipole coefficients yields dense spatial maps of the transverse Jacobian gradient matrix $\nabla \mathbf{H}$ along with propagated $1\sigma$ uncertainty bounds across the beam region ($r \le 2.75\text{ in}$). This operational framework confirms that external magnetometer arrays can reliably monitor magnetic field uniformity and spatial gradients along the 100-meter flight path given appropriate sampling for any complexity order.

Appleby, Darwin [William Rainey Harper Coll.; Ferm↗

DOE OSTI2023

Equivalence between Fermion-to-Qubit Mappings in two Spatial Dimensions

We argue that all locality-preserving mappings between fermionic observables and Pauli matrices on a two-dimensional lattice can be generated from the exact bosonization in Chen et al., whose gauge constraints project onto the subspace of the toric code with emergent fermions. Starting from the exact bosonization and applying Clifford finite-depth generalized local unitary transformation, we can achieve all possible fermion-to-qubit mappings (up to the re-pairing of Majorana fermions). In particular, we discover a new supercompact encoding using 1.25 qubits per fermion on the square lattice. We prove the existence of finite-depth quantum circuits to obtain fermion-to-qubit mappings with qubit-fermion ratios r = 1 + 1/2k for positive integers k, utilizing the trivialness of quantum cellular automata in two spatial dimensions. Also, we provide direct constructions of fermion-to-qubit mappings with ratios arbitrarily close to 1. When the ratio reaches 1, the fermion-to-qubit mapping reduces to the one-dimensional Jordan-Wigner transformation along a certain path in the two-dimensional lattice. Finally, we explicitly demonstrate that the Bravyi-Kitaev superfast simulation, the Verstraete-Cirac auxiliary method, Kitaev’s exactly solved model, the Majorana loop stabilizer codes, and the compact fermion-to-qubit mapping can all be obtained from the exact bosonization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Enzymatically hydrolyzed fluorescence-based chemical probe enables in situ mapping of chitinase activity in the rhizosphere

Rhizosphere microbiomes and root exudates play a pivotal biochemical role in helping to catalyze chitin catabolism. Chitin is a recalcitrant and ubiquitous soil biopolymer, estimated to be the second most abundant organic soil biopolymer on Earth. Despite its abundance, role as a source of C and N in soil, and importance to ecosystem function, the biochemical mechanisms controlling chitin fate in the rhizosphere are elusive and poorly understood. To enable spatial mapping of chitinase activity in the rhizosphere, we designed and synthesized an enzymatically activated fluorogenic substrate, chitotriose-TokyoGreen (chitotriose-TG), by incorporating a fluorescein derivative (TG) onto the trimeric unit of chitin. This non-fluorescent substrate is selectively hydrolyzed by chitinase to release TG and yield a high fluorescence signal, which can be used to spatially image and measure chitinase activity in the rhizosphere. To demonstrate the application of this technique, we grew switchgrass (Panicum virgatum) in rhizoboxes amended with a horizontal layer of chitin. We extracted mobile proteins from the rhizobox using a nitrocellulose membrane blotting technique which offer non-destructive enzyme extraction while preserving the 2D spatial position of the enzymes. We then subjected these membranes to the synthesized chitotriose-TG stain to spatially visualize the distribution of chitinase activity within the rhizosphere. Furthermore, we observed increased chitinase activity near plant roots and higher activity within the soil zone enriched in chitin, showing an adaptive response of chitinase activity with spatial focusing in areas of higher chitin abundance. Thus, the enzyme extraction and visualization strategy we describe here can help enlighten efforts to better understand spatial controls on chitin breakdown in rhizosphere, further elucidating the role of chitin as a C and N source in these systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability: Supporting Data and Code

This repository contains R code and associated datasets for reproducing the analysis described in the manuscript titled “Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability” (DOI: 10.1029/2024JG008604). The provided scripts facilitate a comprehensive analysis of snow depth variability influenced by microtopography and vegetation distribution in a subarctic watershed. Included datasets are high-resolution spatial maps of snow depth, terrain elevation, vegetation height, and distance from shrubs taller than 1 meter, all formatted as text files (.txt). These data are fully describe in doi:10.15485/2316038. Users can adapt the provided R scripts to accommodate different data formats or larger spatial domains, noting that some output files may require modification due to their size.The code includes implementations for boosted regression tree analysis adapted from methods outlined in Elith et al. (2008). Users interested in understanding or modeling landscape-scale snow distribution patterns, particularly in Arctic or subarctic ecosystems, will find this package useful. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

The Influence of Soil Properties on Sea-Breeze Circulations in the Southeast U.S.

Sea-breeze circulations (SBCs) are common weather phenomena at and near coastal regions. They form because of a thermal gradient between the land surface at the coast and the sea surface. In a mid-day regime, a “thermal low” generated at the warm coast will lead to rising air motion, creating a wind shift coming from the sea near the surface displacing the coastal air. A “return flow” moving back towards the sea is generated by upper-level divergence because of the rising motion from the thermal low. SBCs propagate and serve as a method of urban pollutant dispersion in the Los Angeles region of California and are constrained to the coast due to the topography of surrounding mountains serving as a boundary for further inland propagation. Within the northeast U.S. SBCs are seen in the warm season but tend to remain coastally bound due to Coriolis distortion over long distances. Within the southeast U.S. (SEUS), the paradigmatic example of SBCs occurs over the Florida peninsula, where thunderstorms form on a nearly daily occurrence due to the convergence of SBCs from the east and west sides of the peninsula. However, there are further examples of sea-breezes in the SEUS that warrant study. Within the region bordering the SEUS and the Mid-Atlantic, just east of the southern Appalachian Mountains, warm-season SBCs form at the coast of Georgia and the Carolinas. Relatively flat topography ~150-200km inland allows for mostly unimpeded inland SBC propagation. Through visual analysis, Viner et al. catalogued several SBCs that propagated as far inland as the Central Savannah River Area surrounding Augusta, Georgia. Wermter et al. found that while the land-sea thermal gradient at the coast can influence coastal SBC genesis, the inland-coastal thermal gradient over the land is the primary influencer on the speed and depth of inland propagation of SBCs in this region. Additionally, soil moisture itself is a known correlative factor to sea-breeze formation, as it influences the soil temperature and the thermal gradient needed for SBC genesis and inland penetration. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Conversely, drier soil enhances the thermal gradient and promotes SBC formation. However, while there is an inverse relationship between soil moisture and SBCs, higher soil moisture can actually promote more convective rainfall following a SBC if it does not significantly impact the thermal gradient. While the relationship between soil moisture and SBC formation has been conceptually explored and modeled numerically, there is a research gap in observed connections. The Soil Moisture Active Passive (SMAP) satellite mission has been operational since 2015 and has been used to create high-resolution re-analytical Level 4 (L4) datasets of soil moisture and soil temperature at different soil depths: the surface (0-5cm) and rootzone (0-1m). The surface soil temperature effectively acts as the “skin temperature” of the surface at these levels, and a spatial map of the land-sea as well as the coastal-inland thermal gradients can be represented. SMAP data are also assimilated in some atmospheric models such at the High Resolution Rapid Refresh (HRRR) mesoscale model. We propose leveraging the use of SMAP products to fill in spatial gaps left by weather and mesonet stations within the SEUS region, as well as assessing the effectiveness of utilizing SMAP products towards SBC forecasting in both deterministic and machine learning (ML) models.

58 GEOSCIENCES↗

DOE OSTI2021

Spatiotemporal distribution of chemical signatures exhibited by Myxococcus xanthus in response to metabolic conditions

Myxococcus xanthus is a common soil bacterium with a complex life cycle, which is known for production of secondary metabolites. However, little is known about the effects of nutrient availability on M. xanthus metabolite production. In this study, we utilize confocal Raman microscopy (CRM) to examine the spatiotemporal distribution of chemical signatures secreted by M. xanthus and their response to varied nutrient availability. Here, ten distinct spectral features are observed by CRM from M. xanthus grown on nutrient-rich medium. However, when M. xanthus is constrained to grow under nutrient-limited conditions, by starving it of casitone, it develops fruiting bodies, and the accompanying Raman microspectra are dramatically altered. The reduced metabolic state engendered by the absence of casitone in the medium is associated with reduced, or completely eliminated, features at 1140 cm –1 , 1560 cm –1 , and 1648 cm –1 . In their place, a feature at 1537 cm –1 is observed, this feature being tentatively assigned to a transitional phase important for cellular adaptation to varying environmental conditions. In addition, correlating principal component analysis heat maps with optical images illustrates how fruiting bodies in the center co-exist with motile cells at the colony edge. While the metabolites responsible for these Raman features are not completely identified, three M. xanthus peaks at 1004, 1151, and 1510 cm –1 are consistent with the production of lycopene. Thus, a combination of CRM imaging and PCA enables the spatial mapping of spectral signatures of secreted factors from M. xanthus and their correlation with metabolic conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Mapping of the transient electric field causing spark suppression in Micromegas of the ATLAS New Small Wheels

Resistive strips Micromegas are employed in the ATLAS New Small Wheel project. They have already been installed and operate in the experimental cavern of the ATLAS experiment at CERN. This work attempts to describe the mechanism of the surface electric charge spread on a resistive strip of the Micromegas detector and the created transient electric field leading to the suppression of the spark rate in the amplification region. The electric field spatial mapping is calculated versus time by using the accurate 3-layer geometry as a function of the resistive layer thickness around its value used in Micromegas detectors. We also used the approximated method based on 2-layer geometry to investigate its discrepancy from the accurate one.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Probing Plasmonic Near-Fields in Oxide-Modified Aluminum Nanocubes Using Photon-Induced Near-Field Electron Microscopy

Plasmonic nanoparticles generate strong electric fields near their surface upon photoexcitation, enabling applications in sensing, spectroscopy, and photocatalysis. Electron microscopy techniques – such as cathodoluminescence and electron energy loss spectroscopy – have been leveraged to produce nanoscale maps of localized surface plasmon (LSP) modes. More recently, photon-induced near-field electron microscopy (PINEM) has emerged as a powerful technique for imaging evanescent near-fields generated by ultrafast laser excitation. In this work, we employ PINEM within an ultrafast electron microscope, complemented by numerical calculations to investigate how optical polarization, surface modification, and light intensity affect the evanescent fields associated with LSPs on oxide-modified aluminum nanocubes. Polarization control of the incident light field enables spatial mapping of the nanocube’s LSPs at the single particle level. Systematic variation of the oxide layer thickness reveals that increased coating thickness correlates with a stronger PINEM signal and a greater energy gain of probing electrons. Additionally, higher light intensities at fixed polarization and oxide coating further amplify the PINEM signal. These findings demonstrate the utility of PINEM as a high-resolution technique for optical near-field imaging and spectroscopy of single plasmonic nanoparticles. The ability to probe single particle behavior offers new opportunities for advancing the design and characterization of nanophotonic and plasmonic materials.

PINEM↗

DOE OSTI2025

Spatial metabolomics and lipidomics in kidney disease

Kidney disease is a global health issue that affects over 850 million people, and early detection is key to preventing severe disease and complications. Kidney diseases are associated with complex dysregulation of lipid metabolism. Spatial metabolomics through mass spectrometry imaging (MSI) enables spatial mapping of the lipids in tissue and includes a variety of techniques that can be used to image lipids. In the kidney MSI studies often seek to resolve individual functional tissue units such as glomeruli and proximal tubules. Several different MSI techniques such as matrix-assisted laser desorption/ionization and desorption electrospray ionization have been used to characterize lipids and small molecules in chronic kidney disease, acute kidney injury, genetic kidney disease, and cancer. In this review we provide several examples of how spatial metabolomics data can provide critical information concerning localization of changes in disease states. Additionally, when combined with pathology, measurements, transcriptomics, or proteomics, the metabolomic changes can illuminate underlying mechanisms and provide new clinical insights.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Influence of doping and thickness on domain avalanches in lead zirconate titanate thin films

In undoped lead zirconate titanate films of 1–2 μm thick, domain walls move in clusters with a correlation length of approximately 0.5–2 μm. Band excitation piezoresponse force microscopy mapping of the piezoelectric nonlinearity revealed that niobium (Nb) doping increases the average concentration or mobility of domain walls without changing the cluster area of correlated domain wall motion. In comparison, manganese (Mn) doping reduces the contribution of mobile domain walls to the dielectric and piezoelectric responses without changing the cluster area for correlated motion. In both Nb and Mn doped films, the cluster area increases and the cluster density drops as the film thickness increases from 250 to 1250 nm. This is evident in spatial maps generated from the analysis of irreversible to reversible ratios of the Rayleigh coefficients.

36 MATERIALS SCIENCE↗