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De Andrade, Vincent

Publications and source records attributed to De Andrade, Vincent.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Structural aging of human neurons is opposite of the changes in schizophrenia

Human mentality develops with age and is altered in psychiatric disorders, though their underlying mechanism is unknown. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the anterior cingulate cortex from eight schizophrenia and eight control cases. The distribution profiles of neurite curvature of the control cases showed a trend depending on their age, resulting in an age-correlated decrease in the standard deviation of neurite curvature (Pearson’s r = -0.80, p = 0.018). In contrast to the control cases, the schizophrenia cases deviate upward from this correlation, exhibiting a 60% higher neurite curvature compared with the controls (p = 7.8 × 10 -4 ). The neurite curvature also showed a correlation with a hallucination score (Pearson’s r = 0.80, p = 1.8 × 10 -4 ), indicating that neurite structure is relevant to brain function. This report is based on our 3D analysis of human brain tissues over a decade and is unprecedented in terms of the number of cases. We suggest that neurite curvature plays a pivotal role in brain aging and can be used as a hallmark to exploit a novel treatment of schizophrenia.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

3D Quantification of Elemental Gradients within Heterostructured Particles of Battery Cathodes

Heterogenous architectures with elemental gradients tailored within particles have been pursued to combat the instabilities limiting Ni-rich cathode materials for lithium-ion batteries. The growth of different compositional layers is accomplished during the synthesis of hydroxide precursors. However, the extent to which these concentration gradients are modified during high-temperature reactions is difficult to establish in their intact, spherical form. Here, we show the entire three-dimensional structure of a secondary particle can be resolved nondestructively with differential X-ray absorption spectroscopy (XAS) through transmission X-ray microscopy (TXM). The relationship between particle location and elemental content was fully quantified, with high statistical significance, for heterostructures possessing different compositional gradients in the precursors with 90:5:5 Ni:Mn:Co core compositions. Reduced elemental heterogeneity was observed after high-temperature synthesis, but gradients remained. In conclusion, the methodology presented should be used to guide synthesis while assuring that gains in electrochemical performance are linked to precise elemental distributions at the nanoscale.

25 ENERGY STORAGE↗
DOE OSTI2022

X-Ray Induced Chemical Reaction Revealed by In Situ X-Ray Diffraction and Scanning X-Ray Microscopy in 15 nm Resolution

The detection sensitivity of synchrotron-based X-ray techniques has been largely improved due to the ever-increasing source brightness, which has significantly advanced ex situ and in situ research for energy materials such as lithium-ion batteries. However, the strong beam–material interaction arising from the high beam flux can substantially modify the material structure. The beam-induced parasitic effect inevitably interferes with the intrinsic material property, making the interpretation of the experimental results difficult and requiring comprehensive assessments. Here, we present a quantitative study of the beam effect on an electrode material Ag 2 VO 2 PO 4 using four different X-ray characterization methods with different radiation dose rates. The material system exhibits interesting and reversible radiation-induced thermal and chemical reactions, further evaluated under electron microscopy to illustrate the underlying mechanism. Further, the work will provide a guideline for using synchrotron X-rays to distinguish the intrinsic behavior from extrinsic structure change of materials induced by X-rays.

36 MATERIALS SCIENCE↗
DOE OSTI2022

3D LiMn 2 O 4 nThin Film Deposited by ALD: A Road toward High-Capacity Electrode for 3D Li-Ion Microbatteries

Miniaturized electronics suffer from a lack of energy autonomy. In that context, the fabrication of lithium-ion solid-state microbatteries with high performance is mandatory for powering the next generation of portable electronic devices. Here, the fabrication of a thin film positive electrode for 3D Li-ion microbatteries made by the atomic layer deposition (ALD) method and in situ lithiation step is demonstrated. The 3D electrodes based on spinel LiMn 2 O 4 films operate at high working potential (4.1 V vs Li/Li + ) and are capable of delivering a remarkable surface capacity (approximate to 180 mu Ah cm -2 ) at low C-rate while maintaining more than 40 mu Ah cm -2 at C/2 (time constant = 2 h). Both the thickness of the electrode material and the 3D gain of the template are carefully tuned to maximize the electrode performance. Further, advanced characterization techniques such as transmission electron and X-ray transmission microscopies are proposed as perfect tools to study the conformality of the deposited films and the interfaces between each layer: no interdiffusion or segregation are observed. This work represents a major issue towards the fabrication of 3D-lithiated electrode by ALD-without any prelithiation step by electrochemical technique-making it an attractive solution for the fabrication of 3D Li-ion solid-state microbatteries with semiconductor processing methods.

3D Li-ion micro-batteries↗
DOE OSTI2022

Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.

25 ENERGY STORAGE↗
DOE OSTI2022

Evading strength and ductility trade-off in an inverse nacre structured magnesium matrix nanocomposite

Mimicking nacre structure is an often-used strategy for developing materials which combine high strength and high toughness. Here, a novel strategy, named inverse nacre structure, is proposed where elongated and curled soft constituents are embedded in a matrix of hard constituent to form an ordered brick-and-mortar arrangement. This strategy is demonstrated in a particle reinforced metal matrix composite, using pure Mg as the soft phase and SiC nanoparticle reinforced Mg as the hard phase. In this work, the resulting nanocomposite yields high strength, and especially high tensile elongation which is five times that of the homogeneous counterpart. The superior comprehensive strengthening-toughening effect originates from the tailored inverse nacre structure which enables a stable microcrack-multiplication process by sufficiently promoting strain hardening so as to avoid any significant decrease in post-ultimate tensile strength. This strategy, while demonstrated here for the specific case of particle reinforced Mg, is not restricted to any particular material system but constitutes a generic pathway for the development of high-performance materials for engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes

Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation.

25 ENERGY STORAGE↗
DOE OSTI2021

Reaction inhomogeneity coupling with metal rearrangement triggers electrochemical degradation in lithium-rich layered cathode

High-energy density lithium-rich layered oxides are among the most promising candidates for next-generation energy storage. Unfortunately, these materials suffer from severe electrochemical degradation that includes capacity loss and voltage decay during long-term cycling. Present research efforts are primarily focused on understanding voltage decay phenomena while origins for capacity degradation have been largely ignored. Here, we thoroughly investigate causes for electrochemical performance decline with an emphasis on capacity loss in the lithium-rich layered oxides, as well as reaction pathways and kinetics. Advanced synchrotron-based X-ray two-dimensional and three-dimensional imaging techniques are combined with spectroscopic and scattering techniques to spatially visualize the reactivity at multiple length-scales on lithium- and manganese-rich layered oxides. These methods provide direct evidence for inhomogeneous manganese reactivity and ionic nickel rearrangement. Coupling deactivated manganese with nickel migration provides sluggish reaction kinetics and induces serious structural instability in the material. Our findings provide new insights and further understanding of electrochemical degradation, which serve to facilitate cathode material design improvements.

25 ENERGY STORAGE↗
DOE OSTI2021

Replacement of Calcium Carbonate Polymorphs by Cerussite

Calcium carbonate (CaCO 3 ) polymorphs, calcite, aragonite, and vaterite, serve as a major sink to retain various metal ions in natural and engineered systems. Here, we visualize the systematic trends in reactivities of calcite, vaterite, and aragonite to Pb 2+ dissolved in acidic aqueous solutions using in situ optical microscopy combined with ex situ scanning electron and transmission X-ray microscopies. All three polymorphs undergo pseudomorphic replacement by cerussite (PbCO 3 ) but with distinct differences in the evolution of their morphologies. The replacement of calcite and aragonite occurs through the formation of a pseudomorphic cerussite shell (typically 5–10 μm thick) followed by a slower inward propagation of reaction fronts through a thin solution gap (~0.1 μm wide) between the shell and the CaCO 3 substrate. The replacement of vaterite is characterized by the formation of a thinner cerussite shell (≤1 μm thick) and a larger cavity between the shell and the host mineral. These systematic differences in cerussite morphology for different CaCO 3 polymorphs are explained by the relative dissolution and precipitation rates of the reactant and product minerals, coupled with the role of ion transport through the cerussite shells. We also find that the replacement of calcite by cerussite is the slowest when all three polymorphs coexisted. Furthermore, our results provide mechanistic insights into the growth mode of cerussite on dissolving calcium carbonate and demonstrate these CaCO 3 polymorphs as promising substrate materials for removal and recycling of Pb from acidic polluted water and industrial effluents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Multi-modal imaging of a single mouse brain over five orders of magnitude of resolution

Mammalian neurons operate at length scales spanning six orders of magnitude; they project millimeters to centimeters across brain regions, are composed of micrometer-scale-diameter myelinated axons, and ultimately form nanometer scale synapses. Capturing these anatomical features across that breadth of scale has required imaging samples with multiple independent imaging modalities. Translating between the different modalities, however, requires imaging the same brain with each. Here, we imaged the same postmortem mouse brain over five orders of spatial resolution using MRI, whole brain micrometer-scale synchrotron x-ray tomography ($\mu$CT), and large volume automated serial electron microscopy. Using this pipeline, we can track individual myelinated axons previously relegated to axon bundles in diffusion tensor MRI or arbitrarily trace neurons and their processes brain-wide and identify individual synapses on them. This pipeline provides both an unprecedented look across a single brain's multi-scaled organization as well as a vehicle for studying the brain's multi-scale pathologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM)

Four state-of-the-art Deep Learning-based Convolutional Neural Networks (DCNN) were applied to automate the semantic segmentation of a 3D Transmission x-ray Microscopy (TXM) nanotomography image data. The standard U-Net architecture as baseline along with UNet++, PSPNet, and DeepLab v3+ networks were trained to segment the microstructural features of an AA7075 micropillar. A workflow was established to evaluate and compare the DCNN prediction dataset with the manually segmented features using the Intersection of Union (IoU) scores, time of training, confusion matrix, and visual assessment. Comparing all model segmentation accuracy metrics, it was found that using pre-trained models as a backbone along with appropriate training encoder-decoder architecture of the Unet++ can robustly handle large volumes of x-ray radiographic images in a reasonable amount of time. This opens a new window for handling accurate and efficient image segmentation of in situ time-dependent 4D x-ray microscopy experimental datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Fast X-ray Nanotomography with Sub-10 nm Resolution as a Powerful Imaging Tool for Nanotechnology and Energy Storage Applications

In the last decade, transmission X-ray microscopes (TXMs) have come into operation in most of the synchrotrons worldwide. They have proven to be outstanding tools for non-invasive ex and in situ 3D characterization of materials at the nanoscale across varying range of scientific applications. However, their spatial resolution has not improved in many years, while newly developed functional materials and microdevices with enhanced performances exhibit nanostructures always finer. Here, optomechanical breakthroughs leading to fast 3D tomographic acquisitions (85 min) with sub-10 nm spatial resolution, narrowing the gap between X-ray and electron microscopy, are reported. These new achievements are first validated with 3D characterizations of nanolithography objects corresponding to ultrahigh-aspect-ratio hard X-ray zone plates. Then, this powerful technique is used to investigate the morphology and conformality of nanometer-thick film electrodes synthesized by atomic layer deposition and magnetron sputtering deposition methods on 3D silicon scaffolds for electrochemical energy storage applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Catalysts by pyrolysis: Direct observation of chemical and morphological transformations leading to transition metal-nitrogen-carbon materials

Transition metal-nitrogen-carbon materials (M-N-C catalysts) are promising electrocatalysts in polymer electrolyte fuel cells (PEFCs) and electrolyzer applications. High temperature treatment in inert atmosphere (pyrolysis) is the essential, most common method for the synthesis of M-N-C catalysts and critical to achieve high electrocatalytic activity and electronic conductivity. To this day, despite many uses and successful implementations in materials manufacturing, pyrolysis has been an entirely empirical technology, with process control and optimization relying exclusively on “Edisonian” approach. The knowledge gap in the mechanism about how the precursor is being transformed into catalysts hinders further development of the M-N-C catalysts regardless of the precursor class and processing protocols. In this work, we probed the morphological evolution and chemical transformation of a nitrogen-containing charge transfer organic salt, mixed with transition metal (iron) salt and amorphous silica powder (precursor) during the pyrolysis process via a combination of in situ synchrotron and laboratory-based diagnostic techniques. The pyrolysis process is found to be divided into three stages. During a controlled temperature ramp, the selected organic N-C precursor (nicarbazin) began melting and decomposing just below 400 °C, forming a certain number of micrometer-scale pores and pathways. With increase in temperature from 400 °C to 900 °C, amorphous carbon domains started forming, and reduced (metallic) iron nanoclusters appeared, being dispersed uniformly throughout the carbonaceous matrix. When temperature advanced above 900 °C, graphitization of carbon commenced, associated with appearance and evolution of atomically dispersed metal-nitrogen moieties in the carbonaceous matrix. As the graphitization advanced further, a secondary process of agglomeration of metal nanoparticles occurred. Multi-analytical technique observations conducted here provide a base for rational design and optimization of M-N-C electrocatalysts via pyrolysis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Adorym: a multi-platform generic X-ray image reconstruction framework based on automatic differentiation

We describe and demonstrate an optimization-based X-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experimental parameters including probe positions, multiple hologram alignment, and object tilts. It is written with strong support for parallel processing, allowing large datasets to be processed on high-performance computing systems. We demonstrate its use on several experimental datasets to show improved image quality through parameter refinement.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Impact of electrode porosity architecture on electrochemical performances of 1 mm-thick LiFePO4 binder-free Li-ion electrodes fabricated by Spark Plasma Sintering

Thick electrodes with high active material loadings have been intensively studied over the last couple of decades in pursuit of achieving high energy density systems. To optimize and enhance the electrochemical performance of such electrodes, one has to control the pore morphology by, for example, varying the pore size and shape, and the level of porosity. In the present work, the fabrication of thick binder-free LiFePO4 (LFP) electrodes with two different pore sizes (12 and 20 mu m) and porosities (21 vol% and 44 vol%) using Spark Plasma Sintering (SPS) and templating approach is reported. The well-controlled porous architecture inside the thick electrodes is realized by fine-tuning experimental parameters. The impact of porosity architecture on electrochemical performance is quantified and correlated with the 3D tortuosity values determined from both micro-computed tomography and electrochemical impedance-based experimental methods. Based on the micro-computed tomography data analysis, estimated tortuosity values along X, Y, and Z axes reveal an anisotropic effect perpendicularly to the SPS compression axis (Z-direction). This is particularly profoundly observed in the samples with larger pores (20 mu m). The correlation between morphological properties and the rate capability performance is established indicating that the capacity loss happens mainly due to the Li-ion diffusion limitations.

Cathode material↗
DOE OSTI2021

Distributed Optimization for Nonrigid Nano-Tomography

Resolution level and reconstruction quality in nano-computed tomography (nano-CT) are in part limited by the stability of microscopes, because the magnitude of mechanical vibrations during scanning becomes comparable to the imaging resolution, and the ability of the samples to resist radiation induced deformations during data acquisition. In such cases, there is no incentive in recovering the sample state at different time steps like in time-resolved reconstruction methods, but instead the goal is to retrieve a single reconstruction at the highest possible spatial resolution and without any imaging artifacts. Here we propose a distributed optimization solver for tomographic imaging of samples at the nanoscale. Our approach solves the tomography problem jointly with projection data alignment, nonrigid sample deformation correction, and regularization. Projection data consistency is regulated by dense optical flow estimated by Farneback's algorithm, leading to sharp sample reconstructions with less artifacts. Synthetic data tests show robustness of the method to Poisson and low-frequency background noise. We accelerated the solver on multi-GPU systems and validated the method on three nano-imaging experimental data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Structural diverseness of neurons between brain areas and between cases

The cerebral cortex is composed of multiple cortical areas that exert a wide variety of brain functions. Although human brain neurons are genetically and areally mosaic, the three-dimensional structural differences between neurons in different brain areas or between the neurons of different individuals have not been delineated. Here we report a nanometer-scale geometric analysis of brain tissues of the superior temporal gyrus of schizophrenia and control cases. The results of the analysis and a comparison with results for the anterior cingulate cortex indicated that (1) neuron structures are significantly dissimilar between brain areas and that (2) the dissimilarity varies from case to case. The structural diverseness was mainly observed in terms of the neurite curvature that inversely correlates with the diameters of the neurites and spines. The analysis also revealed the geometric differences between the neurons of the schizophrenia and control cases. The schizophrenia cases showed a thin and tortuous neuronal network compared with the controls, suggesting that the neuron structure is associated with the disorder. The area dependency of the neuron structure and its diverseness between individuals should represent the individuality of brain functions.

60 APPLIED LIFE SCIENCES↗