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Results for “cell heterogeneity”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Physical and behavioral adaptations to prevent overheating of the living wings of butterflies

The wings of Lepidoptera contain a matrix of living cells whose function requires appropriate temperatures. However, given their small thermal capacity, wings can overheat rapidly in the sun. Here we analyze butterfly wings across a wide range of simulated environmental conditions, and find that regions containing living cells are maintained at cooler temperatures. Diverse scale nanostructures and non-uniform cuticle thicknesses create a heterogeneous distribution of radiative cooling that selectively reduces the temperature of structures such as wing veins and androconial organs. These tissues are supplied by circulatory, neural and tracheal systems throughout the adult lifetime, indicating that the insect wing is a dynamic, living structure. Behavioral assays show that butterflies use wings to sense visible and infrared radiation, responding with specialized behaviors to prevent overheating of their wings. Our work highlights the physiological importance of wing temperature and how it is exquisitely regulated by structural and behavioral adaptations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials

GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topological quantum devices. However, understanding their intricate structures using density functional theory (DFT) calculations is hindered by spatiotemporal constraints. To overcome this limitation, we develop highly accurate and efficient machine-learning interatomic potentials based on a neuroevolution potential approach with farthest point sampling on a comprehensive DFT data set. The application of the developed machine-learning potential in large-scale atomistic simulations bridges the spatiotemporal gap between modeling and advanced characterizations, and facilitates the discovery of structural intricacies in GeSn alloys. Through extensive statistical sampling, we identify a type of short-range order (SRO) that is distinguished by both its structural signature and electronic band gap from the SRO structure previously predicted. Modeling based on a large simulation cell reveals the coexistence of nano SRO domains with various degrees of ordering, demonstrating a complex spatial heterogeneity of SRO structure. Finally, our study not only reinforces the significance of fine-level structural information in alloys, but it also constitutes an effective framework for exploring SRO in a broad range of complex alloys based on highly accurate and effective machine-learning potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Probing the Role of Multi-scale Heterogeneity in Graphite Electrodes for Extreme Fast Charging

Electrode-scale heterogeneity can combine with complex electrochemical interactions to impede lithium-ion battery performance, particularly during fast charging. This research investigates the influence of electrode heterogeneity at different scales on the lithium-ion battery electrochemical performance under operational extremes. We employ image-based mesoscale simulation in conjunction with a three-dimensional electrochemical model to predict performance variability in 14 graphite electrode X-ray computed tomography data sets. Our analysis reveals that the tortuous anisotropy stemming from the variable particle morphology has a dominating influence on the overall cell performance. Cells with platelet morphology achieve lower capacity, higher heat generation rates, and severe plating under extreme fast charge conditions. On the contrary, the heterogeneity due to the active material clustering alone has minimal impact. Our work suggests that manufacturing electrodes with more homogeneous and isotropic particle morphology will improve electrochemical performance and improve safety, enabling electromobility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Influence of Methylammonium Chloride on Wide-Bandgap Halide Perovskites Films for Solar Cells

Wide-bandgap perovskites are of paramount importance as the photoactive layer of the top cell in high-efficiency tandem solar cells. Comparably high Br contents are required to widen the perovskite bandgap. However, the increase in Br content causes heterogeneous halide distribution and photoinstability. Here, in this study, the positive effect of the additive methylammonium chloride (MACl) on the optical and electronic properties of Br-rich perovskite, deposited using N-methyl-2-pyrrolidone (NMP) as co-solvent and the gas quenching method, is investigated. Simultaneous in situ grazing-incidence wide-angle X-ray scattering and photoluminescence spectroscopy are used to track the evolution of the structural and optoelectronic properties of the perovskites with different amounts of Br and MACl during the spin-coating and thermal annealing steps. The formation mechanism is elucidated in the presence of MACl. It is observed that chloride ions inhibit the intermediate phases, favoring the formation of a perovskite phase with higher crystallinity. Nano X-ray fluorescence mapping recognizes Br-richer and poorer nanometric domains, whose average sizes reduce for samples with MACl. In conclusion, it is demonstrated that adding MACl affects the formation of wide-bandgap perovskites via destabilization of the intermediate phases and acts on the homogenization of the halide distribution, leading to improved solar cell performances.

14 SOLAR ENERGY↗
DOE OSTI2023

A Graph Dynamical neural network approach for decoding dynamical states in ferroelectrics.

Ferroelectric materials such as BaTiO 3 show tremendous potential for emerging advances in memory devices, particular neuromorphic type devices. High density of memory can be obtained by stabilising polar domain walls at the nanoscale, regions of discontinuity between the well-defined polarization order parameter, but little is known about what controls their structure and dynamics in real nanoscale materials. Indeed, chiral polar domain walls have been observed in heterogeneous ferroelectrics, such as oxygen-deficient BaTiO 3 , but very little is known about how such polar-domains walls interact with defects. Indeed, a critical understanding of how dynamics of domain-walls depend on point-defects is crucial to create engineered ferroelectric memory devices. For this work, we perform large-scale simulations of nansocale domain-wall dynamics in pristine and defective BaTiO 3 using reactive force-field developed by us earlier (Phys. Chem. Chem. Phys., 2019, 21, 18240–18249), and capture their dynamical dependence on point defects using a graph dynamical neural-network approach, which we adapted to interrogate solids with well-defined order-parameters, and implemented using Pytorch based libraries. Our machine learning (ML) approach goes beyond the traditional post-processing methods to capture both spatial and temporal heterogeneities of large-scale molecular dynamics simulations of complex defective ferroelectric oxide materials. We crucially find that isolated oxygen vacancies introduce very localized spatial regions (~1–2 unit-cell in length) that show slow dipole relaxation due to formation of defect-dipoles, and that these defect-dipoles in turn slow the intrinsic dynamics of domain walls. Further, the roughness of domain walls, also influenced by vacancies, introduce dynamic heterogeneity along the domain-wall. As such we find a novel mechanism by which quenched disorder due to defects introduce dynamic heterogeneity thereby influencing response to external fields (particularly time varying fields) in a ferroelectric. Our study also emphasizes the need for creating digital twins of dynamical quantities to achieve autonomous in operando control of nanoscale switching.

42 ENGINEERING↗
DOE OSTI2025

Measuring the flatband potential in 2D semiconductors: Pitfalls and a possible SECCM solution

The flatband potential (V fb ) is a critical parameter in semiconductor electrochemistry, defining the potential at which no excess charge exists at the semiconductor/electrolyte interface. It serves as a key reference for interpreting charge transfer kinetics and current–voltage behavior. However, conventional methods like Mott–Schottky analysis fail for atomically thin 2D materials due to the breakdown of the depletion approximation. This perspective examines the limitations of traditional V fb measurements for 2D semiconductors and the experimental challenges that arise. To address these issues, we propose using scanning electrochemical cell microscopy (SECCM) to spatially resolve the potential of zero charge (V pzc ), equivalent to V fb . This approach mitigates sample heterogeneity issues, such as pinholes or multilayer defects, and offers a pathway to more accurate electrochemical characterization. Ultimately, this method will enhance understanding of current–potential behavior in 2D materials, supporting the design of advanced systems for photoelectrocatalysis, energy conversion, and sensing.

2D semiconductors↗
DOE OSTI2021

Structural Polymorphism of Chitin and Chitosan in Fungal Cell Walls From Solid-State NMR and Principal Component Analysis

Chitin is a major carbohydrate component of the fungal cell wall and a promising target for novel antifungal agents. However, it is technically challenging to characterize the structure of this polymer in native cell walls. Here, we recorded and compared 13 C chemical shifts of chitin using isotopically enriched cells of six Aspergillus, Rhizopus, and Candida strains, with data interpretation assisted by principal component analysis (PCA) and linear discriminant analysis (LDA) methods. The structure of chitin is found to be intrinsically heterogeneous, with peak multiplicity detected in each sample and distinct fingerprints observed across fungal species. Fungal chitin exhibits partial similarity to the model structures of α- and γ-allomorphs; therefore, chitin structure is not significantly affected by interactions with other cell wall components. Addition of antifungal drugs and salts did not significantly perturb the chemical shifts, revealing the structural resistance of chitin to external stress. In addition, the structure of the deacetylated form, chitosan, was found to resemble a relaxed two-fold helix conformation. This study provides high-resolution information on the structure of chitin and chitosan in their cellular contexts. The method is applicable to the analysis of other complex carbohydrates and polymer composites.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Crystallization in one-step solution deposition of perovskite films: Upward or downward?

Despite the fast progress of perovskite photovoltaic performances, understanding the crystallization and growth of perovskite films is still lagging. One unanswered fundamental question is whether the perovskite films are grown from top (air side) to bottom (substrate side) or from bottom to top despite 10 years of development. Here, by using grazing incidence x-ray diffraction and morphology characterizations, we unveil that the perovskite films prepared by one-step solution processes, including antisolvent-assisted spin coating and blade coating, follow the downward growth from intermediate phase during thermal annealing. Such a top-to-bottom downward growth is initialized by the evaporation of residual solvent from the top surface of “wet” films and is less sensitive to perovskite compositions and the wettability of underlying substrates. Addressing this fundamental question is important to understand the heterogeneity of perovskite films along the vertical direction, which markedly affects the efficiency and stability of perovskite solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Single-Cell Discovery and Multiomic Characterization of Therapeutic Targets in Multiple Myeloma

Multiple myeloma (MM) is a highly refractory hematologic cancer. Targeted immunotherapy has shown promise in MM but remains hindered by the challenge of identifying specific yet broadly representative tumor markers. We analyzed 53 bone marrow (BM) aspirates from 41 MM patients using an unbiased, high-throughput pipeline for therapeutic target discovery via single-cell transcriptomic profiling, yielding 38 MM marker genes encoding cell-surface proteins and 15 encoding intracellular proteins. Of these, 20 candidate genes were highlighted that are not yet under clinical study, 11 of which were previously uncharacterized as therapeutic targets. The findings were cross-validated using bulk RNA sequencing, flow cytometry, and proteomic mass spectrometry of MM cell lines and patient BM, demonstrating high overall concordance across data types. Independent discovery using bulk RNA sequencing reiterated top candidates, further affirming the ability of single-cell transcriptomics to accurately capture marker expression despite limitations in sample size or sequencing depth. Target dynamics and heterogeneity were further examined using both transcriptomic and immuno-imaging methods. In summary, this study presents a robust and broadly applicable strategy for identifying tumor markers to better inform the development of targeted cancer therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Development of physically transparent, predictive structure-performance relationships for rational design of multi-component catalytic materials. Final Report

Solid surfaces act as catalysts for many important chemical processes, including commodity chemicals production, energy conversion and pollution mitigation. The outcome of catalytic processes is mainly governed by interactions of adsorbates with active sites on the catalyst surface. These interactions can be dramatically affected by the changes in the local characteristics of the active site, which can be accomplished by an introduction of various promoters and poisons, or by alloying with other metal atoms. In fact, many commercial heterogeneous catalysts contain metal nanoparticles promoted by various additives. Our goal is to develop fundamental understanding about the impact of perturbations in the local geometry of a catalytic site on the local surface reactivity. These insights are crucial for a more systematic and rational catalyst design. The project aims to advance the field of catalysis by formulating reliable structure/performance relationships and utilizing these relationships to identify novel catalytic materials. Benefits for DOE and our society are in the development of fundamental theories that guide the process of discovery of energy-efficient and environmentally friendly heterogeneous catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics

Abstract. Microbial activity and chemical reactions in porous media depend on the local conditions at the pore scale and can involve complex feedback with fluid flow and mass transport. We present a modeling framework that quantitatively accounts for the interactions between the bio(geo)chemical and physical processes and that can integrate genome-scale microbial metabolic information into a dynamically changing, spatially explicit representation of environmental conditions. The model couples a lattice Boltzmann implementation of Navier–Stokes (flow) and advection–diffusion-reaction (mass conservation) equations. Reaction formulations can include both kinetic rate expressions and flux balance analysis, thereby integrating reactive transport modeling and systems biology. We also show that the use of surrogate models such as neural network representations of in silico cell models can speed up computations significantly, facilitating applications to complex environmental systems. Parallelization enables simulations that resolve heterogeneity at multiple scales, and a cellular automaton module provides additional capabilities to simulate biofilm dynamics. The code thus constitutes a platform suitable for a range of environmental, engineering and – potentially – medical applications, in particular ones that involve the simulation of microbial dynamics.

58 GEOSCIENCES↗
DOE OSTI2020

Improvement of SCALE-XSPROC Multigroup Cross Section Processing Based on the CENTRM Pointwise Slowing Down Calculation

The SCALE-XSProc multigroup (MG) cross section processing procedure based on the CENTRM pointwise slowing down calculation is the primary procedure to process problem-dependent self-shielded MG cross sections and scattering matrices for neutron transport calculations.This procedure supports various cell-based geometries including slab, 1-D cylindrical, 1-D spherical and 2-D rectangular configurations and doubly heterogeneous particulate fuels. Recently, this procedure has been significantly improved to be applied to any advanced reactor analysis covering thermal and fast reactor systems, and to be comparable to continuous energy (CE) Monte Carlo calculations. Some reactivity bias and reaction rate differences have been observed compared with CE Monte Carlo calculations, and several areas for improvement have been identified in the SCALE-XSProc MG cross section processing: (1) resonance self-shielding calculations within the unresolved resonance range, (2) 10 eV thermal cut-off energy for the free gas model, (3) on-the-fly adjustments to the thermal scattering matrix, (4) normalization of the pointwise neutron flux, and (5) fine MG energy structure. This procedure ensures very accurate MG cross section processing for high-fidelity deterministic reactor physics analysis for various advanced reactor systems.

Kim, Kang Seog↗
DOE OSTI2024

Rapid curing dynamics of PEG-thiol-ene resins allow facile 3D bioprinting and in-air cell-laden microgel fabrication

Thiol-norbornene photoclick hydrogels are highly efficient in tissue engineering applications due to their fast gelation, cytocompatibility, and tunability. In this work, we utilized the advantageous features of polyethylene glycol (PEG)-thiol-ene resins to enable fabrication of complex and heterogeneous tissue scaffolds using 3D bioprinting and in-air drop encapsulation techniques. We demonstrated that photoclickable PEG-thiol-ene resins could be tuned by varying the ratio of PEG-dithiol to PEG norbornene to generate a wide range of mechanical stiffness (0.5–12 kPa) and swelling ratios. Importantly, all formulations maintained a constant, rapid gelation time (<0.5 s). We used this resin in biological projection microstereolithography (BioPµSL) to print complex structures with geometric fidelity and demonstrated biocompatibility by printing cell-laden microgrids. Moreover, the rapid gelling kinetics of this resin permitted high-throughput fabrication of tunable, cell-laden microgels in air using a biological in-air drop encapsulation apparatus (BioIDEA). We demonstrated that these microgels could support cell viability and be assembled into a gradient structure. This PEG-thiol-ene resin, along with BioPµSL and BioIDEA technology, will allow rapid fabrication of complex and heterogeneous tissues that mimic native tissues with cellular and mechanical gradients. The engineered tissue scaffolds with a controlled microscale porosity could be utilized in applications including gradient tissue engineering, biosensing, and in vitro tissue models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mathematical model of a personalized neoantigen cancer vaccine and the human immune system

Cancer vaccines are an important component of the cancer immunotherapy toolkit enhancing immune response to malignant cells by activating CD4 + and CD8 + T cells. Multiple successful clinical applications of cancer vaccines have shown good safety and efficacy. Despite the notable progress, significant challenges remain in obtaining consistent immune responses across heterogeneous patient populations, as well as various cancers. We present a mechanistic mathematical model describing key interactions of a personalized neoantigen cancer vaccine with an individual patient’s immune system. Specifically, the model considers the vaccine concentration of tumor-specific antigen peptides and adjuvant, the patient’s major histocompatibility complexes I and II copy numbers, tumor size, T cells, and antigen presenting cells. We parametrized the model using patient-specific data from a clinical study in which individualized cancer vaccines were used to treat six melanoma patients. Model simulations predicted both immune responses, represented by T cell counts, to the vaccine as well as clinical outcome (determined as change of tumor size). This model, although complex, can be used to describe, simulate, and predict the behavior of the human immune system to a personalized cancer vaccine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Quantitative kinetic rules for plastic strain-induced α - ω phase transformation in Zr under high pressure

Plastic strain-induced phase transformations (PTs) and chemical reactions under high pressure are broadly spread in modern technologies, friction and wear, geophysics, and astrogeology. However, because of very heterogeneous fields of plastic strain $E$ p and stress σ tensors and volume fraction c of phases in a sample compressed in a diamond anvil cell (DAC) and impossibility of measurements of σ and $E$ p , there are no strict kinetic equations for them. Here, we develop a kinetic model, finite element method (FEM) approach, and combined FEM-experimental approaches to determine all fields in strongly plastically predeformed Zr compressed in DAC, and specific kinetic equation for α-ω PT consistent with experimental data for the entire sample. Since all fields in the sample are very heterogeneous, data are obtained for numerous complex 7D paths in the space of 3 components of the plastic strain tensor and 4 components of the stress tensor. Kinetic equation depends on accumulated plastic strain (instead of time) and pressure and is independent of plastic strain and deviatoric stress tensors, i.e., it can be applied for various above processes. Our results initiate kinetic studies of strain-induced PTs and provide efforts toward more comprehensive understanding of material behavior in extreme conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The rigorous upscaling of advection-dominated transport in heterogeneous porous media via the Method of Finite Averages

Systems involving advection-dominated transport through heterogeneous porous and fractured media are ubiquitous in subsurface engineering applications. However, upscaling such systems continues to challenge rigorous modeling efforts, particularly when advection is stronger than diffusion at fine spatial scales (i.e., when the Péclet number is greater than one at length scales that characterize a system’s unit-cells, representative elementary volumes, or averaging regions). Here, in this work, we propose and validate a strategy for extending the Method of Finite Averages (MoFA), a rigorous upscaling methodology for heterogeneous porous media, to upscale transport systems experiencing stronger advection than diffusion at fine scales (i.e., fine-scale Péclet numbers greater than one). We detail the strategy, the physical conditions under which it can be applied while retaining a priori modeling error guarantees, and implement the strategy to obtain a MoFA model for advective-diffusive transport that accommodates advective physics at fine spatial scales. We then perform two numerical experiments considering systems with system-scale Péclet numbers of 300 and 1000 — which correspond to fine-scale Péclet numbers of 30 and 100, respectively — to verify that the error guarantees are met under the strategy. After, we conduct a numerical study to demonstrate the strategy’s advantages over the original MoFA methodology. The results suggest that rigorously-upscaled transport models for heterogeneous porous media experiencing advective physics at finer spatial scales can be derived through MoFA and resolved orders of magnitude faster than their pore-scale counterparts. The results also suggest that the presented strategy is limited to modeling shallow concentration gradients when there are large differences between the time scales related to advection and a system’s temporally-varying boundary conditions. This limitation hinders the strategy’s practicality in modeling more advective systems, and as such, opportunity exists for developing additional strategies that accommodate rapidly-varying boundary conditions — and consequentially, steeper concentration gradients — while modeling advective systems with MoFA.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-resolution 3D strain and orientation mapping within a grain of a directed energy deposition laser additively manufactured superalloy

The industrialization of Laser Additive Manufacturing (LAM) is challenged by the undesirable microstructures and high residual stresses originating from the fast and complex solidification process. Non-destructive assessment of the mechanical performance controlling deformation patterning is therefore critical. Here, we use Dark Field X-ray Microscopy (DFXM) to map the 3D subsurface intragranular orientation and strain variations throughout a surface-breaking grain within a directed energy deposition nickel superalloy. DFXM results reveal a highly heterogenous 3D microstructure in terms of the local orientation and lattice strain. The grain comprises ≈ 5 μm-sized cells with alternating strain states, as high as 5 x10 -3 , and orientation differences <0.5°. The DFXM results are compared to Electron Backscatter Diffraction measurements of the same grain from its cut-off surface. Here, we discuss the microstructure developments during LAM, rationalising the development of the deformation patterning from the extreme thermal gradients during processing and the susceptibility for solute segregation.

36 MATERIALS SCIENCE↗