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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape

Arctic warming is altering vegetation and carbon dynamics with global implications, yet Earth System Model (ESM) predictions in the Arctic remain highly uncertain, in part due to historically limited data for model parameterization and validation. As such, ESMs typically represent Arctic ecosystems in an oversimplified manner. Recently, nine plant functional types (PFTs) designed to realistically represent tundra vegetation were integrated into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) and parameterized using plot-scale observations from a single site. Additional evaluation was needed to determine their transferability across the Arctic. Here, in this study, we evaluated whether refined representation of tundra vegetation improved model accuracy by conducting spatially explicit 100 × 100 m resolution ELM simulations on Alaska's Seward Peninsula. Simulations with the default two-PFT configuration and with the nine Arctic-specific PFTs were benchmarked against observations of net ecosystem exchange, gross primary production, and aboveground biomass from multiple data streams including an eddy covariance flux tower, flux chambers, and aircraft and unoccupied aerial system hyperspectral remote sensing. Evaluation revealed that Arctic-specific PFT simulations produced more realistic landscape-level carbon exchanges, and better captured observed heterogeneity in biomass and productivity, explaining 60%–70% of spatial variance (R 2 = 0.6–0.7) compared to just 12%–18% (R 2 = 0.12–0.18) with the default configuration. However, the refined model failed to reproduce observed aboveground biomass for highly productive alder-willow communities, requiring further evaluation of carbon allocation parameterizations for tall shrubs that are increasingly expanding across tundra landscapes. Our results demonstrate that enhanced representation of vegetation heterogeneity boosts predictive understanding of tundra carbon dynamics, facilitating regional to pan-Arctic model and remote-sensing scaling.

Murphy, Bailey A. [Oak Ridge National Laboratory (↗
DOE OSTI2025

Improving Ionic Conformality Across Polymer Electrolyte|Electrode Interfaces

Maintaining uniform ionic transport at electrode|electrolyte interfaces, i.e., ionic conformality, remains challenging in polymer electrolyte (PE)-based solid-state batteries. Morphological conformality does not necessarily imply ionic conformality. In PEs, which typically consist of a mechanically supporting component and distinct ionically conductive components, the rearrangement or depletion of mobile ion-conductive domains at interfaces can disrupt ionic transport pathways. Such localized ionic depletion contributes to interfacial instability and capacity degradation in high-voltage lithium-metal batteries. Herein, an electrolyte design approach aimed at minimizing interfacial heterogeneities is demonstrated through compositional adjustments, characterized by spatially resolved structural and chemical X-ray techniques and NMR diffusometry to elucidate ion transport dynamics. This approach improves ionic conformality at electrode interfaces, enhancing cycling stability in Li||LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NMC811) coin and pouch cells cycled at high voltages. These results contribute to understanding interfacial behaviors in multiphase PEs and inform strategies for improving stability across solid-state battery interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2021

Experimental evaluation of localized creep deformation in grade 91 steel weldments

Spatially resolved measurement of localized creep deformation in heterogeneous creep resistant steel weldments is crucial but challenging for lifetime assessments of critical steam components in power plants. Here, experimental approaches were established to quantitatively evaluate commonly observed localized creep deformation in multi-pass Grade 91 steel weldments. An in-situ digital image correlation (DIC) system was utilized with a creep testing frame to monitor and measure both full-field strain and localized strain accumulation across the weldments during long-term creep testing at elevated temperatures. The in situ DIC method measured not only the creep deformation behavior of the weld metal, heat affected zone (HAZ), and base metal, but also creep strain evolution for each sub-region within the HAZ itself, including coarse-grained HAZ, fine-grained HAZ (FGHAZ), and intercritical HAZ (ICHAZ). The DIC results revealed that local creep strain in the ICHAZ reached up to 90% strain before final rupture, whereas nominal creep strain measured by the standard extensometer of the tested cross-weld specimen was below 10%, indicative of Type IV cracking of the Grade 91 weld. Microstructural analyses revealed that the faster creep degradation/deformation in the HAZ was caused mainly by accelerated matrix grain recrystallization/growth and a reduced pinning effect from the segregated and coarsened precipitates in the FGHAZ and ICHAZ. The ultimate creep rupture occurred in the ICHAZ owing to its lowest creep resistance induced by the largest recrystallized grain size, the lowest fraction of coincidence site lattice, and the lowest local strain energies/dislocation densities.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Stochastic Inversion of Gaussian Random Media Using Transverse Coherence Functions for Reflected Waves: Theory and Method

The transverse coherence functions (TCFs) of phase and amplitude fluctuations of a seismic wave are powerful to estimate the spatial distribution, length scales, and strength of random heterogeneities. However, TCFs have been formulated for transmitted waves only, not for reflected waves. In this paper, we derive reflection TCFs for Gaussian random media. Furthermore, we propose to invert for Gaussian random media using the reflection TCFs based on the grid search. We validate the new reflection TCF formulas using 2D finite-difference numerical experiments. The numerical example also illustrates the feasibility and efficiency of the inversion. The stochastic inversion using reflected waves can be used in both exploration and global seismology.

58 GEOSCIENCES↗
DOE OSTI2022

Drivers of Future Physical Water Scarcity and Its Economic Impacts in Latin America and the Caribbean

Future water scarcity is a global concern with significant impacts on the energy, water, and land (EWL) sectors. Countries in Latin America and the Caribbean (LAC) are significant producers of agricultural goods consumed around the world, so disruptions resulting from land and water scarcity in LAC are a source of global supply chain risk. Understanding where water scarcity in LAC could occur and what could drive that scarcity to emerge is critical for strategic resource management and planning, both regionally and globally. Assessing future water scarcity impacts in LAC is challenging given the multisector dynamic interactions among the EWL sectors and the multiple uncertainties acting across different spatial scales. We use scenario discovery to explore the drivers of future water scarcity, considering assumptions related to climate, socioeconomics, and the EWL sectors. Understanding what factors drive uncertainty across outcomes can help stakeholders in targeting long-term solutions and future data collection efforts. To illuminate these dynamics, we use a database consisting of a large ensemble of scenarios representing diverse worlds simulated using the Global Change Analysis Model. We quantify future water scarcity and its economic impacts across the scenarios using three metrics: (1) physical water scarcity, (2) water price, and (3) crop profit. We find that physical water scarcity and water price are driven primarily by reservoir storage capacity assumptions, highlighting the importance of strategic water infrastructure development in maintaining future water availability and accessibility. Crop profit is driven by a combination of water supply and demand assumptions, which emphasizes the complex nature of EWL multisector dynamics. While most of LAC is poised to have abundant land and water resources available for development, water basins in Mexico and along the Pacific coast of South America experience high levels of severity and uncertainty across the future scenarios for at least one of the metrics. We find that the drivers of extreme scarcity vary spatially and across the metrics, further highlighting the heterogeneity of the region and the importance of considering multiple metrics to assess water scarcity vulnerability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States

Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large‐scale streamflow projections to inform water resource management and long‐term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications.

CLM5↗
DOE OSTI2023

Correlative chemical and elemental nano-imaging of morphology and disorder at the nacre-prismatic region interface in Pinctada margaritifera

Abstract Understanding biomineralization relies on imaging chemically heterogeneous organic–inorganic interfaces across a hierarchy of spatial scales. Further, organic minority phases are often responsible for emergent inorganic structures from the atomic arrangement of different polymorphs, to nano- and micrometer crystal dimensions, up to meter size mollusk shells. The desired simultaneous chemical and elemental imaging to identify sparse organic moieties across a large field-of-view with nanometer spatial resolution has not yet been achieved. Here, we combine nanoscale secondary ion mass spectroscopy (NanoSIMS) with spectroscopic IR s -SNOM imaging for simultaneous chemical, molecular, and elemental nanoimaging. At the example of Pinctada margaritifera mollusk shells we identify and resolve ~ 50 nm interlamellar protein sheets periodically arranged in regular ~ 600 nm intervals. The striations typically appear ~ 15 µm from the nacre-prism boundary at the interface between disordered neonacre to mature nacre. Using the polymorph distinctive IR-vibrational carbonate resonance, the nacre and prismatic regions are consistently identified as aragonite ( $${\overline{\nu }}_{a}=860$$ ν ¯ a = 860 cm −1 ) and calcite ( $${\overline{\nu }}_{c}=880$$ ν ¯ c = 880 cm −1 ), respectively. We observe previously unreported morphological features including aragonite subdomains encapsulated in extensions of the prism-covering organic membrane and regions of irregular nacre tablet formation coincident with dispersed organics. We also identify a ~ 200 nm region in the incipient nacre region with less well-defined crystal structure and integrated organics. These results show with the identification of the interlamellar protein layer how correlative nano-IR chemical and NanoSIMS elemental imaging can help distinguish different models proposed for shell growth in particular, and how organic function may relate to inorganic structure in other biomineralized systems in general.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Time-domain thermoreflectance

Time-domain thermoreflectance (TDTR) has been instrumental in measuring the heat transfer properties of bulk and nanostructured materials over the past two decades. In this Primer, we describe the optical and thermal aspects of TDTR, with an in-depth discussion on the theory, apparatus design and implementation. We present examples that illustrate the ability of TDTR to measure thermal conductivity tensors, thermal conductance across material interfaces, and volumetric heat capacity of thin films, 2D materials and bulk materials. The ability of TDTR to spatially resolve thermal properties is useful for studying heterogeneous material systems, such as materials processed in or subjected to extreme environments. We consider current limitations of pump–probe metrologies and discuss recent advancements of TDTR, such as time-resolved magneto-optic Kerr effect (TR-MOKE), beam-offset TDTR/TR-MOKE, steady-state thermoreflectance, frequency-domain thermoreflectance and laser-flash TDTR. Lastly, we present an outlook on anticipated technological developments to further expand the ability of TDTR to measure nanoscale thermal properties.

42 ENGINEERING↗
DOE OSTI2020

Hotspot formation due to shock-induced pore collapse in 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane (HMX): Role of pore shape and shock strength in collapse mechanism and temperature

The shock to detonation transition in heterogeneous high energy density solids starts with the spatial localization of mechanical energy into so-called hotspots that form due to the interaction between the leading wave and microstructural features and defects. We used large-scale molecular dynamics to characterize the hotspots resulting from the shock-induced collapse of cylindrical voids and elongated cracks focusing on the effect of shock strength, defect shape, and size. The temperature fields resulting from the collapse of cracks elongated along the shock direction show significantly higher sensitivity to both shock strength and size than cylindrical voids. Cracks 80 nm in length result in temperatures almost three times higher than voids 80 nm in diameter, reaching values corresponding to the ideal case of isentropic recompression of a gas. The molecular dynamics trajectories reveal the atomic origin of this contrasting behavior. While circular voids undergo a transition from viscoelastic pore collapse to a hydrodynamic regime with increasing shock strength, shock focusing in elongated cracks results in jetting and vaporization which, upon recompression, leads to increased heating.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Intrinsically stretchable electrode array enabled in vivo electrophysiological mapping of atrial fibrillation at cellular resolution

Electrophysiological mapping of chronic atrial fibrillation (AF) at high throughput and high resolution is critical for understanding its underlying mechanism and guiding definitive treatment such as cardiac ablation, but current electrophysiological tools are limited by either low spatial resolution or electromechanical uncoupling of the beating heart. To overcome this limitation, here we introduce a scalable method for fabricating a tissue-like, high-density, fully elastic electrode (elastrode) array capable of achieving real-time, stable, cellular level-resolution electrophysiological mapping in vivo. Testing with acute rabbit and porcine models, the device is proven to have robust and intimate tissue coupling while maintaining its chemical, mechanical, and electrical properties during the cardiac cycle. The elastrode array records epicardial atrial signals with comparable efficacy to currently available endocardial-mapping techniques but with 2 times higher atrial-to-ventricular signal ratio and >100 times higher spatial resolution and can reliably identify electrical local heterogeneity within an area of simultaneously identified rotor-like electrical patterns in a porcine model of chronic AF.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Evaluation of the spatial self-shielding impact for TRISO-based nuclear fuel depletion

Reactor physics analyses of nuclear cores with nuclear fuel concepts containing tristructural isotropic (TRISO) particles, such as pebbles or compact fuel elements, rely on various degrees of simplification to keep these highly heterogeneous problems computationally tractable. One such limitation regards the level of spatial discretization employed during burnup calculations, where traditionally only a limited number of spatial zones are modeled at the full core level and assume that the spectrum is constant within the fuel elements and TRISO particles in this depletion zone. This type of assumption neglects the impact of spatial self-shielding effect within the kernels (microscale level) as well as within the compact or pebbles (mesoscale level). Furthermore, the Monte Carlo code Serpent 2 contains many relevant features for efficiently modeling this type of geometry, including a collision-based domain decomposition intended for very large burnup calculations, which we leveraged for this work to quantify the impact of capturing neutron flux variations occurring at the micro- and mesoscale level on a series of high-temperature gas-cooled reactor fuel element depletion problems. While spatial self-shielding is observed at both scales, with differences from a volume-averaged burnup of ±7% within the kernels and ±2% between TRISO particles within the fuel element, the conjugated effect on nuclide inventories and multiplication factor are negligible, hence confirming that assuming a single average spectrum value may be sufficient for most applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Landscape composition and configuration have scale-dependent effects on agricultural pest suppression

Increasing landscape heterogeneity (composition and configuration) can enhance natural enemy populations and support pest suppression in agricultural landscapes. Using a network-based data mining approach, we examined independent gradients of landscape composition and configuration at six spatial scales that were associated with pest suppression services measured at 32 sites in Michigan and Wisconsin, USA. We compared the relative effects of landscape composition and configuration across scales with those of local crop type (corn or grassland). We found that multiple gradients of configurational heterogeneity were independent of composition and strongly associated with pest suppression, with different configuration metrics being predictive of pest suppression depending on the spatial scales and regions considered. Landscapes that were more configurationally heterogeneous at smaller spatial scales consistently supported higher pest suppression. In Michigan, pest suppression increased in landscapes with high edge contrast between annual crops and surrounding habitats and high edge density of grassland within 250-500 m radii. In Wisconsin, pest suppression increased with large core area of grassland and high field density within a 250 m radius. The main compositional effect we found was a positive relationship between grassland cover and pest suppression occurring at larger spatial scales (1000-1500 m) and occurring in Wisconsin but not in Michigan. Our findings demonstrate that effects of landscape composition and configuration on pest suppression differ across spatial scales and vary regionally. The network-based data mining techniques used here could be useful for disentangling intercorrelated landscape metrics in a variety of other contexts in landscape ecology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Exploring wave propagation in heterogeneous metastructures using the relaxed micromorphic model

Metamaterials are artificial structures that can manipulate and control sound waves in ways not possible with conventional materials. While much effort has been undertaken to widen the bandgaps produced by these materials through design of heterogeneities within unit cells, comparatively little work has considered the effect of engineering heterogeneities at the structural scale by combining different types of unit cells. In this paper, we use the relaxed micromorphic model to study wave propagation in heterogeneous metastructures composed of different unit cells. We first establish the efficacy of the relaxed micromorphic model for capturing the salient characteristics of dispersive wave propagation through comparisons with direct numerical simulations for two classes of metamaterial unit cells: namely phononic crystals and locally resonant metamaterials. We then use this model to demonstrate how spatially arranging multiple unit cells into metastructures can lead to tailored and unique properties such as spatially-dependent broadband wave attenuation, rainbow trapping, and pulse shaping. In the case of the broadband wave attenuation application, we show that by building layered metastructures from different metamaterial unit cells, we can slow down or stop wave packets in an enlarged frequency range, while letting other frequencies through. In the case of the rainbow-trapping application, we show that spatial arrangements of different unit cells can be designed to progressively slow down and eventually stop waves with different frequencies at different spatial locations. Finally, in the case of the pulse-shaping application, our results show that heterogeneous metastructures can be designed to tailor the spatial profile of a propagating wave packet. Collectively, these results show the versatility of the relaxed micromorphic model for effectively and accurately simulating wave propagation in heterogeneous metastructures, and how this model can be used to design heterogeneous metastructures with tailored wave propagation functionalities.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Gradient nanostructuring via compositional means

Nanocrystalline metals are inherently unstable against thermal and mechanical stimuli, commonly resulting in significant grain growth. Also, while these metals exhibit substantial Hall-Petch strengthening, they tend to suffer from low ductility and fracture toughness. With regard to the grain growth problem, alloying elements have been employed to stabilize the microstructure through kinetic and/or thermodynamic mechanisms. And to address the ductility challenge, spatially-graded grain size distributions have been developed to facilitate heterogeneous deformation modes: high-strength at the surface and plastic deformation in the bulk. In the present work, we combine these two strategies and present a new methodology for the fabrication of gradient nanostructured metals via compositional means. Here, we have demonstrated that annealing a compositionally stepwise Pt-Au film with a homogenous microstructure results in a film with a spatial microstructural gradient, exhibiting grains which can be twice as wide in the bulk compared to the outer surfaces. Additionally, phase-field modeling was employed for the comparison with experimental results and for further investigation of the competing mechanisms of Au diffusion and thermally induced grain growth. This fabrication method offers an alternative approach for developing the next generation of microstructurally stable gradient nanostructured films.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Isotopic Heterogeneity Imaged in a Uranium Fuel Pellet with Extreme Ultraviolet Laser Ablation and Ionization Time-of-Flight Mass Spectrometry

We use extreme ultraviolet laser ablation and ionization time-of-flight mass spectrometry (EUV TOF) to map uranium isotopic heterogeneity at the nanoscale (=100 nm). Using low-enriched uranium fuel pellets that were made by blending two isotopically distinct feedstocks, we show that EUV TOF can map the 235U/238U content in 100 nm-sized pixels. The two-dimensional (2D) isotope maps reveal U ratio variations in sub-microscale to =1 µm areas of the pellet that had not been fully exposed by microscale or bulk mass spectrometry analyses. Compared to the ratio distribution measured in a homogeneous U reference material, the ratios in the enriched pellet follow a ~3× wider distribution. These results indicate U heterogeneity in the fuel pellet from incomplete blending of the different source materials. EUV TOF results agree well with those obtained on the same enriched pellets by nanoscale secondary ionization mass spectrometry (NanoSIMS), which reveals a comparable U isotope ratio distribution at the same spatial scale. EUV TOF’s ability to assess and map isotopic heterogeneity at the nanoscale makes it a promising tool in fields such as nuclear forensics, geochemistry, and biology that could benefit from uncovering sub-microscale sources of chemical modifications.

Rush, Lydia A.↗
DOE OSTI2025

Operando Infrared Nanospectroscopy of the Silicon/Electrolyte Interface during Initial Stages of Solid-Electrolyte-Interphase Layer Formation

The solid electrolyte interphase (SEI) is a critical component in Li-ion batteries; however, its nanoscale structure and composition and unstable nature make it difficult to characterize and ascertain primary functional mechanisms. We use operando nanoscale Fourier transform infrared spectroscopy (nano-FTIR) with a broadband synchrotron IR source to study the SEI formation on a thin-film Si electrode at nanometer-scale spatial resolution as a function of time and voltage. By probing the Si/carbonate electrolyte interface through a 25 nm-thick amorphous Si window/electrode, we detect molecular vibrational modes within a 10s of nanometers region adjacent to the Si surface and observe that PF6 – anions react to form LiF at 0.5 V. Spatially resolved nano-FTIR spectra showcase subtle nanoscale heterogeneities in the initial solid/liquid interface and the resulting deposited LiF. With its nanoscale resolution and high chemical specificity, operando nano-FTIR provides unique insights into the dynamics and heterogeneous formation of SEIs and opens opportunities for connecting nanoscale interfacial properties to bulk performance metrics.

Dopilka, Andrew↗
DOE OSTI2023

Robust reconstruction of single-cell RNA-seq data with iterative gene weight updates

Single-cell RNA-sequencing technologies have greatly enhanced our understanding of heterogeneous cell populations and underlying regulatory processes. However, structural (spatial or temporal) relations between cells are lost during cell dissociation. These relations are crucial for identifying associated biological processes. Many existing tissue-reconstruction algorithms use prior information about subsets of genes that are informative with respect to the structure or process to be reconstructed. When such information is not available, and in the general case when the input genes code for multiple processes, including being susceptible to noise, biological reconstruction is often computationally challenging. We propose an algorithm that iteratively identifies manifold-informative genes using existing reconstruction algorithms for single-cell RNA-seq data as subroutine. We show that our algorithm improves the quality of tissue reconstruction for diverse synthetic and real scRNA-seq data, including data from the mammalian intestinal epithelium and liver lobules.

59 BASIC BIOLOGICAL SCIENCES↗