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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

WHONDRS Surface Water and Sediment Geochemistry and Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon (v2)

This dataset supports a broader study developing conceptual models for river corridor critical zone processes across spatial scales and was generated in collaboration with the HJ Andrews River Corridor Critical Zone Workshop in 2025. The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen) from 48 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Some of the sites have been impacted by the Holiday Farm Fire and the Lookout Fire in 2020 and 2023, respectively. Related data were collected as part of the workshop and will be published separately in collaboration with other workshop attendees and available at http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. Related genomic data can be found on the National Center for Biotechnology Information (NCBI) under BioProject PRJNA1503030 (see critical details section below for more information). Additional related data collected in 2016 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3377027 and http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1-2019 (Ward et al., 2019). This data package was originally published in March 2026. It was updated in August 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos, (2) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data, (3) a data checks report, (4) a folder of sample data, (5) file-level metadata, (6) data dictionary, (7) field metadata, (8) readme, (9) international generic sample number (IGSN) mapping file; and (10) field protocol. The sample data subfolder contains surface water and sediment (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages, (2) total dissolved nitrogen data and averages, (3) methods codes, (4) FTICR-MS methods; and (5) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the CoreMS processed data and seven subfolders, thee containing .xml files for each sample type (sediment, surface water and blank samples), three containing the sediment CoreMS output files for each sample type (sediment, surface water and blank samples), and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, .json, .jpg, or .jpeg.

Biogeochemistry↗
DOE OSTI2021

Investigation of Delayed Neutron Sensitivities for Several ICSBEP Benchmarks using MCNP

The effective delayed neutron (β$_{eff}$) is a very important parameter for reactor and criticality applications. This parameter is equal to the difference in reactivity between delayed critical ($k_{eff}$ = 1, which requires both prompt and delayed neutrons to achieve criticality) and prompt critical ($k_p$ = 1, which requires only prompt neutrons to achieve criticality). This is often referred to as the delayed critical "window" (the region of criticality between delayed and prompt critical). β$_{eff}$ is a reactor kinetics parameters and depends on the nuclides in the system that undergo fission as well as the spectral characteristics of the system. Measurements of β$_{eff}$ have been performed for many criticality experiments. The EUCLID (Experiments Underpinned by Computational Learning for Improvements in nuclear Data) project at Los Alamos National Laboratory (LANL) aims to constrain nuclear data by using a suite of measurement types beyond $k_{eff}$. Our team has recently investigated the use of pulsed spheres for nuclear data validation. Several other measurement methods are also of interest, including β$_{eff}$ (investigated here) and reactivity coefficients (investigated in a separate work at this same meeting). One focus of our work is to determine if other methods are complimentary to the critical experiments already utilized for nuclear data validation. This is important because if a method has similar sensitivities then it will not be particularly useful for nuclear data validation as it will provide the same information as the critical experiments already being used. Here "similar" could refer to several characteristics, one being the shape of a sensitivity profile over energy. In the future, these methods will be utilized (with both existing and new experiments) in machine learning algorithms for nuclear validation, similar to what is currently done for criticality experiments. In order to use a measurement type for nuclear validation, it is necessary to obtain cross-section sensitivities for that parameter. This work looks at one approach to estimate β$_{eff}$ sensitivities by utilizing $k_{eff}$ sensitivities within Monte Carlo N-Particle ® Code Version 6.2. This is applied to several criticality benchmarks. Results are compared and the benefits and limitations of this approach are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Parallel IO Libraries for Managing HEP Experimental Data

The computing and storage requirements of the energy and intensity frontiers will grow significantly during the Run 4 & 5 and the HL-LHC era. Similarly, in the intensity frontier, with larger trig ger readouts during supernovae explosions, the Deep Underground Neutrino Experiment (DUNE) will have unique computing challenges that could be addressed by the use of parallel and accelerated dataprocessing capabilities. Most of the requirements of the energy and intensity frontier experiments rely on increasing the role of high performance computing (HPC) in the HEP community. In this presentation, we will describe our ongoing efforts that are focused on using HPC resources for the next generation HEP experiments. The HEPCCE (High Energy Physics-Center for Computational Excellence) IOS (Input/Output and Storage) group has been developing approaches to map HEP data to the HDF5 , an IO library optimized for the HPC platforms to store the intermediate HEP data. The complex HEP data products are serialized using ROOT to allow for experiment independent general mapping approaches of the HEP data to the HDF5 format. The mapping approaches can be optimized for high performance parallel IO. Similarly, simpler data can be directly mapped into the HDF5, which can also be suitable for offloading into the GPUs directly. We will present our works on both complex and simple data model models.

Bashyal, Amit↗
DOE OSTI2022

Short GRB Host Galaxies. I. Photometric and Spectroscopic Catalogs, Host Associations, and Galactocentric Offsets

We present a comprehensive optical and near-infrared census of the fields of 90 short gamma-ray bursts (GRBs) discovered in 2005–2021, constituting all short GRBs for which host galaxy associations are feasible (≈60% of the total Swift short GRB population). We contribute 274 new multi-band imaging observations across 58 distinct GRBs and 26 spectra of their host galaxies. Supplemented by literature and archival survey data, the catalog contains 542 photometric and 42 spectroscopic data sets. The photometric catalog reaches 3σ depths of ≳24–27 mag and ≳23–26 mag for the optical and near-infrared bands, respectively. We identify host galaxies for 84 bursts, in which the most robust associations make up 56% (50/90) of events, while only a small fraction, 6.7%, have inconclusive host associations. Based on new spectroscopy, we determine 18 host spectroscopic redshifts with a range of z ≈ 0.15–1.5 and find that ≈23%–41% of Swift short GRBs originate from z > 1. We also present the galactocentric offset catalog for 84 short GRBs. Taking into account the large range of individual measurement uncertainties, we find a median of projected offset of ≈7.7 kpc, for which the bursts with the most robust associations have a smaller median of ≈4.8 kpc. Our catalog captures more high-redshift and low-luminosity hosts, and more highly offset bursts than previously found, thereby diversifying the population of known short GRB hosts and properties. In terms of locations and host luminosities, the populations of short GRBs with and without detectable extended emission are statistically indistinguishable. This suggests that they arise from the same progenitors, or from multiple progenitors, which form and evolve in similar environments. All of the data products are available on the Broadband Repository for Investigating Gamma-Ray Burst Host Traits website.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Permeability of Single–Layer–Free–Standing Meshes at Varying Capillary Pressure via a Novel Method

The permeability of mesh wicks is important for various applications, including two–phase heat transfer. However, the understanding of the permeability of single–layer, free–standing mesh wicks, with liquid–gas interfaces on both sides, is limited. A novel and simpler method is presented to determine the permeability of a free–standing wick and apply it to a representative mesh. This method involves modifying the capillary pressure via elevation and simultaneously measuring the permeability to determine the permeability–capillary pressure relationship. When applied to a copper mesh with plain weave having undergone surface cleaning, the permeability is found to decrease as capillary pressure for deionized water increases. A dimensional analysis is presented to generalize this data for other mesh sizes with similar weaves and fluids. The behavior of mesh in application is modeled, based on the integration of Darcy's law with an analytic function fit to measured data, and parametric studies are conducted to investigate the superficial velocity of liquids through the mesh under varying driving pressures, transport lengths, and liquid viscosity, based on the obtained capillary pressure–permeability relationship. This study provides valuable insights into the transport properties of mesh wicks, with potential applications in fields such as electronics cooling, electrochemical devices, and fluid purification technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deep Learning for Automated Identification of Eels in Sonar Data

Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.

13 HYDRO ENERGY↗
DOE OSTI2022

Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model

The residential buildings sector is one of the largest electricity consumers worldwide and contributes disproportionally to peak electricity demand in many regions. Strongly driven by occupant activities, household energy consumption is stochastic and heterogeneous in nature. However, most residential energy models applied by industry use homogeneous, deterministic activity schedules, which work well for predictions of annual energy consumption, but can result in unrealistic hourly or sub-hourly electric load profiles, with exaggerated or muted peaks. The increasing proportion of variable renewable energy generators means that representing the heterogeneity and stochasticity of occupant behavior is now crucial for reliable planning at both bulk-power and distribution-system scales. This work presents a novel and open-source occupancy simulation approach that can simulate a diverse set of individual occupant and household event schedules for all major electricity, fuel, and hot water end uses. To accomplish this, we evaluated three alternative occupant activity simulation approaches before selecting a hybrid combining time-inhomogeneous Markov chains and probability-sampling of event durations and magnitudes. Further, we integrated the stochastic occupancy simulation with an open-source bottom-up physics-simulation building stock model and published a set of 550,000 diverse household end-use activity schedules representing a national housing stock. The simulator was verified against time-use survey data, and simulation results were validated against measured end-use electricity data for accuracy and reliability. While we use data for the United States, our application demonstrates how similar approaches could be applied using the time-use survey data collected in many countries around the world.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Residual Stress Measurements in Extreme Environments for Hazardous, Layered Specimens

In nuclear fuel plates of low-enriched U-10Mo (LEU) clad with aluminum by hot isostatic pressing (HIP), post-irradiation stresses arising during reactor shutdown are a major concern for safe reactor operations. Measurement of those residual stresses has not previously been possible because the high radioactivity of the plates requires handling only by remote manipulation in a hot cell. The incremental slitting method for measuring through-thickness stress profiles was modified, and a system for automated, remote operation was built and tested. Experimental modifications consisted of replacing electric-discharge machining (EDM) with a small end mill and strain-gauge measurements with cantilever displacement measurements. The inverse method used to calculate stresses was the pulse-regularization method modified to allow discontinuities across material interfaces. The new system was validated by comparing with conventional slitting on a depleted U-10Mo (DU) fuel plate. The new system was applied to two measurements each on six as-fabricated (pre-irradiation) LEU miniature fuel plates. Variations between the measurements at two locations in the same plate were strongly correlated with measured geometrical heterogeneity in the plate—a tilt in the fuel foil. Compressive stresses in the U-10Mo were shown to increase from 20 to 250 MPa as the ratio of aluminum thickness to U-10Mo thickness increased causing increased constraint during cooling. Faster cooling rates during processing also increased stress magnitudes. The measurements trends agreed with data in the literature from similar plates made with DU, which further validates the method. Because other methods are impractical in a hot cell, the modified slitting method is now poised for the first measurements of post-irradiation stresses.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Emulation of neutron damage with proton irradiation and its effects on microstructure and microchemistry of Zircaloy-4

This work assesses the potential of proton irradiation to simulate the neutron damage to the matrix and laves phase Zr(Fe,Cr) 2 precipitates in Zircaloy-4. Isothermal proton irradiation has been performed on Zircaloy-4 samples at irradiation temperatures ranging from 250 to 350 °C. Two-step proton irradiation was also performed to enhance the amorphization of and iron loss from the laves phase Zr(Fe,Cr) 2 precipitates. The irradiated microstructures, including dislocation loops and rafts near SPPs, were observed in proton irradiated Zircaloy-4, which are consistent with neutron irradiated material at a similar damage level. The amount of irradiation-induced hardening after proton irradiation was similar to post neutron irradiated data. The significant amorphization of the SPPs and concurrent Fe redistribution observed on neutron irradiated materials can be effectively emulated using a two-step proton irradiation on Zircaloy-4. Hence, the neutron irradiation effect on Zircaloy-4 can be mostly captured using the two-step proton irradiation described in this study.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Big Data Seismology

The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Currently, Big Data Seismology is an emergent revolution involving the use of large, data-dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data-dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long-duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up-to-speed with how the Big Data revolution is advancing the field of seismology.

58 GEOSCIENCES↗
DOE OSTI2025

Effect of Ice Number Concentration on the Evolution of Boundary Layer Clouds During Arctic Marine Cold‐Air Outbreaks

Abstract Marine cold‐air outbreaks (MCAOs) are crucial for Arctic Ocean heat loss, featuring convective cloud rolls that transition into convection cells downstream. Understanding factors controlling this transformation is the key for improving MCAO cloud representation in climate models. This study employs large‐eddy simulations to investigate how cloud ice number concentrations () affect cloud evolution using a case from the Cold‐Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) campaign. The simulations, performed in a Lagrangian framework following an air mass trajectory, are driven by ERA5 reanalysis data. Initially, all simulations produce similar cloud patterns, but higher leads to earlier breakup of cloud rolls. Between 4 and 10 hr, surface precipitation rates are similar across simulations, but precipitation initiates earlier, and the cloud‐base precipitation rates are higher when is higher. The stronger precipitation evaporation leads to increased stability of the boundary layer and reduced intensity of vertical mixing between the surface and cloud layer. An increased sink of cloud layer moisture via precipitation and decreased source through diminished vertical transport result in earlier cloud breakup in higher conditions. Simulations with different sea surface temperatures (SST) indicate that this cloud breakup mechanism remains valid for MCAOs of different strengths, although the cloud organization is more sensitive to SST changes in low environments. This work highlights the importance of accurate representations of ice processes in simulating MCAO clouds and suggests the need for observational constraints of ice nucleating particles and over the mixed‐phase cloud regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3' RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of timeseries transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNcseq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Source localization for neutron imaging systems using convolutional neural networks

The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium–tritium shots.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

CasPEDIA Database: a functional classification system for class 2 CRISPR-Cas enzymes

Abstract CRISPR-Cas enzymes enable RNA-guided bacterial immunity and are widely used for biotechnological applications including genome editing. In particular, the Class 2 CRISPR-associated enzymes (Cas9, Cas12 and Cas13 families), have been deployed for numerous research, clinical and agricultural applications. However, the immense genetic and biochemical diversity of these proteins in the public domain poses a barrier for researchers seeking to leverage their activities. We present CasPEDIA (http://caspedia.org), the Cas Protein Effector Database of Information and Assessment, a curated encyclopedia that integrates enzymatic classification for hundreds of different Cas enzymes across 27 phylogenetic groups spanning the Cas9, Cas12 and Cas13 families, as well as evolutionarily related IscB and TnpB proteins. All enzymes in CasPEDIA were annotated with a standard workflow based on their primary nuclease activity, target requirements and guide-RNA design constraints. Our functional classification scheme, CasID, is described alongside current phylogenetic classification, allowing users to search related orthologs by enzymatic function and sequence similarity. CasPEDIA is a comprehensive data portal that summarizes and contextualizes enzymatic properties of widely used Cas enzymes, equipping users with valuable resources to foster biotechnological development. CasPEDIA complements phylogenetic Cas nomenclature and enables researchers to leverage the multi-faceted nucleic-acid targeting rules of diverse Class 2 Cas enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Correlation between mean transverse momentum and anisotropic flow in heavy-ion collisions

The correlation between the mean transverse momentum of outgoing particles, $\langle{p_t}\rangle$, and the magnitude of anisotropic flow, v n , has recently been measured in Pb+Pb collisions at the CERN Large Hadron Collider, as a function of the collision centrality. Here, we confirm the previous observation that event-by-event hydrodynamics predicts a correlation between v n and $\langle{p_t}\rangle$ that is similar to that measured in data. We show that the magnitude of this correlation can be directly predicted from the initial condition of the hydrodynamic calculation, for n = 2, 3, if one replaces v n by the corresponding initial-state anisotropy, ε n , and $\langle{p_t}\rangle$ by the total energy per unit rapidity of the fluid at the beginning of the hydrodynamic expansion.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Automatic Loss Factor Modeling and Attribution on Unlabeled PV Energy Data

We present a novel approach for modeling the loss factors of photovoltaic power generation systems (PV systems). This method is a white-box machine learning model built on convex optimization that is fast, interpretable, and auditable. It takes as an input the measured daily energy produced by the system, over a multi-year period, and returns a multiplicative decomposition model of the daily energy signal and full attribution of the total energy loss to each feature. The methods section of this paper has two major components: (1) the description of the signal decomposition (SD) model, expressed in the SD framework, and (2) the attribution of total energy losses via Shapley values. We validate the method on synthetic and open-source data sets and compare to similar methods from the literature.

artificial intelligence↗
DOE OSTI2025

Root‐Pore Interactions, the Underestimated Driver for Rhizosphere Structure and Rhizosheath Development

Physical characteristics of rhizosphere and rhizosheath, that is, root-adhering soil, are crucial for plant performance. Yet, the drivers of the rhizosphere's structural properties and their relationships with rhizosheath development remain unclear. We used X-ray computed micro-tomography (i) to explore two drivers of rhizosphere porosity: root-induced changes vs. preferential root growth into soil with certain pore characteristics and (ii) to estimate their contributions to rhizosphere macroporosity gradients and rhizosheath formation. Rhizosheath development was assessed in relation to rhizosphere macroporosity and rhizodeposition after ¹⁴C labeling. Our results confirmed that both root-induced changes and growth preferences shape rhizosphere structure, with their relative significance depending on the inherent macropore availability. In intact soils, growth preferences were the dominant factor, while in sieved soils the root-induced changes became equally important. Rhizosheath formation was associated with roots compacting their surrounding and releasing carbon. However, no correlation was found between rhizosheath formation and the actual rhizosphere, that is, the volume of soil adjacent to the roots. The study offers new process-level understanding of rhizosphere porosity gradients, while emphasizing caution in interpreting root growth data from sieved soil studies. Similarly, traditional destructively sampled rhizosheath may not fully capture the true characteristics of the actual rhizosphere, underscoring importance of intact-soil analyses.

macroporosity gradients↗