Search NASA⌕ Search

SEARCH · Search NASA

Results for “Landscape Characterization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9

Observing and modeling the effects of production infrastructure in electromagnetic surveys

Electromagnetic (EM) methods are among the original techniques for subsurface characterization in exploration geophysics because of their particular sensitivity to the earth electrical conductivity, a physical property of rocks distinct yet complementary to density, magnetization, and strength. However, this unique ability also makes them sensitive to metallic artifacts — infrastructure such as pipes, cables, and other forms of cultural clutter — the EM footprint of which often far exceeds their diminutive stature when compared to that of bulk rock itself. In the hunt for buried treasure or unexploded ordnance, this is an advantage; in the long-term monitoring of mature oil fields after decades of production, it is quite troublesome indeed. In this study we consider the latter through the lens of an evolving energy industry landscape in which the traditional methods of EM characterization for the exploration geophysicist are applied toward emergent problems in well-casing integrity, carbon capture and storage, and overall situational awareness in the oil field. We introduce case studies from these exemplars, showing how signals from metallic artifacts can dominate those from the target itself and impose significant burdens on the requisite simulation complexity. Furthermore, we show how recent advances in numerical methods mitigate the computational explosivity of infrastructure modeling, providing feasible and real-time analysis tools for the desktop geophysicist. Lastly, we demonstrate through comparison of field data and simulation results that incorporation of infrastructure into the analysis of such geophysical data is, in a growing number of cases, a requisite but now manageable step.

58 GEOSCIENCES↗

Energy landscapes and functions of supramolecular systems

By means of two supramolecular systems—peptide amphiphiles engaged in hydrogen-bonded β-sheets, and chromophore amphiphiles driven to assemble by π-orbital overlaps—we show that the minima in the energy landscapes of supramolecular systems are defined by electrostatic repulsion and the ability of the dominant attractive forces to trap molecules in thermodynamically unfavourable configurations. Furthermore, these competing interactions can be selectively switched on and off, with the order of doing so determining the position of the final product in the energy landscape. Within the same energy landscape, the peptide-amphiphile system forms a thermodynamically favoured product characterized by long bundled fibres that promote biological cell adhesion and survival, and a metastable product characterized by short monodisperse fibres that interfere with adhesion and can lead to cell death. Our findings suggest that, in supramolecular systems, functions and energy landscapes are linked, superseding the more traditional connection between molecular design and function.

36 MATERIALS SCIENCE↗

Determining flow directions in river channel networks using planform morphology and topology

Abstract. The abundance of global, remotely sensed surface water observations has accelerated efforts toward characterizing and modeling how water moves across the Earth's surface through complex channel networks. In particular, deltas and braided river channel networks may contain thousands of links that route water, sediment, and nutrients across landscapes. In order to model flows through channel networks and characterize network structure, the direction of flow for each link within the network must be known. In this work, we propose a rapid, automatic, and objective method to identify flow directions for all links of a channel network using only remotely sensed imagery and knowledge of the network's inlet and outlet locations. We designed a suite of direction-predicting algorithms (DPAs), each of which exploits a particular morphologic characteristic of the channel network to provide a prediction of a link's flow direction. DPAs were chained together to create “recipes”, or algorithms that set all the flow directions of a channel network. Separate recipes were built for deltas and braided rivers and applied to seven delta and two braided river channel networks. Across all nine channel networks, the recipe-predicted flow directions agreed with expert judgement for 97 % of all tested links, and most disagreements were attributed to unusual channel network topologies that can easily be accounted for by pre-seeding critical links with known flow directions. Our results highlight the (non)universality of process–form relationships across deltas and braided rivers.

54 ENVIRONMENTAL SCIENCES↗

Mapping the Complete Reaction Energy Landscape of a Metal–Organic Framework Phase Transformation

Crystalline materials undergo valuable phase transformations, and the energetic processes that underlie these transformations can be fully characterized through a combination of thermodynamic and kinetic studies. Here, we report the first complete reaction energy landscape of metal–organic framework (MOF) interpenetration, specifically in the phase transformation of NU-1200 to its doubly interpenetrated counterpart, STA-26. We characterized the thermodynamics of this phase transformation by pairing experiments with density functional theory (DFT) calculations. This analysis revealed that factors such as the increase in crystal density likely drive Zr- and Hf-NU-1200 to STA-26 interpenetration, while other chemical interactions such as steric repulsions prevent Th-NU-1200 from interpenetrating. Using time-resolved in situ X-ray diffraction, we monitored phase transformation reaction profiles and extracted quantitative kinetic information using the Avrami-Erofe’ev model. As a result, we obtained activation energies for the Zr- and Hf-NU-1200 transformations to Zr- and Hf-STA-26, respectively, revealing slower phase change kinetics for MOFs with stronger bonds. Finally, we paired the kinetic data with experimental observations to classify the mechanistic model of this phase transformation as partial dissolution. Here, we anticipate that this thermodynamic, kinetic, and mechanistic understanding will broadly inform further studies on the energetics of crystallization.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers

Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient’s specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens. The genomic data were divided into a training set of N = 835 cases and a validation set consisting of 1315 hormone sensitive, 634 triple negative breast cancer (TNBC) and 1365 breast cancer patients with information on neoadjuvant chemotherapy responses. Patients were selected by the following criteria: estrogen receptor (ER) status, lymph node invasion, recurrence free survival. The k-means classification algorithm delineated clusters with low- and high- expression of genes related to recurrence of disease; a multivariate Cox’s proportional hazard model defined recurrence risk for disease. Classifier genes were validated by Immunohistochemistry (IHC) using tissue microarray sections containing both normal and cancerous tissues and by evaluating findings deposited in the human protein atlas repository. Based on the leave-on-out cross validation procedure of 4 independent data sets we identified 51-genes associated with disease relapse and selected 10, i.e. TOP2A, AURKA, CKS2, CCNB2, CDK1 SLC19A1, E2F8, E2F1, PRC1, KIF11 for in depth validation. Expression of the mechanistically linked disease regulated genes significantly correlated with recurrence free survival among ER-positive and triple negative breast cancer patients and was independent of age, tumor size, histological grade and node status. Importantly, the classifier genes predicted pathological complete responses to neoadjuvant chemotherapy (P < 0.001) with high expression of these genes being associated with an improved therapeutic response toward two different anthracycline-taxane regimens; thus, highlighting the prospective for precision medicine. Our study demonstrates the potential of classifier genes to predict risk for disease relapse and treatment response to chemotherapies. The classifier genes enable rational selection of patients who benefit best from a given chemotherapy thus providing the best possible care. The findings encourage independent clinical validation.

59 BASIC BIOLOGICAL SCIENCES↗

Airborne Imagery Collections, Utqiagvik (Barrow), Alaska, 2013

The data here are orthomosaics, digital surface models (DSMs), and individual frames captured during low altitude airborne flights in 2013 at the Barrow Environmental Observatory. The orthomosaics, thermal IR mosaics, and DSMs were generated from the individual frames using Structure from Motion techniques. Data were collected to characterize the optical and thermal properties of the landscape and how they differ between two points in time with different vegetative and hydrologic conditions. The orthomosaic tiles, thermal IR mosaics, and DSMs are geotiffs in NAD 83 UTM Zone 4 named by their lower-left coordinate. Metadata for the raw imagery are contained in the .exif headers in the files for the optical imagery only. XYZ files contain the IR temperatures at various longitudes and latitudes. Supported by the U.S. DOE Arctic NGEE program and the Alaska Climate Science Center. The LiDAR dataset used for accuracy assessment was provided by Cathy Wilson(cjw@lanl.gov) and Chandana Gangodagamage(chandana@lanl.gov). File types included: *.tif; *.png; *.kmz; *.bmp; *.pgw, *xyz, *.jpg. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

BOREAS RSS-19 1994 Seasonal Understory Reflectance Data

The BOREAS RSS-19 team collected airborne remotely sensed images and ground reflectance data for characterizing the radiometric properties of the boreal forest landscape. One objective of BOREAS is to further the understanding of the spectral bidirectional reflectance of typical boreal ecosystem stands in the visible/near-infrared regime. An essential input for any canopy BRDF model is an accurate estimate of the average understory reflectance, both for sunlit and shaded conditions. These variables can be expected to vary seasonally because of species-dependent differences in the phenological cycle of foliar display. In response to these requirements, the average understory reflectance for the flux tower sites of both the NSA (Thompson, Manitoba) and the SSA (Candle Lake, Saskatchewan) was observed throughout the year during five field campaigns. This was done by measuring the nadir reflectance (400 to 850 nm) of sunlit and shaded understory (vegetation and snow cover) along a surveyed LAI transect line (Chen, RSS-07) at each site near solar noon and documenting an average site reflectance. Comparisons between sites reveal differences in the green and infrared regions of the spectra, because of the differing species in the understory for each site. Temporal (seasonal) variation for each site was also observed (06-Feb-1994 to 16-Sep-1994), indicating the changing flora mixtures and changing spectral signatures as the understory matures during the growing season. The data are stored in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884) or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Miller, John R.↗

From Protein Design to the Energy Landscape of a Cold Unfolding Protein

Understanding protein folding is crucial for protein sciences. The conformational spaces and energy landscapes of cold (unfolded) protein states, as well as the associated transitions, are hardly explored. Furthermore, it is not known how structure relates to the cooperativity of cold transitions, if cold and heat unfolded states are thermodynamically similar, and if cold states play important roles for protein function. We created the cold unfolding 4-helix bundle DCUB1 with a de novo designed bipartite hydrophilic/hydrophobic core featuring a hydrogen bond network which extends across the bundle in order to study the relative importance of hydrophobic versus hydrophilic protein–water interactions for cold unfolding. Structural and thermodynamic characterization resulted in the discovery of a complex energy landscape for cold transitions, while the heat unfolded state is a random coil. Below ~0 °C, the core of DCUB1 disintegrates in a largely cooperative manner, while a near-native helical content is retained. The resulting cold core-unfolded state is compact and features extensive internal dynamics. Below -5 °C, two additional cold transitions are seen, that is, (i) the formation of a water-mediated, compact, and highly dynamic dimer, and (ii) the onset of cold helix unfolding decoupled from cold core unfolding. Our results suggest that cold unfolding is initiated by the intrusion of water into the hydrophilic core network and that cooperativity can be tuned by varying the number of core hydrogen bond networks. Protein design has proven to be invaluable to explore the energy landscapes of cold states and to robustly test related theories.

59 BASIC BIOLOGICAL SCIENCES↗

Observed effects of landscape variability on convective clouds

A case is presented in which clouds are observed to form first over a mesoscale-size area (100 x 300 km) of harvested wheat in Oklahoma, where the ground temperature is warmer than adjoining areas dominated by growing vegetation. In addition, clouds are suppressed over relatively long bands downwind of small man-made lakes and areas characterized by heavy tree cover. The observed variability of cloud relative to landscape type is compared with that simulated with a one-dimensional boundary-layer model. Clouds form earliest over regions characterized by high, sensible heat flux, and are suppressed over regions characterized by high, latent heat flux during relatively dry atmospheric conditions. This observation has significance in gaining understanding of the feedback mechanisms of land modification on climate, as well as understanding relatively short-range weather forecasting.

Rabin, Robert M.↗

Spatial top-down proteomics for the functional characterization of human kidney

Background: The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. Results: Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. Conclusions: We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.

59 BASIC BIOLOGICAL SCIENCES↗

Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach

Exploring water fluxes between hydrological model (HM) components is essential to assess and improve model realism. Many classical metrics for HM diagnosis rely solely on streamflow and hence provide limited insights into model performance across processes. This study applies an information theory measure known as “transfer entropy” (TE) to systematically quantify the transfer of information among major HM components. To test and demonstrate the benefits of TE, we use the Framework for Understanding Structural Errors (FUSE) model to mimic and compare four commonly used HM structures, VIC, PRMS, SACRAMENTO, and TOPMODEL, across 671 catchments spanning a variety of hydrologic regimes in the conterminous United States. We explore connections between HM components and catchment landscape characteristics (e.g., climate, topography, soil, and vegetation) and characterize their nonlinear associations using distance correlation and Spearman correlation coefficients. Our results indicate that while the information transferred from precipitation to runoff is similar across model structures (likely as a result of calibration), the information transferred among other components can vary significantly from a FUSE structure to another. We find that aridity, precipitation duration and frequency, snow fraction, mean elevation, forest area, and leaf area index are often significantly associated with TE between the main HM components. Here, we propose that the presence of meaningful nonlinear associations can be used to diagnose process representation in HMs. Our results highlight the necessity to enhance the conventional streamflow-only calibration approach for a more realistic representation of water dynamics in the models.

large-sample hydrology↗

Microbial diversity in an intensively managed landscape is structured by landscape connectivity

Intensively managed land increases the rate of nutrient and particle transport within a basin, but the impact of these changes on microbial community assembly patterns at the basin scale is not yet understood. The objective of this study was to investigate how landscape connectivity and dispersal impacts microbial diversity in an agricultural-dominated watershed. We characterized soil, sediment and water microbial communities along the Upper Sangamon River basin in Illinois-a 3600 km 2 watershed strongly influenced by human activity, especially landscape modification and extensive fertilization for agriculture. We employed statistical and network analyses to reveal the microbial community structure and interactions in the critical zone (water, soil and sediment media). Using a Bayesian source tracking approach, we predicted microbial community connectivity within and between the environments. We identified strong connectivity within environments (up to 85.4 ± 13.3% of sequences in downstream water samples sourced from upstream samples, and 44.7 ± 26.6% in soil and sediment samples), but negligible connectivity across environments, which indicates that microbial dispersal was successful within but not between environments. Species sorting based on sample media type and environmental parameters was the dominant driver of community dissimilarity. Lastly, we constructed operational taxonomic unit association networks for each environment and identified a number of co-occurrence relationships that were shared between habitats, suggesting that these are likely to be ecologically significant.

59 BASIC BIOLOGICAL SCIENCES↗

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗

Exploring the spatial variability of soil properties in an Alfisol Catena

Detailed digital soil maps showing the spatial heterogeneity of soil properties consistent with the landscape are required for site-specific management of plant nutrients, land use planning and process-based environmental modeling. We characterized the short-scale spatial heterogeneity of soil properties in an Alfisol catena in a tropical landscape of Sri Lanka. The impact of different land-uses (paddy, vegetable and un-cultivated) was examined to assess the impact of anthropogenic activities on the variability of soil properties at the catenary level. Conditioned Latin hypercube sampling was used to collect 58 geo-referenced topsoil samples (0–30 cm) from the study area. Soil samples were analyzed for pH, electrical conductivity (EC), organic carbon (OC), cation exchange capacity (CEC) and texture. The spatial correlation between soil properties was analyzed by computing crossvariograms and subsequent fitting of theoretical model. Spatial distribution maps were developed using ordinary kriging. The range of soil properties, pH: 4.3–7.9; EC: 0.01–0.18 dS m –1 ; OC: 0.1–1.37%; CEC: 0.44– 11.51 cmol (+) kg –1 ; clay: 1.5–25% and sand: 59.1–84.4% and their coefficient of variations indicated a large variability in the study area. Electrical conductivity and pH showed a strong spatial correlation which was reflected by the cross-variogram close to the hull of the perfect correlation. Moreover, cross-variograms calculated for EC and Clay, CEC and OC, CEC and clay and CEC and pH indicated weak positive spatial correlation between these properties. Relative nugget effect (RNE) calculated from variograms showed strongly structured spatial variability for pH, EC and sand content (RNE < 25%) while CEC, organic carbon and clay content showed moderately structured spatial variability (25% < RNE < 75%). Spatial dependencies for examined soil properties ranged from 48 to 984 m. The mixed effects model fitting followed by Tukey's post-hoc test showed significant effect of land use on the spatial variability of EC. Our study revealed a structured variability of topsoil properties in the selected tropical Alfisol catena. Except for EC, observed variability was not modified by the land uses. Investigated soil properties showed distinct spatial structures at different scales and magnitudes of strength. Our results will be useful for digital soil mapping, site specific management of soil properties, developing appropriate land use plans and quantifying anthropogenic impacts on the soil system.

Geo-statistical analysis↗

Integrating mRNA and protein sequencing enables the detection and quantitative profiling of natural protein sequence variants of Populus trichocarpa

The availability of next-generation sequencing technologies has rapidly transformed our ability to link genotypes to phenotypes, and as such, promises to facilitate the dissection of genetic contribution to complex traits. Although discoveries of genetic associations will further our understanding of biology, once candidate variants have been identified, investigators are faced with the challenge of characterizing the functional effects on proteins encoded by such genes. Here we show how next-generation RNA sequencing data can be exploited to construct genotype-specific protein sequence databases, which provide a clearer picture of the molecular toolbox underlying cellular and organismal processes and their variation in a natural population. For this study, we used two individual genotypes (DENA-17-3 and VNDL-27-4) from a recent genome wide association (GWA) study of Populus trichocarpa, an obligate outcrosser that exhibits tremendous phenotypic variation across the natural population. This strategy allowed us to comprehensively catalogue proteins containing single amino acid polymorphisms (SAAPs) and insertions and deletions (INDELS). Based on large-scale identification of SAAPs, we profiled the frequency of 128 types of naturally occurring amino acid substitutions, with a subset of SAAPs occurring in regions of the genome having strong polymorphism patterns consistent with recent positive and/or divergent selection. In addition, we were able to explore the diploid landscape of Populus at the proteome-level, allowing the characterization of heterozygous variants.

59 BASIC BIOLOGICAL SCIENCES↗

Characterization of Coal Refuse Sites in West Virginia: Stream Loss, Volume Estimation, and Land Cover Analysis for Restoration Feasibility

Coal refuse disposal across Appalachia has resulted in widespread environmental degradation, including stream burial, landscape destabilization, and long-term hydrologic impacts. This study provides a GIS-based spatial characterization of 623 coal refuse sites in West Virginia, evaluating their potential for ecological and economic restoration. Stream loss was quantified through digitization of historic topographic maps, revealing over 194,500 meters of historically present streams lost, with 105,000 meters directly buried beneath refuse piles. Volume estimation using empirical Bayesian kriging and modern LiDAR surfaces indicated approximately 1.96 billion cubic meters of deposited refuse. Land cover analysis based on NAIP-derived classification showed that 48% of the total site area was forested, while 15% remained barren. These results highlight not only the environmental footprint of legacy refuse deposits but also their potential for restoration through stream daylighting and beneficial reuse. Emerging opportunities, including rare earth element recovery and mitigation banking, may offer economic incentives to facilitate reclamation. This work establishes a spatial framework to prioritize refuse sites for restoration, balancing ecological uplift with feasible material management strategies.

58 GEOSCIENCES↗

The impact of proinflammatory cytokines on the ß-cell regulatory landscape provides new insights into the genetics of type 1 diabetes

Early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic ß cells. We show here that exposure to pro-inflammatory cytokines unmasks a marked plasticity of the ß-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulusresponsive enhancers linked to changes in the ß-cell transcriptome, proteome and 3D chromatin structure. Our data indicates that the ß cell response to cytokines is mediated by the induction of novel regulatory regions as well as the activation of primed regulatory elements pre-bound by islet-specific transcription factors. We found that T1D-associated loci are enriched of the newly mapped cis-regulatory regions and identify T1Dassociated variants disrupting cytokine-responsive enhancer activity in human ß cells. Our study illustrates how ß cells respond to a proinflammatory environment and implicate a role for stimulus-response islet enhancers in T1D.

Romas-Rodriquez, Mireia↗

Lithium Iron Aluminum Nickelate, LiNi x Fe y Al z O 2 —New Sustainable Cathodes for Next–Generation Cobalt–Free Li–Ion Batteries

In recent years, cobalt has become a critical constraint on the supply chain of the Li-ion battery industry. Here, with the ever-increasing projections for electric vehicles, the dependency of current Li-ion batteries on the ever-fluctuating cobalt prices poses serious environmental and sustainability issues. To address these challenges, a new class of cobalt-free materials with general formula of LiNi x Fe y Al z O 2 ( x + y + z = 1), termed as the lithium iron aluminum nickelate (NFA) class of cathodes, is introduced. These cobalt-free materials are synthesized using the sol–gel process to explore their compositional landscape by varying aluminum and iron. These NFA variants are characterized using electron microscopy, neutron and X-ray diffraction, and Mössbauer and X-ray photoelectron spectroscopy to investigate their morphological, physical, and crystal-structure properties. Operando experiments by X-ray diffraction, Mössbauer spectroscopy, and galvanostatic intermittent titration have been also used to study the crystallographic transitions, electrochemical activity, and Li-ion diffusivity upon lithium removal and uptake in the NFA cathodes. NFA compositions yield specific capacities of ≈200 mAh g -1 , demonstrating reasonable rate capability and cycling stability with ≈80% capacity retention after 100 charge/discharge cycles. While this is an early stage of research, the potential that these cathodes could have as viable candidates in next-generation cobalt-free lithium-ion batteries is highlighted here.

25 ENERGY STORAGE↗