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Visualization of metabolic interaction networks in microbial communities using VisANT 5.0
Here, the complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique meta-graph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues.
Ultrafast Heterogeneous Nucleation Enables a Hierarchical Surface Configuration of Lithium‐Rich Layered Oxide Cathode Material for Enhanced Electrochemical Performances
Abstract Lithium‐rich layered oxides have attracted much attention for the high energy density, but they still suffer from cycling degradation and sluggish kinetic capability. In the present study, a novel modification of Li 1.143 Mn 0.544 Ni 0.136 Co 0.136 O 2 sphere cathode materials by a hierarchical surface configuration is proposed, which integrates advantages of dual oxide layers of both amorphous and nanocrystalline Al 2 O 3, as well as an inner spinel membrane. Mainly promoted by an ultrafast heterogeneous nucleation of Al(NO) 3 nanoseeds caused by huge solubility differences, it only takes several minutes for pretreatment. After common annealing at low temperature, the obtained material is capable to deliver a high discharge capacity of 296.3 mAh g −1 with initial Coulombic efficiency of 92.9% at 0.1 C rate, and maintain stable cycling at both 0.2 and 0.5 C rates. Besides, rate performances are also enhanced as a result of the superior interface gained after surface modification, which produces reduced polarization during cycling. Through comprehensive analyses, improved surface property and stability are confirmed as the key to these enhancements. This strategy is anticipated to inspire new modification designs in future high energy density cathode materials.
Computational Methodology to Simulate Pyrometallurgical Processes in a Secondary Lead Furnace
Pyrometallurgical recovery of nonferrous metals involves a combination of thermally intensive transformations during exothermic gas-phase reactions, endothermic decomposition of solid charge, and melting of simpler solids. In the recovery of secondary lead, simultaneous thermal effects in a reverberatory-style furnace cause a melt pool to accumulate at the bottom, with lighter solids (slag) floating above and gaseous products from decomposition of the charge diffusing through the gas–slag interface. Species from oxy-fuel combustion of natural gas, species profiles from smelting reactions, and the formation of a melt pool consisting primarily of lead are simulated via a time-averaged formulation. Predictions of outflow are compared with preset inflow profiles to ensure conservation of mass. Thermal profiles for solid, liquid, and gas phases are presented by species. A novel method is implemented to model the latent heat of fusion using a heterogeneous chemical reaction. The simulation is conducted in Simcenter STAR-CCM+ v. 16.02.009-R8.
Structure-guided utilization of lignocellulose for catalysis, energy, and biomaterials
As a complex composite of cellulose, hemicellulose, and lignin, plant lignocellulose has long served as a major resource for biomass conversion, materials engineering, and bio-based product development. High-resolution structural insights enabled by solid-state nuclear magnetic resonance (ssNMR) now allow the mapping of polymer interfaces, identification of functional group accessibility, and tracking of molecular organization during processing, all of which are critical factors for optimizing catalytic strategies. These insights could drive transformative progress in lignocellulose-based applications, including selective depolymerization, improved pretreatment design, and efficient upcycling of lignin into resins, plastics, and biomedical materials. In industry-relevant contexts, such as biofuel generation and renewable material manufacturing, understanding the hydration dynamics, cross-linking patterns, and structural heterogeneity is also essential. The ability to visualize these features in native biomass presents a unique opportunity to develop new strategies for sustainability and performance. As the structural toolbox continues to expand, it is becoming a central enabler for innovations in renewable energy, green chemistry, and advanced bioproducts.
Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning
Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis–nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.
Intradomain Textures in Block Copolymers: Multizone Alignment and Biaxiality
Block copolymer (BCP) melt assembly has been studied for decades, focusing largely on self-organized spatial patterns of periodically ordered segment density. Here, we demonstrate that underlying the well-known composition profiles (i.e., ordered lamella, cylinders, spheres, and networks) are generic and heterogeneous patterns of segment orientation that couple strongly to morphology, even in the absence of specific factors that promote intra or interchain segment alignment. We employ both self-consistent field theory and coarse-grained simulation methods to measure polar and nematic order parameters of segments in a freely jointed chain model of diblock melts. We show that BCP morphologies have a multizone texture, with segments predominantly aligned normal and parallel to interdomain interfaces in the respective brush and interfacial regions of the microdomain. Further, morphologies with anisotropically curved interfaces (i.e., cylinders and networks) exhibit biaxial order that is aligned to the principal curvature axes of the interface.
Nonadiabatic rate constants for proton transfer and proton-coupled electron transfer reactions in solution: Effects of quadratic term in the vibronic coupling expansion
Rate constant expressions for vibronically nonadiabatic proton transfer and proton-coupled electron transfer reactions are presented and analyzed. The regimes covered include electronically adiabatic and nonadiabatic reactions, as well as high-frequency and low-frequency proton donor-acceptor vibrational modes. These rate constants differ from previous rate constants derived with the cumulant expansion approach in that the logarithmic expansion of the vibronic coupling in terms of the proton donor-acceptor distance includes a quadratic as well as a linear term. The analysis illustrates that inclusion of this quadratic term in the framework of the cumulant expansion framework may significantly impact the rate constants at high temperatures for proton transfer interfaces with soft proton donor-acceptor modes that are associated with small force constants and weak hydrogen bonds. The effects of the quadratic term may also become significant in these regimes when using the vibronic coupling expansion in conjunction with a thermal averaging procedure for calculating the rate constant. In this case, however, the expansion of the coupling can be avoided entirely by calculating the couplings explicitly for the range of proton donor-acceptor distances sampled. The effects of the quadratic term for weak hydrogen-bonding systems are less significant for more physically realistic models that prevent the sampling of unphysical short proton donor-acceptor distances. Additionally, the rigorous relation between the cumulant expansion and thermal averaging approaches is clarified. In particular, the cumulant expansion rate constant includes effects from dynamical interference between the proton donor-acceptor and solvent motions and becomes equivalent to the thermally averaged rate constant when these dynamical effects are neglected. This analysis identifies the regimes in which each rate constant expression is valid and thus will be important for future applications to proton transfer and proton-coupled electron transfer in chemical and biological processes.
Formation of (Rh–Fe)–FeO x Complex Sites Enables Methanol Synthesis from CO 2
Here, we addressed the challenges of designing catalysts for selective CO 2 hydrogenation by incorporating oxide Fe species onto Rh nanoparticles. Nanoscopic FeO x domains created a “reverse catalyst” structure (i.e., a metal oxide supported on a metal) that increased the density of interfacial sites compared to traditional supported catalysts. The contact between the metal nanoparticle and the oxide overlayer induced the formation of a surface Rh-Fe alloy that stabilize methoxy groups while suppressing hydrogenolysis to methane. Sites at FeO x -metal interfaces interact with CO 2 sevenfold stronger than sites on metal surfaces, show larger energy barriers to cleave the C-O bonds, and offer a barrierless pathway for hydrogenation of methoxy species to methanol. Consequently, the multifunctional sites over FeO x /Rh-Fe catalysts highlight and meet the requirements of a selective methanol catalyst: strong interaction with CO 2 to ensure high density of transition states; metal sites to activate and make hydrogen available to surface intermediates; and high energy barriers for C-O bond cleavage to form carbides. These synthesis and catalytic chemistries, demonstrated for Rh-Fe-FeO x interfaces, enable us to overcome the limitations to the design of methanol production catalysts.
Framework for Extensible, Asynchronous Task Scheduling (FEATS) in Fortran
Most parallel scientific programs contain compiler directives (pragmas) such as those from OpenMP, explicit calls to runtime library procedures such as those implementing the Message Passing Interface (MPI), or compiler-specific language extensions such as those provided by CUDA. By contrast, the recent Fortran standards empower developers to express parallel algorithms without directly referencing lower-level parallel programming models. Fortran’s parallel features place the language within the Partitioned Global Address Space (PGAS) class of programming models. When writing programs that exploit data-parallelism, application developers often find it straightforward to develop custom parallel algorithms. Problems involving complex, heterogeneous, staged calculations, however, pose much greater challenges. Such applications require careful coordination of tasks in a manner that respects dependencies prescribed by a directed acyclic graph. When rolling one’s own solution proves difficult, extending a customizable framework becomes attractive. The paper presents the design, implementation, and use of the Framework for Extensible Asynchronous Task Scheduling (FEATS), which we believe to be the first task-scheduling tool written in modern Fortran. We describe the benefits and compromises associated with choosing Fortran as the implementation language, and we propose ways in which future Fortran standards can best support the use case in this paper.
Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments
The annual Workshop on Workflows in Support of Large-Scale Science (WORKS) is a premier venue for the scientific workflow community to present the latest advances in research and development on the many facets of scientific workflows throughout their life-cycle. The Lightning Talks at WORKS focus on describing a novel tool, scientific workflow, or concept, which are work-in-progress and address emerging technologies and frameworks to foster discussion in the community. This paper summarizes the lightning talks at the 2023 edition of WORKS, covering five topics: leveraging large language models to build and execute workflows; developing a common workflow scheduler interface; scaling uncertainty workflow applications on exascale computing systems; evaluating a transcriptomics workflow for cloud vs. HPC systems; and best practices in migrating legacy workflows to workflow management systems.
Amino mapping: possibility to visualize amino-N compounds in the rhizosphere of Zea Mays L.
Abstract Understanding N uptake by plants, the N cycle, and their relationship to soil heterogeneity has generated a great deal of interest in the distribution of amino-N compounds in soil. Visualization of the spatial distribution of amino-N in soil can provide insights into the role of labile N in plant-microbial mechanisms of N acquisition and plant N uptake, but until now, it has remained technically challenging. Here, we describe a novel technique to visualize the amino-N distribution at the root-soil interface. The technique is based on time-lapse amino mapping (TLAM) using membranes saturated with the fluorogenic OPAME reagent (O-phthalaldehyde and β-mercaptoethanol). OPAME in the membrane reacts with organic compounds containing a NH 2 functional group at the membrane-soil interface, generating a fluorescent product visible under UV light and detectable by a digital camera. The TLAM amino-mapping technique was applied to visualize and quantify the concentration of amino-N compounds in the rhizosphere of maize (Zea MaysL.). A ten times greater amino-N concentration was detected in the rhizosphere compared to non-rhizosphere soil. The high content of amino-N was mainly associated with the root tips and was 3 times larger than the average amino-N content at seminal roots. The amino-N rhizosphere was 2 times broader around the root tips than around other parts of the roots. We concluded that TLAM is a promising approach for monitoring the fate of labile N in soils. However, the technique needs to be standardized for different soil types, plant species, and climate conditions to allow wider application.
Observing crystal nucleation in four dimensions using atomic electron tomography
Nucleation plays a critical role in many physical and biological phenomena that range from crystallization, melting and evaporation to the formation of clouds and the initiation of neurodegenerative diseases. However, nucleation is a challenging process to study in experiments, especially in the early stage when several atoms or molecules start to form a new phase from a parent phase. A number of experimental and computational methods have been used to investigate nucleation processes, but experimental determination of the three-dimensional atomic structure and the dynamics of early stage nuclei has been unachievable. Here we use atomic electron tomography to study early stage nucleation in four dimensions (4D: that is, including time) at atomic resolution. Using FePt nanoparticles as a model system, we find that early stage nuclei are irregularly shaped, each has a core of one to a few atoms with the maximum order parameter, and the order parameter gradient points from the core to the boundary of the nucleus. We capture the structure and dynamics of the same nuclei undergoing growth, fluctuation, dissolution, merging and/or division, which are regulated by the order parameter distribution and its gradient. These experimental observations are corroborated by molecular dynamics simulations of heterogeneous and homogeneous nucleation in liquid–solid phase transitions of Pt. Our experimental and molecular dynamics results differ from classical nucleation theory, indicating that a theory beyond this is needed to describe early stage nucleation at the atomic scale. Looking forward, we anticipate that the reported approach will open the door to the study of many fundamental problems in materials science, nanoscience, condensed matter physics and chemistry, such as phase transition, atomic diffusion, grain boundary dynamics, interface motion, defect dynamics and surface reconstruction with 4D atomic resolution.
Development of a semi-empirical potential for simulation of Ni solute segregation into grain boundaries in Ag
An Ag–Ni semi-empirical potential was developed to simulate the segregation of Ni solutes at Ag grain boundaries (GBs). The potential combines a new Ag potential fitted to correctly reproduce the stable and unstable stacking fault energies in this metal and the existing Ni potential from Mendelev et al (2012 Phil. Mag. 92 4454–69). The Ag–Ni cross potential functions were fitted to ab initio data on the liquid structure of the Ag 80 Ni 20 alloy to properly incorporate the Ag–Ni interaction at small atomic separations, and to the Ni segregation energies at different sites within a high-energy Σ9 (221) symmetric tilt GB. By deploying this potential with hybrid Monte Carlo/molecular dynamics simulations, it was found that heterogeneous segregation and clustering of Ni atoms at GBs and twin boundary defects occur at low Ni concentrations, 1 and 2 at%. This behavior is profoundly different from the homogeneous interfacial dispersion generally observed for the Cu segregation in Ag. A GB transformation to amorphous intergranular films was found to prevail at higher Ni concentrations (10 at%). In conclusion, the developed potential opens new opportunities for studying the selective segregation behavior of Ni solutes in interface-hardened Ag metals and its effect on plasticity.
New Architecture to Support Integration and Processing of Seismic Data from Heterogeneous Sources
The Geophysical Monitoring Program (GMP) at Lawrence Livermore National Lab (LLNL) maintains a database and supporting infrastructure for geophysical data used in support of the Nuclear Detonation Detection mission. This database includes data from multiple sources, many of which do not distribute data to the public or for which there is no automated means of access. For example, Figure 1 shows (left) the distribution of waveform data in our database by source. The Incorporated Research Institutions for Seismology Data Management Center (IRISDMC) is our major source of waveform data and those data may be retrieved at will using the Federated Digital Seismograph Networks FDSN web Application Programming Interface (API). However, the next 6 most important sources of waveform data have no or only limited automated access to waveforms. As Figure 1 (right) shows, it is very common for waveform records associate with an event in our database to come from two or more sources, and in some cases data come from 10 sources. This diversity of data sources drives our need for efficient and correct integration of metadata, parametric data, and waveform data.
Pore-scale supercritical CO2 dissolution and mass transfer under imbibition conditions
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Computational investigation on CO 2 adsorption in titanium carbide-derived carbons with residual titanium
We develop a new approach for modeling titanium carbide derived-carbon (TiC-CDC) systems with residual titanium by the generation of modified atomistic structures based on a silicon carbide derived-carbon (SiC-CDC) model and the application of weighted combinations of these structures. In our approach, the original SiC-CDC structure is modified by (i) removing carbon, (ii) adding carbon and (iii) adding titanium. The new atomic scale carbide-derived carbon (CDC) structures are investigated using classical molecular dynamics simulations, and their pure CO 2 adsorption isotherms are calculated using grand canonical Monte Carlo simulations. The system of TiC-CDC with residual titanium is modeled as weighted combinations of pure carbon CDC structures, CDC structures with titanium and a TiC crystalline structure. Our modeling is able to produce both structural properties and adsorption isotherms in accordance with experimental data. The fraction of different models in the systems successfully reflects the structural differences in various experimental TiC-CDC samples. The modeling also suggests that in partially etched TiC-CDC systems, the titanium that may be accessible to CO 2 gas at the transitional interface may provide significant interaction sites for CO 2 and may lead to more efficient overall gas adsorption.
Tunable and laser-reconfigurable 2D heterocrystals obtained by epitaxial stacking of crystallographically incommensurate Bi 2 Se 3 and MoS 2 atomic layers
Vertical stacking is widely viewed as a promising approach for designing advanced functionalities using two-dimensional (2D) materials. Combining crystallographically commensurate materials in these 2D stacks has been shown to result in rich new electronic structure, magnetotransport, and optical properties. In this context, vertical stacks of crystallographically incommensurate 2D materials with well-defined crystallographic order are a counterintuitive concept and, hence, fundamentally intriguing. We show that crystallographically dissimilar and incommensurate atomically thin MoS 2 and Bi 2 Se 3 layers can form rotationally aligned stacks with long-range crystallographic order. Our first-principles theoretical modeling predicts heterocrystal electronic band structures, which are quite distinct from those of the parent crystals, characterized with an indirect bandgap. Experiments reveal striking optical changes when Bi 2 Se 3 is stacked layer by layer on monolayer MoS 2 , including 100% photoluminescence (PL) suppression, tunable transmittance edge (1.1→0.75 eV), suppressed Raman, and wide-band evolution of spectral transmittance. Disrupting the interface using a focused laser results in a marked the reversal of PL, Raman, and transmittance, demonstrating for the first time that in situ manipulation of interfaces can enable “reconfigurable” 2D materials. We demonstrate submicrometer resolution, “laser-drawing” and “bit-writing,” and novel laser-induced broadband light emission in these heterocrystal sheets.