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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integrative teaching of metabolic modeling and flux analysis with interactive python modules

Abstract The modeling of rates of biochemical reactions—fluxes—in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint‐based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to‐date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C‐metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands‐on computer laboratory component of a four‐day metabolic modeling workshop and participant survey results showed improvements in learners' self‐assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.

Kaste, Joshua A. M.↗
DOE OSTI2023

Project: Corbomite - Product: ConsoleWorks REACT

TDi Technologies presents ConsoleWorks REACT, an advanced platform designed to tackle the complexities of cyber and operational risk assessment. This comprehensive solution goes beyond asset-focused approaches by considering the impact of both assets and people have on the security and operation of critical infrastructure, specifically targeting preventing gird mis-operation by evaluating real-time human interaction or commands with critical assets. The hypothesis suggests that by integrating the assessment of user commands into the overall risk assessment process, organizations can make more informed decisions, prioritize resources effectively, and respond promptly to potential threats. This hypothesis forms the basis for the development of a proactive and holistic risk management approach that is more comprehensive and context aware than traditional models which only look at assets, patch levels, configuration and threat intel by collecting that information off the network vs directly from the asset, human, and human interaction all in real-time. ConsoleWorks' unique man-in-the-middle architecture is a key feature that sets it apart in the cybersecurity landscape. This architecture enables the real-time observation, enforcement, and commands or interaction risk transparency of user interaction with critical infrastructure to be risk mitigated and audited as they are aggregated with device and human risk factors for a more comprehensive risk threat score across a device or group of devices. This architecture allows ConsoleWorks to act as a secure intermediary between users and critical assets, monitoring all interactions and ensuring that only authorized commands are sent to the asset to be executed. This not only enhances security but also provides a comprehensive audit trail of all user activities, contributing to compliance efforts and facilitating incident investigation and risk management. By integrating this unique architecture with our comprehensive risk assessment methodology, ConsoleWorks REACT provides a powerful solution for managing cyber and operational risks, enabling organizations to maintain a robust security posture and effectively mitigate potential threats. To that end, the primary objective of this project was to research and develop a robust solution that enables the energy industry to mitigate the risks associated with human actions that can compromise the security or operations of assets critical to energy delivery, generation, transmission and operation. ConsoleWorks REACT plays a pivotal role in achieving this goal by leveraging its unique capabilities to monitor and track all user activity. Through its Zero Trust approach, which emphasizes continuous verification, the platform ensures secure access to assets and serves as the centralized human response and notification platform for addressing cyber and operational issues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

Linville, Lisa M.↗
DOE OSTI2016

Biochemical Roles for Conserved Residues in the Bacterial Fatty Acid-binding Protein Family

Fatty acid kinase (Fak) is a ubiquitous Gram-positive bacterial enzyme consisting of an ATP-binding protein (FakA) that phosphorylates the fatty acid bound to FakB. In Staphylococcus aureus, Fak is a global regulator of virulence factor transcription and is essential for the activation of exogenous fatty acids for incorporation into phospholipids. The 1.2-Å x-ray structure of S. aureus FakB2, activity assays, solution studies, site-directed mutagenesis, and in vivo complementation were used to define the functions of the five conserved residues that define the FakB protein family (Pfam02645). The fatty acid tail is buried within the protein, and the exposed carboxyl group is bound by a Ser-93-fatty acid carboxyl-Thr-61-His-266 hydrogen bond network. The guanidinium of the invariant Arg-170 is positioned to potentially interact with a bound acylphosphate. The reduced thermal denaturation temperatures of the T61A, S93A, and H266A FakB2 mutants illustrate the importance of the hydrogen bond network in protein stability. The FakB2 T61A, S93A, and H266A mutants are 1000-fold less active in the Fak assay, and the R170A mutant is completely inactive. All FakB2 mutants form FakA(FakB2)2 complexes except FakB2(R202A), which is deficient in FakA binding. Allelic replacement shows that strains expressing FakB2 mutants are defective in fatty acid incorporation into phospholipids and virulence gene transcription. These conserved residues are likely to perform the same critical functions in all bacterial fatty acid-binding proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Parameterization and analysis of 3-D radiative transfer in clouds

This report provides a summary of major accomplishments from the project. The project examines the impact of radiative interactions between neighboring atmospheric columns, for example clouds scattering extra sunlight toward nearby clear areas. While most current cloud models don't consider these interactions and instead treat sunlight in each atmospheric column separately, the resulting uncertainties have remained unknown. This project has provided the first estimates on the way average solar heating is affected by interactions between nearby columns. These estimates have been obtained by combining several years of cloud observations at three DOE Atmospheric Radiation Measurement (ARM) Climate Research Facility sites (in Alaska, Oklahoma, and Papua New Guinea) with simulations of solar radiation around the observed clouds. The importance of radiative interactions between atmospheric columns was evaluated by contrasting simulations that included the interactions with those that did not. This study provides lower-bound estimates for radiative interactions: It cannot consider interactions in cross-wind direction, because it uses two-dimensional vertical cross-sections through clouds that were observed by instruments looking straight up as clouds drifted aloft. Data from new DOE scanning radars will allow future radiative studies to consider the full three-dimensional nature of radiative processes. The results reveal that two-dimensional radiative interactions increase overall day-and-night average solar heating by about 0.3, 1.2, and 4.1 Watts per meter square at the three sites, respectively. This increase grows further if one considers that most large-domain cloud simulations have resolutions that cannot specify small-scale cloud variability. For example, the increases in solar heating mentioned above roughly double for a fairly typical model resolution of 1 km. The study also examined the factors that shape radiative interactions between atmospheric columns and found that local effects were often much larger than the overall values mentioned above, and were especially large for high sun and near convective clouds such as cumulus. The study also found that statistical methods such as neural networks appear promising for enabling cloud models to consider radiative interactions between nearby atmospheric columns. Finally, through collaboration with German scientists, the project found that new methods (especially one called stepwise kriging) show great promise in filling gaps between cloud radar scans. If applied to data from the new DOE scanning cloud radars, these methods can yield large, continuous three-dimensional cloud structures for future radiative simulations.

58 GEOSCIENCES↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Consequences of adsorbate-adsorbate interactions for apparent kinetics of surface catalytic reactions

Lateral adsorbate interactions at catalyst surfaces are known to influence adsorption energies and reaction rates. Lattice-based kinetic Monte Carlo (kMC) simulations are able to capture these influences, but such models are typically parameterized for a specific reaction network and catalyst surface. Here we report kMC simulations to probe the influence of lateral interactions on simulated rates, rate orders, apparent activation energies, and Sabatier plots. We construct a simple, two-step reaction network involving a single adsorbate and rate- limiting diatomic dissociation, employ a generic repulsive lateral interaction model consistent with known adsorbate-adsorbate interactions on metal surfaces, and a rate model consistent with known Brønsted-Evans-Polyani relationships for diatomic dissocations. Furthermore, we juxtapose reaction kinetics over a wide range of reaction conditions and catalyst binding energies, as a function of interaction strength. We find that at a given zero-coverage binding energy and external conditions, adsorbate coverage decreases monotonically with increasing interaction strength, but absolute rates can vary linearly or nonlinearly. Interactions flatten the Sabatier volcano and shift the maximum towards stronger binding. Influences on apparent rate orders and activation energies are modest and are sensitive to interaction-induced adsorbate ordering. Model predictions are sensitive to lattice size effects at high coverages. The results, modelled for a simple reaction system, highlight the generic consequences of lateral interactions and guidance for identifying their signatures in observed kinetics.

kinetic Monte Carlo↗
DOE OSTI2024

Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks

Fractional and tempered fractional partial differential equations (PDEs) are effective models of long-range interactions, anomalous diffusion, and non-local effects. Traditional numerical methods for these problems are mesh-based, thus struggling with the curse of dimensionality (CoD). Physics-informed neural networks (PINNs) offer a promising solution due to their universal approximation, generalization ability, and mesh-free training. In principle, Monte Carlo fractional PINN (MC-fPINN) estimates fractional derivatives using Monte Carlo methods and thus could lift CoD. However, this may cause significant variance and errors, hence affecting convergence; in addition, MC-fPINN is sensitive to hyperparameters. In general, numerical methods and specifically PINNs for tempered fractional PDEs are under-developed. Herein, we extend MC-fPINN to tempered fractional PDEs to address these issues, resulting in the Monte Carlo tempered fractional PINN (MC-tfPINN). To reduce possible high variance and errors from Monte Carlo sampling, we replace the one-dimensional (1D) Monte Carlo with 1D Gaussian quadrature, applicable to both MC-fPINN and MC-tfPINN. We validate our methods on various forward and inverse problems of fractional and tempered fractional PDEs, scaling up to 100,000 dimensions. Our improved MC-fPINN/MC-tfPINN using quadrature consistently outperforms the original versions in accuracy and convergence speed in very high dimensions.

42 ENGINEERING↗
DOE OSTI2015

Structural changes of linear DNA molecules induced by cisplatin

Interaction between long DNA molecules and activated cisplatin is believed to be crucial to anticancer activity. However, the exact structural changes of long DNA molecules induced by cisplatin are still not very clear. In this study, structural changes of long linear double-stranded DNA (dsDNA) and short single-stranded DNA (ssDNA) induced by activated cisplatin have been investigated by atomic force microscopy (AFM). The results indicated that long DNA molecules gradually formed network structures, beads-on-string structures and their large aggregates. Electrostatic and coordination interactions were considered as the main driving forces producing these novel structures. An interesting finding in this study is the beads-on-string structures. Moreover, it is worth noting that the beads-on-string structures were linked into the networks, which can be ascribed to the strong DNA–DNA interactions. This study expands our knowledge of the interactions between DNA molecules and cisplatin. - Highlights: • We investigate structural changes of dsDNA and ssDNA induced by cisplatin. • AFM results indicated long dsDNA formed network, beads-on-string and aggregates. • ssDNA can form very similar structures as those of long linear dsDNA. • A possible formation process of theses novel structure is proposed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Formation and surface melting of nanoparticle superlattices in a solution

The wisdom in the saying of “There are no two snowflakes alike” lies in the importance of history or kinetic pathways in the phase transitions of solids. Likewise, “artificial solids,” namely superlattices consisting of functional nanoparticles, have lattice size, surface morphology, crystallinity, symmetry, and structural reconfiguration (for example, transition into a disordered state) highly dependent on the kinetic pathways as the nanoparticles interact with each other in solution [1]. Great progresses have been made in understanding the formation pathways of superlattices using liquid-phase transmission electron microscopy (TEM) [2-4]. For example, by tracking single nanoparticle’s trajectories, especially aided by U-net neural network-based machine learning, previous studies mapped the fundamental nanoparticle interactions at nanometer resolution [5]. Nonclassical, two-step nucleation pathway has also been elucidated in the system of nanoprisms, by optimizing protocols such as loading nanoparticle suspensions over the supersaturation threshold and minimizing particle‒substrate interaction [2]. Surface morphologies or exposed facets of superlattices have been shown to follow the principles of Wulff construction rule, where the facet-dependent surface energy can be measured based on the capillary wave theory [4]. However, the reverse process of crystallization of superlattices, the conversion from crystalline to disordered state, has been much less explored. On one hand, the melting of nanoparticle superlattices can provide a preferred pathway to induce structural reorganization or shuffling of building blocks for them to transform into different types of crystal structures. On the other hand, understanding nanoscale superlattice melting and comparing such behaviors with the prevailing surface melting theories developed for atomic/molecular solids can provide a potent way to engineer phase transitions of supra- and hierarchical structures constructed from nanoscale entities (e.g., DNA-coated nanoparticles, proteins), for their applications in reprogrammable and switchable materials with multifunctional properties [6, 7]. The experimental challenges to observe melting of superlattices are twofold. Practically it is difficult to load the initial superlattice form, in an intact manner, into the highly confined liquid-phase TEM chamber for in-situ observation. Here, the triggering of melting also needs meticulous manipulation of nanoparticle concentration, interparticle interaction, and solution environment.

Kim, Ahyoung↗
DOE OSTI2025

Engineering a new tripartite split-ccGFP system from Corynactis californica for detecting protein–protein interactions

Protein-protein interactions (PPIs) are critical to a range of biological processes and, consequently, aberrant interactions are implicated in many disorders. The study of the complex networks of PPIs promises to elucidate undiscovered roles in cellular processes and the mechanisms of disease. To accomplish this, tools to effectively sense PPIs are necessary. Effective PPI sensors must rapidly detect interactions in real-time with high sensitivity without perturbing the proteins of interest (POIs) under study. Split fluorescent proteins have previously been used to successfully monitor PPIs, in part due to the small size of the tags. Here, we developed an optimized tripartite split GFP system based on Corynactis californica GFP (ccGFP) to detect PPIs in vitro. In this sensor system, ccGFP fragments ccGFP10 and ccGFP11 are tagged to two POIs. PPIs can then be detected via fluorescence by complementation to the third fragment, ccGFP1-9, which reconstitutes functional ccGFP. The optimized ccGFP system shows improved detection kinetics and pH and temperature stability compared to a previous system. We then validated the sensor by monitoring PPIs in two model systems: attractive/repulsive coiled-coils and rapamycin-inducible FRB/FKBP heterodimerization. Finally, we developed an anti-tripartite ccGFP single-chain variable fragment (scFv), which could enable versatile detection of identified protein-protein complexes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Towards Scalable Deep Learning via I/O Analysis and Optimization

Deep learning systems have been growing in prominence as a way to automatically characterize objects, trends, and anomalies. Given the importance of deep learning systems, researchers have been investigating techniques to optimize such systems. An area of particular interest has been using large supercomputing systems to quickly generate effective deep learning networks: a phase often referred to as “training” of the deep learning neural network. As we scale existing deep learning frameworks—such as Caffe—on these large supercomputing systems, we notice that the parallelism can help improve the computation tremendously, leaving data I/O as the major bottleneck limiting the overall system scalability. In this paper, we first present a detailed analysis of the performance bottlenecks of Caffe on large supercomputing systems. Our analysis shows that the I/O subsystem of Caffe—LMDB—relies on memory-mapped I/O to access its database, which can be highly inefficient on large-scale systems because of its interaction with the process scheduling system and the network-based parallel filesystem. Based on this analysis, we then present LMDBIO, our optimized I/O plugin for Caffe that takes into account the data access pattern of Caffe in order to vastly improve I/O performance. Our experimental results show that LMDBIO can improve the overall execution time of Caffe by nearly 20-fold in some cases.

Pumma, Sarunya↗
DOE OSTI2014

Structural basis for the inhibition of poly(ADP-ribose) polymerases 1 and 2 by BMN 673, a potent inhibitor derived from dihydropyridophthalazinone

BMN 673, a novel PARP1/2 inhibitor in clinical development with substantial tumor cytotoxicity, forms extensive hydrogen-bonding and π-stacking in the nicotinamide pocket, with its unique disubstituted scaffold extending towards the less conserved edges of the pocket. These interactions might provide structural insight into the ability of BMN 673 to both inhibit catalysis and affect DNA-binding activity. Poly(ADP-ribose) polymerases 1 and 2 (PARP1 and PARP2), which are involved in DNA damage response, are targets of anticancer therapeutics. BMN 673 is a novel PARP1/2 inhibitor with substantially increased PARP-mediated tumor cytotoxicity and is now in later-stage clinical development for BRCA-deficient breast cancers. In co-crystal structures, BMN 673 is anchored to the nicotinamide-binding pocket via an extensive network of hydrogen-bonding and π-stacking interactions, including those mediated by active-site water molecules. The novel di-branched scaffold of BMN 673 extends the binding interactions towards the outer edges of the pocket, which exhibit the least sequence homology among PARP enzymes. The crystallographic structural analyses reported here therefore not only provide critical insights into the molecular basis for the exceptionally high potency of the clinical development candidate BMN 673, but also new opportunities for increasing inhibitor selectivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1993

A programming environment for distributed complex computing. An overview of the Framework for Interdisciplinary Design Optimization (FIDO) project. NASA Langley TOPS exhibit H120b

The Framework for Interdisciplinary Design Optimization (FIDO) is a general programming environment for automating the distribution of complex computing tasks over a networked system of heterogeneous computers. For example, instead of manually passing a complex design problem between its diverse specialty disciplines, the FIDO system provides for automatic interactions between the discipline tasks and facilitates their communications. The FIDO system networks all the computers involved into a distributed heterogeneous computing system, so they have access to centralized data and can work on their parts of the total computation simultaneously in parallel whenever possible. Thus, each computational task can be done by the most appropriate computer. Results can be viewed as they are produced and variables changed manually for steering the process. The software is modular in order to ease migration to new problems: different codes can be substituted for each of the current code modules with little or no effect on the others. The potential for commercial use of FIDO rests in the capability it provides for automatically coordinating diverse computations on a networked system of workstations and computers. For example, FIDO could provide the coordination required for the design of vehicles or electronics or for modeling complex systems.

Townsend, James C.↗
DOE OSTI2026

Dynamic Interfacial Design in Adaptive Hybrid Materials Enables Reversible and Tunable Mechano-Optic Smart Responses

Next-generation polymeric materials are shifting toward adaptive and interactive behaviors of living systems; however, designing materials that can reversibly modulate optical properties under mechanical deformation while maintaining mechanical robustness remains a key challenge. Here, we report a mechanically robust vitrimer-based adaptive hybrid material (AHM) that exhibits a stretch-induced reversible transparency-to-opacity transition, enabled by the integration of dynamic interactions at the polymer–silica nanoparticle interface and controlled nanoparticle self-assembly. The AHM combines boronic ester–functionalized polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (S-Bpin) with diol-functionalized silica nanoparticles (diol-SiNPs) to form a hybrid network hosting both dynamic boronic ester and hydrogen-bonding interactions. These reversible linkages facilitate controlled nanoparticle self-assembly and enable strain-induced nanoparticle alignment/aggregation. Upon stretching, SiNP-rich domains align and aggregate within the polymer matrix, while local modulus mismatch between stiff aggregated SiNP/borylated-styrene-rich regions and the softer elastomeric midblock induces surface microwrinkle formation. These internal aggregates and surface wrinkles cooperatively enhance light scattering, producing the opaque state under strain. Furthermore, the tailored AHM exhibits high toughness, thermomechanical stability, reprocessability, and programmable shape-memory behavior. This work presents a dynamic interfacial design strategy for mechanically robust, optically reconfigurable, and reusable soft materials for adaptive optics, smart windows, sensing, soft robotics, and circular smart-material platforms.

adaptive hybrid materials↗
DOE OSTI2017

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↗
DOE OSTI2025

Electronic Configurational Transformation of Network Modifiers in Aluminate Glass above Megabar Pressures

Electronic responses of glasses under extreme pressures differ from those of crystalline analogs. Their distinct electronic environments are found in network formers with well-defined, covalent-bonded coordination environments (e.g., [4] Si and [4] Al) and in network modifiers with more disordered, ionic-bonded configurations (e.g., [5,6,7] Ca). Deciphering the evolution of the bonding environment of network modifier cations upon compression provides atomic insights into the pressure-driven hardening and transport properties of glasses. Despite the importance, in contrast to extensive efforts to uncover how network formers behave under pressure, considerable structural disorder around network modifiers makes it challenging to probe their electronic bonding environments under compression. Our understanding of the evolution of network modifiers above megabars is currently absent. Here, we report a discovery of highly densified electronic configurations of network modifier Ca in aluminate glass under extreme compression via the first inelastic X-ray scattering at the Ca L-edge up to 140 GPa. As evidenced by the prominent pressure-driven increases in electronic dispersion and delocalization, densified calcium environments are characterized by a decreased average Ca–O distance, the formation of highly coordinated calcium, a broader distribution of topological variables, and a greater distortion of Ca polyhedra above megabars. The spectral features for the Ca environments reveal significant electronic and bonding modifications, including pressure-driven increases in the ligand field interaction, the covalence characteristic of the Ca–O bond, and the electron–hole Coulomb interaction. These densification paths identify the electronic adaptation of network modifiers above megabars, shedding light on the origins of enhanced electron transport and the electron-storing capacity of glasses under pressure.

amorphous materials↗
DOE OSTI2015

A 3D chiral metal-organic framework based on left-handed helices containing 3-amino-1 H-1,2,4-triazole ligand

A chiral metal-organic framework, [Cu(atr)(OH)]·0.5H{sub 2}O·0.5en (1) (Hatr=3-amino-1 H-1,2,4-triazole, en=ethylenediamine), was constructed via diffusion reaction of the achiral Hatr ligand and CuSO{sub 4} as starting materials. Compound 1 crystallizes in the chiral space group P3{sub 2}21 and features a porous metal-organic framework with 44.1% solvent-accessible volume fabricated by left-handed helices with a pitch height of l{sub p}=10.442 Å. Six helices gather around in a cycle forming a large honeycomb channel with a 6.58 Å inner diameter. Cu(II) center and atr{sup ‒} ligand regarded as 3-connected nodes, compound 1 can be simplified to a 3-c uninodal (4.12{sup 2}) (qtz-h) topological network. A gradual decreasing in the magnetic moment depending on temperature decreasing indicates an antiferromagnetic interaction in 1. The powder XRD confirms the bulk sample is a single crystal pure phase, and the thermogravimetric analysis shows the thermal stability of 1 is up to ca. 240 °C. - Highlights: • The present 3D chiral MOF is built from achiral Hatr ligand. • Six left-handed helices gather into a honeycomb channel in chiral sp P3{sub 2}21. • Compound 1 shows a 3-c uninodal (4.12{sup 2}) or qtz-h topological network. • Compound 1 indicates an antiferromagnetic interaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗