SEARCH · Search NASA

Results for “matrix structures”

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.

SourceResearch connectionFrom yearThrough year

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

At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

DOE OSTI2024

The NuMI Flux Prediction at ICARUS

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment seeking to probe fundamental symmetries within the structure of the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) mixing matrix, and perform precision measurements its parameters including the neutrino mass ordering via the sign of $\Delta m^2_{31}$, and the charge-parity violating phase, $\delta_{CP}$. To make these measurements with high precision, DUNE will require external $\nu$-Ar scattering cross section data as a crucial input to the oscillation fit. Imaging Cosmic And Rare Underground Signals (ICARUS) is a 476 t liquid argon neutrino detector located at Fermi National Accelerator Laboratory (FNAL) where it is serving as the far detector for the Short-Baseline Neutrino (SBN) program along the Booster Neutrino Beam (BNB) axis. ICARUS additionally lies 795 m downstream and 100.1 mrad off-axis of the Neutrinos at the Main Injector (NuMI) neutrino beam. From this position, ICARUS is exposed to a large flux of NuMI (anti-)electron and (anti-)muon neutrinos, and poses a unique opportunity to provide high-statistics measurements of quasi-elastic and single pion-production cross sections for four neutrino flavors ($\nu_{\mu}$, $\nu_{e}$, $\bar{\nu}_{\mu}$, $\bar{\nu}_{e}$). This dissertation is centered around accurately characterizing the models and estimating their precision for use in making these measurements. This includes identifying major sources of uncertainty in the models such that they can be properly propagated to the cross section measurements. Specifically, this work focused on the model of the NuMI beamline and its impact on the neutrino fluxes, but also delved into the detector response model and its impact on reconstructed observables in the detector. Significant efforts were made to improve the characterization to enhance precision and thus reduce the level of propagated uncertainty. In particular, the NuMI flux was determined to be composed of 57% $\nu_{\mu}$, 38% $\bar{\nu}_{\mu}$, 3% $\nu_{e}$, and 2% $\bar{\nu}_{e}$ while the horns are operating in the positive-particle focusing configuration. The total uncertainty on the $\nu_{\mu} + \bar{\nu}_{\mu}$ ($\nu_{e} + \bar{\nu}_{e}$) flux while operating in the forward horn operating mode was determined to be 10.84% (9.04%). Compared to the on-axis flux, mesons that eventually decay to neutrinos more frequently reinteract within the NuMI structure, resulting in elevated uncertainty as these processes are not well-constrained by existing hadron interaction cross section measurements. Covariance matrices were calculated to propagate the flux uncertainty characterization to NuMI analyses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Structural Heterogeneity in Medium-Entropy AgMnSbPbTe 4 for Glassy Thermal Transport and High Thermoelectric Performance

Medium-entropy semiconductors represent a unique category of entropy-engineered materials. They possess a considerable level of randomness in atomic mixing, although this is not sufficient to conclusively achieve single-phase structure stabilization, in contrast to high-entropy materials. This introduces strong competition between the formation of different phases, which can potentially lead to structural heterogeneity. Here, in this work, we uncover endotaxial nanoprecipitates in the microscopically identified homogeneous medium-entropy semiconductor AgMnSbPbTe 4 . These nanoprecipitates initially crystallize in a cubic phase $(Fm\bar{3}m)$ within kinetically stabilized AgMnSbPbTe 4 , subsequently evolving into a thermodynamically stable monoclinic phase $(P2_1/c)$ during thermal annealing while maintaining an endotaxial relationship with the matrix lattice. This nanophase segregation and the resultant lattice mismatch at interfaces introduce strain fluctuations up to 5% at intervals of 20 nm across the entire microstructure. Within the matrix phase, atomic displacement of up to 23 pm was observed. This structural heterogeneity results in glass-like thermal transport behavior, achieving an ultralow lattice thermal conductivity κ L = 0.312 Wm –1 K –1 at 800 K, which is in accordance with the amorphous limit predicted by the Cahill model. The synergy of band convergence effect and well-maintained carrier mobility leads to a maximum ZT of 1.72 at 800 K and an average ZT avg of 1.02 over the temperature range of 300–825 K. This study highlights that the underexplored structural heterogeneity in medium-entropy semiconductors can potentially yield beneficial phenomena, such as the phonon-glass electron-crystal transport behavior in this case, which holds promise for advancing thermoelectric applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Systematic Study of the Self-Renormalized Nucleon Gluon PDF in Large-Momentum Effective Theory

We present a systematic study of the nucleon gluon parton distribution function (PDF) using the self-renormalized large-momentum effective theory (LaMET) approach in lattice QCD. This work extends previous gluon-PDF extractions by performing a detailed analysis of key systematic effects, including gauge-link smearing, lattice spacing, pion mass, and nucleon boost momentum. The self-renormalization framework mitigates ultraviolet divergences associated with Wilson-line self-energy and renormalon contributions by combining lattice matrix elements with perturbative short-distance information, thereby preserving the correct infrared structure. Calculations are performed on $N_f=2+1+1$ HISQ ensembles generated by the MILC Collaboration at three lattice spacings and two pion masses, with boosted nucleon states reaching momenta up to 2.2~GeV. We determine renormalization factors from zero-momentum matrix elements and apply hybrid renormalization to suppress discretization artifacts. After extrapolating large-separation behavior and performing Fourier transforms, we reconstruct quasi-PDFs and match them to lightcone PDFs using next-to-leading order Wilson coefficients. Our results demonstrate that smearing and lattice-spacing effects are under control, and pion-mass and lattice-spacing dependence is mild relative to the current $O(10^6)$ statistics; however, momentum dependence remains a significant source of uncertainty. Future work including even larger boost momenta will be essential to reduce systematics in lattice determinations of the gluon PDF and to advance toward precision QCD phenomenology at the LHC and the future Electron-Ion Collider.

FOS: Physical sciences↗

DOE OSTI2024

Quantifying Quantum Chaos through Microcanonical Distributions of Entanglement

A characteristic feature of “quantum chaotic” systems is that their eigenspectra and eigenstates display universal statistical properties described by random matrix theory (RMT). However, eigenstates of local systems also encode structure beyond RMT. To capture this feature, we introduce a framework that allows us to compare the properties of eigenstates in local systems with those of pure random states. In particular, our framework defines a notion of distance between quantum state ensembles that utilizes the Kullback-Leibler divergence to compare the microcanonical distribution of entanglement entropy (EE) of eigenstates with a reference RMT distribution generated by pure random states (with appropriate constraints). This notion gives rise to a quantitative metric for quantum chaos that not only accounts for averages of the distributions but also higher moments. The differences in moments are compared on a highly resolved scale set by the standard deviation of the RMT distribution, which is exponentially small in system size. As a result, the metric can distinguish between chaotic and integrable behaviors and, in addition, quantify and compare the of chaos (in terms of proximity to RMT behavior) between two systems that are assumed to be chaotic. We implement our framework in local, minimally structured, Floquet random circuits, as well as a canonical family of many-body Hamiltonians, the mixed-field Ising model (MFIM). Importantly, for Hamiltonian systems, we find that the reference random distribution must be appropriately constrained to incorporate the effect of energy conservation in order to describe the ensemble properties of midspectrum eigenstates. The metric captures deviations from RMT across all models and parameters, including those that have been previously identified as strongly chaotic, and for which other diagnostics of chaos such as level spacing statistics look strongly thermal. In Floquet circuits, the dominant source of deviations is the second moment of the distribution, and this persists for all system sizes. For the MFIM, we find significant variation of the KL divergence in parameter space. Notably, we find a small region where deviations from RMT are minimized, suggesting that “maximally chaotic” Hamiltonians may exist in fine-tuned pockets of parameter space. Published by the American Physical Society 2024

Physics↗

DOE OSTI2025

Sensitivity of magnetic islands in permanent magnet stellarators using the gradient and Hessian methods

Stellarator plasmas are known to be very sensitive to perturbations in the magnetic field. The permanent magnet stellarator was in part developed as a solution to high machining tolerances placed on the shape properties of electromagnetic coils in traditional stellarators. However, as a consequence of this high sensitivity to the field structure, sensitivities of permanent magnet stellarator plasmas to perturbations of permanent magnet properties must necessarily be well-understood. The gradient and Hessian matrix methods have been previously demonstrated to be useful sensitivity analysis methods for modular coils. We apply these two methods to the study of island width sensitivities in both the MUSE and PM4STELL permanent magnet stellarator projects. These sensitivity methods were used to determine the relative impacts of permanent magnet parameter perturbations on island widths in the vacuum field approximation of both stellarator equilibria. The square of resonant magnetic field perturbation is used here as a proxy for island width. In particular, gradients of magnetizations of individual magnets were examined in MUSE, as well as gradients of magnet group displacements informed by device design. Three different forms of permanent magnet magnetization perturbations are investigated for MUSE, and the flux surface response to perturbations is demonstrated. The Hessian matrix method is applied to PM4STELL, illustrating the sensitivity of dominant island widths to displacements of toroidal wedge structures. These methods allow for selective direction of experimental resources toward regions of heightened sensitivity, while constraints on less impactful permanent magnet parameters can be relaxed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

The AUREX cell: a versatile operando electrochemical cell for studying catalytic materials using X-ray diffraction, total scattering and X-ray absorption spectroscopy under working conditions

Understanding the structure–property relationship in electrocatalysts under working conditions is crucial for the rational design of novel and improved catalytic materials. This paper presents the Aarhus University reactor for electrochemical studies using X-rays (AUREX) operando electrocatalytic flow cell, designed as an easy-to-use versatile setup with a minimal background contribution and a uniform flow field to limit concentration polarization and handle gas formation. The cell has been employed to measure operando total scattering, diffraction and absorption spectroscopy as well as simultaneous combinations thereof on a commercial silver electrocatalyst for proof of concept. This combination of operando techniques allows for monitoring of the short-, medium- and long-range structure under working conditions, including an applied potential, liquid electrolyte and local reaction environment. The structural transformations of the Ag electrocatalyst are monitored with non-negative matrix factorization, linear combination analysis, the Pearson correlation coefficient matrix, and refinements in both real and reciprocal space. Upon application of an oxidative potential in an Ar-saturated aqueous 0.1 M KHCO 3 /K 2 CO 3 electrolyte, the face-centered cubic (f.c.c.) Ag gradually transforms first to a trigonal Ag 2 CO 3 phase, followed by the formation of a monoclinic Ag 2 CO 3 phase. A reducing potential immediately reverts the structure to the Ag (f.c.c.) phase. Following the electrochemical-reaction-induced phase transitions is of fundamental interest and necessary for understanding and improving the stability of electrocatalysts, and the operando cell proves a versatile setup for probing this. In addition, it is demonstrated that, when studying electrochemical reactions, a high energy or short exposure time is needed to circumvent beam-induced effects.

Frank, Sara (ORCID:0000000163218363)↗

DOE OSTI2025

RE-INTEGRATE EMT Simulation Software: Graph Convolutional Network for Sparse Matrix Pattern Detection

The increasing complexity of power networks, driven by proliferation of inverters, presents analytical challenges that simplified models often fail to capture, necessitating Electromagnetic Transient (EMT) simulations. EMT models are represented as discretized differential-algebraic equations (DAEs), forming a linear system Ax = b that is computationally intensive to solve. Due to inherent sparsity of adjacency matrix A, distinct patterns emerge that, when accurately identified, enable efficient solver selection to minimize computation time. However, identifying ideal pattern is complicated by numerous reordering algorithms and limited structural insights. To address this, we introduce a Graph Convolutional Network (GCN) model for classifying sparse matrix patterns common in power system analysis. The model, achieving 96% test accuracy, is validated using PV plant models of 125 MW capacities connected to New England 39-bus transmission system (TS), and further scaled to a 4,992-bus network with 384 PV plants, yielding 191, 616 × 191, 616 sized A matrix. For all cases, the GCN model accurately identifies the matrix’s intrinsic sparse pattern, demonstrating its potential to enhance solver performance in EMT analysis.

Hossain, Md Rifat [Florida International Universit↗

DOE OSTI2026

Block-Type Antiferromagnetism in Single Chain Quasi-One-Dimensional K 3 ⁢Fe 2 ⁢Se 4

One-dimensional (1D) structures provide a unique platform to study the correlated quantum interactions and phase transitions such as unconventional magnetism and superconducting states. Here, we report that iron chalcogenide K 3 ⁢Fe 2 ⁢Se 4 exhibits an unusual block-type canted antiferromagnetic (AFM) order with a clear single chain quasi-1D structure, which is structurally different from the two-leg ladder BaFe 2 ⁢Se 3 , through both experimental measurements and density matrix renormalization group (DMRG) calculations. The narrow bandgap semiconductor K 3 ⁢Fe 2 ⁢Se 4 has a quasi-1D edge-shared FeSe4 tetrahedra chain structure and orders antiferromagnetically below 110 K. The magnetic moments couple antiferromagnetically along the quasi-1D chain direction of the 𝑏 axis and form an up-down-down-up (↑−↓−↓−↑)–like spin structure with a commensurate propagation vector 𝒌=⁢(0,0,0), where block-type spin ↑−↑ or ↓−↓ coupling are between the longer Fe-Fe bonds of the quasi-1D chain. DMRG results show that block antiferromagnetic state is stable in K 3 ⁢Fe 2 ⁢Se 4 and reveal that the block-ordered arrangement of Fe 2.5+ ions spins arise from the competition between ferromagnetic and AFM interaction in the presence of strong electronic correlation. Our research results not only report the discovery of a clear block-type canted antiferromagnetic structure in a real quasi-1D chain material but also provide a theoretical approach to understand the block-type antiferromagnetism in quasi-1D iron chalcogenides.

antiferromagnetism↗

DOE OSTI2026

Simplex‐based model for nanoparticle grain identification in four‐dimensional scanning transmission electron microscopy data

Grain identification in polycrystalline nanoparticles, for example, determining which crystal phases are present at each spatial location, is fundamental to materials characterisation. This is particularly challenging when grains overlap extensively, as commonly occurs in four-dimensional scanning transmission electron microscopy (4D-STEM) datasets. We propose a simplex-based model (SBM) in which each simplex vertex represents the diffraction pattern (DP) of a pure grain, and the simplex edges and interior represent overlapping grains. Our SBM grain identification algorithm operates on the Bragg disk (BD) data matrix distilled from the 4D-STEM data to identify the grain membership at each scan position, together with a BD feature matrix whose columns represent the DPs for each constituent grain, which is important for identifying the crystal structure of each grain. We solve the model using a two-stage algorithm. In Stage 1, we adapt a linear mixing algorithm to estimate an initial BD feature matrix whose columns represent DPs of potentially overlapping grains. Our Stage 2 algorithm incorporates sparsity considerations to transform the initial BD feature matrix so that its columns represent DPs of pure grains. Using simulated datasets with various grain configurations, we demonstrate that SBM recovers both the BD feature matrix and membership maps more accurately than existing methods, even when a grain lacks any pure region and completely overlaps with other grains.

4D-STEM segmentation↗

DOE OSTI2025

A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites

Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.

Hybrid composites↗

DOE OSTI2025

Pultrusion and Vitrimer Composites: Emerging Pathways for Sustainable Structural Materials

Pultrusion is a manufacturing process used to produce fiber-reinforced polymer composites with excellent mechanical, thermal, and chemical properties. The resulting materials are lightweight, durable, and corrosion-resistant, making them valuable in aerospace, automotive, construction, and energy sectors. However, conventional thermoset composites remain difficult to recycle due to their infusible and insoluble cross-linked structure. This review explores integrating vitrimer technology a novel class of recyclable thermosets with dynamic covalent adaptive networks into the pultrusion process. As only limited studies have directly reported vitrimer pultrusion to date, this review provides a forward-looking perspective, highlighting fundamental principles, challenges, and opportunities that can guide future development of recyclable high-performance composites. Vitrimers combine the mechanical strength (tensile strength and modulus) of thermosets with the reprocessability and reshaping of thermoplastics through dynamic bond exchange mechanisms. These polymers offer high-temperature reprocessability, self-healing, and closed-loop recyclability, where recycling efficiency can be evaluated by the recovery yield retention of mechanical properties and reuse cycles meeting the demand for sustainable manufacturing. Key aspects discussed include resin formulation, fiber impregnation, curing cycles, and die design for vitrimer systems. The temperature-dependent bond exchange reactions present challenges in achieving optimal curing and strong fiber–matrix adhesion. Recent studies indicate that vitrimer-based composites can maintain structural integrity while enabling recycling and repair, with mechanical performance such as flexural and tensile strength comparable to conventional composites. Incorporating vitrimer materials into pultrusion could enable high-performance, lightweight products for a circular economy. The remaining challenges include optimizing curing kinetics, improving interfacial adhesion, and scaling production for widespread industrial adoption.

Fiber composites↗

DOE OSTI2026

Molecularly engineered ZnO–carbon nanosheets from fumaric acid precursors for efficient photocatalytic water purification

The photocatalytic breakdown of organic contaminants is crucial for the development of water purification technology. Zinc oxide (ZnO) is an extensively researched photocatalyst; however, its efficacy is hindered by rapid charge recombination and limited utilization of UV irradiation. Resolving these issues necessitates integrating ZnO with conductive carbon phases via scalable, low-temperature synthesis. We provide a molecularly designed sol–gel method that converts zinc–fumarate coordination networks into two-dimensional ZnO–carbon nanosheets utilizing solely aqueous precursors and mild annealing temperatures (400–600 °C). This method utilizes fumaric acid as a dicarboxylate linker and polyvinylpyrrolidone (PVP) as a structural carbon source to produce ultrathin wurtzite ZnO nanosheets embedded inside an amorphous carbon matrix. The resultant ZnO-C hybrid achieves nearly complete methylene blue degradation within 10 min under UV-A illumination, demonstrating first-order kinetics and outstanding recyclability. Compared with commercial ZnO, the ZnO–C nanosheets exhibit comparable rapid photocatalytic degradation, enhanced adsorption behavior, a porous nanosheet morphology, and an integrated ZnO–carbon interfacial structure. These findings provide a viable molecular-templating approach to fabricating various metal oxide–carbon photocatalysts and underscore substantial enhancements in semiconductor efficacy in eco-friendly water treatment systems.

Ozcan, Muca [ORNL] (ORCID:0000000320020474)↗

DOE OSTI2025

Evidence for triaxial shape coexistence in 74 Ge

The deformation properties of the low-lying states in 74 Ge have been investigated using multistep Coulomb excitation. The measurements were carried out with the advanced 𝛾-ray tracking array, GRETINA, and the CHICO2 particle detector. A comprehensive set of 𝐸⁢2 transition and diagonal matrix elements was deduced following an analysis with the semiclassical coupled-channels code GOSIA. The data were compared with results of calculations carried out within the framework of the generalized triaxial rotor model as well as with the configuration interaction shell model and the symmetric rotor model. Results from calculations with covariant density functional theory were used to construct a five-dimensional collective Hamiltonian for further comparisons with the data. Collectively, the calculations provide an accurate reproduction of the experimental matrix elements and further support an understanding in terms of the coexistence of two axially asymmetric shapes. In conclusion, this leads to an overall interpretation of the underlying structure of 74 Ge requiring triaxiality, as is also the case in the neighboring even-mass Ge isotopes.

59 ≤ A ≤ 89↗

DOE OSTI2024

Influence of gadolinium doping on structural, optical, and electronic properties of polymeric graphitic carbon nitride

Polymeric graphitic carbon nitride (gCN) materials have received great attention in the fields of photo and electrocatalysis due to their distinct properties in metal-free systems with high physicochemical stability. Nevertheless, the activity of undoped gCN is limited due to its relatively low specific surface area, low conductivity, and poor dispersibility. Doping Gd atoms in a gCN matrix is an efficient strategy to fine-tune its catalytic activity and its electronic structure. Herein, the influence of various wt% of gadolinium (Gd) doped in melon-type carbon nitride was systematically investigated. Gadolinium-doped graphitic carbon nitride (GdgCN) was synthesized by adding gadolinium nitrate to dicyandiamide during polymerization. The X-ray diffraction (XRD) and transmission electron microscopy (TEM) results revealed that the crystallinity and the morphological properties are influenced by the % of Gd doping. Furthermore, X-ray photoelectron spectroscopy (XPS) studies revealed that the gadolinium ions bonded with nitrogen atoms. Complementary density functional theory (DFT) calculations illustrate possible bonding configurations of Gd ions both in bulk material and on ultrathin melon layers and provide evidence for the corresponding bandgap modifications induced by gadolinium doping.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Enhancing cold spray coatings: Microstructural dynamics and performance attributes of Inconel 625 with chromium carbide incorporation for hydropower applications

The incorporation of chromium carbide (CrC) particles into the cold spray (CS) process is known to mitigate nozzle clogging, although at the expense of deposition efficiency. This study explores the intricate microstructural changes induced by varying amounts of CrC powder (12.5 % and 6 %) in conjunction with Inconel-625 (Inc-625) powder. The deposition was carried out onto A27 cast steel under different CS parameters. Microstructural characterization, including detailed electron microscopy studies, reveals a complex yet structurally stable coating. Noteworthy features include grain fragmentation and a cellular structure enriched with Nb and Mo, with minimal plastic deformation of CrC in the matrix. The cold-sprayed coatings exhibit a significant (~4 times) increase in microhardness compared to the A27 substrate. Mechanical and cavitation erosion properties were systematically investigated. Coatings subjected to higher particle energy conditions with a gas pressure of 600 psi and gas temperature of 650 °C, demonstrated superior resistance to cavitation erosion. This resistance is attributed to a combination of factors, including microstructural characteristics and porosity. Altogether, the study provides valuable insights into the structural dynamics and performance of CS coatings enriched with CrC particles.

A27 cast steel↗

DOE OSTI2025

Additive manufacturing of metal matrix composites

Although Metal matrix composites (MMCs) are superior to most sought-after metallic alloys, their challenging fabricability has limited their widespread use in bulk-form applications. Among the many advanced fabrication techniques, Additive Manufacturing (AM), owing to its unique capabilities to produce near-net shapes, has drawn significant traction in the past two decades, especially for materials that are difficult to process using traditional methods. However, unlike pure metal/alloy systems, MMCs are highly sensitive to the processing conditions prevailing in AM techniques due to factors such as the high melting point of reinforcement particles and the potential for in-situ reactions. Therefore, it may be a while before metal matrix composites are commercially produced via AM. This review will discuss the current state-of-the-art design, fabricability, and performance of various additively manufactured MMCs. A particular focus will be on microstructural evolution and microstructure-property relationships. The most employed AM techniques, such as directed energy deposition, powder bed fusion, binder jetting, sheet lamination, and solid-state friction stir processing, are fundamentally different in terms of thermo-kinetics, forming the perspective for this review. A detailed comparison of microstructural evolution and process parameter optimization, including feedstock preparation methods and the role of machine learning and modeling among the different AM processes, is also presented. Finally, a critical evaluation of emerging AM technologies for MMCs is also provided, highlighting their potential advantages and challenges.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Mechanistic Insights into Visible-Light-Induced ATRA Reactions Powered by the Symbiotic Relationship between Cu(II)/Cu(I) Phenanthroline Complexes

As photoredox catalysis continues to yield promising chemical transformations, there is an increased need to understand how specific photocatalysts function to improve reaction efficiencies while expanding their scope. Copper-phenanthroline-based photocatalysts such as Cu II (dap)Cl 2 (dap = 2,9-di(p-anisyl)-1,10-phenanthroline) and [Cu I (dap) 2 ]Cl were both found to be equally capable of olefin activation through electrophilic atom transfer radical addition (ATRA) reactions. Although these molecular catalysts have proven successful, many intermediates suggested in the proposed catalytic cycles have never been detected. One undetermined aspect in this chemistry is related to how one equivalent of Cu II (dap)Cl 2 generates half of an equivalent of [Cu I (dap) 2 ] + during the photocatalytic sequence. To this end, we initially used more synthetically accessible model systems, namely, [Cu I (dpp) 2 ]Cl and Cu II (dpp)Cl 2 (dpp = 2,9-diphenyl-1,10-phenanthroline), to glean detailed mechanistic insights into this unusual symbiotic relationship. We directly detected several intermediates involved in the ATRA photocatalytic cycle using these model chromophores in conjunction with electronic spectroscopy, infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) mass spectrometry, electronic structure calculations, EPR spin-trap experiments, and 1 H NMR spectroscopy. We found that the unique ligand lability and coordinating properties of acetonitrile enable both the in situ oxidation of [Cu I (dpp) 2 ] + by tosyl chloride into Cu II (dpp)Cl 2 and the visible-light-induced homolysis of the Cu II –Cl bond, which initiates the conversion to the Cu I species [Cu I (dpp) 2 ][Cu I Cl 2 ]. Here, the combined findings from the present study of the catalytic cycle demonstrate that the symbiotic relationship between Cu II (dpp)Cl 2 and [Cu I (dpp) 2 ] + , as well as between Cu II (dap)Cl 2 and [Cu I (dap) 2 ] + , is the critical factor enabling the ATRA photoreaction by departing from either photocatalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Statistical properties of filaments in the cosmic web

ABSTRACT In the context of the cosmological and constrained Exploring the Local Universe with the reConstructed Initial Density field (ELUCID) simulation, this study explores the statistical characteristics of filaments within the cosmic web, focussing on aspects such as the distribution of filament lengths and their radial density profiles. Using the classification of the cosmic web environment through the Hessian matrix of the density field, our primary focus is on how cosmic structures react to the two variables $R_{\rm s}$ and $\lambda _{\rm th}$. The findings show that the volume fractions of knots, filaments, sheets, and voids are highly influenced by the threshold parameter $\lambda _{\rm th}$, with only a slight influence from the smoothing length $R_{\rm s}$. The central axis of the cylindrical filament is pinpointed using the medial-axis thinning algorithm of the COsmic Web Skeleton (COWS) method. It is observed that median filament lengths tend to increase as the smoothing lengths increase. Analysis of filament length functions at different values of $R_{\rm s}$ indicates a reduction in shorter filaments and an increase in longer filaments as $R_{\rm s}$ increases, peaking around $2.5R_{\rm s}$. The study also shows that the radial density profiles of filaments are markedly affected by the parameters $R_{\rm s}$ and $\lambda _{\rm th}$, showing a valley at approximately $2R_{\rm s}$, with increases in the threshold leading to higher amplitudes of the density profile. Moreover, shorter filaments tend to have denser profiles than their longer counterparts.

Zhang, Youcai (ORCID:0000000319674091)↗