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At least 1,459 records · Page 81

Role of catalytic nitrile decomposition in tricopper complex mediated direct partial oxidation of methane to methanol

Synthetic homogeneous system known to date performing methane to methanol conversion using O 2 as terminal oxidant is unique and based on copper complex with piperazine-based ligand (Cu 3 L in Fig. 1) in a medium of acetonitrile. Prior work have shown that in order to achieve catalytic turnover, hydrogen peroxide is needed to regenerate the active site. We show in this paper that reaction solvent based on organic nitrile decompose concurrently with methane activation and that in the absence of either acetonitrile, Cu complex or hydrogen peroxide, the catalytic turnover does not happen. We show in this manuscript that the direct methane oxidation to methanol might have been mediated by catalytic Radziszewski oxidation between acetonitrile and H 2 O 2 . Additionally we have discovered that in the absence of methane, peroxide mediated acetonitrile decomposition also makes methanol via a background reaction which was hitherto unknown.

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Decentralized digital twins of complex dynamical systems

Abstract In this article, we introduce a decentralized digital twin (DDT) modeling framework and its potential applications in computational science and engineering. The DDT methodology is based on the idea of federated learning, a subfield of machine learning that promotes knowledge exchange without disclosing actual data. Clients can learn an aggregated model cooperatively using this method while maintaining complete client-specific training data. We use a variety of dynamical systems, which are frequently used as prototypes for simulating complex transport processes in spatiotemporal systems, to show the viability of the DDT framework. Our findings suggest that constructing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems may be made possible by federated machine learning.

97 MATHEMATICS AND COMPUTING↗

Machine learning surrogates for surface complexation model of uranium sorption to oxides

Abstract The safety assessments of the geological storage of spent nuclear fuel require understanding the underground radionuclide mobility in case of a leakage from multi-barrier canisters. Uranium, the most common radionuclide in non-reprocessed spent nuclear fuels, is immobile in reduced form (U(IV) and highly mobile in an oxidized state (U(VI)). The latter form is considered one of the most dangerous environmental threats in the safety assessments of spent nuclear fuel repositories. The sorption of uranium to mineral surfaces surrounding the repository limits their mobility. We quantify uranium sorption using surface complexation models (SCMs). Unfortunately, numerical SCM solvers often encounter convergence problems due to the complex nature of convoluted equations and correlations between model parameters. This study explored two machine learning surrogates for the 2-pK Triple Layer Model of uranium retention by oxide surfaces if released as U(IV) in the oxidizing conditions: random forest regressor and deep neural networks. Our surrogate models, particularly DNN, accurately reproduce SCM model predictions at a fraction of the computational cost without any convergence issues. The safety assessment of spent fuel repositories, specifically the migration of leaked radioactive waste, will benefit from having ultrafast AI/ML surrogates for the computationally expensive sorption models that can be easily incorporated into larger-scale contaminant migration models. One such model is presented here.

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Complexity-calibrated benchmarks for machine learning reveal when prediction algorithms succeed and mislead

Abstract Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form of recurrent neural network. Recently, a “next-generation” reservoir computer was introduced in which the memory trace involves only a finite number of previous symbols. We explore the inherent limitations of finite-past memory traces in this intriguing proposal. A lower bound from Fano’s inequality shows that, on highly non-Markovian processes generated by large probabilistic state machines, next-generation reservoir computers with reasonably long memory traces have an error probability that is at least $$\sim 60\%$$ ∼ 60 % higher than the minimal attainable error probability in predicting the next observation. More generally, it appears that popular recurrent neural networks fall far short of optimally predicting such complex processes. These results highlight the need for a new generation of optimized recurrent neural network architectures. Alongside this finding, we present concentration-of-measure results for randomly-generated but complex processes. One conclusion is that large probabilistic state machines—specifically, large $$\epsilon$$ ϵ -machines—are key to generating challenging and structurally-unbiased stimuli for ground-truthing recurrent neural network architectures.

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Crystal structure of the human PRPK–TPRKB complex

Mutations of the p53-related protein kinase (PRPK) and TP53RK-binding protein (TPRKB) cause Galloway-Mowat syndrome (GAMOS) and are found in various human cancers. We have previously shown that small compounds targeting PRPK showed anti-cancer activity against colon and skin cancer. Here we present the 2.53 Å crystal structure of the human PRPK-TPRKB-AMPPNP (adenylyl-imidodiphosphate) complex. The structure reveals details in PRPK-AMPPNP coordination and PRPK-TPRKB interaction. PRPK appears in an active conformation, albeit lacking the conventional kinase activation loop. We constructed a structural model of the human EKC/KEOPS complex, composed of PRPK, TPRKB, OSGEP, LAGE3, and GON7. Disease mutations in PRPK and TPRKB are mapped into the structure, and we show that one mutation, PRPK K238Nfs*2, lost the binding to OSGEP. Our structure also makes the virtual screening possible and paves the way for more rational drug design.

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Topological structure of complex predictions

Abstract Current complex prediction models are the result of fitting deep neural networks, graph convolutional networks or transducers to a set of training data. A key challenge with these models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into a simplified topological view of the prediction landscape. The result is a map of the predictions that enables inspection of the model results with more specificity than dimensionality-reduction methods such as tSNE and UMAP. The methods scale up to large datasets across different domains. We present a case study of a transformer-based model previously designed to predict expression levels of a piece of DNA in thousands of genomic tracks. When the model is used to study mutations in the BRCA1 gene, our topological analysis shows that it is sensitive to the location of a mutation and the exon structure of BRCA1 in ways that cannot be found with tools based on dimensionality reduction. Moreover, the topological framework offers multiple ways to inspect results, including an error estimate that is more accurate than model uncertainty. Further studies show how these ideas produce useful results in graph-based learning and image classification.

Computer Science↗

Concerted proton–electron transfer reactions of manganese–hydroxo and manganese–oxo complexes

The enzymes manganese superoxide dismutase and manganese lipoxygenase use MnIII–hydroxo centres to mediate proton-coupled electron transfer (PCET) reactions with substrate. As manganese is earth-abundant and inexpensive, manganese catalysts are of interest for synthetic applications. Recent years have seen exciting reports of enantioselective C–H bond oxidation by Mn catalysts supported by aminopyridyl ligands. Such catalysts offer economic and environmentally-friendly alternatives to conventional reagents and catalysts. Mechanistic studies of synthetic catalysts highlight the role of Mn–oxo motifs in attacking substrate C–H bonds, presumably by a concerted proton–electron transfer (CPET) step. (CPET is a sub-class of PCET, where the proton and electron are transferred in the same step.) Knowledge of geometric and electronic influences for CPET reactions of Mn–hydroxo and Mn–oxo adducts enhances our understanding of biological and synthetic manganese centers and informs the design of new catalysts. In this Feature article, we describe kinetic, spectroscopic, and computational studies of MnIII–hydroxo and MnIV–oxo complexes that provide insight into the basis for the CPET reactivity of these species. Systematic perturbations of the ligand environment around MnIII–hydroxo and MnIV–oxo motifs permit elucidation of structure–activity relationships. For MnIII–hydroxo centers, electron-deficient ligands enhance oxidative reactivity. However, ligand perturbations have competing consequences, as changes in the MnIII/II potential, which represents the electron-transfer component for CPET, is offset by compensating changes in the pKa of the MnII–aqua product, which represents the proton-transfer component for CPET. For MnIV–oxo systems, a multi-state reactivity model inspired the development of significantly more reactive complexes. Weakened equatorial donation to the MnIV–oxo unit results in large rate enhancements for C–H bond oxidation and oxygen-atom transfer reactions. These results demonstrate that the local coordination environment can be rationally changed to enhance reactivity of MnIII–hydroxo and MnIV–oxo adducts.

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Multiscale investigations of europium(III) complexation with tetra- n -octyl diglycolamide confined in porous solid supports

The microscopic, short-range coordination environments and mesoscopic, long-range structures of Eu 3+ contacted with tetra-n-octyl diglycolamide (TODGA) confined on ordered mesoporous carbon (OMC) nanoparticles and Amberchrom CG 71 resin were probed using Eu L 3 -edge extended X ray absorption fine structure (EXAFS) as well as small-angle and wide-angle X ray scattering, SAXS and WAXS, respectively. A homoleptic Eu(TODGA) 3 3+ coordination complex typical of liquid-liquid extraction (LLE) chemistry is present under low Eu 3+ loading conditions on both solid supports. Deviations from this traditional structure motif appear at hyperstoichiometric Eu 3+ loadings, above the 1:3 Eu 3+ to TODGA mole ratio. Microcrystalline-like domains with multinuclear Eu speciation are templated by use of these high Eu 3+ loading conditions with the functionalized OMC materials, highlighting a major departure of liquid-solid extraction chromatography (EXC) from the coordination chemistry of LLE. No such long-range spatial coherence was observed for analogous polyacrylic resin materials. Furthermore, these results demonstrate both the similarities and differences between multiscale structures in LLE and EXC, underscoring the opportunities for improved separation techniques based on solid supports with long-range spatial coherence (e.g., OMC systems) and without it (e.g., resin materials). Since crystallization was the first approach to adjacent lanthanide separations, the prospect of templating microcrystalline TODGA-lanthanide complexes that are otherwise not prone to crystallization by use of OMC nanoparticles represents a new entry to addressing long-standing issues in purification by liquid-solid phase separation.

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The synthesis and versatile reducing power of low-valent uranium complexes

This Perspective provides a detailed overview of the chemistry of low-valent (di- and trivalent) uranium. The reactivity of uranium(II) and uranium(III) complexes is discussed both to illustrate the general types of reactions that might be expected and to highlight the many unusual modes of reactivity observed with this element. A particular emphasis is given to redox reactions with uranium(III) species, including reduction of small molecules, multi-electron reductions involving redox-active ligands, and formation of uranium–ligand multiple bonds. Redox-neutral adduct formation with uranium(III) complexes as well as the current state of the young field of uranium(II) redox chemistry are also covered. In addition, synthetic protocols to prepare a wide range of low-valent compounds are presented.

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Expanding manganese(iv) aqueous chemistry: unusually stable water-soluble hexahydrazide clathrochelate complexes

Mn cage complexes are rare, and the ones successfully isolated in the solid state are not stable in water and organic solvents. In this work, we present the first report of mononuclear Mn clathrochelates, in which the encapsulated metal exists in the oxidation state +4. The complexes are extremely stable in the crystalline state and in solutions and show rich redox chemistry.

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Nickel(ii) complexes based on dithiolate–polyamine binary ligand systems: crystal structures, hirshfeld surface analysis, theoretical study, and catalytic activity study on photocatalytic hydrogen generation

To ascertain the influence of binary ligand systems [1,1-dicyanoethylene-2,2-dithiolate (i-mnt -2 ) and polyamine {tetraen = tris(2-aminoethyl)amine, tren = diethylene triamine and opda = o-phenylenediamine}] on the coordination modes of the Ni(II) metal center and resulting supramolecular architectures, a series of nickel(II) thiolate complexes [Ni(tetraen)(i-mnt)](DMSO) (1), [Ni 2 (tren) 2 (i-mnt) 2 ] (2), and [Ni 2 (i-mnt) 2 (opda) 2 ] n (3) have been synthesized in high yield in one step in water and structurally characterized by single crystal X-ray crystallography and spectroscopic techniques. X-ray diffraction studies disclose the diverse i-mnt -2 coordination to the Ni +2 center in the presence of active polyamine ligands, forming a slightly distorted octahedral geometry (NiN 4 S 2 ) in 1, square planar (NiS 4 ) and distorted octahedral geometries (NiN 6 ) in the bimetallic co-crystallized aggregate of cationic [Ni(tren) 2 ] +2 and anionic [Ni(i-mnt) 2 ] -2 in 2, and a one dimensional (1D) polymeric chain along the [100] axis in 3, having consecutive square planar (NiS 4 ) and octahedral (NiN 6 ) coordination kernels. The N–H···O, N–H···S, N–H···N, N–H···S, N–H···N, and N–H···O type hydrogen bonds stabilize the supramolecular assemblies in 1, 2, and 3 respectively imparting interesting graph-set-motifs. The molecular Hirshfeld surface analyses (HS) and 2D fingerprint plots were utilized for decoding all types of non-covalent contacts in the crystal networks. Atomic HS analysis of the Ni +2 centers reveals significant Ni–N metal–ligand interactions compared to Ni–S interactions. We have also studied the unorthodox interactions observed in the solid state structures of 1–3 by QTAIM and NBO analyses. Moreover, all the complexes proved to be highly active water reduction co-catalysts (WRC) in a photo-catalytic hydrogen evolution process involving iridium photosensitizers, wherein 2 and 3 having a square planar arrangement around the nickel center(s) – were found to be the most active ones, achieving 1000 and 1119 turnover numbers (TON), respectively.

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Unveiling the complex configurational landscape of the intralayer cavities in a crystalline carbon nitride

The in-depth understanding of the reported photoelectrochemical properties of the layered carbon nitride, poly(triazine imide)/LiCl (PTI/LiCl), has been limited by the apparent disorder of the Li/H atoms within its framework. To understand and resolve the current structural ambiguities, an optimized one-step flux synthesis (470 °C, 36 h, LiCl/KCl flux) was used to prepare PTI/LiCl and deuterated-PTI/LiCl in high purity. Its structure was characterized by a combination of neutron/X-ray diffraction and transmission electron microscopy. The range of possible Li/H atomic configurations was enumerated for the first time and, combined with total energy calculations, reveals a more complex energetic landscape than previously considered. Experimental data were fitted against all possible structural models, exhibiting the most consistency with a new orthorhombic model (Sp. Grp. Ama2) that also has the lowest total energy. In addition, a new Cu(I)-containing PTI (PTI/CuCl) was prepared with the more strongly scattering Cu(I) cations in place of Li, and most closely matching with the partially-disorder structure in Cmc2 1 . Thus, a complex configurational landscape of PTI is revealed to consist of a number of ordered crystalline structures that are new potential synthetic targets, such as with the use of metal-exchange reactions.

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Quantitative U=O bond activation in uranyl complexes via silyl radical transfer

Reductive silylation of the uranyl dication with 1,4-bis(trimethylsilyl)dihydropyrazine, or “Mashima's reagent”, is detailed. The reagent simultaneously delivers silylium ions and electrons to multiple uranyl complexes, i.e. a pyridine dipyrrolide uranyl complex and a common uranyl-containing starting material, UO 2 Cl 2 (OPPh 3 ) 2 . This reaction results in quantitative activation of thermodynamically robust U=O bonds. Furthermore, the reductive functionalization of environmentally persistent actinyl species with Mashima's reagent is seen as a promising solution for nuclear waste remediation.

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Isostructural σ-hydrocarbyl phospholide complexes of uranium, neptunium, and plutonium

σ-Hydrocarbyl complexes of the form [M(η 5 -PC 4 Me 4 ) 2 (μ-η 1 :η 6 -CH 2 Ph) 2 K(η 6 -arene)] (M = La, Ce, Pr, U, Np, Pu; arene = benzene or toluene) were synthesised in one-pot reactions from [MI 3 (THF) 4 ], or [U(BH 4 ) 3 (toluene)] (M = U). All complexes were examined by multinuclear ( 1 H, 13 C{ 1 H}, 31 P{ 1 H}) NMR and UV-vis-NIR spectroscopy, as well as single-crystal X-ray diffraction from which molecular metal–phosphorus bonds for Np and Pu, and a σ-hydrocarbyl metal–carbon bond for Pu, have been structurally authenticated.

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Influence of intermolecular interactions on the infrared complex indices of refraction for binary liquid mixtures

This paper investigates the accuracy of deriving the composite optical constants of binary mixtures from only the complex indices of refraction of the neat materials. These optical constants enable the reflectance spectra of the binary mixtures to be modeled for multiple scenarios (e.g., different substrates, thicknesses, volume ratios), which is important for contact and standoff chemical detection. Using volume fractions, each mixture's complex index of refraction was approximated via three different mixing rules. To explore the impact of intermolecular interactions, these predictions are tested by experimental measurements for two representative sets of binary mixtures: (1) tributyl phosphate combined with n-dodecane, a non-polar medium, to represent mixtures which primarily interact via dispersion forces and (2) tributyl phosphate and 1-butanol to represent mixtures with polar functional groups that can also interact via dipole–dipole interactions, including hydrogen bonding. The residuals and the root-mean-square error between the experimental and calculated index values are computed and demonstrate that for miscible liquids in which the average geometry of the cross-interactions can be considered isotropic (e.g., dispersion), the refractive indices of the mixtures can be modeled using composite n and k values derived from volume fractions of the neat liquids. Conversely, in spectral regions where the geometry of the cross-interactions is more restricted and anisotropic (e.g., hydrogen bonding), the calculated n and k values vary from the measured values. The impact of these interactions on the reflectance spectra are then compared by modeling a thin film of the binary mixtures on an aluminum substrate using both the measured and the mathematically computed indices of refraction.

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