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HDO of lignin-derived phenolics on stepped M@Pt(211) (M = Co, Fe, Mo) single-atom alloy was computationally explored. Either C–O bond length or *OH binding energy was confirmed as an effective catalytic descriptor for predicting HDO performance.
Conventional lithium ion battery separators are microporous polyolefin membranes that play a passive role in the electrochemical cell. Next generation separators should offer significant performance enhancements, while being fabricated through facile, low cost approaches with the ability to readily tune physicochemical properties. This study presents a single-step manufacturing technique based on UV-initiated polymerization-induced phase separation (PIPS), wherein microporous separators are fabricated from multifunctional monomers and ethylene carbonate (EC), which functions as both the pore-forming agent (porogen) and electrolyte component in the electrochemical cell. By controlling the ratio of the 1,4-butanediol diacrylate (BDDA) monomer to ethylene carbonate, monolithic microporous membranes are readily prepared with 25 μm thickness and pore sizes and porosities ranging from 6.8 to 22 nm and 15.4% to 38.5%, respectively. With 38.5% apparent porosity and an average pore size of 22 nm, the poly(1,4-butanediol diacrylate) (pBDDA) separator takes up 127% liquid electrolyte, resulting in an ionic conductivity of 1.98 mS cm –1 , which is greater than in conventional Celgard 2500. Lithium ion battery half cells consisting of LiNi 0.5 Mn 0.3 Co 0.2 O 2 cathodes and pBDDA separators were shown to undergo reversible charge/discharge cycling with an average discharge capacity of 142 mA h g –1 and a capacity retention of 98.4% over 100 cycles – comparable to cells using state-of-the-art separators. Moreover, similar discharge capacities were achieved in rate performance tests due to the high ionic conductivity and electrolyte uptake of the film. Finally, the pBDDA separators were shown to be thermally stable to 374 °C, lack low temperature thermal transitions that can compromise cell safety, and exhibit no thermal shrinkage up to 150 °C.
We report once limited to chain-growth polymerizations, fine control over polymerization-induced phase separation (PIPS) has recently been demonstrated in rubber-toughened thermoset materials formed through step-growth polymerizations. The domain length scales of these thermoset materials can be elegantly tuned by utilizing a binary mixture of curing agents (CAs) that individually yield disparate morphologies. Importantly, varying the composition of the binary mixture affects characteristics of the materials such as glass transition temperature and tensile behavior. Here, we establish a full phase diagram of PIPS in a rubber-toughened epoxy system tuned by a binary CA mixture to provide a robust framework of phase behaviour. X-Ray scattering in situ and post-PIPS is employed to elucidate the PIPS mechanism whereby an initial polymerization-induced compositional fluctuation causes nanoscale phase separation of rubber and epoxy components prior to local chain crosslinking and potential macrophase separation. We further demonstrate the universality of this approach by alternatively employing binary epoxy or binary rubber mixtures to achieve broad variations in morphology and glass transitions.
In this work the authors combined in-solution ligand exchange with an electrically driven film deposition technique to produce conformal PbSe quantum dot films on non-flat substrates in a single step without the need for post deposition processing.
The anchoring of the Cu cluster on Mo 2 C substrate can achieve a planar configuration, effectively boosting urea synthesisviaa one-step mechanism.
We show how the multi-step direct reaction series can be evaluated using Monte Carlo methods. Nucleon-nucleon collisions occur according to the random selection of a nucleon’s attenuation factor along its classical trajectory. The particles and hole excited in a collision are selected from a local Fermi distribution. We assume that the particles continue to propagate and possibly collide again before leaving the nucleus. We assume that holes collide in place, to possibly produce other particles and holes.
The generation of accurate kinetic parameters such as mean generation time Λ and effective delayed neutron fraction β eff via Monte Carlo codes is established. Employing these in downstream deterministic codes warrants another step to ensure no additional error is introduced by the low-order transport operator when computing forward and adjoint fluxes for bilinear weighting of these parameters. Another complexity stems from applying superhomogenization (SPH) equivalence in non-fundamental mode approximations, where reference and low-order calculations rely on a 3D full core model. In these cases, SPH factors can optionally be computed for only part of the geometry while preserving reaction rates and K-effective, but the impact of such approximations on kinetics parameters has not been thoroughly studied. This paper aims at studying the preservation of bilinearly-weighted quantities in the Serpent–Griffin calculation procedure. Diffusion and transport evaluations of IPEN/MB-01, Godiva, and Flattop were carried out with the Griffin reactor physics code, testing available modeling options using Serpent-generated multigroup cross sections and equivalence data. Verifying Griffin against Serpent indicates sensitivities to multigroup energy grid selection and regional application of SPH equivalence, introducing significant errors; these were demonstrated to be reduced through the use of a transport method together with a finer energy grid.
Gallium oxide (Ga 2 O 3 ) is being actively explored for electronics that can operate at high power, temperature, and frequency as well as for deep-ultraviolet optoelectronics and other applications due to its ultra-wide bandgap (UWBG) and low projected fabrication cost of large-size and high-quality crystals. Efficient n-type doping of monoclinic beta-phase of Ga 2 O 3 has been achieved, but p-type doping faces fundamental obstacles due to compensation, deep acceptor levels, and the polaron transport mechanism of free holes. However, aside from the challenges of achieving p-type conductivity, plenty of opportunity exists to engineer the position of the Fermi level for improved design of Ga 2 O 3 -based devices. We use first-principles defect theory and defect equilibrium calculations to simulate a three-step growth-annealing-quench synthesis protocol for hydrogen-assisted Mg doping in ß-Ga 2 O 3 . The simulations take into account the gas phase equilibrium between H 2 , O 2 , and H 2 O, which determines the H chemical potential. We predict Ga 2 O 3 doping-type conversion to a net p-type regime after growth under reducing conditions in the presence of H2 followed by O-rich annealing, which is a similar process to Mg acceptor activation by H removal in GaN. For equilibrium annealing with re-equilibration of compensating O vacancies, there is an optimal temperature that maximizes the Ga 2 O 3 net acceptor density for a given Mg doping level; the acceptor density is further increased in the non-equilibrium annealing scenario without re-equilibration. After quenching to operating temperature, the Ga 2 O 3 Fermi level drops below mid-gap down to about 1.5 eV above the valence band maximum, creating a significant number of uncompensated neutral Mg Ga 0 acceptors. The resulting free hole concentration in Ga 2 O 3 is very low even at elevated operating temperature (~10 8 cm -3 at 400°C) due to the deep energy level of these Mg acceptors, and hole conductivity is further impeded by the polaron hopping mechanism. However, the Fermi-level reduction and suppression of free electron density in this doping-type converted (NA>ND) Ga 2 O 3 material are important for improved designs of Ga 2 O 3 electronic devices. These results illustrate the power of computational predictions not only for new materials but also for their synthesis science.
The time-dependent exchange–correlation potential has the unusual task of directing fictitious non-interacting electrons to move with exactly the same probability density as true interacting electrons. This has intriguing implications for its structure, especially in the non-perturbative regime, leading to step and peak features that cannot be captured by bootstrapping any ground-state functional approximation. Here, we review what has been learned about these features in the exact exchange–correlation potential of time-dependent density functional theory in the past decade or so and implications for the performance of simulations when electrons are driven far from any ground state.
Here, we present a Monte Carlo toolkit for validating step range filter (SRF) spectrometer designs. Geant4 is used to transport charged particles through the SRF filters to generate synthetic SRF data that include realistic CR-39 effects. Synthetic SRF spectra generated by this method inherently account for instrument response and allow for the quantification of SRF performance before shots. The usefulness of this toolkit is demonstrated through its application to a number of problems. A new broadband SRF for the ∼10 MeV wide 3He3He proton spectrum is validated, and an analysis method for analyzing 3He3He-p SRF data that accounts for instrument response is put forth. In addition, an SRF design for the compact recoil-proton spectrometer (CRS) on the Z-machine is validated. Finally, a new calibration technique for the DD-p SRF is proposed and validated.
A high-voltage generator of nanosecond pulses utilizing a drift step recovery diode (DSRD) as an opening switch in the compression section is designed to operate at peak voltages of 4 kV and repetition rates of 2 MHz in burst mode at reduced voltages. The generator is used to perform empirical studies on the output metrics of a DSRD generator with a focus on matching the circuit of the reverse current loop parameters and DSRD to the load. The dynamic impedance introduced by nonlinear elements in the DSRD was investigated with respect to the driving circuit by varying the number of DSRDs connected in series. Experimental results indicate that optimization of the driving circuit for the DSRD is key to minimizing losses and achieving optimal pulse compression. Furthermore, the results also indicate trends in DSRD performance as larger storage inductances and longer pumping times are used, limiting the pulse compression capabilities of a single stage DSRD generator.
Significance In nature, mutation is the first step of evolution, where it provides the genetic variation for the natural selection to act. Here we take a system of artificial self-replicating tiles, DNA origami, that exhibit templated reproduction. We can generate a small fraction of mutations by introducing a mismatch in hybridization between parent and daughter. We can modify the origami functionality to affect the growth rate of the mutated species, giving it less or more evolutionary advantage, and to become dominant in several generations. The introduction of mutations into an artificial self-replicating system provides new directions for research into self-assembly processes.
Enzymes catalyze biochemical reactions through precise positioning of substrates, cofactors, and amino acids to modulate the transition-state free energy. However, the role of conformational dynamics remains poorly understood due to poor experimental access. This shortcoming is evident with Escherichia coli dihydrofolate reductase (DHFR), a model system for the role of protein dynamics in catalysis, for which it is unknown how the enzyme regulates the different active site environments required to facilitate proton and hydride transfer. Here, we describe ligand-, temperature-, and electric-field-based perturbations during X-ray diffraction experiments to map the conformational dynamics of the Michaelis complex of DHFR. We resolve coupled global and local motions and find that these motions are engaged by the protonated substrate to promote efficient catalysis. This result suggests a fundamental design principle for multistep enzymes in which pre-existing dynamics enable intermediates to drive rapid electrostatic reorganization to facilitate subsequent chemical steps.
The valorization of CO2 to chemicals beyond C1-2 products is receiving significant interest; however, the direct electrosynthesis of Cn molecules (n > 4) remains a challenge. Here, we present a two-step abiotic-biotic system for upgrading CO2 into the biopolymer, poly(3-hydroxybutyrate). In the electrolysis system, CO2 is converted into C2 oxygenates using a Cu-Ag tandem electrocatalyst. The electrolysis process generates a liquid stream containing ~ 200 mM acetate in a bio-compatible electrolyte. This electrosynthesized acetate is then fed to a bioreactor, where the substrate is upgraded by Cupriavidus necator to biopolymer with a maximum rate of 32 ± 3.5 mg L-1 h-1. We further demonstrate the purification of the resulting biopolymer into a powder. The high productivity of the abiotic-biotic system demonstrates its feasibility for sustainable chemical manufacturing.
Here we investigate using deep learning, a type of machine-learning algorithm employing multiple layers of artificial neurons, for the mathematical representation of multigroup cross sections for use in the Griffin reactor multiphysics code for two-step deterministic neutronics calculations. A three-dimensional fuel element typical of a high-temperature gas reactor as well as a two-dimensional sodium-cooled fast reactor lattice are modeled using the Serpent Monte Carlo code, and multigroup macroscopic cross sections are generated for various state parameters to produce a training data set and a separate validation data set. A fully connected, feedforward neural network is trained using the open-source PyTorch machine-learning framework, and its accuracy is compared against the standard piecewise linear interpolation model. Additionally, we provide in this work a generic technique for propagating the cross-section model errors up to the k eff using sensitivity coefficients with the first-order uncertainty propagation rule. Quantifying the eigenvalue error due to the cross-section regression errors is especially practical for appropriately selecting the mathematical representation of the cross sections. We demonstrate that the artificial neural network model produces lower errors and therefore enables better accuracy relative to the piecewise linear model when the cross sections exhibit nonlinear dependencies; especially when a coarse grid is employed, where the errors can be halved by the artificial neural network. However, for linearly dependent multigroup cross sections as found for the sodium-cooled fast reactor case, a simpler linear regression outperforms deeper networks.
Here the stepped-pressure equilibrium code (SPEC) (Hudson et al 2012 Phys. Plasmas 19 , 112 502) is extended to enable free-boundary multi-region relaxed magnetohydrodynamic (MRxMHD) equilibrium calculations. The vacuum field surrounding the plasma inside an arbitrary 'computational boundary', , is computed, and the virtual-casing principle is used iteratively to compute the normal field on so that the equilibrium is consistent with an externally produced magnetic field. Recent modifications to SPEC are described, such as the use of Chebyshev polynomials to describe the radial dependence of the magnetic vector potential, and a variety of free-boundary verification calculations are presented.
Like most magnetic confined fusion experiments, the ST40 tokamak started off with a small subset of diagnostics and gradually increased the diagnostic set to include more complex and comprehensive systems. To make the most of each operational phase, forward models of various diagnostics are used and developed to aid design, provide consistency-checks during commissioning, test analysis methods, and build workflows to constrain high-level parameters to inform interpretation, theory and modelling. For new models and new analysis workflows, minimum-viable-products are released early, and their complexity is increased in a step-wise manner, facilitating the support of all programme phases on multiple parallel applications, while enabling learning opportunities and feedback loops. In this contribution we review the philosophy, scope and architecture of the framework under development. We discuss the details of some forward models, with examples on how they are used to aid diagnostic design, to investigate analysis methodologies through synthetic data, and how they are embedded in experimental analysis workflows. We compare previously published experimental results with new, more advanced analysis workflows employing more recent, detailed models and new diagnostic data, providing confirmation of the published material from the 2021–22 experimental campaign.
Abstract Underlying policy efforts to address global climate change is the scientific need to develop the methods to accurately measure and model carbon stocks and fluxes across the wide range of spatial and temporal scales in the Earth system. Initiated in 2010, the NASA Carbon Monitoring System is one of the most ambitious relevant science initiatives to date, exploiting the satellite remote sensing resources, computational capabilities, scientific knowledge, airborne science capabilities, and end-to-end system expertise that are major strengths of the NASA Earth Science program. Here we provide a synthesis of ‘Phase 2’ activities (2011–2019), encompassing 79 projects, 482 publications, and 136 data products. Our synthesis addresses four key questions: What has been attempted? What major results have been obtained? What major gaps and uncertainties remain? and What are the recommended next steps? Through this review, we take stock of what has been accomplished and identify future priorities toward meeting the nation’s needs for carbon monitoring reporting and verification.