Combinatorial-Entropy-Driven Aggregation in DNA-Grafted Nanoparticles
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99 GENERAL AND MISCELLANEOUS↗
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Metal–Organic Framework Based Hydrogen-Bonding Nanotrap for Efficient Acetylene Storage and Separation
The removal of carbon dioxide (CO 2 ) from acetylene (C 2 H 2 ) is a critical industrial process for manufacturing high purity C 2 H 2 . However, it remains challenging to address the trade-off between adsorption capacity and selectivity, on account of their similar physical properties and molecular sizes. To overcome this difficulty, here we report a novel strategy involving the regulation of hydrogen-bonding nanotrap on the pore surface to promote the separation of C 2 H 2 /CO 2 mixtures, in three isostructural metal-organic frameworks (MOFs, named as MIL-160, CAU-10H, and CAU-23, respectively). Among them, MIL-160, which has abundant hydrogen-bonding acceptors as nanotraps, can selectively capture acetylene molecules and demonstrates ultra-high C 2 H 2 storage capacity (191 cm 3 g –1 , or 213 cm 3 cm –3 ) but much less CO 2 uptake (90 cm 3 g –1 ) under ambient conditions. The C 2 H 2 adsorption amount of MIL-160 is remarkably higher than the other two isostructural MOFs (86 cm 3 g –1 and 119 cm 3 g –1 for CAU-10H and CAU-23 respectively) under the same conditions. More importantly, both simulation and experimental breakthrough results show that MIL-160 sets a new benchmark for equimolar C 2 H 2 /CO 2 separation in terms of the separation potential (Δq break = 5.02 mol/kg) and C 2 H 2 productivity (6.8 mol/kg). In addition, in-situ FT-IR experiments combined with computational modeling further reveal that the unique host-guest multiple hydrogen-bonding interactions between the nanotrap and C 2 H 2 is the key factor for achieving extraordinary acetylene storage capacity and superior C 2 H 2 /CO 2 selectivity. Furthermore, this work provides a novel and powerful approach to address the trade-off of this extremely challenging gas separation.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Mapping the Morphology of DNA on Carbon Nanotubes in Solution Using X-ray Scattering Interferometry
Single-walled carbon nanotubes (SWCNTs) with adsorbed single-stranded DNA (ssDNA) are applied as sensors to investigate biological systems, with potential applications ranging from clinical diagnostics to agricultural biotechnology. Unique ssDNA sequences render SWCNTs selectively responsive to target analytes such as (GT)n-SWCNTs recognizing the neuromodulator, dopamine. It remains unclear how the ssDNA conformation on the SWCNT surface contributes to functionality, as observations have been limited to computational models or experiments under dehydrated conditions that differ substantially from the aqueous biological environments in which the nanosensors are applied. We demonstrate a direct mode of measuring in-solution ssDNA geometries on SWCNTs via X-ray scattering interferometry (XSI), which leverages the interference pattern produced by AuNP tags conjugated to ssDNA on the SWCNT surface. We employ XSI to quantify distinct surface-adsorbed morphologies for two (GT) n ssDNA oligomer lengths (n = 6, 15) that are used on SWCNTs in the context of dopamine sensing and measure the ssDNA conformational changes as a function of ionic strength and during dopamine interaction. Here, we show that the shorter oligomer, (GT) 6 , adopts a more periodically ordered ring structure along the SWCNT axis (inter-ssDNA distance of 8.6 ± 0.3 nm), compared to the longer (GT) 15 oligomer (most probable 5'-to-5' distance of 14.3 ± 1.1 nm). During molecular recognition, XSI reveals that dopamine elicits simultaneous axial elongation and radial constriction of adsorbed ssDNA on the SWCNT surface. Our approach using XSI to probe solution-phase morphologies of polymer-functionalized SWCNTs can be applied to yield insights into sensing mechanisms and inform future design strategies for nanoparticle-based sensors.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Intermediate selectivity in the oxidation of phenols using plasmonic Au/ZnO photocatalysts
Tunable reaction selectivity on a single catalyst is a continual goal in chemical syntheses. Herein, we report an unexpected light-directed intermediate selectivity using well-known plasmonic photocatalysts. We observed distinct intermediate selectivity behaviors between using UV and visible light irradiations. Chemical computations and quenching experiments suggest that the radicals generated by the plasmonic excitation govern the light-directed selectivity. As a result, the broader impact of this work ranges from selective yield of desirable intermediates for subsequent syntheses without tedious separation procedures, to arousing interest in examining new opportunities for plasmonic photocatalysts.
36 MATERIALS SCIENCE↗
Boundary layer description of directional polymer crystallisation
Nearly fifty years ago Lovinger and Gryte suggested that the directional crystallization of a polymer was analogous to the quiescent isothermal crystallization experiment but at a supercooling where the crystal growth velocity was equal to the velocity of the moving front. Our experiments showed that this equivalence holds in a detailed manner at low directional velocities. To understand the underlying physics of these situations, we modeled the motion of a crystallization front in a liquid where the left side boundary is suddenly lowered below the melting point (Stefan's problem) but with the modification that the crystallization kinetics follow a version of the Avrami model. Our numerical results surprisingly showed that the results of the polymer analog track with the Stefan results which were derived for a simple liquid that crystallizes completely at its melting point; in particular, the position of the crystal growth-front evolved with time exactly as in the Stefan problem. The numerical solution also showed that the temperature in the immediate vicinity of the growth-front decreased with increasing front velocity, which is in line with Lovinger and Gryte's ansatz. To provide a clear theoretical understanding of these numerical results we derive a boundary layer solution to the governing coupled differential equations of the polymer problem. The analytical results are in agreement with our observations from experiments and numerical computations but show that this equivalence between the small molecule and polymer analog only holds in the limit where the crystallization enthalpy is much larger than the rate at which heat is conducted away in the polymer. In particular, in the context of the temperature profile, the enthalpy generated by the crystallisation process which is spread out over a narrow spatial region can be approximated as a point source whose location and temperature correspond to the Lovinger–Gryte ansatz.
36 MATERIALS SCIENCE↗
Curiosity driven exploration to optimize structure–property learning in microscopy
Rapidly determining structure–property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure–property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure–property relationships in materials science.
36 MATERIALS SCIENCE↗
Understanding the impact of nuclear-data covariances on various integral responses using adjustment
The EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project created a library of sensitivities for nine different integral responses with respect to nuclear data. These integral responses were obtained from measurements at LLNL (Lawrence Livermore National Laboratory) pulsed spheres, critical and sub-critical assemblies. At the same time, covariances for ENDF/B-VIII.0 were processed at LANL (Los Alamos National Laboratory). The combination of these data allow us to study the impact of nuclear-data covariances on various integral responses, either by forward-propagating covariances via sensitivities, or by using nuclear data, integral responses, and sensitivities for adjustment. Here, we will present: the impact of 1 H, 9 Be, 12 C, 27 Al, 56 Fe, 235,238 U, and 239,240 Pu ENDF/B-VIII.0 covariances on simulated bounds of the following integral responses: LLNL pulsed-spheres neutron-leakage spectra, the effective neutron multiplication factor, reaction rates, and reactivity coefficients of ICSBEP critical assemblies. Also, adjustment results with the same nuclear-data covariances and responses will be discussed.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
How can a diverse set of integral and semi-integral measurements inform identification of discrepant nuclear data?
Nuclear data are used for a variety of applications, including criticality safety, reactor performance, and material safeguards. Despite the breadth of use-cases, the effective neutron multiplication factor, keff, of ICSBEP critical assemblies are primarily used for nuclear data validation; these are sensitive to specific energy regions and nuclides and are unable to uniquely constrain nuclear data. As a consequence, general-purpose nuclear data libraries, such as ENDF/B-VIII.0, may have deficiencies that, while not apparent in criticality applications, negatively impact other applications, such as non-destructive analysis of special nuclear material and neutron diagnosed subcritical experiments. Recent work by the Experiments Underpinned by Computational Learning for Improvements in Nuclear Data (EUCLID) project developed a machine learning tool, RAFIEKI, which uses random forests and the SHAP metric to determine which nuclear data contribute most to predicted bias between measured and simulated responses (e.g. keff). This paper contrasts RAFIEKI analysis applied to keff only against RAFIEKI analysis with keff paired with either LLNL pulsed sphere measurements or subcritical benchmarks. Two examples show that a) including pulsed sphere measurements substantially increases 9Be nuclear data importance to bias between 2 and 15 MeV, and b) including subcritical benchmarks has the potential for disentangling compensating errors between 240Pu (n,el) and (n,il) cross-sections between 0.1 and 10 MeV. These results show that RAFIEKI analysis applied to response sets that include, but go beyond, keff can aid nuclear data evaluators in identifying issues in nuclear data.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Three-dimensional measurements of fundamental plasma parameters in pulsed ICP operation
Radio frequency inductively coupled plasma sources are widely used in low temperature industrial processing. Recent computer simulations and experiments indicate significant improvement in processing results with the use of pulsed plasmas. We report three dimensional spatial and temporal measurements of fundamental plasma parameters in a pulsed Argon plasma in an industrial etch tool modified for diagnostic access. The pulse repetition rate is 1 kHz with 50% duty cycle. The density does not decay to zero at the end of the afterglow period, and this affects the initial conditions for the beginning of the next powered phase. At 5 mTorr, the plasma starts in the E-mode for each pulse and grows into a “ring” shape density profile. Subsequently, the profile evolves to peak in the middle of the chamber as it reaches a steady state toward the end of the active-glow. At 25 mTorr, the plasma density profile always peaks at the center of the chamber throughout the entire pulse.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
A Gaussian process based surrogate approach for the optimization of cylindrical targets
Simulating direct-drive inertial confinement experiments presents significant computational challenges, both due to the complexity of the codes required for such simulations and the substantial computational expense associated with target design studies. Machine learning models, and in particular, surrogate models, offer a solution by replacing simulation results with a simplified approximation. In this study, we apply surrogate modeling and optimization techniques that are well established in the existing literature to one-dimensional simulation data of a new cylindrical target design containing deuterium–tritium fuel. These models predict yields without the need for expensive simulations. We find that Bayesian optimization with Gaussian process surrogates enhances sampling efficiency in low-dimensional design spaces but becomes less efficient as dimensionality increases. Nonetheless, optimization routines within two-dimensional and five-dimensional design spaces can identify designs that maximize yield, while also aligning with established physical intuition. Optimization routines, which ignore constraints on hydrodynamic instability growth, are shown to lead to unstable designs in 2D, resulting in yield loss. However, routines that utilize 1D simulations and impose constraints on the in-flight aspect ratio converge on novel cylindrical target designs that are stable against hydrodynamic instability growth in 2D and achieve high yield.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Expansion dynamics of femtosecond laser-induced plasmas: Influence of thermophysical plasma properties
This study investigates the expansion dynamics of femtosecond laser-induced plasmas, emphasizing the impact of plasma thermophysical properties and ambient gas composition. Through shadowgraphy experiments and multiphase computational fluid dynamics (CFD) simulations, the influence of parameters such as heat capacity, molecular weight, and thermal conductivity on plume morphology, shockwave evolution, and energy dissipation mechanisms is examined. A mixture multiphase model is implemented to capture the interaction between the plasma and the surrounding gas. Simulation results reveal that plasma expansion is strongly inertia-driven. Results show that differences in plasma properties and ambient conditions affect the shape and temperature distribution of the expanding plume. The early-stage dynamics are primarily dictated by pressure forces, whereas thermal and viscous effects play a growing role in the plume's behavior during later stages of expansion. The CFD findings show the necessity of accurate initial condition characterization, including crater geometry and plasma pressure and temperature, for reliable modeling of plasma evolution in laser ablation processes.
CFD modeling↗
Charting C–C coupling pathways in electrochemical CO 2 reduction on Cu(111) using embedded correlated wavefunction theory
The electrochemical CO 2 reduction reaction (CO 2 RR) powered by excess zero-carbon-emission electricity to produce especially multicarbon (C 2+ ) products could contribute to a carbon-neutral to carbon-negative economy. Foundational to the rational design of efficient, selective CO 2 RR electrocatalysts is mechanistic analysis of the best metal catalyst thus far identified, namely, copper (Cu), via quantum mechanical computations to complement experiments. Here, we apply embedded correlated wavefunction (ECW) theory, which regionally corrects the electron exchange-correlation error in density functional theory (DFT) approximations, to examine multiple C–C coupling steps involving adsorbed CO (*CO) and its hydrogenated derivatives on the most ubiquitous facet, Cu(111). We predict that two adsorbed hydrogenated CO species, either *COH or *CHO, are necessary precursors for C–C bond formation. The three kinetically feasible pathways involving these species yield all three possible products: *COH–CHO, *COH–*COH, and *OCH–*OCH. The most kinetically favorable path forms *COH–CHO. In contrast, standard DFT approximations arrive at qualitatively different conclusions, namely, that only *CO and *COH will prevail on the surface and their C–C coupling paths produce only *COH–*COH and *CO–*CO, with a preference for the first product. This work demonstrates the importance of applying qualitatively and quantitatively accurate quantum mechanical method to simulate electrochemistry in order ultimately to shed light on ways to enhance selectivity toward C 2+ product formation via CO 2 RR electrocatalysts.
30 DIRECT ENERGY CONVERSION↗
Photoselective isotope fractionation dynamics of N 2 with cosmo and atmospheric chemistry perspectives
Stable isotope ratio measurements provide valuable insights into a broad range of natural processes, from planetary atmospheres and climate to interstellar chemistry. Nitrogen, which has two stable isotopes, exhibits varying isotope ratios across the solar system. To model these observations, the isotope fraction as a function of energy is essential. At the Advanced Light Source (ALS), we measured the photodissociation of molecular nitrogen (N 2 ) with vacuum UV photons where a single photon is sufficiently energetic to dissociate the strong bond. The nitrogen atoms produced are scavenged with H 2 to form ammonia, whose isotopic makeup is determined. Blending the experiments with dynamical computations that include the shielding of light, we examine the isotopic composition and electronic atomic states produced. The measured photodissociation of N 2 at a natural isotopic composition with a frequency broad light beam exceptionally strongly favors the formation of the heavier nitrogen isotope, 15 N. Computations concur and suggest that the maximum in the quantum yield reflects significant variations in the specific electronic quantum states of the product N atoms that have quite different reactivities. Our quantum computations show that at similar energies, photodissociation of 14 N 14 N and 15 N 14 N can lead to different product channels. The computed dynamics include extensive state-selective spin–orbit and nonadiabatic couplings affecting the light absorption and dissociation pathways that proceed via the triplet manifold of states. Our results are relevant for future exploration missions, both in situ and sample-return and for other molecules such as O 2 and CO.
Komarova, Ksenia [Hebrew Univ. of Jerusalem (Israe↗
Exploring the relation between transonic dislocation glide and stacking fault width in FCC metals
Theory predicts limiting gliding velocities that dislocations cannot overcome. Computational and recent experiments have shown that these limiting velocities are soft barriers and dislocations can reach transonic speeds in high rate plastic deformation scenarios. In this paper we systematically examine the mobility of edge and screw dislocations in several face centered cubic (FCC) metals (Al, Au, Pt, and Ni) in the extreme large-applied-stress regime using molecular dynamics simulations. Our results show that edge dislocations are more likely to move at transonic velocities due to their high mobility and lower limiting velocity than screw dislocations. Importantly, among the considered FCC metals, the dislocation core structure determines the dislocation’s ability to reach transonic velocities. This is likely due to the variation in stacking fault width due to relativistic effects near the limiting velocities.
36 MATERIALS SCIENCE↗
Granger causal inference for climate change attribution
Abstract Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and focus on different types of weather and climate events and each of which are conditional to varying extents. Some methods are based on Pearl causality (direct experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses relative to Pearl-causal attribution and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality.
Risser, Mark D. (ORCID:0000000319561783)↗
Electronic spin transition in FeO 2 : Evidence for Fe(II) with peroxide O 2 2 –
The discovery of FeO 2 containing more oxygen than hematite (Fe 2 O 3 ), which was previously believed to be the most oxygen rich iron compound, has important implications for the study of deep lower mantle compositions. Compared to other iron compounds, there are limited reports on FeO 2 , making studies of its physical properties of great interest in fundamental condensed matter physics and geoscience. Even the oxidation state of Fe in FeO 2 is the subject of debate in theoretical works and there have not been reports from experimental electronic and magnetic properties measurements. Here, we report the pressure-induced spin state transition from synchrotron experiments and our computational results explain the underlying mechanism. Using density functional theory and dynamical mean field theory, we calculated spin states of Fe with volume and Hubbard interaction U change, which clearly demonstrate that Fe in FeO 2 consists of Fe(II) and peroxide O 2– 2 . In conclusion, our paper suggests that the localized nature of both Fe 3d orbitals and O 2 molecular orbitals should be correctly treated for unveiling the structural and electronic properties of FeO 2 .
74 ATOMIC AND MOLECULAR PHYSICS↗
Increased localization of Majorana modes in antiferromagnetic chains on superconductors
Magnet-superconductor hybrid (MSH) systems are a key platform for custom-designed topological superconductors. Ideally, the ends of a one-dimensional MSH structure will host Majorana zero-modes (MZMs), the fundamental unit of topological quantum computing. However, some experiments with ferromagnetic (FM) chains show a more complicated picture. Due to tiny gap sizes and hence long coherence lengths, MZMs might hybridize and lose their topological protection. Recent experiments on a niobium surface have shown that both FM and antiferromagnetic (AFM) chains may be engineered, with the magnetic order depending on the crystallographic direction of the chain. While FM chains are well understood, AFM chains are less so. Here, we study two models inspired by the niobium surface: A minimal model to elucidate the general topological properties of AFM chains and an extended model to more closely simulate a real system by mimicking the proximity effect. Here, we find that, in general, for AFM chains, the topological gap is larger than for FM ones, and thus, coherence lengths are shorter for AFM chains, yielding more pronounced localization of MZMs in these chains. While for some parameters AFM chains may be topologically trivial, we find in these cases that adding an adjacent chain can result in a nontrivial system, with a single MZM at each chain end.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Brittle-ductile transitions in a metallic glass
Recent computational and laboratory experiments have shown that brittle-ductile transitions in the notch toughnesses of metallic glasses such as Vitreloy 1 are strongly sensitive to the initial effective disorder (or “fictive”) temperature. Glasses with lower effective temperatures are weak and brittle; those with higher effective temperatures are strong and ductile. The analysis of this phenomenon presented here examines the onset of fracture at the tip of a notch as predicted by the shear-transformation-zone theory of spatially varying plastic deformation. The central ingredient of this analysis is an approximation for the dynamics of the plastic zone formed by stress concentration at the notch tip. As a result, this zone first shields the tip but then, with increasing stress, expands suddenly, producing a discontinuous transition between brittle and ductile failure in satisfactory agreement with the numerical and experimental observations.
36 MATERIALS SCIENCE↗