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At least 865 records · Page 48

Understanding the local structure of disordered carbons from cellulose and lignin

An electron microscopy investigation was performed to understand the relationship between the microstructure and properties of carbonized cellulose and lignin (softwood kraft lignin) relative to the structure of the original biomass components. Structure details at micro- and molecular levels were investigated by scanning transmission electron microscopy. Atomic-resolution images revealed the presence of random disordered carbon in carbonized cellulose (C-CNC) and of large domains of well-ordered carbon with graphite sheet structure in carbonized lignin (C-Lignin). These structural differences explain why C-CNC exhibits higher surface area and porosity than C-Lignin. The presence of certain well-ordered carbon in carbonized lignin indicates some of the carbon in lignin are graphitized with heat treatment temperature up to 950 °C. This result is encouraging for future endeavors of attaining acceptable modulus of carbon fiber from lignin given suitable modifications to the chemistry and structure of lignin. The results of this research contribute to an improved understanding of the carbonization mechanism of the key cellulose and lignin components of biomass materials.

54 ENVIRONMENTAL SCIENCES↗

Twinned nanostructure of VO 2 thin films grown on r-cut sapphire

Due to strong electron-phonon interactions, strain engineering is a powerful tool to control quantum critical phenomena in strongly correlated oxides. Exploring this possibility requires understanding the nanoscale structure of quantum materials and the role it plays in forming the strain landscape. In this work, we used a combination of x-ray nanoimaging and reciprocal space mapping to study the nanostructure of the archetypal Mott insulator VO 2 featuring an insulator-to-metal and structural phase transitions. We found that VO 2 thin films grown on r-cut sapphire consist of two intertwined crystal lattices, permanently inclined with respect to each other. This persistent pattern of twin domains stands out from the symmetry breaking induced by the structural phase transition and conceivably originates from the post-growth strain relaxation process. We propose a model explaining the formation of twin domains and the emergence of anisotropy in the film nanostructure. Our work suggests using miscut substrates to suppress either one or the other twin that can serve as a new tool to control strain in VO 2 films.

36 MATERIALS SCIENCE↗

Role of Intra-Domain Heterogeneity on Ion and Polymer Dynamics in Block Polymer Electrolytes: An Approach for Spatially Resolving Dynamics and Ion Transport

The design of safe and high-performance, nanostructured, block polymer (BP) electrolytes for lithium-ion batteries requires a thorough understanding of the key parameters that govern local structure and dynamics. Yet, the interfaces between microphase separated domains can introduce complexities in this local behavior that can be challenging to quantify. Herein, the local polymer, cation (Li + ), and anion dynamics were described in salt-doped polystyrene block poly(oligo-oxyethylene methyl ether methacrylate) (PS-b-POEM) through a quantitative framework that considered the effects of polymer architecture, segmental mixing, chain stretching, and confinement on polymer mobility and ion transport. This framework was validated through nuclear magnetic resonance (NMR) spectroscopy measurements on solid (dry) polymer electrolyte samples. Notably, a mobility transition temperature (T mobility ) was identified through NMR spectroscopy that captured the local dynamics more accurately than the thermal glass transition temperature. Additionally, the approach quantitatively described the mobility gradient across a domain when segmental mixing effects were combined with chain stretching and confinement information, especially at higher segregation strengths – facilitating the assessment of local ion diffusion and conductivity. As a result, spatially averaged local ion diffusion predictions quantitatively matched NMR-measured ion diffusivities in the BP samples, while spatially summed ionic conductivity predictions across a domain qualitatively captured trends in the measured ionic conductivities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Asymmetric Dual-Grating Dielectric Laser Accelerator Optimization

Although hundreds of keV in energy gain have already been demonstrated in dielectric laser accelerators (DLAs), the challenge of creating structures that can confine electrons for multiple millimeters remains. We focus here on dual gratings with single-sided drive, which have experimentally demonstrated energy modulation numerous times. Using a Finite-Difference Time-Domain simulation to find the fields within various DLA structures and correlating these results with particle tracking simulation, we look at the impact of teeth height and width, as well as gap and offset, on the performance of these structures. We find a tradeoff between electron throughput and acceleration; however, we also find that for any given grating geometry, there is a gap and offset that will allow some charge acceleration. For our 780 nm laser wavelength, this results in a 1200 nm optimal gap size for most gratings.

Crisp, Sophie (ORCID:0000000327503727)↗

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials

GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topological quantum devices. However, understanding their intricate structures using density functional theory (DFT) calculations is hindered by spatiotemporal constraints. To overcome this limitation, we develop highly accurate and efficient machine-learning interatomic potentials based on a neuroevolution potential approach with farthest point sampling on a comprehensive DFT data set. The application of the developed machine-learning potential in large-scale atomistic simulations bridges the spatiotemporal gap between modeling and advanced characterizations, and facilitates the discovery of structural intricacies in GeSn alloys. Through extensive statistical sampling, we identify a type of short-range order (SRO) that is distinguished by both its structural signature and electronic band gap from the SRO structure previously predicted. Modeling based on a large simulation cell reveals the coexistence of nano SRO domains with various degrees of ordering, demonstrating a complex spatial heterogeneity of SRO structure. Finally, our study not only reinforces the significance of fine-level structural information in alloys, but it also constitutes an effective framework for exploring SRO in a broad range of complex alloys based on highly accurate and effective machine-learning potentials.

36 MATERIALS SCIENCE↗

The State of the Art in Visualizing Dynamic Multivariate Networks

Abstract Most real‐world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real‐world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.

Kale, Bharat↗

Evolutionary Diversification of Host-Targeted Bartonella Effectors Proteins Derived from a Conserved FicTA Toxin-Antitoxin Module

Proteins containing a FIC domain catalyze AMPylation and other post-translational modifications (PTMs). In bacteria, they are typically part of FicTA toxin-antitoxin modules that control conserved biochemical processes such as topoisomerase activity, but they have also repeatedly diversified into host-targeted virulence factors. Among these, Bartonella effector proteins (Beps) comprise a particularly diverse ensemble of FIC domains that subvert various host cellular functions. However, no comprehensive comparative analysis has been performed to infer molecular mechanisms underlying the biochemical and functional diversification of FIC domains in the vast Bep family. Here, we used X-ray crystallography, structural modelling, and phylogenetic analyses to unravel the expansion and diversification of Bep repertoires that evolved in parallel in three Bartonella lineages from a single ancestral FicTA toxin-antitoxin module. Our analysis is based on 99 non-redundant Bep sequences and nine crystal structures. Inferred from the conservation of the FIC signature motif that comprises the catalytic histidine and residues involved in substrate binding, about half of them represent AMP transferases. A quarter of Beps show a glutamate in a strategic position in the putative substrate binding pocket that would interfere with triphosphate-nucleotide binding but may allow binding of an AMPylated target for deAMPylation or another substrate to catalyze a distinct PTM. The β-hairpin flap that registers the modifiable target segment to the active site exhibits remarkable structural variability. The corresponding sequences form few well-defined groups that may recognize distinct target proteins. The binding of Beps to promiscuous FicA antitoxins is well conserved, indicating a role of the antitoxin to inhibit enzymatic activity or to serve as a chaperone for the FIC domain before translocation of the Bep into host cells. Taken together, our analysis indicates a remarkable functional plasticity of Beps that is mostly brought about by structural changes in the substrate pocket and the target dock. These findings may guide future structure–function analyses of the highly versatile FIC domains.

59 BASIC BIOLOGICAL SCIENCES↗

Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections

Abstract Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state‐of‐the‐art knowledge in an internally self‐consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain‐specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low‐emissions SSP1‐1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long‐term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

54 ENVIRONMENTAL SCIENCES↗

Visualizing nucleation, condensation and propagation of β-tubulin folding in chaperonin TRiC

AbstractThe folding nucleus (FN) initiates protein folding and enables an efficient folding pathway. Here we directly visualize the tubulin FN consisting of a nonnative, partially assembled Rossmann fold, in the closed chamber of human chaperonin TRiC. Chaperonin TRiC interacts with non-natively folded secondary structural elements, stabilizing the nucleus for transition into its first native domain. Through progressive folding, the unfolded sequence goes through drastic spatial arrangement in the TRiC chamber to sample the conformational space, mediated by the highly dynamic CCT tails. The observed presence of individual nonnative secondary structures first in the nonnative FN and then around the incrementally folded native domains supports the hypothesis that tubulin folding in TRiC is a hierarchical process of nucleation, condensation and propagation in cooperation with TRiC subunits.

Zhao, Yanyan (ORCID:0000000323021763)↗

Multimodal Approach Reveals the Symmetry-Breaking Pathway to the Broken Helix in EuIn 2 ⁢As 2

Understanding and manipulating emergent phases, which are themes at the forefront of quantum-materials research, rely on identifying their underlying symmetries. This general principle has been particularly prominent in materials with coupled electronic and magnetic degrees of freedom, in which magnetic order influences the electronic band structure and can lead to exotic topological effects. However, identifying symmetry of a magnetically ordered phase can pose a challenge, particularly in the presence of small domains. Here we introduce a multimodal approach for determining magnetic structures, which combines symmetry-sensitive optical probes, scattering, and group-theoretical analysis. We apply it to EuIn 2 ⁢As 2 , a material that has received attention as a candidate axion insulator. While first-principles calculations predict this state on the assumption of a simple collinear antiferromagnetic structure, subsequent neutron-scattering measurements reveal a much more intricate magnetic ground state characterized by two coexisting magnetic wave vectors reached by successive thermal phase transitions. The proposed high- and low-temperature phases are a spin helix and a state with interpenetrating helical and Néel antiferromagnetic order termed a “broken helix,” respectively. Employing a multimodal approach, we identify the magnetic structure associated with these two phases of EuIn 2 ⁢As 2 . We find that the higher-temperature phase is characterized by a variation of the magnetic moment amplitude from layer to layer, with the moment vanishing entirely in every third Eu layer. The lower-temperature structure is similar to the broken helix, with one important difference: Because of local strain, the relative orientation of the magnetic structure and the lattice is not fixed. Consequently, the symmetry required to protect the axion phase is not generically protected in EuIn 2 ⁢As 2 , but we show that it can be restored if the magnetic structure is tuned with uniaxial strain. Finally, we present a spin Hamiltonian that identifies the spin interactions that account for the complex magnetic order in EuIn 2 ⁢As 2 . Our work highlights the importance of a multimodal approach in determining the symmetry of complex order parameters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Markovian transport processes in a heterogeneous, variably saturated watershed: A multi-domain spatial Markov model

Transport processes in watersheds remain challenging to describe accurately with upscaled models because of their intricate complexities at multiple scales that can lead to multi-modal, non-Fickian breakthrough curves. One of the recent advances in solute transport modeling has been the Lagrangian spatial Markov model (SMM), which describes transport using velocity increments and models the correlated transitions between them. The method has been applied previously to saturated porous media and fractured media, but these successful applications suggest it may be useful for describing watershed scale processes as well. An existing, 3-D, heterogeneous variably saturated model of a headwaters catchment and classical random walk particle tracking were used to generate a forward simulation and the resulting transport was analyzed with a spatial Markov framework. Existing SMM methods were used to analyze the transport paths through the complex, unconfined domain and we found that the vadose zone plays an important role in the correlation structure. As such, a new kind of SMM is proposed, termed a multi-domain SMM (MD-SMM), that uses separate correlation models for auto-transitions in the vadose zone and the saturated zone, and for the cross-transitions between them. The MD-SMM improves representation of transport relative to the single domain model and can naturally delineate times when a particle is in the vadose zone or the saturated zone, adding an additional degree of realism to the upscaled model. Lastly, we show that the transition probability matrix for transport in the saturated portion of this unconfined system is approximately Markovian, which is an important validation of the proposed framework.

54 ENVIRONMENTAL SCIENCES↗

Perspective—Emergent Phases in Rare Earth Nickelate Heterostructure

The prediction of high T c superconductivity in layers of LaNiO 3 through orbital engineering has led to extensive research efforts over the last fifteen years. During this period, a plethora of thin films and heterostructures based rare-earth nickelate family with perovskite structure has been synthesized and explored. Here, in this short perspective, we briefly review the complexity of bulk RENiO 3 , spotlighting several recent findings of emergent phenomena in heterostructures containing the interface between RENiO 3 and another transition metal oxide. Finally, we outline potentially interesting future directions linked to time-domain dynamics to harness new Mott and topological phases in artificial structures of RENiO 3 .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Development of Nanocrystalline Graphite from Lignin Sources

Carbon composites are attractive to a variety of high-impact applications, such as carbon fibers, batteries, and vehicle parts, due to their multifunctional properties. The properties of carbon are highly dependent on the allotrope the carbon takes and the functionality, impurities, and defects contained within the structure. The increase in demand for sustainable carbon sources in energy storage devices motivates interest in understanding synthesis parameters of lignin value-added products. Also, as the dependence on oil for fuel decreases, alternative sources for carbon in many applications will be needed. In this work, the thermochemical conversion of lignin powders from different feedstocks was evaluated via small and wide-angle X-ray scattering techniques to resolve the amorphous, disordered, and crystalline domains present in the lignin carbons. Scattering analyses indicated an evolution of hierarchical structures along with an increase in ordered domains as a function of carbonization temperature. Qualitative and quantitative methods were used to describe isotropic scattering intensity profiles at multiple length scales. The use of power law models in the mesoscopic region served as the basis to describe morphological changes related to structural features, for example, graphene stacking, degree of roughness, and surface fractals. Kraft softwood and switchgrass produced carbon powder with the most crystalline domains and the least surface roughness. Softwoods reached the highest degree of crystallinity followed by switchgrass samples and had less variability in particle sizes. These results suggest lignin carbons extracted from softwoods and switchgrass are viable substitutes for graphite. Interpretation of X-ray scattering data from lignin carbon powders elucidates feedstock- and processing-dependent morphological features across multiple length scales providing a straightforward framework to evaluate the feasibility of leveraging lignin carbons for producing tunable application-specific materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Ligand‐induced CaMKIIα hub Trp403 flip, hub domain stacking, and modulation of kinase activity

Abstract γ‐Hydroxybutyric acid (GHB) analogs are small molecules that bind competitively to a specific cavity in the oligomeric CaMKIIα hub domain. Binding affects conformation and stability of the hub domain, which may explain the neuroprotective action of some of these compounds. Here, we describe molecular details of interaction of the larger‐type GHB analog 2‐(6‐(4‐chlorophenyl)imidazo[1,2‐b]pyridazine‐2‐yl)acetic acid (PIPA). Like smaller‐type analogs, PIPA binding to the CaMKIIα hub domain promoted thermal stability. PIPA additionally modulated CaMKIIα activity under sub‐maximal CaM concentrations and ultimately led to reduced substrate phosphorylation. A high‐resolution X‐ray crystal structure of a stabilized CaMKIIα (6x mutant) hub construct revealed details of the binding mode of PIPA, which involved outward placement of tryptophan 403 (Trp403), a central residue in a flexible loop close to the upper hub cavity. Small‐angle X‐ray scattering (SAXS) solution structures and mass photometry of the CaMKIIα wild‐type hub domain in the presence of PIPA revealed a high degree of ordered self‐association (stacks of CaMKIIα hub domains). This stacking neither occurred with the smaller compound 3‐hydroxycyclopent‐1‐enecarboxylic acid (HOCPCA), nor when Trp403 was replaced with leucine (W403L). Additionally, CaMKIIα W403L hub was stabilized to a larger extent by PIPA compared to CaMKIIα hub wild type, indicating that loop flexibility is important for holoenzyme stability. Thus, we propose that ligand‐induced outward placement of Trp403 by PIPA, which promotes an unforeseen mechanism of hub domain stacking, may be involved in the observed reduction in CaMKIIα kinase activity. Altogether, this sheds new light on allosteric regulation of CaMKIIα activity via the hub domain.

Biochemistry & Molecular Biology↗

Asymmetric Composition of Ionic Aggregates and the Origin of High Correlated Transference Number in Water-in-Salt Electrolytes

"Water-in-salt" electrolytes open up exciting new avenues for expanding the electrochemical window of aqueous electrolytes. We investigated the solvation structure and dynamics of highly concentrated lithium bis(trifluoromethane)sulfonimide aqueous electrolyte using experimentally corroborated molecular dynamics simulations. The simulations revealed that the heterogeneous structure of the electrolyte comprises percolating networks of ion and water domains/aggregates. Interestingly, the ionic regions are composed of more TFSI - ions than Li + ions. The Li + -ion transport mechanism was further explored. Li + ions can hop along the coordinated TFSI- ions in the ionic aggregates. The calculated correlated transference number of the 20 m electrolyte is similar to 0.32, which is reasonably high for the high concentration due to a weak negative correlation between the motion of cations and anions within the heterogeneous microscopic domains. These molecular dynamics results connect the heterogeneous structure of the electrolyte to the correlated dynamics of the Li + ion and provide a new understanding of the Li + -ion transport mechanism in this novel electrolyte.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The emergence of three-dimensional chiral domain walls in polar vortices

Abstract Chirality or handedness of a material can be used as an order parameter to uncover the emergent electronic properties for quantum information science. Conventionally, chirality is found in naturally occurring biomolecules and magnetic materials. Chirality can be engineered in a topological polar vortex ferroelectric/dielectric system via atomic-scale symmetry-breaking operations. We use four-dimensional scanning transmission electron microscopy (4D-STEM) to map out the topology-driven three-dimensional domain walls, where the handedness of two neighbor topological domains change or remain the same. The nature of the domain walls is governed by the interplay of the local perpendicular (lateral) and parallel (axial) polarization with respect to the tubular vortex structures. Unique symmetry-breaking operations and the finite nature of domain walls result in a triple point formation at the junction of chiral and achiral domain walls. The unconventional nature of the domain walls with triple point pairs may result in unique electrostatic and magnetic properties potentially useful for quantum sensing applications.

36 MATERIALS SCIENCE↗

An evolutionarily conserved tryptophan cage promotes folding of the extended RNA recognition motif in the hnRNPR ‐like protein family

Abstract The heterogeneous nuclear ribonucleoprotein (hnRNP) R‐like family is a class of RNA binding proteins in the hnRNP superfamily with diverse functions in RNA processing. Here, we present the 1.90 Å X‐ray crystal structure and solution NMR studies of the first RNA recognition motif (RRM) of human hnRNPR. We find that this domain adopts an extended RRM (eRRM1) featuring a canonical RRM with a structured N‐terminal extension (N ext ) motif that docks against the RRM and extends the β‐sheet surface. The adjoining loop is structured and forms a tryptophan cage motif to position the N ext motif for docking to the RRM. Combining mutagenesis, solution NMR spectroscopy, and thermal denaturation studies, we evaluate the importance of residues in the N ext –RRM interface and adjoining loop on eRRM folding and conformational dynamics. We find that these sites are essential for protein solubility, conformational ordering, and thermal stability. Consistent with their importance, mutations in the N ext –RRM interface and loop are associated with several cancers in a survey of somatic mutations in cancer studies. Sequence and structure comparison of the human hnRNPR eRRM1 to experimentally verified and predicted hnRNPR‐like proteins reveals conserved features in the eRRM.

Biochemistry & Molecular Biology↗

Physics-guided dual implicit neural representations for source separation

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

47 OTHER INSTRUMENTATION↗