HIV-1 matrix-tRNA complex structure reveals basis for host control of Gag localization
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High-power lasers require enormous beam sizes to avoid optics damage; without plasma control of light, future higher-power systems will have prohibitive costs. The laser-induced formation of ionization patterns in gases provides an opportunity to manipulate the spatial and temporal properties of femtosecond to picosecond beams with precise control at high intensity, providing a path towards compact multi-petawatt lasers. In this project, we proposed new concepts of diffractive optics elements relying on ionization-based structures, including a holographic lens and a compression grating. The theoretical characterization of these optical elements was supported by particle-in-cell simulations. Experiments have been carried out to achieve reflective elements (i.e., plasma-based gratings) that demonstrated high robustness and efficiencies on the order of 50%, which validates the feasibility of our new concepts and paves the way for future plasma-based high-power laser systems.
A hallmark of retrovirus replication is the translation of two different polyproteins from one RNA through programmed –1 frameshifting. This is a mechanism in which the actively translating ribosome is induced to slip in the 5′ direction at a defined codon and then continues translating in the new reading frame. Programmed frameshifting controls the stoichiometry of viral proteins and is therefore under stringent evolutionary selection. Forty years ago, the first frameshifting stimulatory element was discovered in the Rous sarcoma virus. The ~120 nt RNA segment was predicted to contain a pseudoknot, but its 3D structure has remained elusive. Now, we have determined cryoEM and X-ray crystallographic structures of this classic retroviral element, finding that it adopts a butterfly-like double-pseudoknot fold. One “wing” contains a dynamic pyrimidine-rich helix, observed crystallographically in two conformations and in a third conformation via cryoEM. The other wing encompasses the predicted pseudoknot, which interacts with a second unexpected pseudoknot through a toggle residue, A2546. This key purine switches conformations between structural states and tunes the stability of interacting residues in the two wings. We find that its mutation can modulate frameshifting by as much as 50-fold, likely by altering the relative abundance of different structural states in the conformational ensemble of the RNA. Taken together, our structure–function analyses reveal how a dynamic double pseudoknot junction stimulates frameshifting by taking advantage of conformational heterogeneity, supporting a multistate model in which high Shannon entropy enhances frameshifting efficiency.
The photoisomerization reaction of a fluorescent protein chromophore occurs on the ultrafast timescale. The structural dynamics that result from femtosecond optical excitation have contributions from vibrational and electronic processes and from reaction dynamics that involve the crossing through a conical intersection. The creation and progression of the ultrafast structural dynamics strongly depends on optical and molecular parameters. When using X-ray crystallography as a probe of ultrafast dynamics, the origin of the observed nuclear motions is not known. Now, high-resolution pump–probe X-ray crystallography reveals complex sub-ångström, ultrafast motions and hydrogen-bonding rearrangements in the active site of a fluorescent protein. However, we demonstrate that the measured motions are not part of the photoisomerization reaction but instead arise from impulsively driven coherent vibrational processes in the electronic ground state. A coherent-control experiment using a two-colour and two-pulse optical excitation strongly amplifies the X-ray crystallographic difference density, while it fully depletes the photoisomerization process. A coherent control mechanism was tested and confirmed the wave packets assignment.
The intensity of light from the highest-power lasers is limited by the size of the optics required to amplify, compress, transport, and focus a laser beam. Indeed, optics damage puts strict constraints on the laser energy density that solid optics can sustain; this leads to enormous optics sizes and limits the maximum intensity that these lasers can achieve. In this project we investigated new types of diffractive optics based on mixtures of neutral gas and plasmas. The basic idea is to interfere laser beams in a gas near the ionization threshold, so that plasma is created in the constructive interference regions (i.e., the “bright” fringes of the interference pattern) while the destructive interference regions remain a neutral gas. The resulting refractive index modulation, due to the different indices in gas vs. plasma, effectively turns such structures into diffractive optics elements. We proposed several new types of optics based on this general concept: i) reflection gratings, which can act as mirrors of “pulse cleaners” to eliminate laser pre-pulse; ii) transmission gratings, as part of a new type of pulse compression scheme with the potential to achieve laser intensities orders of magnitude above what is currently feasible using solid-state optics; and iii) holographic lenses, to focus high-power lasers. We conducted laboratory experiments which demonstrated high efficiency reflection gratings, paving the way for practical applications of our new concepts.
The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.
The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.
Topological spin/polarization structures in ferroic materials continue to draw great attention as a result of their fascinating physical behaviors and promising applications in the field of high-density nonvolatile memories as well as future energy-efficient nanoelectronic and spintronic devices. Such developments have been made, in part, based on recent advances in theoretical calculations, the synthesis of high-quality thin films, and the characterization of their emergent phenomena and exotic phases. Herein, progress over the last decade in the study of topological structures in ferroic thin films and heterostructures is explored, including the observation of topological structures and control of their structures and emergent physical phenomena through epitaxial strain, layer thickness, electric, magnetic fields, etc. First, the evolution of topological spin structures (e.g., magnetic skyrmions) and associated functionalities (e.g., topological Hall effect) in magnetic thin films and heterostructures is discussed. Then, the exotic polar topologies (e.g., domain walls, closure domains, polar vortices, bubble domains, and polar skyrmions) and their emergent physical properties in ferroelectric oxide films and heterostructures are explored. Lastly, a brief overview and prospectus of how the field may evolve in the coming years is provided.
Abstract Aerosol jet printing is a popular digital additive manufacturing method for flexible and hybrid electronics, but it lacks sophisticated real‐time process control schemes that would enable more widespread adoption in manufacturing environments. Here, an optical measurement system is introduced to track the aerosol density upstream of the printhead. The measured optical extinction, combined with the aerosol flow rate, is directly related to deposition rate and accurately predicts functional materials properties such as the electrical resistance of printed graphene films. This real‐time system offers a compelling solution for process drift and batch‐to‐batch variability, rendering it a valuable tool for both real‐time control of aerosol jet printing and fundamental studies of the underlying process science.
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The cycling performance of batteries is largely determined by electrode-electrolyte interphase associated with the chemical and electrochemical properties of electrolyte salts and solvents. In this work, we used a localized high concentration electrolyte (LHCE) that enables high performance of Si anodes as a model system and investigated the scientific mechanism behind the optimization of electrolyte. The correlation between electrode (cathode/anode)-electrolyte interphase and ion coordination structure and diffusion kinetics were elucidated systematically. Among all the LHCEs tested, the one with 1.8M LiFSI concentration and 1:2 molar ratio of carbonate solvents and fluorinated diluents has the highest proportion of solvent-separated ion pairs and contact ion pairs and fastest ion diffusion. It enables the most stable electrode-electrolyte interphase and hence the best Si anode performance. These results reveal the fundamental mechanism behind the stability of LHCEs and provide guidance on the new electrolyte design for Si anodes and other battery systems.
Numerous experimental investigations indicated that expansive clays such as montmorillonite can intercalate CO 2 preferentially into their interlayers and therefore potentially act as a material for CO 2 separation, capture, and storage. However, an understanding of the energy–structure relationship during the intercalation of CO 2 into clay interlayers remains elusive. Here, in this study, we use metadynamics molecular dynamics simulations to elucidate the energy landscape associated with CO 2 intercalation. Our free energy calculations indicate that CO 2 favorably partitions into nanoconfined water in clay interlayers from a gas phase, leading to an increase in the CO 2 /H 2 O ratio in clay interlayers as compared to that in bulk water. CO 2 molecules prefer to be located at the centers of charge-neutral hydrophobic siloxane rings, whereas interlayer spaces close to structural charges tend to avoid CO 2 intercalation. The structural charge distribution significantly affects the amount of CO 2 intercalated in the interlayers. These results provide a mechanistic understanding of CO 2 intercalation in clays for CO 2 separation, capture, and storage.
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The chemical structure of donors and acceptors limit the power conversion efficiencies achievable with active layers of binary donor-acceptor mixtures. Here, using quaternary blends, double cascading energy level alignment in bulk heterojunction organic photovoltaic active layers are realized, enabling efficient carrier splitting and transport. Numerous avenues to optimize light absorption, carrier transport, and charge-transfer state energy levels are opened by the chemical constitution of the components. Record-breaking PCEs of 18.07% are achieved where, by electronic structure and morphology optimization, simultaneous improvements of the open-circuit voltage, short-circuit current and fill factor occur. The donor and acceptor chemical structures afford control over electronic structure and charge-transfer state energy levels, enabling manipulation of hole-transfer rates, carrier transport, and non-radiative recombination losses.
Grubbs 3rd-generation (G3) pre-catalyst-initiated ring-opening metathesis polymerization (ROMP) remains an indispensable tool in the polymer chemist's toolbox. Tricyclononenes (TCN) and tricyclononadienes (TCND) represent under-explored classes of monomers for ROMP that have the potential to both advance fundamental knowledge (e.g., structure-polymerization kinetics relationships) and serve as practical tools for the polymer chemist (e.g., post-polymerization functionalization). In this work, a library of TCN and TCND imides, monoesters, and diesters, along with their exo-norbornene counterparts, were synthesized to compare their behaviors in G3-initiated ROMP. Real-time 1 H NMR was used to study their polymerization kinetics; propagation rates ( k p ) were extracted for each monomer. To understand the relationships between monomer structure and ROMP propagation rates, density functional theory methods were used to calculate a variety of electronic and steric parameters for each monomer. While electronic parameters (e.g., HOMO energy levels) correlated positively with the measured k p values, steric parameters generally gave improved correlations, which indicates that monomer size and shape are better predictors for k p than electronic parameters for this data set. Furthermore, the TCND diester—which contains an electron-deficient cyclobutene that is resistant to ROMP—and its polymer p(TCND) are shown to be highly reactive toward DBU-catalyzed conjugate addition reactions with thiols, providing a protecting- and activating-group free strategy for post-polymerization modification.