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Abstract Due to their diverse potential in advanced electronics and energy technologies, electrically conducting metal‐organic frameworks (MOFs) are drawing significant attention. Although hexagonal 2D MOFs generally display impressive electrical conductivity because of their dual in‐plane (through bonds) and out‐of‐plane (through π‐stacked ligands) charge transport pathways, notable differences between these two orthogonal conduction routes cause anisotropic conductivity and lower bulk conductivity. To address this issue, we have developed the first redox‐complementary dual‐ligand 2D MOF Cu₃(HHTP)(HHTQ), featuring a π‐donor hexahydroxytriphenylene (HHTP) ligand and a π‐acceptor hexahydroxytricycloquinazoline (HHTQ) ligand located at alternate corners of the hexagons, which form either parallel HHTP and HHTQ stacks (AA stacking) or alternating HHTP/HHTQ stacks (AB stacking) along the c‐axis. Regardless of the stacking pattern, Cu₃(HHTP)(HHTQ) supports more effective out‐of‐plane conduction through either separate π‐donor and π‐acceptor stacks or alternating π‐donor/acceptor stacks, while promoting in‐plane conduction through the pushpull‐like heteroleptic coordination network. As a result, Cu₃(HHTP)(HHTQ) exhibits higher bulk conductivity (0.12 S/m at 295 K) than single‐ligand MOFs Cu₃(HHTP)₂(7.3 × 10⁻²S/m) and Cu₃(HHTQ)₂(5.9 × 10⁻⁴S/m). This work introduces a new design approach to improve the bulk electrical conductivity of 2D MOFs by supporting charge transport in both in‐ and out‐of‐plane direcations. 

Metal halide perovskites represent a promising class of gain media for next‐generation nonepitaxial laser diodes. However, fully electrically pumped perovskite laser diodes have not been achieved yet. Herein, the use of sodium fluoride (NaF) is explored as an efficient additive in halide perovskite films to improve their optical and light amplification properties. The incorporation of NaF in perovskites leads to a remarkable threefold increase in light‐emitting intensity. The threshold of amplified spontaneous emission (ASE) by optical pumping is reduced by more than 20%, from ≈13.5 to 10.4 μJ cm⁻². Furthermore, the NaF‐modified perovskites exhibit stable ASE emission, even after exposure to 1.5 billion optical pulses, highlighting substantial improvements in the material's photostability. Finally, optically pumped ASE is observed from a full perovskite light‐emitting diode stack, including lossy metal electrodes. This work demonstrates significant progress toward the development of electrically pumped perovskite lasers. 

Power systems with utility-scale solar photovoltaic (PV) can significantly influence the operating points (OPs) of synchronous generators, particularly during periods of high solar PV generation. A sudden drop in solar PV output due to cloud cover or other transient conditions will alter the generation of synchronous generators shifting their OPs. These shifted OPs can become a challenge for stability as the system may operate closer to its stability limits. If a disturbance occurs while the system is operating at the shifted OP, with reduced stability margins, it will be more vulnerable to increased oscillations, loss of synchronism of its generator(s) and system instability. This study introduces a scalable delta-automatic generation control (delta-AGC) logic method designed to address stability challenges arising from shifts in the OPs of synchronous generators during abrupt drops in PV generation. By temporarily adjusting the OPs of synchronous generators through modification of their participation factors (PFs) in the AGC logic dispatch, the proposed method enhances power system stability. The proposed delta-AGC logic method focuses on the optimal determination of delta-PFs in power systems with large number of generators, using the concept of coherency and employing a hierarchical optimization strategy that includes both inter-coherent and intra-coherent group optimization. Additionally, a new electromechanical oscillation index (EMOI), integrating both time response analysis (TRA) and frequency response analysis (FRA), is utilized as an online situational awareness tool (SAT) for optimizing the system’s stability under various conditions. This online SAT has been implemented in a decentralized manner at the area level, limiting wide-area communication overheads and any cybersecurity concerns. The delta-AGC logic method is illustrated on a modified IEEE 68 bus system, incorporating large utility-scale solar PV plants, and is validated through real-time simulation. Various cases, including high-loading conditions with and without power system stabilizers, conventional AGC logic, and delta-AGC logic, are carried out to evaluate the effectiveness of the proposed delta-AGC logic method. The results illustrate the performance and benefits of the delta-AGC logic method, highlighting its potential to significantly enhance power system stability. 

Predicting materials’ microstructure from the desired properties is critical for exploring new materials. Herein, a novel regression‐based prediction of scanning electron microscopy (SEM) images for the target hardness using generative adversarial networks (GANs) is demonstrated. This article aims at generating realistic SEM micrographs, which contain rich features (e.g., grain and neck shapes, tortuosity, spatial configurations of grain/pores). Together, these features affect material properties but are difficult to predict. A high‐performance GAN, named ‘Microstructure‐GAN’ (or M‐GAN), with residual blocks to significantly improve the details of synthesized micrographs is established . This algorithm was trained with experimentally obtained SEM micrographs of laser‐sintered alumina. After training, the high‐fidelity, feature‐rich micrographs can be predicted for an arbitrary target hardness. Microstructure details such as small pores and grain boundaries can be observed even at the nanometer scale (∼50 nm) in the predicted 1000× micrographs. A pretrained convolutional neural network (CNN) was used to evaluate the accuracy of the predicted micrographs with rich features for specific hardness. The relative bias of the CNN‐evaluated value of the generated micrographs was within 2.1%–2.7% from the values for experimental micrographs. This approach can potentially be applied to other microscopy data, such as atomic force, optical, and transmission electron microscopy. 

ABSTRACT Exosomes, a subset of extracellular vesicles (EVs) ranging in size from 30 to 150 nm, are of significant interest for biomedical applications such as diagnostic testing and therapeutics delivery. Biofluids, including urine, blood, and saliva, contain exosomes that carry biomarkers reflective of their host cells. However, isolation of EVs is often a challenge due to their size range, low density, and high hydrophobicity. Isolations can involve long separation times (ultracentrifugation) or result in impure eluates (size exclusion chromatography, polymer‐based precipitation). As an alternative to these methods, this study evaluates the first use of nylon‐6 capillary‐channeled polymer (C‐CP) fiber columns to separate EVs from human urine via a step‐gradient hydrophobic interaction chromatography method. Different from previous efforts using polyester fiber columns for EV separations, nylon‐6 shows potential for increased isolation efficiency, including somewhat higher column loading capacity and more gentle EV elution solvent strength. The efficacy of this approach to EV separation has been determined by scanning electron and transmission microscopy, nanoparticle flow cytometry (NanoFCM), and Bradford protein assays. Electron microscopy showed isolated vesicles of the expected morphology. Nanoparticle flow cytometry determined particle densities of eluates yielding up to 5 × 10⁸particles mL⁻¹, a typical distribution of vesicle sizes in the eluate (60–100 nm), and immunoconfirmation using fluorescent anti‐CD81 antibodies. Bradford assays confirmed that protein concentrations in the EV eluate were significantly reduced (approx. sevenfold) from raw urine. Overall, this approach provides a low‐cost and time‐efficient (< 20 min) column separation to yield urinary EVs of the high purities required for downstream applications, including diagnostic testing and therapeutics. 

Abstract This study explores the impact of deep (5–40 m) critical zone (CZ) structure on vegetation distribution in a semiarid snow‐dominated climate. Utilizing seismic refraction surveys, we identified a significant negative correlation between seismically derived saprolite thickness and light detecting and ranging‐derived vegetation heights (R= −0.66). We argue that CZ structure, specifically shallow fractured bedrock under valley bottoms, provides moisture near the surface where trees are established—suggesting the trees are situated in locations with access to nutrients and water. This work provides a unique spatially exhaustive perspective and adds to growing evidence that in addition to other factors such as slope, aspect, and climate, deep CZ structure plays a vital role in ecosystem development. 

Abstract The rise of exascale supercomputing has motivated an increase in high‐fidelity computational fluid dynamics (CFD) simulations. The detail in these simulations, often involving shape‐dependent, time‐variant flow domains and low‐speed, complex, turbulent flows, is essential for fueling innovations in fields like wind, civil, automotive, or aerospace engineering. However, the massive amount of data these simulations produce can overwhelm storage systems and negatively affect conventional data management and postprocessing workflows, including iterative procedures such as design space exploration, optimization, and uncertainty quantification. This study proposes a novel sampling method harnessing the signed distance function (SDF) concept: SDF‐biased flow importance sampling (BiFIS) and implicit compression based on implicit neural network representations for transforming large‐size, shape‐dependent flow fields into reduced‐size shape‐agnostic images. Designed to alleviate the above‐mentioned problems, our approach achieves near‐lossless compression ratios of approximately :, reducing the size of a bridge aerodynamics forced‐vibration simulation from roughly to about while maintaining low reproduction errors, in most cases below , which is unachievable with other sampling approaches. Our approach also allows for real‐time analysis and visualization of these massive simulations and does not involve decompression preprocessing steps that yield full simulation data again. Given that image sampling is a fundamental step for any image‐based flow field prediction model, the proposed BiFIS method can significantly improve the accuracy and efficiency of such models, helping any application that relies on precise flow field predictions. The BiFIS code is available onGitHub. 

Abstract The biomolecules interact with their partners in an aqueous media; thus, their solvation energy is an important thermodynamics quantity. In previous works (J. Chem. Theory Comput. 14(2): 1020–1032), we demonstrated that the Poisson–Boltzmann (PB) approach reproduces solvation energy calculated via thermodynamic integration (TI) protocol if the structures of proteins are kept rigid. However, proteins are not rigid bodies and computing their solvation energy must account for their flexibility. Typically, in the framework of PB calculations, this is done by collecting snapshots from molecular dynamics (MD) simulations, computing their solvation energies, and averaging to obtain the ensemble‐averaged solvation energy, which is computationally demanding. To reduce the computational cost, we have proposed Gaussian/super‐Gaussian‐based methods for the dielectric function that use the atomic packing to deliver smooth dielectric function for the entire computational space, the protein and water phase, which allows the ensemble‐averaged solvation energy to be computed from a single structure. One of the technical difficulties associated with the smooth dielectric function presentation with respect to polar solvation energy is the absence of a dielectric border between the protein and water where induced charges should be positioned. This motivated the present work, where we report a super‐Gaussian regularized Poisson–Boltzmann method and use it for computing the polar solvation energy from single energy minimized structures and assess its ability to reproduce the ensemble‐averaged polar solvation on a dataset of 74 high‐resolution monomeric proteins. 

Abstract We investigated the effects of storm‐time diffuse auroral electron precipitation on ionospheric Pedersen and Hall conductivity and conductance during the CME‐driven St. Patrick's Day storms of 2013 (minDst = −131 nT) and 2015 (minDst = −233 nT). These storms were simulated using the magnetically and electrically self‐consistent RCM‐E model with STET modifications, alongside the B3C auroral transport code to compute ionospheric conductivities and height‐integrated conductance. The simulation results were validated against conductance inferred from Poker Flat Incoherent Scatter Radar (PFISR) and Millstone Hill Incoherent Scatter Radar (MHISR) measurements. Our simulations show that the magnetic latitude and local time distribution of Pedersen and Hall auroral conductance strongly correlate with diffuse electron precipitation flux, with the plasmapause marking the low‐latitude boundary of conductance. Simulated Pedersen/Hall conductance agrees reasonably well with PFISR measurements at 65.9° MLAT during diffuse auroral precipitation. During the intense 2015 storm, diffuse aurora extended down to 52.5° MLAT, with simulated conductance agreeing within a factor of two with MHISR observations. Discrete auroral arcs observed during both storms enhanced PFISR conductance by tens of siemens, though these enhancements were not captured by the model. Additionally, the simulated electric intensity showed development of sub‐auroral polarization streams (SAPS) and dawn SAPS features and followed the general trend of Poker Flat electric intensity at 65.9° MLAT during diffuse aurora, despite being updated every 5 min. The overall agreement between simulated ionospheric conductance and electric intensity with observations highlights the model's capability during diffuse auroral precipitation.