Electrolyte Dependence of Li+ Transport Mechanisms in Small Molecule Solvents from Classical Molecular Dynamics.

As demands on Li-ion battery performance increase, the need for electrolytes with high ionic conductivity and a high Li+ transference number (tLi) becomes crucial to boost power density. Unfortunately, tLi in liquid electrolytes is typically <0.5 due to Li+ migrating via a vehicular mechanism, whereby Li+ diffuses along with its solvation shell, making its diffusivity slower than the counteranion. Designing liquid electrolytes where the Li+ ion diffuses independently of its solvation shell is of significant interest to enhance the transference number. In this work, we elucidate how the properties of the solvent influence the Li+ transport mechanism. Using classical molecular dynamics simulations, we find that a vehicular mechanism can be increasingly preferred with a decreasing solvent viscosity and increasing interaction energy between the solvent and Li+. Thus, a weaker interaction energy can enhance tLi through a solvent-exchange mechanism, ultimately improving Li-ion battery performance. Finally, metadynamics simulations show that in electrolytes where a solvent-exchange mechanism is preferable, the energy barrier to changing the coordination environment of Li+ is much lower than in electrolytes where a vehicular mechanism dominates.

Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity.

Graph neural networks (GNNs) have recently been used to learn the representations of crystal structures through an end-to-end data-driven approach. However, a systematic top-down approach to evaluate and understand the limitations of GNNs in accurately capturing crystal structures has yet to be established. In this study, we introduce an approach using human-designed descriptors as a compendium of human knowledge to investigate the extent to which GNNs can comprehend crystal structures. Our findings reveal that current state-of-the-art GNNs fall short in accurately capturing the periodicity of crystal structures. We analyze this failure by exploring three aspects: local expressive power, long-range information processing, and readout function. To address these identified limitations, we propose a straightforward and general solution: the hybridization of descriptors with GNNs, which directly supplements the missing information to GNNs. The hybridization enhances the predictive accuracy of GNNs for specific material properties, most notably phonon internal energy and heat capacity, which heavily rely on the periodicity of materials.

Chemical and Physical Drivers for Improvement in Permeance and Stability of Linker-Free Graphene Oxide Membranes.

Graphene oxide (GO) is a promising membrane material for chemical separations, including water treatment. However, GO has often required postsynthesis chemical modifications, such as linkers or intercalants, to improve either the permeability, performance, or mechanical integrity of GO membranes. In this work, we explore two different feedstocks of GO to investigate chemical and physical differences, where we observe up to a 100× discrepancy in the permeability-mass loading trade-off while maintaining nanofiltration capacity. GO membranes also show structural stability and chemical resilience to harsh pH conditions and bleach treatment. We probe GO and the resulting assembled membranes through a variety of characterization approaches, including a novel scanning-transmission-electron-microscopy-based visualization approach, to connect differences in sheet stacking and oxide functional groups to significant improvements in permeability and chemical stability.

Effect of Methyl Groups on Formation of Ordered or Layered Graphitic Materials from Aromatic Molecules.

Developing functionally complex carbon materials from small aromatic molecules requires an understanding of how the chemistry and structure of its constituent molecules evolve and crosslink, to achieve a tailorable set of functional properties. Here, molecular dynamics (MD) simulations are used to isolate the effect of methyl groups on condensation reactions during the oxidative process and evaluate the impact on elastic modulus by considering three monodisperse pyrene-based systems with increasing methyl group fraction. A parameter to quantify the reaction progression is designed by computing the number of new covalent bonds formed. Utilizing the previously developed MD framework, it is found that increasing methylation leads to an almost doubling of bond formation, a larger fraction of the new bonds oriented in the direction of tensile stress, and a higher basal plane alignment of the precursor molecules along the direction of tensile stress, resulting in enhanced tensile modulus. Additionally, via experiments, it is demonstrated that precursors with a higher fraction of methyl groups result in a higher alignment of molecules. Moreover, increased methylation results in the lower spread of single molecule alignment which may lead to smaller variations in tensile modulus and more consistent properties in carbon materials derived from methyl-rich precursors.

Ultrafine Filteration of Metal Catalysts Enables Fabrication of Silicon Nanowires with Diameter Approaching a Unit Cell Size.

Advanced functionalities of silicon nanowires are size-dependent and downscaling of the nanostructure often leads to higher device performances. Single-crystal silicon nanowires with diameters approaching a single unit cell are fabricated using membrane-filtrated catalyst assisted chemical etching. Atomically filtrated gold is used as uniform pattern to direct anisotropic etching of dense silicon nanowire arrays. The size of the nanowires can be controlled by engineering the molecular weight of Poly(methyl methacrylate) used to fabricate the polymer globule membranes. The smallest silicon nanowires with 0.9 nm diameters exhibit direct, and wide band gap of 3.55 eV and establishes a new record. The experimentally obtained silicon nanowires in this size fill the valuable gap below the few-nanometer regime where to date only theoretical predictions have been available. This fabrication approach could provide facile access to atomic-scale silicon, which can bring further advancement to next generation nanodevices.

Bayesian-Optimization-Assisted Laser Reduction of Poly(acrylonitrile) for Electrochemical Applications.

Laser reduction of polymers has recently been explored to rapidly and inexpensively synthesize high-quality graphitic and carbonaceous materials. However, in past work, laser-induced graphene has been restricted to semiaromatic polymers and graphene oxide; in particular, poly(acrylonitrile) (PAN) is claimed to be a polymer that cannot be laser-reduced successfully to form electrochemically active material. In this work, three strategies to surmount this barrier are employed: (1) thermal stabilization of PAN to increase its sp2 content for improved laser processability, (2) prelaser treatment microstructuring to reduce the effects of thermal stresses, and (3) Bayesian optimization to search the parameter space of laser processing to improve performance and discover morphologies. Based on these approaches, we successfully synthesize laser-reduced PAN with a low sheet resistance (6.5 Ω sq-1) in a single lasing step. The resulting materials are tested electrochemically, and their applicability as membrane electrodes for vanadium redox flow batteries is demonstrated. This work demonstrates electrodes that are processed in air, below 300 °C, which are cycled stably over 2 weeks at 40 mA cm-2, motivating further development of laser reduction of porous polymers for membrane electrode applications such as RFBs.

2D Monolayers for Superior Transparent Electromagnetic Interference Shielding.

With countless modern technologies utilizing wireless communication, materials that can selectively allow transmission of visible light and prevent transmission of low frequency GHz electromagnetic interference (EMI) are needed. Recently, 2D materials such as graphene, transition metal dichalcogenides, and MXenes have shown promise for such applications. Despite the rapid advances, little progress has been made in identifying 2D monolayers with intrinsically higher visible transmittance (Tvis) and shielding effectiveness (SE). With endless variations in structure and composition, the 2D materials space is too large for systematic experimental investigation. To tackle this challenge, we perform a high-throughput computational screening. Using an atomistic first-principles method, we simultaneously calculate Tvis and SE of 7000 2D monolayer materials. We identify 26 monolayer materials with excellent properties of >98% Tvis and >5 dB SE (∼70% EMI attenuation). The top candidate, an AgSe2 monolayer with predicted 98.53% Tvis and 12.53 dB SE (∼94% EMI attenuation), is a significant improvement over the state-of-the-art, graphene, with 96.7% Tvis and 3.04 dB SE (∼40% EMI attenuation). Additionally, we gain physical insights into the transparent EMI shielding performance of 2D monolayers and their electronic structure, elucidating the role of surface terminations and nearly free electron states.

Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning.

The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew-Burke-Ernzerhof (PBE) functional with linear correction, PBEsol, and meta-generalized gradient approximation (meta-GGA) functionals (SCAN and r2SCAN), and it outperforms the hotly studied deep neural network-based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database and discover materials with underestimated stability. The multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.

Conformal Encapsulation of Silver Nanowire Transparent Electrodes by Nanosized Reduced Graphene Oxide Leading to Improved All-Round Stability.

Solution-processed silver nanowire (AgNW) networks are promising as next-generation transparent conductive electrodes due to their excellent optoelectronic properties, mechanical flexibility, as well as low material and processing costs. However, AgNWs are prone to thermally induced fragmentation and chemical degradation, necessitating a conformal protective coating typically achieved by low-throughput methods such as sputtering or atomic layer deposition. Herein, we report a facile all-solution-based approach to synthesize a conformally coated AgNW network by nanosized reduced graphene oxide R(nGO). In this method, probe ultrasonication is used to obtain nanosized GO, which is coated on AgNWs by a layer-by-layer approach and then chemically treated to form R(nGO)/AgNW. We show that our transparent electrode has excellent transmittance (85-92%) and sheet resistance (17.5 Ω/sq), combined with outstanding thermal and electrothermal stability, thanks to the conformal nature of the R(nGO) film, and demonstrate its use as a transparent heater with a high maximum temperature. This, in conjunction with improved long-term chemical and mechanical bending stability of R(nGO)/AgNW, indicates that our newly developed process represents an effective and low-cost strategy to improve the overall operational resilience of metal nanowire-based transparent conductive electrodes.

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties.

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

Atoms to fibers: Identifying novel processing methods in the synthesis of pitch-based carbon fibers.

Understanding and optimizing the key mechanisms used in the synthesis of pitch-based carbon fibers (CFs) are challenging, because unlike polyacrylonitrile-based CFs, the feedstock for pitch-based CFs is chemically heterogeneous, resulting in complex fabrication leading to inconsistency in the final properties. In this work, we use molecular dynamics simulations to explore the processing and chemical phase space through a framework of CF models to identify their effects on elastic performance. The results are in excellent agreement with experiments. We find that density, followed by alignment, and functionality of the molecular constituents dictate the CF mechanical properties more strongly than their size and shape. Last, we propose a previously unexplored fabrication route for high-modulus CFs. Unlike graphitization, this results in increased sp3 fraction, achieved via generating high-density CFs. In addition, the high sp3 fraction leads to the fabrication of CFs with isometric compressive and tensile moduli, enabling their potential applications for compressive loading.

Kinetics of Sorption in Hygroscopic Hydrogels.

Hygroscopic hydrogels hold significant promise for high-performance atmospheric water harvesting, passive cooling, and thermal management. However, a mechanistic understanding of the sorption kinetics of hygroscopic hydrogels remains elusive, impeding an optimized design and broad adoption. Here, we develop a generalized two-concentration model (TCM) to describe the sorption kinetics of hygroscopic hydrogels, where vapor transport in hydrogel micropores and liquid transport in polymer nanopores are coupled through the sorption at the interface. We show that the liquid transport due to the chemical potential gradient in the hydrogel plays an important role in the fast kinetics. The high water uptake is attributed to the expansion of hydrogel during liquid transport. Moreover, we identify key design parameters governing the kinetics, including the initial porosity, hydrogel thickness, and shear modulus. This work provides a generic framework of sorption kinetics, which bridges the knowledge gap between the fundamental transport and practical design of hygroscopic hydrogels.

Electronic, Structural, and Magnetic Upgrading of Coal-Based Products through Laser Annealing.

Most coal-to-product routes require complex thermal treatment to carbonize the raw materials. However, the lack of unified comparison of products made from different kinds of coals downplays the role of initial coal chemistry in high-temperature reactions. Here, we used a CO2 laser to investigate the roles that aromatic content and maturity play in the structural evolution and doping of coals during annealing. Results show that a bituminous coal (DECS 19) with aromatic content and maturity in between higher rank, more mature anthracite (DECS 21) and lower rank, lower maturity lignite (DECS 25) leads to more graphite-like structure observed from the highest 2D peak on the Raman spectrum and conductivity (sheet resistance ∼30 ohm sq-1) after lasing. When nitrogen dopants are incorporated with saturated urea dopants into coals through laser ablation, nitrogen preferentially incorporates at the edge sites of graphitic grains. Furthermore, oxide nanoparticles can be incorporated into the graphitic backbone of coal to modify their electronic and magnetic properties through laser annealing. Leveraging tunable magnetic behavior, we demonstrate a soft actuator using both conductive and magnetic coal-Fe/Co oxide. Through laser annealing, we propose a paradigm to understand and control coal chemistry toward flexible and tunable doping and magnetism.

Printing Air-Stable High-Tc Molecular Magnet with Tunable Magnetic Interaction.

High-Tc molecular magnets have amassed much promise; however, the long-standing obstacle for its practical applications is the inaccessibility of high-temperature molecular magnets showing dynamic and nonvolatile magnetization control. In addition, its functional durability is prone to degradation in oxygen and heat. Here, we introduce a rapid prototyping and stabilizing strategy for high Tc (360 K) molecular magnets with precise spatial control in geometry. The printed molecular magnets are thermally stable up to 400 K and air-stable for over 300 days, a significant improvement in its lifetime and durability. X-ray magnetic circular dichroism and computational modeling reveal the water ligands controlling magnetic exchange interaction of molecular magnets. The molecular magnets also show dynamical and reversible tunability of magnetic exchange interactions, enabling a colossal working temperature window of 86 K (from 258 to 344 K). This study provides a pathway to flexible, lightweight, and durable molecular magnetic devices through additive manufacturing.

Oxynitride-Encapsulated Silver Nanowire Transparent Electrode with Enhanced Thermal, Electrical, and Chemical Stability.

Silver nanowire (AgNW) networks have been explored as a promising technology for transparent electrodes due to their solution-processability, low-cost implementation, and excellent trade-off between sheet resistance and transparency. However, their large-scale implementation in applications such as solar cells, transparent heaters, and display applications has been hindered by their poor thermal, electrical, and chemical stability. In this work, we present reactive sputtering as a method for fast deposition of metal oxynitrides as an encapsulant layer on AgNWs. Because O2 cannot be used as a reactive gas in the presence of oxidation-sensitive materials such as Ag, N2 is used under moderate sputtering base pressures to leverage residual H2O on the sample and chamber to deposit Al, Ti, and Zr oxynitrides (AlOxNy, TiOxNy, and ZrOxNy) on Ag nanowires on glass and polymer substrates. All encapsulants improve AgNW networks' electrical, thermal, and chemical stability. In particular, AlOxNy-encapsulated networks present exceptional chemical stability (negligible increase in resistance over 7 days at 80% relative humidity and 80 °C) and transparency (96% for 20 nm films on AgNWs), while TiOxNy demonstrates exceptional thermal and electrical stability (stability up to over temperatures 100 °C more than that of bare AgNW networks, with a maximum areal power density of 1.72 W/cm2, and no resistance divergence at up to 20 V), and ZrOxNy presents intermediate properties in all metrics. In summary, a novel method of oxynitride deposition, leveraging moderate base pressure reactive sputtering, is demonstrated for AgNW encapsulant deposition, which is compatible with roll-to-roll processes that are operated at commercial scales, and this technique can be extended to arbitrary, vacuum-compatible substrates and device architectures.

Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning.

Understanding and broad screening Li interaction energetics with surfaces are key to the development of materials for a wide range of applications including Li-based electrochemical capacitors, Li sensors, Li separation membranes, and Li-ion batteries. In this work, we build a high-throughput screening scheme to screen Li adsorption energetics on 2D metallic materials. First, density functional theory and graph convolution networks are utilized to calculate the minimum Li adsorption energies for some 2D metallic materials. The data is then used to find a dependence of the minimum Li adsorption energies on the sum of ionization potential, work function of the 2D metal, and coupling energy between Li+ and substrate, and the dependence is used to screen all 2D metallic materials. Physics-simplified learning by splitting the property into different contributions and learning or calculating each component is shown to have higher accuracy and transferability for machine learning of complex materials properties.

Cation- and pH-Dependent Hydrogen Evolution and Oxidation Reaction Kinetics.

The production of molecular hydrogen by catalyzing water splitting is central to achieving the decarbonization of sustainable fuels and chemical transformations. In this work, a series of structure-making/breaking cations in the electrolyte were investigated as spectator cations in hydrogen evolution and oxidation reactions (HER/HOR) in the pH range of 1 to 14, whose kinetics was found to be altered by up to 2 orders of magnitude by these cations. The exchange current density of HER/HOR was shown to increase with greater structure-making tendency of cations in the order of Cs+ < Rb+ < K+ < Na+ < Li+, which was accompanied by decreasing reorganization energy from the Marcus-Hush-Chidsey formalism and increasing reaction entropy. Invoking the Born model of reorganization energy and reaction entropy, the static dielectric constant of the electrolyte at the electrified interface was found to be significantly lower than that of bulk, decreasing with the structure-making tendency of cations at the negatively charged Pt surface. The physical origin of cation-dependent HER/HOR kinetics can be rationalized by an increase in concentration of cations on the negatively charged Pt surface, altering the interfacial water structure and the H-bonding network, which is supported by classical molecular dynamics simulation and surface-enhanced infrared absorption spectroscopy. This work highlights immense opportunities to control the reaction rates by tuning interfacial structures of cation and solvents.

Emerged Metallicity in Molecular Ferromagnetic Wires.

Supramolecular engineering bridges molecular assembly with macromolecular charge-transfer salts, promising the design to construct supramolecular architectures that integrate cooperative properties difficult or impossible to find in conventional lattices. Here, we report the crystal engineering design and kinetic growth of one-dimensional supramolecular wires composed of bis(ethylenedithio)tetrathiafulvalene (ET+) cation and polymeric Cu[N(CN)2]2- anion. A bulk ferromagnetic order is discovered for filling up the gap where strong ferromagnetism is missing in such ET molecule-based charge-transfer salts. Metallicity is induced by electric current from the semiconducting wire, which is attributed to strain effect by tuning its close molecular contact. This structural feature is evidenced through the combination of various mechanistic spectroscopic studies. Electric dipole is established from the close molecular contacts and is suggestive to stabilize ferromagnetic spin interaction through anions bridging spin sites. The breakthrough shown here provides a pathway to explore low-dimensional supramolecular materials exhibiting strong electron correlation, metallicity, and ferromagnetism.

Atomic Structure of Dislocations and Grain Boundaries in Two-Dimensional PtSe2.

Each 2D material has a distinct structure for its grain boundary and dislocation cores, which is dictated by both the crystal lattice geometry and the elements that participate in bonding. For the class of noble metal dichalcogenides, this has yet to be thoroughly investigated at the atomic scale. Here, we examine the atomic structure of the dislocations and grain boundaries (GBs) in two-dimensional PtSe2, using atomic-resolution annular dark field scanning transmission electron microscopy, combined with density functional theory and empirical force field calculations. The PtSe2 we study adopts the 1T phase in large-area polycrystalline films with numerous planar tilt GB distinct dislocations, including 5|7+Se and 4|4|8+Se polygons, in tilt-angle monolayer GBs, with features sharply distinguished from those in 2H-phase TMDs. On the basis of dislocation cores, the GB structures are investigated in terms of pathways of dislocation chain arrangement, dislocation core distributions in different misorientation angles, and 2D strain fields induced. Based on the Frank-Bilby equation, the deduced Burgers vector magnitude is close to the lattice constant of 1T-PtSe2, building the quantitative relationship of dislocation spacings and small GB angles. The 30° GBs are most frequently formed as a stitched interface between the armchair and zigzag lattices, constructed by a string of 5|7+Se dislocations asymmetrically with a small deviation angle. Another special angle GB, mirror twin 60° GB, is also mapped linearly by metal-condensed asymmetric or Se-rich symmetric dislocations. This report gives atomic-level insights into the GBs and dislocations in 1T-phase noble metal TMD PtSe2, which is a promising material to underpin extending properties of 2D materials by local structure engineering.

Successful, Easy to Access, Online Publication of COVID-19 Data During the Pandemic, New York City, 2020.

Making public health data easier to access, understand, and use makes it more likely that the data will be influential. Throughout the COVID-19 pandemic, the New York City (NYC) Department of Health and Mental Hygiene's Web-based data communication became a cornerstone of NYC's response and allowed the public, journalists, and researchers to access and understand the data in a way that supported the pandemic response and brought attention to the deeply unequal patterns of COVID-19's morbidity and mortality in NYC. (Am J Public Health. 2021;111(S3):S193-S196. https://doi.org/10.2105/AJPH.2021.306446).