A structural expose of noncanonical molecular reactivity within the protein tyrosine phosphatase WPD
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Redox flow batteries are attractive technologies for grid energy storage since they use solutions of redox-active molecules that enable a superior scalability and the decoupling of power and energy density. However, the reaction mechanisms of the redox active components at RFB electrodes are complex, and there is currently a pressing need to understand how interfacial processes impact the kinetics and operational reversibility of RFB systems. Here, we developed a combined electrochemical imaging methodology rooted in scanning electrochemical microscopy (SECM) and atomic force microscopy (AFM) for exploring the impact of electrode structure and conditioning on the electron transfer properties of model redox-active dialkoxybenzene derivatives, 2,5-di-tert-butyl-1,4-bis(2-methoxyethoxy)benzene (C1) and 2,3-dimethyl-1,4-dialkoxybenzene (C7). Using AFM and secondary-ion mass spectrometry (SIMS), we observed the formation of interfacial films with distinct mechanical properties compared to those of cleaved graphitic surfaces, and exclusively during reduction of electrogenerated radical cations. These films had an impact on the median rate and distribution of the electron transfer rate constant at the basal plane of multilayer and single layer graphene electrodes, displaying kinetically-limited values that did not yield the activation expected per the Butler–Volmer model with a transfer coefficient ~0.5. These changes were dependent on redoxmer structure: furthermore, SECM showed strong attenuation of C7 kinetics by a surface layer on MLG and SLG, while C1 kinetics were only affected by SLG. SECM and AFM results together show that these limiting films operate exclusively on the basal plane of graphite, with the edge plane showing a relative insensitivity to cycling and operation potential. Ultimately, this integrated electrochemical imaging methodology creates new opportunities to understand the unique role of interfacial processes on the heterogeneous reactivity of redoxmers at electrodes for RFBs, with a future role in elucidating phenomena at high active concentrations and spatiotemporal variations in electrode dynamics.
Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. Here, we show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.
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Interactive molecular dynamics in virtual reality (IMD-VR) simulations provide a digital molecular playground for students as an alternative or complement to traditional molecular modeling kits or 2D illustrations. Previous IMD-VR studies have used molecular mechanics to enable simulations of macromolecules such as proteins and nanostructures for the classroom setting with considerable success. Here, we present the InteraChem molecular visualizer, intended for reactive IMD-VR simulation using semiempirical and ab initio methods. InteraChem visualizes not only the molecular geometry but also (1) isosurfaces such as molecular orbitals and electrostatic potentials and (2) two-dimensional graphs of time-varying simulation quantities such as kinetic/potential energy, internal coordinates, and user-applied force. Additionally, InteraChem employs speech recognition to facilitate user interaction and introduces a novel “atom happiness” visualization using emojis to indicate the energetic feasibility of a particular bonding arrangement. Furthermore, we include a set of accompanying exercises that we have used to teach chemical reactivity in small molecular systems.
A reactive molecular dynamics approach is used to simulate cross-linking of acrylate polymer networks. By employing the same force field and reactive scheme and studying three representative multifunctional acrylate monomers, we isolate the importance of the nonreactive moieties within these model monomers. Analyses of reactive trajectories benchmark the estimated gel points, cyclomatic character, and spatially resolved cross-linking tendencies of the acrylates as a function of conversion. The insights into the similarities and differences of the polymerization and resulting networks presented here suggest molecular mechanics as a useful tool in the rational design of photopolymerization resins.
When exposed to fire, the integrity of cement-based materials is governed by thermally-induced changes in the mechanical properties of their binding phase, i.e., the calcium–silicate–hydrate (C–S–H) gel. However, the effect of temperature on the structure, density, and mechanical properties of C–S–H remains only partially known. Here, based on reactive molecular dynamics simulations, we reveal the nature of thermally-induced damage in C–S–H gels. In general, we show that, at the atomic scale, exposure to high temperature results in partial dehydration, volumetric shrinkage, disordering, and stiffening in the C–S–H grains. However, we show that the thermal response of C–S–H strongly depends on its chemical composition, wherein C–S–H systems associated with lower Ca/Si molar ratios are able to undergo higher temperatures before amorphization. Based on these results, we demonstrate that the stiffness of C–S–H gels (i.e., including porosity—as probed by nanoindentation) is governed by a competition between the stiffening of the grains and the decrease in packing density—wherein the latter eventually become predominant.
Abstract Understanding the formation and evolution of the cathode‐electrolyte interphase (CEI), which forms at the interface between the cathode and electrolyte, is crucial for revealing degradation mechanisms in cathode materials, especially for developing strategies to stabilize the interphase in the strongly oxidizing conditions that evolve at high operating voltages in next‐generation Li‐ion batteries. However, The present understanding of the CEI is challenged by its complex and dynamic nature. In this work, near‐edge X‐ray absorption fine structure spectroscopy, electrochemical characterization, and reactive molecular dynamics simulations are combined to reveal a mechanism for CEI formation and evolution above model LiMn 2 O 4 (LMO) thin‐film electrodes in contact with conventional carbonate‐based electrolytes. It is found that Mn dissolution from LMO can be understood in terms of repetitive Mn 3 O 4 formation and dissolution behavior during cycling, which is closely connected to electrolyte decomposition and a key aspect of the CEI formation and growth. The behavior of the CEI in this model system offers detailed insight into the dynamic chemistry of the interphase, underscoring the important role of electrolyte composition and cathode surface structure in interphase degradation.
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Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.
Molecular insights into graphene-catalyst surface interactions can provide useful information for the efficient design of copper current collectors with graphitic anode interfaces. As graphene bending can affect the local electron density, it should reflect its local reactivity as well. Using ReaxFF reactive molecular simulations, we have investigated the possible bending of graphene in vacuum and near copper surfaces. We describe the energy cost for graphene bending and the binding energy with hydrogen and copper with two different ReaxFF parameter sets, demonstrating the relevance of using the more recently developed ReaxFF parameter sets for graphene properties. Moreover, the draping angle at copper step edges obtained from our atomistic simulations is in good agreement with the draping angle determined from experimental measurements, thus validating the ReaxFF results.
Mesoporous silica materials (MSMs) are well-suited for biomedical applications due to their unique features, including a large surface area and tunable pore size. To enhance their durability, the small pores in MSMs are filled with carbon precursors and then carbonized to prevent them from interacting with unreacted silicic acid. Here, in this study, we synthesized and healed MSMs using a combination of non-reactive and reactive molecular dynamics (MD) simulations. The non-reactive MD simulation revealed that the self-assembly of Pluronic® L64 polymers in water resulted in nearly 80 % hydrogen bonds between the hydrophilic sections of the micelle and water. In the bond-boosted ReaxFF MD simulations, silicic acid precursors were condensed on the micelle surface, with over 60 % of them leading to the creation of periodic mesoporous silica within the system. Condensation of silicic acid precursors at 300 K with bond-boosting and at 1500 K without it both significantly promoted the polymerization of Si(OH) 4 , with the latter doubling the rate compared to the former. Subsequently, we healed the MSM surface by carbonizing carbon precursors inside an MSM pore. Polyethylene (PE) and high-rank lignite were identified as the most suitable precursors due to their ability to form turbostratic graphene structures. High-rank lignite exhibited the highest carbon conversion ratio to 6-membered rings, closely followed by PE, in the carbon ring formation analysis. Additionally, the production of gases, such as H 2 , increased significantly for PE at both 2200 K and 2600 K, indicating the conversion of a considerable portion of carbon into graphitic or turbostratic structures. The carbonization of PE primarily led to the formation of planar (sp 2 ) structures, while sucrose yielded the least planar structures. Finally, we studied the protective blocking of unreacted silicic acid precursor by considering a PET turbostratic graphene structure in a silica mesopore formed at 2600 K. The trajectory analysis showed that the surface of the silica was effectively coated with PET tar, preventing unreacted silicic acid from interacting with the inner silica pore surface. These findings offer valuable insights into the synthesis and carbonization-based healing processes of MSMs, enhancing their potential for various biomedical applications.
Triangular nanovoids in 2D materials transition metal dichalcogenides have vertex points that cause stress concentration and lead to sharp crack propagation and failure. Here, the atomistic mechanics of back folding around triangular nanovoids in monolayer WS 2 sheets is examined. Combining atomic-resolution images from annular dark-field scanning transmission electron microscopy with reactive molecular modelling, it is revealed that the folding edge formation has statistical preferences under geometric conditions based on the orientation mismatch. It is further investigated how loading directions and strong interlayer friction, interplay with WS 2 lattice's crack preference, govern the deformation and fracture pattern around folding edges. Finally, these results provide fundamental insights into the combination of fracture and folding in flexible monolayer crystals and the resultant Moiré lattices.
Functional layered material (LM) architectures will dominate nanomaterials science in this century. We have developed theory, modeling, simulation, and software and data tools that enhance understanding and AI guide synthesis, enable characterization of complex structures, and improve capabilities in the predictive design and growth of LMs. Research at the Center has focused on: Computational synthesis and characterization: AI guided synthesis and experimental synthesis of stacked LMs with tailored properties via optimized chemical vapor deposition (CVD) growth and liquid-phase exfoliation; study defects, edges, grain boundaries, wrinkling of atomic layers and their effects on chemical, mechanical, electrical, and optical properties. Far-from-equilibrium processes: Joint experimental and simulation based probe of electronic processes with NAQMD and ultrafast X-ray free-electron laser (XFEL) and ultrafast electron diffraction (UED) facilities at Stanford. Experimentally validate NAQMD by ultrafast electron diffraction and X-ray spectroscopy studies of structural and excited state dynamics, shape fluctuations, and phonon dynamics. Scalable software: Simulation engines for desktop-to-exascale platforms using low-overhead, linear-scaling QMD algorithms; divide-conquer-recombine NAQMD with electronic excitations; extended-Lagrangian reactive molecular dynamics (RMD), machine learning (ML) based neural-network quantum molecular dynamics (NNQMD), and super-state accelerated molecular dynamics (AMD) and kinetic Monte Carlo codes; thermal and electrical transport software; and design 3D architectures of LMs with desired functionality using scalable software. Distribution of software and data, and training: Software and simulation-experimental data generated within the Center are distributed to the materials science community via Berkeley Materials Project (MP) framework. We have also organized three workshops for software distribution and training at USC (Nov. 2017, Mar. 2018) and Gaithersburg, MD (Nov. 2018) to train researchers, with the last one in focused on underrepresented groups, in collaboration with Howard University which is one of the largest HBCUs. The Center supported a total of 46 personnel and 6 undergraduate students. These include 14 faculty, 11 postdoctoral research associates, 20 graduate research assistants, and mentored 6 undergraduate students. This resulted in the publications of 63 research papers that include 46 publications on Reactive and Quantum Dynamics Simulations, 13 publications on Machine Learning for Quantum Materials, and 4 publications on Quantum Computing.
Tailoring the grain boundaries (GBs) and twist angles between two-dimensional (2D) crystals are two crucial synthetic challenges to deterministically enable envisioned applications such as moiré excitons, emerging magnetism, or single-photon emission. Here, we reveal how twisted 2D bilayers can be synthesized from the collision and coalescence of two growing monolayer MoS 2 crystals during chemical vapor deposition. The twisted bilayer (TB) moiré angles are found to preserve the misorientation angle (θ) of the colliding crystals. The shapes of the TB regions are rationalized by a kink propagation model that predicts the GB formed by the coalescing crystals. Optical spectroscopy measurements reveal a θ-dependent long-range strain in crystals with stitched grain boundaries and a sharp (θ > 20°) threshold for the appearance of TBs, which relieves this strain. Reactive molecular dynamics simulations explain this strain from the continued growth of the crystals during coalescence due to the insertion of atoms at unsaturated defects along the GB, a process that self-terminates when the defects become saturated. The simulations also reproduce atomic-resolution electron microscopy observations of faceting along the GB, which is shown to arise from the growth-induced long-range strain. These facets align with the axes of the colliding crystals to provide favorable nucleation sites for second-layer growth of a TB with twist angles that preserve the misorientation angle θ. This interplay between strain generation and aligned nucleation provides a synthetic pathway for the growth of TBs with deterministic angles.