Potential economic impact of COVID-19-related school closures

Severe disruptions in school education during the coronavirus disease (COVID–19) pandemic has impacted children through their formative years which will affect their employment opportunities and earning potential for many years after school ages. This paper examines the medium-to-long-term economic scarring effects, using data available through the Global Trade Analysis Project, a computable general equilibrium model, with empirical study focusing on the impact of school closures on economic growth and employment. The estimated results show significant declines in global gross domestic product (GDP) and employment. Moreover, the losses in global GDP and employment increase over time. Declines in global GDP amount to 0.19% in 2024, 0.64% in 2028, and 1.11% in 2030. In absolute terms, the cost to the global economy in 2030 alone is $943 billion. The scarring effects are greater in economies with significant student populations from rural areas, those in the poorest and second wealth quintile. Learning and earning losses are also significant in economies where the share of unskilled labor employment in the overall labor force is high.</p

Heterogeneous Pyrolysis: A Route for Epitaxial Growth of hBN Atomic Layers on Copper Using Separate Boron and Nitrogen Precursors

Growth of hBN on metal substrates is often performed via chemical vapor deposition from a single precursor (e.g., borazine) and results in hBN monolayers limited by the substrates catalyzing effect. Departing from this paradigm, we demonstrate close control over the growth of mono-, bi-, and trilayers of hBN on copper using triethylborane and ammonia as independent sources of boron and nitrogen. Using density functional theory (DFT) calculations and reactive force field molecular dynamics, we show that the key factor enabling the growth beyond the first layer is the activation of ammonia through heterogeneous pyrolysis with boron-based radicals at the surface. The hBN layers grown are in registry with each other and assume a perfect or near perfect epitaxial relation with the substrate. From atomic force microscopy (AFM) characterization, we observe a moiré superstructure in the first hBN layer with an apparent height modulation and lateral periodicity of ∼10 nm. While this is unexpected given that the moiré pattern of hBN/Cu(111) does not have a significant morphological corrugation, our DFT calculations reveal a spatially modulated interface dipole layer which determines the unusual AFM response. These findings have improved our understanding of the mechanisms involved in growth of hBN and may help generate new growth methods for applications in which control over the number of layers and their alignment is crucial (such as tunneling barriers, ultrathin capacitors, and graphene-based devices)

Configurational-Bias Monte Carlo Back-Mapping Algorithm for Efficient and Rapid Conversion of Coarse-Grained Water Structures into Atomistic Models

Coarse-grained molecular dynamics (MD) simulations represent a powerful approach to simulate longer time scale and larger length scale phenomena than those accessible to all-atom models. The gain in efficiency, however, comes at the cost of atomistic details. The reverse transformation, also known as back mapping, of coarse-grained beads into their atomistic constituents represents a major challenge. Most existing approaches are limited to specific molecules or specific force fields and often rely on running a long-time atomistic MD of the back-mapped configuration to arrive at an optimal solution. Such approaches are problematic when dealing with systems with high diffusion barriers. Here, we introduce a new extension of the configurational-bias Monte Carlo (CBMC) algorithm, which we term the crystalline-configurational-bias Monte Carlo (C-CBMC) algorithm, which allows rapid and efficient conversion of a coarse-grained model back into its atomistic representation. Although the method is generic, we use a coarse-grained water model as a representative example and demonstrate the back mapping or reverse transformation for model systems ranging from the ice–liquid water interface to amorphous and crystalline ice configurations. A series of simulations using the TIP4P/Ice model are performed to compare the new CBMC method to several other standard Monte Carlo and molecular dynamics-based back-mapping techniques. In all of the cases, the C-CBMC algorithm is able to find optimal hydrogen-bonded configuration many thousand evaluations/steps sooner than the other methods compared within this paper. For crystalline ice structures, such as a hexagonal, cubic, and cubic-hexagonal stacking disorder structures, the C-CBMC was able to find structures that were between 0.05 and 0.1 eV/water molecule lower in energy than the ground-state energies predicted by the other methods. Detailed analysis of the atomistic structures shows a significantly better global hydrogen positioning when contrasted with the existing simpler back-mapping methods. The errors in the radial distribution functions (RDFs) of back-mapped configuration relative to reference configuration for the C-CBMC, MD, and MC were found to be 6.9, 8.7, and 12.9, respectively, for the hexagonal system. For the cubic system, the relative errors of the RDFs for the C-CBMC, MD, and MC were found to be 18.2, 34.6, and 39.0, respectively. Our results demonstrate the efficiency and efficacy of our new back-mapping approach, especially for crystalline systems where simple force-field-based relaxations have a tendency to get trapped in local minima

<i>Ab Initio</i>-Based Bond Order Potential to Investigate Low Thermal Conductivity of Stanene Nanostructures

We introduce a bond order potential (BOP) for stanene based on an <i>ab initio</i> derived training data set. The potential is optimized to accurately describe the energetics, as well as thermal and mechanical properties of a free-standing sheet, and used to study diverse nanostructures of stanene, including tubes and ribbons. As a representative case study, using the potential, we perform molecular dynamics simulations to study stanene’s structure and temperature-dependent thermal conductivity. We find that the structure of stanene is highly rippled, far in excess of other 2-D materials (e.g., graphene), owing to its low in-plane stiffness (stanene: ∼ 25 N/m; graphene: ∼ 480 N/m). The extent of stanene’s rippling also shows stronger temperature dependence compared to that in graphene. Furthermore, we find that stanene based nanostructures have significantly lower thermal conductivity compared to graphene based structures owing to their softness (i.e., low phonon group velocities) and high anharmonic response. Our newly developed BOP will facilitate the exploration of stanene based low dimensional heterostructures for thermoelectric and thermal management applications

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Theme 1 - reseaux et systemes - Projet ResedasSIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.2000 n.3869 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Defect Dynamics in 2‑D MoS₂ Probed by Using Machine Learning, Atomistic Simulations, and High-Resolution Microscopy

Structural defects govern various physical, chemical, and optoelectronic properties of two-dimensional transition-metal dichalcogenides (TMDs). A fundamental understanding of the spatial distribution and dynamics of defects in these low-dimensional systems is critical for advances in nanotechnology. However, such understanding has remained elusive primarily due to the inaccessibility of (a) necessary time scales <i>via</i> standard atomistic simulations and (b) required spatiotemporal resolution in experiments. Here, we take advantage of supervised machine learning, <i>in situ</i> high-resolution transmission electron microscopy (HRTEM) and molecular dynamics (MD) simulations to overcome these limitations. We combine genetic algorithms (GA) with MD to investigate the extended structure of point defects, their dynamical evolution, and their role in inducing the phase transition between the semiconducting (2H) and metallic (1T) phase in monolayer MoS₂. GA-based structural optimization is used to identify the long-range structure of randomly distributed point defects (sulfur vacancies) for various defect densities. Regardless of the density, we find that organization of sulfur vacancies into extended lines is the most energetically favorable. HRTEM validates these findings and suggests a phase transformation from the 2H-to-1T phase that is localized near these extended defects when exposed to high electron beam doses. MD simulations elucidate the molecular mechanism driving the onset of the 2H to 1T transformation and indicate that finite amounts of 1T phase can be retained by increasing the defect concentration and temperature. This work significantly advances the current understanding of defect structure/evolution and structural transitions in 2D TMDs, which is crucial for designing nanoscale devices with desired functionality

Development of a Modified Embedded Atom Force Field for Zirconium Nitride Using Multi-Objective Evolutionary Optimization

Zirconium nitride (ZrN) exhibits exceptional mechanical, chemical, and electrical properties, which make it attractive for a wide range of technological applications, including wear-resistant coatings, protection from corrosion, cutting/shaping tools, and nuclear breeder reactors. Despite its broad usability, an atomic scale understanding of the superior performance of ZrN, and its response to external stimuli, for example, temperature, applied strain, and so on, is not well understood. This is mainly due to the lack of interatomic potential models that accurately describe the interactions between Zr and N atoms. To address this challenge, we develop a modified embedded atom method (MEAM) interatomic potential for the Zr–N binary system by training against formation enthalpies, lattice parameters, elastic properties, and surface energies of ZrN (and, in some cases, also Zr₃N₄) obtained from density functional theory (DFT) calculations. The best set of MEAM parameters are determined by employing a multiobjective global optimization scheme driven by genetic algorithms. Our newly developed MEAM potential accurately reproduces structure, thermodynamics, energetic ordering of polymorphs, as well as elastic and surface properties of Zr–N compounds, in excellent agreement with DFT calculations and experiments. As a representative application, we employed molecular dynamics simulations based on this MEAM potential to investigate the atomic scale mechanisms underlying fracture of bulk and nanopillar ZrN under applied uniaxial strains, as well as the impact of strain rate on their mechanical behavior. These simulations indicate that bulk ZrN undergoes brittle fracture irrespective of the strain rate, while ZrN nanopillars show quasi-plasticity owing to amorphization at the crack front. The MEAM potential for Zr–N developed in this work is an invaluable tool to investigate atomic-scale mechanisms underlying the response of ZrN to external stimuli (e.g, temperature, pressure etc.), as well as other interesting phenomena such as precipitation

Ultrafast Three-Dimensional X‑ray Imaging of Deformation Modes in ZnO Nanocrystals

Imaging the dynamical response of materials following ultrafast excitation can reveal energy transduction mechanisms and their dissipation pathways, as well as material stability under conditions far from equilibrium. Such dynamical behavior is challenging to characterize, especially <i>operando</i> at nanoscopic spatiotemporal scales. In this letter, we use X-ray coherent diffractive imaging to show that ultrafast laser excitation of a ZnO nanocrystal induces a rich set of deformation dynamics including characteristic “hard” or inhomogeneous and “soft” or homogeneous modes at different time scales, corresponding respectively to the propagation of acoustic phonons and resonant oscillation of the crystal. By integrating the 3D nanocrystal structure obtained from the ultrafast X-ray measurements with a continuum thermo-electro-mechanical finite element model, we elucidate the deformation mechanisms following laser excitation, in particular, a torsional mode that generates a 50% greater electric potential gradient than that resulting from the flexural mode. Understanding of the time-dependence of these mechanisms on ultrafast scales has significant implications for development of new materials for nanoscale power generation

Ultrafast Three-Dimensional X‑ray Imaging of Deformation Modes in ZnO Nanocrystals

Imaging the dynamical response of materials following ultrafast excitation can reveal energy transduction mechanisms and their dissipation pathways, as well as material stability under conditions far from equilibrium. Such dynamical behavior is challenging to characterize, especially <i>operando</i> at nanoscopic spatiotemporal scales. In this letter, we use X-ray coherent diffractive imaging to show that ultrafast laser excitation of a ZnO nanocrystal induces a rich set of deformation dynamics including characteristic “hard” or inhomogeneous and “soft” or homogeneous modes at different time scales, corresponding respectively to the propagation of acoustic phonons and resonant oscillation of the crystal. By integrating the 3D nanocrystal structure obtained from the ultrafast X-ray measurements with a continuum thermo-electro-mechanical finite element model, we elucidate the deformation mechanisms following laser excitation, in particular, a torsional mode that generates a 50% greater electric potential gradient than that resulting from the flexural mode. Understanding of the time-dependence of these mechanisms on ultrafast scales has significant implications for development of new materials for nanoscale power generation

Effect of the Hydrofluoroether Cosolvent Structure in Acetonitrile-Based Solvate Electrolytes on the Li⁺ Solvation Structure and Li–S Battery Performance

We evaluate hydrofluoroether (HFE) cosolvents with varying degrees of fluorination in the acetonitrile-based solvate electrolyte to determine the effect of the HFE structure on the electrochemical performance of the Li–S battery. Solvates or sparingly solvating electrolytes are an interesting electrolyte choice for the Li–S battery due to their low polysulfide solubility. The solvate electrolyte with a stoichiometric ratio of LiTFSI salt in acetonitrile, (MeCN)₂–LiTFSI, exhibits limited polysulfide solubility due to the high concentration of LiTFSI. We demonstrate that the addition of highly fluorinated HFEs to the solvate yields better capacity retention compared to that of less fluorinated HFE cosolvents. Raman and NMR spectroscopy coupled with ab initio molecular dynamics simulations show that HFEs exhibiting a higher degree of fluorination coordinate to Li⁺ at the expense of MeCN coordination, resulting in higher free MeCN content in solution. However, the polysulfide solubility remains low, and no crossover of polysulfides from the S cathode to the Li anode is observed