Unlocking High Capacity and Reversible Alkaline Iron Redox using Silicate-Sodium Hydroxide Hybrid Electrolytes.

Alkaline iron (Fe) batteries are attractive due to the high abundance, low cost, and multiple valent states of Fe but show limited columbic efficiency and storage capacity when forming electrochemically inert Fe3O4 on discharging and parasitic H2 on charging. Herein, sodium silicate is found to promote Fe(OH)2/FeOOH against Fe(OH)2/Fe3O4 conversions. Electrochemical experiments, operando X-ray characterization, and atomistic simulations reveal that improved Fe(OH)2/FeOOH conversion originates from (i) strong interaction between sodium silicate and iron oxide and (ii) silicate-induced strengthening of hydrogen-bond networks in electrolytes that inhibits water transport. Furthermore, the silicate additive suppresses hydrogen evolution by impairing energetics of water dissociation and hydroxyl de-sorption on iron surfaces. This new silicate-assisted redox chemistry mitigates H2 and Fe3O4formation, improving storage capacity (199 mAh g-1 in half-cells) and coulombic efficiency (94% after 400 full-cell cycles), paving a path to realizing green battery systems built from earth-abundant materials.

Fast-charging aluminium-chalcogen batteries resistant to dendritic shorting.

Although batteries fitted with a metal negative electrode are attractive for their higher energy density and lower complexity, the latter making them more easily recyclable, the threat of cell shorting by dendrites has stalled deployment of the technology1,2. Here we disclose a bidirectional, rapidly charging aluminium-chalcogen battery operating with a molten-salt electrolyte composed of NaCl-KCl-AlCl3. Formulated with high levels of AlCl3, these chloroaluminate melts contain catenated AlnCl3n+1- species, for example, Al2Cl7-, Al3Cl10- and Al4Cl13-, which with their Al-Cl-Al linkages confer facile Al3+ desolvation kinetics resulting in high faradaic exchange currents, to form the foundation for high-rate charging of the battery. This chemistry is distinguished from other aluminium batteries in the choice of a positive elemental-chalcogen electrode as opposed to various low-capacity compound formulations3-6, and in the choice of a molten-salt electrolyte as opposed to room-temperature ionic liquids that induce high polarization7-12. We show that the multi-step conversion pathway between aluminium and chalcogen allows rapid charging at up to 200C, and the battery endures hundreds of cycles at very high charging rates without aluminium dendrite formation. Importantly for scalability, the cell-level cost of the aluminium-sulfur battery is projected to be less than one-sixth that of current lithium-ion technologies. Composed of earth-abundant elements that can be ethically sourced and operated at moderately elevated temperatures just above the boiling point of water, this chemistry has all the requisites of a low-cost, rechargeable, fire-resistant, recyclable battery.

Controlled Formation of Conduction Channels in Memristive Devices Observed by X-ray Multimodal Imaging.

Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software-based AI. The key to improving accuracy is to reduce the intrinsic randomness of memristive devices, emulating synapses in the brain for neuromorphic computing. Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X-ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. This work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing.

Revitalizing Iron Redox by Anion-Insertion-Assisted Ferro- and Ferri-Hydroxides Conversion at Low Alkalinity.

Iron hydroxides are desirable alkaline battery electrodes for low cost and environmental beneficence. However, hydrogen evolution on charging and Fe3O4 formation on discharging cause low storage capacity and poor cycling life. We report that green rust (GR) (Fe2+4Fe3+2 (HO-)12SO4), formed via sulfate insertion, promotes Fe(OH)2/FeOOH conversion and shows a discharge capacity of ∼211 mAh g-1 in half-cells and Coulombic efficiency of 93% after 300 cycles in full-cells. Theoretical calculations show that Fe(OH)2/FeOOH conversion is facilitated by intercalated sulfate anions. Classical molecular dynamics simulations reveal that electrolyte alkalinity strongly impacts the energetics of sulfate solvation, and low alkalinity ensures fast transport of sulfate ions. Anion-insertion-assisted Fe(OH)2/FeOOH conversion, also achieved with Cl- ion, paves a pathway toward efficient utilization of Fe-based electrodes for sustainable applications.

Investigations of Bis(alkylthiocarbamato)copper Linkage Isomers.

Linkage isomers are coordination compounds with the same composition but different donor atoms, resulting in distinct physical and electronic structures. A pair of linkage isomers, CuL555 and CuL465, derived from phenylglyoxal bis(ethylthiocarbamate) were synthesized, isolated, and characterized by structural, electrochemical, and spectroscopic methods. The isomers are stable in solution under ambient conditions, but CuL465 converts to CuL555 in acid, consistent with quantum-chemical calculations. The complexes were screened against a lung adenocarcinoma cell line (A549) and a nonmalignant lung fibroblast cell line (IMR-90) to evaluate the antiproliferation activity. CuL555 and CuL465 possessed EC50 values of 0.113 ± 0.030 and 0.115 ± 0.038 µM for A549 and 1.87 ± 0.29 and 0.77 ± 0.22 µM for IMR-90, respectively.

Synthesis of Fluorine-Doped Lithium Argyrodite Solid Electrolytes for Solid-State Lithium Metal Batteries.

Solid-state lithium metal batteries (SSLMBs) that utilize novel solid electrolytes (SEs) have garnered much attention because of their potential to yield safe and high-energy-density batteries. Sulfide-based argyrodite-class SEs are an attractive option because of their impressive ionic conductivity. Recent studies have shown that LiF at the interface between Li and SE enhances electrochemical stability. However, the synthesis of F-doped argyrodites has remained challenging because of the high temperatures used in the state-of-the-art solid-state synthesis methods. In this work, for the first time, we report F-doped Li5+yPS5Fy argyrodites with a tunable doping content and dual dopants (F-/Cl- and F-/Br-) that were synthesized through a solvent-based approach. Among all compositions, Li6PS5F0.5Cl0.5 exhibits the highest Li-ion conductivity of 3.5 × 10-4 S cm-1 at room temperature (RT). Furthermore, Li symmetric cells using Li6PS5F0.5Cl0.5 show the best cycling performance among the tested cells. X-ray photoelectron spectroscopy and ab initio molecular dynamics simulations revealed that the enhanced interfacial stability of Li6PS5F0.5Cl0.5 SE against Li metal can be attributed to the formation of a stable solid electrolyte interphase (SEI)-containing conductive species (Li3P), alongside LiCl and LiF. These findings open new opportunities to develop high-performance SSLMBs using a novel class of F-doped argyrodite electrolytes.

Visualization of Solid-State Synthesis for Chalcogenide Na Superionic Conductors by in-situ Neutron Diffraction.

Chalcogenide superionic sodium (Na) conductors have great potential as solid electrolytes (SEs) in all-solid-state Na batteries with advantages of high energy density, safety, and cost effectiveness. The crystal structures and ionically conductive properties of solid Na-ion conductors are strongly influenced by synthetic approaches and processing parameters. Thus, understanding the synthesis process is essential to control the structures and phases and to obtain Na-ion conductors with desirable properties. Thanks to the high-flux and deep-penetrating time-of-flight neutron diffraction (ND), in-situ experiments were able to track real-time structural changes of two chalcogenide SEs (Na3 SbS4 and Na3 SbS3.5 Se0.5 ) during the solid-state synthesis. For these two conductors, the ND results revealed a fast one-step reaction for the synthesis and the molten process when heating up, and the recrystallization as well as the cubic-to-tetragonal phase transition up on cooling. Moreover, Se-doping was found to influence the reaction temperatures, lattice parameter, and structure stability based on neutron experimental observations and theoretical simulation. This work presents a detailed structural study using in-situ ND technology for the solid synthesis process of chalcogenide Na-ion conductors, beneficial for the design and synthesis of new solid-state conductors.

A method to analyze the sectoral impact of Fiscal support for COVID-19 affected economies: The case of Oceania.

In this paper, we apply the method of computable general equilibrium (CGE) modeling in economics to ascertain how fiscal support measures such as wage subsidies, small business loans, and finance guarantee schemes have impacted at an economy-wide and sectoral level for 8 COVID-19 affected economies in Oceania. We model our scenarios based on IMF World economic outlook projections, combined with the fiscal stimulus packages offered to counter this global health pandemic's recessionary effect. Our study confirms that the adverse impact of COVID-19 on output is cushioned through a large fiscal stimulus package wherever offered. This package would still be inadequate to avoid unemployment and job losses in tourism and education services in Oceania, with continued support essential for their survival in 2021.•The approach entails steps (1) to (3), as outlined in the paper.•Future researchers will find this method useful in evaluating the adverse impact of not only COVID-19 but any other external shocks to the economy, either directly or indirectly, that involves fiscal support mechanisms.

One-Dimensional Lateral Force Anisotropy at the Atomic Scale in Sliding Single Molecules on a Surface.

Using a q+ atomic force microscopy at low temperature, a sexiphenyl molecule is slid across an atomically flat Ag(111) surface along the direction parallel to its molecular axis and sideways to the axis. Despite identical contact area and underlying surface geometry, the lateral force required to move the molecule in the direction parallel to its molecular axis is found to be about half of that required to move it sideways. The origin of the lateral force anisotropy observed here is traced to the one-dimensional shape of the molecule, which is further confirmed by molecular dynamics simulations. We also demonstrate that scanning tunneling microscopy can be used to determine the comparative lateral force qualitatively. The observed one-dimensional lateral force anisotropy may have important implications in atomic scale frictional phenomena on materials surfaces.

Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models.

The solvation properties of molecules, often estimated using quantum chemical simulations, are important in the synthesis of energy storage materials, drugs, and industrial chemicals. Here, we develop machine learning models of solvation energies to replace expensive quantum chemistry calculations with inexpensive-to-compute message-passing neural network models that require only the molecular graph as inputs. Our models are trained on a new database of solvation energies for 130,258 molecules taken from the QM9 dataset computed in five solvents (acetone, ethanol, acetonitrile, dimethyl sulfoxide, and water) via an implicit solvent model. Our best model achieves a mean absolute error of 0.5 kcal/mol for molecules with nine or fewer non-hydrogen atoms and 1 kcal/mol for molecules with between 10 and 14 non-hydrogen atoms. We make the entire dataset of 651,290 computed entries openly available and provide simple web and programmatic interfaces to enable others to run our solvation energy model on new molecules. This model calculates the solvation energies for molecules using only the SMILES string and also provides an estimate of whether each molecule is within the domain of applicability of our model. We envision that the dataset and models will provide the functionality needed for the rapid screening of large chemical spaces to discover improved molecules for many applications.

Defect dynamics in two-dimensional black phosphorus under argon ion irradiation.

Fundamental understanding of the atomic-scale mechanisms underlying production, accumulation, and temporal evolution of defects in phosphorene during noble-gas ion irradiation is crucial to design efficient defect engineering routes to fabricate next-generation materials for energy technologies. Here, we employed classical molecular dynamics (CMD) simulations using a reactive force field to unravel the effect of defect dynamics on the structural changes in a monolayer of phosphorene induced by argon-ion irradiation, and its subsequent relaxation during post-radiation annealing treatment. Analysis of our CMD trajectories using unsupervised machine learning methods showed that radiation fluence strongly influences the types of defect that form, their dynamics, and their relaxation mechanisms during subsequent annealing. Low ion fluences yielded a largely crystalline sheet featuring isolated small voids (up to 2 nm), Stone-Wales defects, and mono-/di-vacancies; while large nanopores (∼10 nm) can form beyond a critical fluence of ∼1014 ions per cm2. During post-radiation annealing, we found two distinct relaxation mechanisms, depending on the fluence level. The isolated small voids (1-2 nm) formed at low ion-fluences heal via local re-arrangement of rings, which is facilitated by a cooperative mechanism involving a series of atomic motions that include thermal rippling, bond formation, bond rotation, angle bending and dihedral twisting. On the other hand, damaged structures obtained at high fluences exhibit pronounced coalescence of nanopores mediated by 3D networks of P-centered tetrahedra. These findings provide new perspectives to use ion beams to precisely control the concentration and distribution of specific defect types in phosphorene for emerging applications in electronics, batteries, sensing, and neuromorphic computing.

High temperature stability, metallic character and bonding of the Si2BN planar structure.

The family of monolayered Si2BN structures constitute a new class of 2D materials exhibiting metallic character with remarkable stability. Topologically, these structures are very similar to graphene, forming a slightly distorted honeycomb lattice generated by a union of two basic motifs with AA and AB stacking. In the present work we study in detail the structural and electronic properties of these structures in order to understand the factors which are responsible for their structural differences as well as those which are responsible for their metallic behavior and bonding. Their high temperature stability is demonstrated by the calculations of finite temperature phonon modes which show no negative contributions up to and beyond 1000 K. Presence of the negative thermal expansion coefficient, a common feature of one-atom thick 2D structures, is also seen. Comparison of the two motifs reveal the main structural differences to be the differences in their bond angles, which are affected by the third nearest neighbor interactions ofcis-transtype. On the other hand, the electronic properties of these two structures are very similar, including the charge transfers occurring between orbitals and between atoms. Their metallicity is mainly due to thepzorbitals of Si with a minor contribution from thepzorbitals of B, while the contribution from thepzorbitals of N atoms is negligible. There is almost no contributions from the Npzelectrons to the energy states near the Fermi level, and they form a band well below it. I.e., thepzelectrons of N are localized mostly at the N atoms and therefore cannot be considered as mobile electrons of thepzcloud. Moreover, we show that due to the relative positions in the energy axis of the atomic energies of thepzorbitals of B, N and Si atoms, the density of states (DOS) of Si2BN can be considered qualitatively as a combination of the DOS of planar hexagonal BN (h-BN) and hypothetically planar silicene (ph-Si). As a result, the Si2BN behaves electronically at the Fermi level as slightly perturbed ph-Si, having very similar electronic properties as silicene, but with the advantage of having kinetic stability in planar form. As for the bonding, the Si-Si bonds are covalent, while theπback donation mechanism occurs for the B-N bonding, in accordance with the B-N bonding in h-BN.

Mitigating Scatter in Mechanical Properties in AISI 410 Fabricated via Arc-Based Additive Manufacturing Process.

Wire-based metal additive manufacturing utilizes the ability of additive manufacturing to fabricate complex geometries with high deposition rates (above 7 kg/h), thus finding applications in the fabrication of large-scale components, such as stamping dies. Traditionally, the workhorse materials for stamping dies have been martensitic steels. However, the complex thermal gyrations induced during additive manufacturing can cause the evolution of an inhomogeneous microstructure, which leads to a significant scatter in the mechanical properties, especially the toughness. Therefore, to understand these phenomena, arc-based additive AISI 410 samples were fabricated using robotic gas metal arc welding (GMAW) and were subjected to a detailed characterization campaign. The results show significant scatter in the tensile properties as well as Charpy V-notch impact toughness data, which was then correlated to the microstructural heterogeneity and delta (δ) ferrite formation. Post-processing (austenitizing and tempering) treatments were developed and an ~70% reduction in the scatter of tensile data and a four-times improvement in the toughness were obtained. The changes in mechanical properties were rationalized based on the microstructure evolution during additive manufacturing. Based on these, an outline to tailor the composition of "printable" steels for tooling with isotropic and uniform mechanical properties is presented and discussed.

Carbon pricing and planetary boundaries.

Human activities are threatening to push the Earth system beyond its planetary boundaries, risking catastrophic and irreversible global environmental change. Action is urgently needed, yet well-intentioned policies designed to reduce pressure on a single boundary can lead, through economic linkages, to aggravation of other pressures. In particular, the potential policy spillovers from an increase in the global carbon price onto other critical Earth system processes has received little attention to date. To this end, we explore the global environmental effects of pricing carbon, beyond its effect on carbon emissions. We find that the case for carbon pricing globally becomes even stronger in a multi-boundary world, since it can ameliorate many other planetary pressures. It does however exacerbate certain planetary pressures, largely by stimulating additional biofuel production. When carbon pricing is allied with a biofuel policy, however, it can alleviate all planetary pressures.

Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms.

High-fidelity quantum-chemical calculations can provide accurate predictions of molecular energies, but their high computational costs limit their utility, especially for larger molecules. We have shown in previous work that machine learning models trained on high-level quantum-chemical calculations (G4MP2) for organic molecules with one to nine non-hydrogen atoms can provide accurate predictions for other molecules of comparable size at much lower costs. Here we demonstrate that such models can also be used to effectively predict energies of molecules larger than those in the training set. To implement this strategy, we first established a set of 191 molecules with 10-14 non-hydrogen atoms having reliable experimental enthalpies of formation. We then assessed the accuracy of computed G4MP2 enthalpies of formation for these 191 molecules. The error in the G4MP2 results was somewhat larger than that for smaller molecules, and the reason for this increase is discussed. Two density functional methods, B3LYP and ωB97X-D, were also used on this set of molecules, with ωB97X-D found to perform better than B3LYP at predicting energies. The G4MP2 energies for the 191 molecules were then predicted using these two functionals with two machine learning methods, the FCHL-Δ and SchNet-Δ models, with the learning done on calculated energies of the one to nine non-hydrogen atom molecules. The better-performing model, FCHL-Δ, gave atomization energies of the 191 organic molecules with 10-14 non-hydrogen atoms within 0.4 kcal/mol of their G4MP2 energies. Thus, this work demonstrates that quantum-chemically informed machine learning can be used to successfully predict the energies of large organic molecules whose size is beyond that in the training set.

Perovskite neural trees.

Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic matter is challenging due to the need to host a complex energy landscape capable of learning, memory and electrical interrogation. We report experimental realization of tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses. This demonstration represents physical realization of ultrametric trees, a concept from number theory applied to the study of spin glasses in physics that inspired early neural network theory dating almost forty years ago. We apply the tree-like memory features in spiking neural networks to demonstrate high fidelity object recognition, and in future can open new directions for neuromorphic computing and artificial intelligence.

Economic and environmental effects of reduction in smoking prevalence in Tanzania.

BACKGROUND: In Tanzania, strong tobacco control measures that would lead to a reduction in prevalence (consumption) have so far not been implemented due to concern about possible economic effects on gross domestic product and employment. The aim of this study is to analyse the economic effects of reducing tobacco consumption in Tanzania. METHODS: The study uses computable general equilibrium (CGE) modelling to arrive at the effects of decreasing tobacco prevalence. A full-fledged global CGE model was developed, including comprehensive details on tobacco and tobacco products/sectors using the Global Trade Analysis Program-Environment model and database. RESULTS: The results indicate that a 30% reduction in prevalence could lead to employment losses of about 20.8% in tobacco and 7.8% in the tobacco products sector. However, when compensated by increases in other sectors the overall decline in employment is only 0.5%. The decline in the economy as a whole is negligible at -0.3%. CONCLUSION: Initially, some assistance from the Tanzanian government may be needed for the displaced workers from the tobacco sector as a result of the decline in smoking prevalence. However, these results should be taken as a lower bound since the economic burden of diseases caused by tobacco may be far higher than the sectoral losses. The results do not include the health benefits of lower smoking prevalence. In addition, the revenues from higher taxes, as part of measures to decrease prevalence, would provide more fiscal space that can be used to finance assistance for displaced tobacco farmers and workers.

Accurate quantum chemical energies for 133 000 organic molecules.

The energies of the 133 000 molecules in the GDB-9 database have been calculated at the G4MP2 level of theory and then were used to calculate their enthalpies of formation. This database contains organic molecules having nine or less atoms of carbon, nitrogen, oxygen, and fluorine, as well as hydrogen atoms. The accuracy of the G4MP2 energies was investigated on a subset of 459 of the molecules having experimental enthalpies of formation with small uncertainties. On this subset the G4MP2 enthalpies of formation have an accuracy of 0.79 kcal mol-1, which is similar to its accuracy previously reported for the smaller G3/05 test set. An error analysis of the theoretical enthalpies of formation of the 459 molecules is presented in terms of the size and type of the molecules. Three different density functionals (B3LYP, ωB97X-D, M06-2X) were also assessed on 459 molecules of accurate enthalpy data for comparison with the G4MP2 results. The G4MP2 energies for the 133 K molecules provide a database that can be used to calculate accurate reaction energies as well as to assess new or existing experimental enthalpies of formation. Several examples are given of types of reactions that can be predicted using the G4MP2 database of energies. The G4MP2 energies of the GDB-9 molecules will also be useful in future investigations of applications of machine learning to quantum chemical data.

A Long-Cycle-Life Lithium-CO2 Battery with Carbon Neutrality.

Lithium-CO2 batteries are attractive energy-storage systems for fulfilling the demand of future large-scale applications such as electric vehicles due to their high specific energy density. However, a major challenge with Li-CO2 batteries is to attain reversible formation and decomposition of the Li2 CO3 and carbon discharge products. A fully reversible Li-CO2 battery is developed with overall carbon neutrality using MoS2 nanoflakes as a cathode catalyst combined with an ionic liquid/dimethyl sulfoxide electrolyte. This combination of materials produces a multicomponent composite (Li2 CO3 /C) product. The battery shows a superior long cycle life of 500 for a fixed 500 mAh g-1 capacity per cycle, far exceeding the best cycling stability reported in Li-CO2 batteries. The long cycle life demonstrates that chemical transformations, making and breaking covalent CO bonds can be used in energy-storage systems. Theoretical calculations are used to deduce a mechanism for the reversible discharge/charge processes and explain how the carbon interface with Li2 CO3 provides the electronic conduction needed for the oxidation of Li2 CO3 and carbon to generate the CO2 on charge. This achievement paves the way for the use of CO2 in advanced energy-storage systems.

Machine learning a bond order potential model to study thermal transport in WSe2 nanostructures.

Nanostructures of transition metal di-chalcogenides (TMDCs) exhibit exotic thermal, chemical and electronic properties, enabling diverse applications from thermoelectrics and catalysis to nanoelectronics. The thermal properties of these nanoscale TMDCs are of particular interest for thermoelectric applications. Thermal transport studies on nanotubes and nanoribbons remain intractable to first principles calculations whereas existing classical molecular models treat the two chalcogen layers in a monolayer with different atom types; this imposes serious limitations in studying multi-layered TMDCs and dynamical phenomena such as nucleation and growth. Here, we overcome these limitations using machine learning (ML) and introduce a bond order potential (BOP) trained against first principles training data to capture the structure, dynamics, and thermal transport properties of a model TMDC such as WSe2. The training is performed using a hierarchical objective genetic algorithm workflow to accurately describe the energetics, as well as thermal and mechanical properties of a free-standing sheet. As a representative case study, we perform molecular dynamics simulations using the ML-BOP model to study the structure and temperature-dependent thermal conductivity of WSe2 tubes and ribbons of different chiralities. We observe slightly higher thermal conductivities along the armchair direction than zigzag for WSe2 monolayers but the opposite effect for nanotubes, especially of smaller diameters. We trace the origin of these differences to the anisotropy in thermal transport and the restricted momentum selection rules for phonon-phonon Umpklapp scattering. The developed ML-BOP model is of broad interest and will facilitate studies on nucleation and growth of low dimensional WSe2 structures as well as their transport properties for thermoelectric and thermal management applications.