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.

A lithium-oxygen battery with a long cycle life in an air-like atmosphere.

Lithium-air batteries are considered to be a potential alternative to lithium-ion batteries for transportation applications, owing to their high theoretical specific energy. So far, however, such systems have been largely restricted to pure oxygen environments (lithium-oxygen batteries) and have a limited cycle life owing to side reactions involving the cathode, anode and electrolyte. In the presence of nitrogen, carbon dioxide and water vapour, these side reactions can become even more complex. Moreover, because of the need to store oxygen, the volumetric energy densities of lithium-oxygen systems may be too small for practical applications. Here we report a system comprising a lithium carbonate-based protected anode, a molybdenum disulfide cathode and an ionic liquid/dimethyl sulfoxide electrolyte that operates as a lithium-air battery in a simulated air atmosphere with a long cycle life of up to 700 cycles. We perform computational studies to provide insight into the operation of the system in this environment. This demonstration of a lithium-oxygen battery with a long cycle life in an air-like atmosphere is an important step towards the development of this field beyond lithium-ion technology, with a possibility to obtain much higher specific energy densities than for conventional lithium-ion batteries.

Perovskite nickelates as electric-field sensors in salt water.

Designing materials to function in harsh environments, such as conductive aqueous media, is a problem of broad interest to a range of technologies, including energy, ocean monitoring and biological applications. The main challenge is to retain the stability and morphology of the material as it interacts dynamically with the surrounding environment. Materials that respond to mild stimuli through collective phase transitions and amplify signals could open up new avenues for sensing. Here we present the discovery of an electric-field-driven, water-mediated reversible phase change in a perovskite-structured nickelate, SmNiO3. This prototypical strongly correlated quantum material is stable in salt water, does not corrode, and allows exchange of protons with the surrounding water at ambient temperature, with the concurrent modification in electrical resistance and optical properties being capable of multi-modal readout. Besides operating both as thermistors and pH sensors, devices made of this material can detect sub-volt electric potentials in salt water. We postulate that such devices could be used in oceanic environments for monitoring electrical signals from various maritime vessels and sea creatures.

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.

Carbon-based tribofilms from lubricating oils.

Moving mechanical interfaces are commonly lubricated and separated by a combination of fluid films and solid 'tribofilms', which together ensure easy slippage and long wear life. The efficacy of the fluid film is governed by the viscosity of the base oil in the lubricant; the efficacy of the solid tribofilm, which is produced as a result of sliding contact between moving parts, relies upon the effectiveness of the lubricant's anti-wear additive (typically zinc dialkyldithiophosphate). Minimizing friction and wear continues to be a challenge, and recent efforts have focused on enhancing the anti-friction and anti-wear properties of lubricants by incorporating inorganic nanoparticles and ionic liquids. Here, we describe the in operando formation of carbon-based tribofilms via dissociative extraction from base-oil molecules on catalytically active, sliding nanometre-scale crystalline surfaces, enabling base oils to provide not only the fluid but also the solid tribofilm. We study nanocrystalline catalytic coatings composed of nitrides of either molybdenum or vanadium, containing either copper or nickel catalysts, respectively. Structurally, the resulting tribofilms are similar to diamond-like carbon. Ball-on-disk tests at contact pressures of 1.3 gigapascals reveal that these tribofilms nearly eliminate wear, and provide lower friction than tribofilms formed with zinc dialkyldithiophosphate. Reactive and ab initio molecular-dynamics simulations show that the catalytic action of the coatings facilitates dehydrogenation of linear olefins in the lubricating oil and random scission of their carbon-carbon backbones; the products recombine to nucleate and grow a compact, amorphous lubricating tribofilm.

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.

Strongly correlated perovskite lithium ion shuttles.

Solid-state ion shuttles are of broad interest in electrochemical devices, nonvolatile memory, neuromorphic computing, and biomimicry utilizing synthetic membranes. Traditional design approaches are primarily based on substitutional doping of dissimilar valent cations in a solid lattice, which has inherent limits on dopant concentration and thereby ionic conductivity. Here, we demonstrate perovskite nickelates as Li-ion shuttles with simultaneous suppression of electronic transport via Mott transition. Electrochemically lithiated SmNiO3 (Li-SNO) contains a large amount of mobile Li+ located in interstitial sites of the perovskite approaching one dopant ion per unit cell. A significant lattice expansion associated with interstitial doping allows for fast Li+ conduction with reduced activation energy. We further present a generalization of this approach with results on other rare-earth perovskite nickelates as well as dopants such as Na+ The results highlight the potential of quantum materials and emergent physics in design of ion conductors.

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.

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.

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).

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 operando 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.

Self-assembly of collagen on flat surfaces: the interplay of collagen-collagen and collagen-substrate interactions.

Fibrillar collagens, common tissue scaffolds in live organisms, can also self-assemble in vitro from solution. While previous in vitro studies showed that the pH and the electrolyte concentration in solution largely control the collagen assembly, the physical reasons why such control could be exerted are still elusive. To address this issue and to be able to simulate self-assembly over large spatial and temporal scales, we have developed a microscopic model of collagen with explicit interactions between the units that make up the collagen molecules, as well as between these units and the substrate. We have used this model to investigate assemblies obtained via molecular dynamics deposition of collagen on a substrate at room temperature using an implicit solvent. By comparing the morphologies from our molecular dynamics simulations with those from our atomic-force microscopy experiments, we have found that the assembly is governed by the competition between the collagen-collagen interactions and those between collagen and the substrate. The microscopic model developed here can serve for guiding future experiments that would explore new regions of the parameter space.

Atomistic origin of superior performance of ionic liquid electrolytes for Al-ion batteries.

Encouraged by recent experimental findings, here we report on an in silico investigation to probe the atomistic origin behind the superior performance of ionic liquids (ILs) over traditional carbonate electrolytes for Al-ion batteries. Fundamental insights from computationally derived thermodynamic and kinetic considerations coupled with an atomistic-level description of the solvation dynamics is used to elucidate the performance improvements. The formation of low-stability ion-solvent complexes in ILs facilitates rapid Al-ion solvation-desolvation and translates into favorable transport properties (viscosity and ionic conductivity). Our results offer encouraging prospects for this approach in the a priori prediction of optimal IL formulations for Al-ion batteries.

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.

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.

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.

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.

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.