A solid-state Li-air battery: computational studies of interfaces and relevance to discharge mechanism.

There is much interest in developing new energy storage systems to replace currently available ones that mainly work based on Li-ion intercalations. One attractive area is the Li-air battery for which most of the research has involved liquid electrolytes. There have been few studies on the use of a solid electrolyte in a Li-air battery. Recently, we reported the successful use of a solid-state electrolyte in a Li-air battery resulting in a Li2O product and potentially much higher energy density than in a Li-air battery based on either a Li2O2 or LiO2 product (Science, 2023, 379, 499). In this paper we discuss how the discharge mechanism involved in this solid-state Li-air battery differs from that of a Li-air battery with a liquid electrolyte. The solid-state mechanism is further explored with density functional studies of various interfaces involving the discharge product. We discuss the relevance of the results to the discharge mechanism in the solid-state Li-air battery.

Template assisted lithium superoxide growth for lithium-oxygen batteries.

Developing batteries with energy densities comparable to internal combustion technology is essential for a worldwide transition to electrified transportation. Li-O2 batteries are seen as the 'holy grail' of battery technologies since they have the highest theoretical energy density of all battery technologies. Current lithium-oxygen (Li-O2) batteries suffer from large charge overpotentials related to the electronic resistivity of the insulating lithium peroxide (Li2O2) discharge product. One potential solution is the formation and stabilization of a lithium superoxide (LiO2) discharge intermediate that exhibits good electronic conductivity. However, LiO2 is reported to be unstable at ambient temperature despite its favorable formation energy at -1.0 eV per atom. In this paper - based on our recent work on the development of cathode materials for aprotic lithium oxygen batteries including two intermetallic compounds, LiIr3 and LiIr, that are found to form good template interfaces with LiO2 - a simple goodness of fit R factor to gauge how well a template surface structure can support LiO2 growth, is developed. The R factor is a quantitative measurement to calculate the geometric difference in the unit cells of specific Miller Index 2D planes of the template surface and LiO2. Using this as a guide, the R factors for LiIr3, LiIr, and La2NiO4+δ, are found to be good. This guide is attested by simple extension to other noble metal intermetallics with electrochemical cycling data including LiRh3, LiRh, and Li2Pd. Finally, the template concept is extended to main group elements and the R factors for LiO2 (111) and Li2Ca suggest that Li2Ca is a possible candidate for the template assisted LiO2 growth strategy.

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.

Light- and Chemical-Doping-Induced Magnetic Behavior of Eu Molecular Systems.

Variable temperature electron paramagnetic resonance (VT-EPR) was used to investigate the role of the environment and oxidation states of several coordinated Eu compounds. We find that while Eu(III) chelating complexes are diamagnetic, simple chemical reduction results in the formation of paramagnetic species. In agreement with the distorted D3h symmetry of Eu molecular complexes investigated in this study, the EPR spectrum of reduced complexes showed axially symmetric signals (g⊥ = 2.001 and g∥ = 1.994) that were successfully simulated with two Eu isotopes with nuclear spin 5/2 (151Eu and 153Eu with 48% and 52% natural abundance, respectively) and nuclear g-factors 151Eu/153Eu = 2.27. Illumination of water-soluble complex Eu(dipic)3 at 4 K led to the ligand-to-metal charge transfer (LMCT) that resulted in the formation of Eu(II) in a rhombic environment (gx = 2.006, gy = 1.995, gz = 1.988). The existence of LMCT affects the luminescence of Eu(dipic)3, and pre-reduction of the complex to Eu(II)(dipic)3 reversibly reduces red luminescence with the appearance of a weak CT blue luminescence. Furthermore, encapsulation of a large portion of the dipic ligand with Cucurbit[7]uril, a pumpkin-shaped macrocycle, inhibited ligand-to-metal charge transfer, preventing the formation of Eu(II) upon illumination.

Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning.

In previous work (Dandu et al., J. Phys. Chem. A, 2022, 126, 4528-4536), we were successful in predicting accurate atomization energies of organic molecules using machine learning (ML) models, obtaining an accuracy as low as 0.1 kcal/mol compared to the G4MP2 method. In this work, we extend the use of these ML models to adiabatic ionization potentials on data sets of energies generated using quantum chemical calculations. Atomic specific corrections that were found to improve atomization energies from quantum chemical calculations have also been used in this study to improve ionization potentials. The quantum chemical calculations were performed on 3405 molecules containing eight or fewer non-hydrogen atoms derived from the QM9 data set, using the B3LYP functional with the 6-31G(2df,p) basis set for optimization. Low-fidelity IPs for these structures were obtained using two density functional methods: B3LYP/6-31+G(2df,p) and ωB97XD/6-311+G(3df,2p). Highly accurate G4MP2 calculations were performed on these optimized structures to obtain high-fidelity IPs to use in ML models based on the low-fidelity IPs. Our best performing ML methods gave IPs of organic molecules within a mean absolute deviation of 0.035 eV from the G4MP2 IPs for the whole data set. This work demonstrates that ML predictions assisted by quantum chemical calculations can be used to successfully predict IPs of organic molecules for use in high throughput screening.

Nanostructured Conductive Metal Organic Frameworks for Sustainable Low Charge Overpotentials in Li-Air Batteries.

Lithium-oxygen batteries are among the most attractive alternatives for future electrified transportation. However, their practical application is hindered by many obstacles. Due to the insulating nature of Li2 O2 product and the slow kinetics of reactions, attaining sustainable low charge overpotentials at high rates becomes a challenge resulting in the battery's early failure and low round trip efficiency. Herein, outstanding characteristics are discovered of a conductive metal organic framework (c-MOF) that promotes the growth of nanocrystalline Li2 O2 with amorphous regions. This provides a platform for the continuous growth of Li2 O2 units away from framework, enabling a fast discharge at high current rates. Moreover, the Li2 O2 structure works in synergy with the redox mediator (RM). The conductivity of the amorphous regions of the Li2 O2 allows the RM to act directly on the Li2 O2 surface instead of catalyst edges and then transport through the electrolyte to the Li2 O2 surface. This direct charge transfer enables a small charge potential of <3.7 V under high current densities (1-2 A g-1 ) sustained for a long cycle life (100-300 cycles) for large capacities (1000-2000 mAh g-1 ). These results open a new direction for utilizing c-MOFs towards advanced energy storage systems.

Concentration-dependent ion correlations impact the electrochemical behavior of calcium battery electrolytes.

Ion interactions strongly determine the solvation environments of multivalent electrolytes even at concentrations below that required for practical battery-based energy storage. This statement is particularly true of electrolytes utilizing ethereal solvents due to their low dielectric constants. These solvents are among the most commonly used for multivalent batteries based on reactive metals (Mg, Ca) due to their reductive stability. Recent developments in multivalent electrolyte design have produced a variety of new salts for Mg2+ and Ca2+ that test the limits of weak coordination strength and oxidative stability. Such electrolytes have great potential for enabling full-cell cycling of batteries based on these working ions. However, the ion interactions in these electrolytes exhibit significant and non-intuitive concentration relationships. In this work, we investigate a promising exemplar, calcium tetrakis(hexafluoroisopropoxy)borate (Ca(BHFIP)2), in the ethereal solvents 1,2-dimethoxyethane (DME) and tetrahydrofuran (THF) across a concentration range of several orders of magnitude. Surprisingly, we find that effective salt dissociation is lower at relatively dilute concentrations (e.g. 0.01 M) than at higher concentrations (e.g. 0.2 M). Combined experimental and computational dielectric and X-ray spectroscopic analyses of the changes occurring in the Ca2+ solvation environment across these concentration regimes reveals a progressive transition from well-defined solvent-separated ion pairs to de-correlated free ions. This transition in ion correlation results in improvements in both conductivity and calcium cycling stability with increased salt concentration. Comparison with previous findings involving more strongly associating salts highlights the generality of this phenomenon, leading to important insight into controlling ion interactions in ether-based multivalent battery electrolytes.

Improving the Accuracy of Composite Methods: A G4MP2 Method with G4-like Accuracy and Implications for Machine Learning.

G4MP2 theory has proven to be a reliable and accurate quantum chemical composite method for the calculation of molecular energies using an approximation based on second-order perturbation theory to lower computational costs compared to G4 theory. However, it has been found to have significantly increased errors when applied to larger organic molecules with 10 or more nonhydrogen atoms. We report here on an investigation of the cause of the failure of G4MP2 theory for such larger molecules. One source of error is found to be the "higher-level correction (HLC)", which is meant to correct for deficiencies in correlation contributions to the calculated energies. This is because the HLC assumes that the contribution is independent of the element and the type of bonding involved, both of which become more important with larger molecules. We address this problem by adding an atom-specific correction, dependent on atom type but not bond type, to the higher-level correction. We find that a G4MP2 method that incorporates this modification of the higher-level correction, referred to as G4MP2A, becomes as accurate as G4 theory (for computing enthalpies of formation) for a test set of molecules with less than 10 nonhydrogen atoms as well as a set with 10-14 such atoms, the set of molecules considered here, with a much lower computational cost. The G4MP2A method is also found to significantly improve ionization potentials and electron affinities. Finally, we implemented the G4MP2A energies in a machine learning method to predict molecular energies.

A lithium-oxygen battery based on lithium superoxide.

Batteries based on sodium superoxide and on potassium superoxide have recently been reported. However, there have been no reports of a battery based on lithium superoxide (LiO2), despite much research into the lithium-oxygen (Li-O2) battery because of its potential high energy density. Several studies of Li-O2 batteries have found evidence of LiO2 being formed as one component of the discharge product along with lithium peroxide (Li2O2). In addition, theoretical calculations have indicated that some forms of LiO2 may have a long lifetime. These studies also suggest that it might be possible to form LiO2 alone for use in a battery. However, solid LiO2 has been difficult to synthesize in pure form because it is thermodynamically unstable with respect to disproportionation, giving Li2O2 (refs 19, 20). Here we show that crystalline LiO2 can be stabilized in a Li-O2 battery by using a suitable graphene-based cathode. Various characterization techniques reveal no evidence for the presence of Li2O2. A novel templating growth mechanism involving the use of iridium nanoparticles on the cathode surface may be responsible for the growth of crystalline LiO2. Our results demonstrate that the LiO2 formed in the Li-O2 battery is stable enough for the battery to be repeatedly charged and discharged with a very low charge potential (about 3.2 volts). We anticipate that this discovery will lead to methods of synthesizing and stabilizing LiO2, which could open the way to high-energy-density batteries based on LiO2 as well as to other possible uses of this compound, such as oxygen storage.

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.

High Performance Air Breathing Flexible Lithium-Air Battery.

Lithium-oxygen (Li-O2 ) batteries possess the highest theoretical energy density (3500 Wh kg-1 ), which makes them attractive candidates for modern electronics and transportation applications. In this work, an inexpensive, flexible, and wearable Li-O2 battery based on the bifunctional redox mediator of InBr3 , MoS2 cathode catalyst, and Fomblin-based oxygen permeable membrane that enable long-cycle-life operation of the battery in pure oxygen, dry air, and ambient air is designed, fabricated, and tested. The battery operates in ambient air with an open system air-breathing architecture and exhibits excellent cycling up to 240 at the high current density of 1 A g-1 with a relative humidity of 75%. The electrochemical performance of the battery including deep-discharge capacity, and rate capability remains almost identical after 1000 cycle in a bending fatigue test. This finding opens a new direction for utilizing high performance Li-O2 batteries for applications in the field of flexible and wearable electronics.

Theoretical evidence of water serving as a promoter for lithium superoxide disproportionation in Li-O2 batteries.

Experimental evidence has demonstrated that the presence of water in non-aqueous electrolytes significantly affects Li-O2 electrochemistry. Understanding the reaction mechanism for Li2O2 formation in the presence of water impurities is important to understand Li-O2 battery performance. A recent experiment has found that very small amounts of water (as low as 40 ppm) can significantly affect the product formation in Li-O2 batteries as opposed to essentially no water (1 ppm). Although experimental as well as theoretical work has proposed mechanisms of Li2O2 formation in the presence of much larger amounts of water, none of the mechanisms provide an explanation for the observations for very small amounts of water. In this work, density functional theory (DFT) was utilized to obtain a mechanistic understanding of the Li-O2 discharge chemistry in a dimethoxyethane (DME) electrolyte containing an isolated water and no water. The reaction pathways for Li2O2 formation from LiO2 on a model system were carefully evaluated with different level of theories, i.e. PBE (PW), B3LYP/6-31G(2df,p), B3LYP/6-311++G(2df,p) and G4MP2. The results indicate that the LiO2 disproportionation reaction to Li2O2 can be promoted by the water in DME electrolyte, which explains why there is a significant difference compared to when no water is present in the experimentally observed discharge product distributions. Ab initio molecular dynamics calculations were also used to investigate the disproportionation of LiO2 dimer in explicit DME. This work adds to the fundamental understanding of the discharge chemistry of a Li-O2 battery.

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.

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.

Cation Additive Enabled Rechargeable LiOH-Based Lithium-Oxygen Batteries.

Lithium-oxygen (Li-O2 ) batteries have attracted extensive research interest due to their high energy density. Other than Li2 O2 (a typical discharge product in Li-O2 batteries), LiOH has proved to be electrochemically active as an alternative product. Here we report a simple strategy to achieve a reversible LiOH-based Li-O2 battery by using a cation additive, sodium ions, to the lithium electrolyte. Without redox mediators in the cell, LiOH is detected as the sole discharge product and it charges at a low charge potential of 3.4 V. A solution-based reaction route is proposed, showing that the competing solvation environment of the catalyst and Li+ leads to LiOH precipitation at the cathode. It is critical to tune the cell chemistry of Li-O2 batteries by designing a simple system to promote LiOH formation/decomposition.

Hydrogen Activation by Silica-Supported Metal Ion Catalysts: Catalytic Properties of Metals and Performance of DFT Functionals.

Single-site heterogeneous catalysts (SSHC) have received increasing attention due to their well-defined active sites and potentially high specific activity. Detailed computational studies were carried out on a set of potential SSHC's, i.e., silica-supported metal ions, to investigate the reactivity of these catalysts with H2 as well as to evaluate the performance of density functional theory (DFT) methods in conjunction with triple-ζ quality basis sets (i.e., cc-pVTZ) on reaction energetics. The ions considered include 4d and 5d metals as well as several post-transition metal ions. A representative cluster model of silica is used to calculate reaction free energies of the metal hydride formation that results from the heterolytic cleavage of H2 on the M-O bond. The hydride formation free energy is previously shown to be strongly correlated with the catalytic activity of such catalysts for alkene hydrogenation. ONIOM calculations (CCSD(T)//MP2) are used to assess the accuracy and reliability of the MP2 results and it is found that MP2 is a suitable level of theory for gauging the performance of DFT functionals. The performance of various DFT functionals is assessed relative to MP2 results and it is found that the wB97xd and PBE0 functionals have the lowest standard deviation (STD) value while the MN12SX and PBE functionals have the lowest mean absolute deviation (MAD) values. The B3LYP functional is shown to have similar MAD and STD values as the top performing functionals. Potential active SSHC's for exergonic hydrogen activation predicted in this study include mostly late and post transition metal ions, i.e., Au3+, Pd2+, Pt4+, Pd4+, Ir4+, Hg2+, Rh3+, Pb4+, Tl3+, In3+, Ir3+, Os4+, Cd2+, Ru2+, and Ga3+. This study provides important guidance to future computational studies of such catalyst systems.

In Situ Formed Ir3Li Nanoparticles as Active Cathode Material in Li-Oxygen Batteries.

Lithium-oxygen (Li-O2) batteries are a promising class of rechargeable Li batteries with a potentially very high achievable energy density. One of the major challenges for Li-O2 batteries is the high charge overpotential, which results in a low energy efficiency. In this work size-selected subnanometer Ir clusters are used to investigate cathode materials that can help control lithium superoxide formation during discharge, which has good electronic conductivity needed for low charge potentials. It is found that Ir particles can lead to lithium superoxide formation as the discharge product with Ir particle sizes of ∼1.5 nm giving the lowest charge potentials. During discharge these 1.5 nm Ir nanoparticles surprisingly evolve to larger ones while incorporating Li to form core-shell structures with Ir3Li shells, which probably act as templates for growth of lithium superoxide during discharge. Various characterization techniques including DEMS, Raman, titration, and HRTEM are used to characterize the LiO2 discharge product and the evolution of the Ir nanoparticles. Density functional calculations are used to provide insight into the mechanism for formation of the core-shell Ir3Li particles. The in situ formed Ir3Li core-shell nanoparticles discovered here provide a new direction for active cathode materials that can reduce charge overpotentials in Li-O2 batteries.

Perspective: Size selected clusters for catalysis and electrochemistry.

Size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization, and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition, cluster-support interactions, and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modeling based on density functional theory sampling of local minima and energy barriers or ab initio molecular dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Finally, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.