Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

Revealing the Dynamic Evolution of Electrolyte Configuration on the Cathode-Electrolyte Interface by Visualizing (De) Solvation Processes

Electrolyte engineering is crucial for improving cathode electrolyte interphase (CEI) to enhance the performance of lithium-ion batteries, especially at high charging cut-off voltages. However, typical electrolyte modification strategies always focus on the solvation structure in the bulk region, but consistently neglect the dynamic evolution of electrolyte solvation configuration at the cathode-electrolyte interface, which directly influences the CEI construction. Herein, we reveal an anti-synergy effect between Li+-solvation and interfacial electric field by visualizing the dynamic evolution of electrolyte solvation configuration at the cathode-electrolyte interface, which determines the concentration of interfacial solvated-Li+. The Li+ solvation in the charging process facilitates the construction of a concentrated (Li+-solvent/anion-rich) interface and anion-derived CEI, while the repulsive force derived from interfacial electric field induces the formation of a diluted (solvent-rich) interface and solvent-derived CEI. Modifying the electrochemical protocols and electrolyte formulation, we regulate the "inflection voltage" arising from the anti-synergy effect and prolong the lifetime of the concentrated interface, which further improves the functionality of CEI architecture.

A machine learning potential construction based on radial distribution function sampling

Sampling reference data is crucial in machine learning potential (MLP) construction. Inadequate coverage of local configurations in reference data may lead to unphysical behaviors in MLP-based molecular dynamics (MLP-MD) simulations. To address this problem, this study proposes a new on-the-fly reference data sampling method called radial distribution function (RDF)-based data sampling for MLP construction. This method detects and extracts anomalous structures from the trajectories of MLP-MD simulations by focusing on the shapes of RDFs. The detected structures are added to the reference data to improve the accuracy of the MLP. This method allows us to realize a reasonable MLP construction for liquid water with minimal additional data. We prepare data from an H2O molecular cluster system and verify whether the constructed MLPs are practical for bulk water systems. MLP-MD simulations without RDF-based data sampling show unphysical behaviors, such as atomic collisions. In contrast, after applying this method, we obtain MLP-MD trajectories with features, such as RDF shapes and angle distributions, that are comparable to those of ab initio MD simulations. Our simulation results demonstrate that the RDF-based data sampling approach is useful for constructing MLPs that are robust to extrapolations from molecular cluster systems to bulk systems without any specialized know-how.

Interpretable Surrogate Learning for Electronic Material Generation

Despite many accessible AI models that have been developed, it is an open challenge to fully exploit interpretable insights to enable effective materials design and develop materials with desired properties for target applications. Here, we introduce an interpretable surrogate learning framework that can actively design and generate electronic materials (EMGen), akin to producing updated materials with requirements by screening all possible elements and fractions. Taking the materials system with required band gaps as a case study, EMGen exhibits a benchmarking predictive error and a running time of 1.7 min for designing and producing a structure with a desired band gap. Using EMGen, we establish a large hybrid functional band gap database, and more uplifting is that the proposed EMGen effectively designs GaxOy with a wide band gap (>5.0 eV) for deep ultraviolet (DUV) optoelectronic devices, enabling a breakthrough extension of the applicability of GaxOy films in photodetectors to DUV light below 240 nm. The augmented band gap also helps improve the breakdown voltage and the heat resilience performance of the amorphous GaxOy film, thereby achieving considerable potential within the realm of power electronics applications. The proposed EMGen, as a specialized, interpretable AI model for the generation of electronic materials, is demonstrated to be an essential tool for on-demand semiconductor materials design.

A comprehensive guide for accurate conformational energies of microsolvated Li+ clusters with organic carbonates

Organic carbonates and their mixtures are frequently used in electrolyte solutions in lithium-ion batteries. Rationalization and tuning of the related Li+ solvation processes are rooted in the proper identification of the representative low-energy spatial structures of the microsolvated Li+(S)n clusters. In this study, we introduce an automatically generated database of conformational energies (CEs), LICARBCONF806, comprising 806 diverse conformers of Li+ clusters with 7 common organic carbonates. A number of standard and composite density functional theory (DFT) approaches and fast semi-empirical methods are examined to reproduce the reference CEs obtained at the RI-SCS-MP2/CBS level of theory. A hybrid PBE0-D4 functional paired with the def2-QZVP basis set is the most robust in reproducing the reference values while composite B97-3c demonstrates the best cost-benefit ratio. Contemporary tight-binding semi-empirical methods GFNn-xTB can be used for the filtering of high-energy structures, but their performance worsens significantly when the limited number of low-energy (CE < 3 kcal mol-1) conformers are to be sorted. Thermal corrections used to convert electronic energies to respective Gibbs free energies and especially corrections imposed by a continuum solvation model can significantly influence both the conformer ranking and the width of the CE distribution. These should be appropriately taken into account to identify lowest energy conformers in solution and at non-zero temperatures. The almost black-box conformation generation workflow used in this work successfully predicts representitative low-energy four-coordinated conformers of Li+ clusters with cyclic carbonates and unravels the complex conformational nature of the clusters with flexible linear carbonates.

A Highly Compatible Deep Eutectic Solvent-Based Poly(ethylene) Oxide Polymer Electrolyte to Enable the Stable Operation of 4.5 V Lithium Metal Batteries

Insufficient ionic conductivity, limited Li+ transfer number (tLi+), and narrow electrochemical windows have heavily restricted the actual application of PEO （poly(ethylene) oxide）-based polymer electrolytes (PEs). Herein, a novel deep eutectic solvent (DES)-based PEO PE for stabilizing high voltage lithium metal battery (LMB) is designed. The DES reduces the crystallinity of PEO while promoting the dissociation of LiTFSI to release more free Li+, thus facilitating the transport of Li+ in the PEO matrix. In addition, the interaction between DES and the PEO, thereby improving the stability of the PEO-based PE under high voltage. Consequently, the PEO-DES-FEC (for short PDF) PE possesses satisfactory ionic conductivity, good mechanical properties, and high electrochemical stability. Meanwhile, PDF PE can build a robust/uniform LiF-rich solid electrolyte interface (SEI) to ensure electrode/electrolyte interface stability. As a concept proof, the Li symmetrical battery and Li||LiFePO4 LMBs of PDF PE exhibit good cycle stability. Applied to the high voltage Li||NCM811 LMBs, the PDF PE-based cell has excellent cycling performance at 4.3 and 4.5 V. This tactic is one of the successful demonstrations of PEO-based electrolytes under 4.5 V high voltage conditions, which breaks through the voltage constraint of conventional PEO-based PEs.

Underlying Mechanism of Electrolyte Compositional Engineering Based on Additive Solvation-Structure Governing Solid Electrolyte Interphase Formation in Lithium-Ion Batteries

Substantial efforts are dedicated to optimizing the additive dosage in the electrolyte and studying its effect on solid electrolyte interphase (SEI) formation in Li-ion batteries (LIBs). This study reveals that the decomposition characteristics of the additive based on its lithium-ion solvation nature significantly contribute to controlling SEI formation. During SEI formation, the strong lithium-ion solvating additive spontaneously migrates to the negative electrode due to negative charge accumulation on the surface, and SEI reinforcement is feasible by increasing the additive dosage. In contrast, population-based SEI formation occurs with a weaker solvating additive, so dosage-dependent modification of the SEI is not effective. These findings demonstrate that compositional electrolyte engineering based on the solvation properties of the additive can be more effective than empirical and experimental studies based on trial and error.

Application of graph neural network in computational heterogeneous catalysis

Heterogeneous catalysis, as a key technology in modern chemical industries, plays a vital role in social progress and economic development. However, its complex reaction process poses challenges to theoretical research. Graph neural networks (GNNs) are gradually becoming a key tool in this field as they can intrinsically learn atomic representation and consider connection relationship, making them naturally applicable to atomic and molecular systems. This article introduces the basic principles, current network architectures, and datasets of GNNs and reviews the application of GNN in heterogeneous catalysis from accelerating the materials screening and exploring the potential energy surface. In the end, we summarize the main challenges and potential application prospects of GNNs in future research endeavors.

Synergistic Anion and Solvent-Derived Interphases Enable Lithium-Ion Batteries under Extreme Conditions

Lithium-ion batteries (LIBs) face increasingly stringent demands as their application expands into new areas, including extreme temperatures and fast charging. To meet these demands, the electrolyte should enable fast lithium-ion transport and form stable interphases on electrodes simultaneously. In practice, however, improving one aspect often compromises another. For instance, the trend toward electrolytes forming anion-derived interphases typically reduces transport efficiency due to weak-solvating solvents. We propose that instead of relying on anions to form the interphase, leveraging both solvents and anions to form interphases can potentially lead to a balancing point between robust interphase formation and effective ion transport. Guided by this design principle, 2,2-difluoroethyl ethyl carbonate (DFDEC) was identified as the promising solvent. With the new electrolyte using DFDEC as the major solvent and lithium bis(fluorosulfonyl) imide (LiFSI) as the salt, graphite||LiNi0.8Mn0.1Co0.1O2 (NMC811) full cells are capable of fast charging and demonstrate long-term cycling stability with a cutoff voltage of 4.5 V. Notably, the battery shows a capacity retention of 84.3% after 500 cycles with an average Coulombic efficiency (CE) as high as 99.93%. This new electrolyte also enables stable battery cycling across a wide temperature range (-20 to 60 °C), with excellent capacity retention.

From Data to Discovery: Recent Trends of Machine Learning in Metal-Organic Frameworks

Renowned for their high porosity and structural diversity, metal-organic frameworks (MOFs) are a promising class of materials for a wide range of applications. In recent decades, with the development of large-scale databases, the MOF community has witnessed innovations brought by data-driven machine learning methods, which have enabled a deeper understanding of the chemical nature of MOFs and led to the development of novel structures. Notably, machine learning is continuously and rapidly advancing as new methodologies, architectures, and data representations are actively being investigated, and their implementation in materials discovery is vigorously pursued. Under these circumstances, it is important to closely monitor recent research trends and identify the technologies that are being introduced. In this Perspective, we focus on emerging trends of machine learning within the field of MOFs, the challenges they face, and the future directions of their development.

Microenvironment Engineering of Heterogeneous Catalysts for Liquid-Phase Environmental Catalysis

Environmental catalysis has emerged as a scientific frontier in mitigating water pollution and advancing circular chemistry and reaction microenvironment significantly influences the catalytic performance and efficiency. This review delves into microenvironment engineering within liquid-phase environmental catalysis, categorizing microenvironments into four scales: atom/molecule-level modulation, nano/microscale-confined structures, interface and surface regulation, and external field effects. Each category is analyzed for its unique characteristics and merits, emphasizing its potential to significantly enhance catalytic efficiency and selectivity. Following this overview, we introduced recent advancements in advanced material and system design to promote liquid-phase environmental catalysis (e.g., water purification, transformation to value-added products, and green synthesis), leveraging state-of-the-art microenvironment engineering technologies. These discussions showcase microenvironment engineering was applied in different reactions to fine-tune catalytic regimes and improve the efficiency from both thermodynamics and kinetics perspectives. Lastly, we discussed the challenges and future directions in microenvironment engineering. This review underscores the potential of microenvironment engineering in intelligent materials and system design to drive the development of more effective and sustainable catalytic solutions to environmental decontamination.

Strong Lewis-acid coordinated PEO electrolyte achieves 4.8 V-class all-solid-state batteries over 580 Wh kg-1

Polyethylene oxide (PEO) based electrolytes critically govern the security and energy density of solid-state batteries, but typically suffer from poor oxidation resistance at high voltages, which limits the energy density of batteries. Here, we report a Lewis-acid coordinated strategy to significantly improve the cyclic stability of 4.8 V-class PEO-based battery. The introduced Mg2+ and Al3+ with strong electron-withdrawing capability weaken the electron density of ether oxygen (EO) chains via chelation in the coordination structure, resulting in a locally limited interaction between the EO chains and the surface of cathodes at high state of charge. The batteries using Lewis-acid coordinated electrolytes and Ni-rich cathodes achieve high voltage stability of 4.8 V over 300 cycles. Further, the realization of industrial-scale electrolyte membranes, and Ah-level pouch cells over 586 Wh kg‒1 with good cyclic stability, suggests the potential of our strategy in practical applications of all-solid-state batteries.

Multifunctional Self-Assembled Bio-Interfacial Layers for High-Performance Zinc Metal Anodes

Rechargeable aqueous zinc-ion (Zn-ion) batteries are widely regarded as important candidates for next-generation energy storage systems for low-cost renewable energy storage. However, the development of Zn-ion batteries is currently facing significant challenges due to uncontrollable Zn dendrite growth and severe parasitic reactions on Zn metal anodes. Herein, we report an effective strategy to improve the performance of aqueous Zn-ion batteries by leveraging the self-assembly of bovine serum albumin (BSA) into a bilayer configuration on Zn metal anodes. BSA's hydrophilic and hydrophobic fragments form unique and intelligent ion channels, which regulate the migration of Zn ions and facilitate their desolvation process, significantly diminishing parasitic reactions on Zn anodes and leading to a uniform Zn deposition along the Zn (002) plane. Notably, the Zn||Zn symmetric cell with BSA as the electrolyte additive demonstrated a stable cycling performance for up to 2400 hours at a high current density of 10 mA cm-2. This work demonstrates the pivotal role of self-assembled protein bilayer structures in improving the durability of Zn anodes in aqueous Zn-ion batteries.

A Knowledge-Data Dual-Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes

Developing rechargeable batteries that operate within a wide temperature range and possess high safety has become necessary with increasing demands. Rapid and accurate assessment of the melting points (MPs), boiling points (BPs), and flash points (FPs) of electrolyte molecules is essential for expediting battery development. Herein, we introduce Knowledge-based electrolyte Property prediction Integration (KPI), a knowledge-data dual-driven framework for molecular property prediction of electrolytes. Initially, the KPI collects molecular structures and properties, and then automatically organizes them into structured datasets. Subsequently, interpretable machine learning further explores the structure-property relationships of molecules from a microscopic perspective. Finally, by embedding the discovered knowledge into property prediction models, the KPI achieved very low mean absolute errors of 10.4, 4.6, and 4.8 K for MP, BP, and FP predictions, respectively. The KPI reached state-of-the-art results in 18 out of 20 datasets. Utilizing molecular neighbor search and high-throughput screening, 15 and 14 promising molecules, with and without Chemical Abstracts Service Registry Number, respectively, were predicted for wide-temperature-range and high-safety batteries. The KPI not only accurately predicts molecular properties and deepens the understanding of structure-property relationships but also serves as an efficient framework for integrating artificial intelligence and domain knowledge.

When Metal Nanoclusters Meet Smart Synthesis

Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall nanoparticles with molecule-like properties, bridging conventional metal-ligand complexes and nanocrystals. Despite their potential for various applications, synthesis challenges such as a precise understanding of varied synthetic parameters and property-driven synthesis persist, hindering their full exploitation and wider application. Incorporating smart synthesis methodologies, including a closed-loop framework of automation, data interpretation, and feedback from AI, offers promising solutions to address these challenges. In this perspective, we summarize the closed-loop smart synthesis that has been demonstrated in various nanomaterials and explore the research frontiers of smart synthesis for MNCs. Moreover, the perspectives on the inherent challenges and opportunities of smart synthesis for MNCs are discussed, aiming to provide insights and directions for future advancements in this emerging field of AI for Science, while the integration of deep learning algorithms stands to substantially enrich research in smart synthesis by offering enhanced predictive capabilities, optimization strategies, and control mechanisms, thereby extending the potential of MNC synthesis.

Modeling solvation dynamics of transition metal redox ion through on-the-fly multi-objective Bayesian-optimized force field

Modeling solvation dynamics and properties is crucial for developing electrolytes for electrochemical energy storage and conversion devices. This work reports an on-the-fly multi-objective Bayesian optimization (OTF-MOBO) method to parameterize force fields for modeling ionic solvation structures, thermodynamics, and transport properties using molecular dynamics simulations. By leveraging solvation-free energy and solvation radii as training data, we employ the data-driven OTF-MOBO algorithm to actively optimize the force field parameters. The modeling accuracy was evaluated in molecular dynamics simulations until the Pareto front in the parameter space was reached through minimized prediction errors in both solvation-free energy and solvation radii. Using transition metal redox ions (Fe3+/Fe2+, Cr3+/Cr2+, and Cu2+/Cu+) in aqueous solution as examples, we demonstrate that simple force fields combining the Lenard-Jones potential and Coulombic potential can achieve relative error below 2% in both solvation free energy and solvation radii. The optimized force fields can be further extrapolated to predict solvation entropy and diffusivities with relative error below 10% compared with experiments.

Synergistic Integration of Physical Embedding and Machine Learning Enabling Precise and Reliable Force Field

Machine-learning force fields have achieved significant strides in accurately reproducing the potential energy surface with quantum chemical accuracy. However, this approach still faces several challenges, e.g., extrapolating to uncharted chemical spaces, interpreting long-range electrostatics, and mapping complex macroscopic properties. To address these issues, we advocate for a synergistic integration of physical principles and machine learning techniques within the framework of a physically informed neural network (PINN). This approach involves incorporating physical knowledge into the parameters of the neural network, coupled with an efficient global optimizer, the Tabu-Adam algorithm, proposed in this work to augment optimization under strict physical constraint. We choose the AMOEBA+ force field as the physics-based model for embedding and then train and test it using the diethylene glycol dimethyl ether (DEGDME) data set as a case study. The results reveal a breakthrough in constructing a precise and noise-robust machine learning force field. Utilizing two training sets with hundreds of samples, our model exhibits remarkable generalization and density functional theory (DFT) accuracy in describing molecular interactions and enables a precise prediction of the macroscopic properties such as the diffusion coefficient with minimal cost. This work provides valuable insight into establishing a fundamental framework of the PINN force field.

Molecule Design for Non-Aqueous Wide-Temperature Electrolytes via the Intelligentized Screening Method

Operating a lithium-ion battery (LIB) in a wide temperature range is essential for ensuring a stable electricity supply amidst fluctuating temperatures caused by climate or terrain changes. Electrolyte plays a pivotal role in determining the temperature durability of batteries. However, specialized electrolytes designed for either low or high temperatures typically possess distinct features. Therefore, wide-temperature electrolytes (WTEs) are necessary as they encompass a combination of diverse properties, which complicates the clear instruction of WTE design. Here we represent an artificial intelligence (Al)-assisted workflow of WTE design through stepwise parameterizations and calculations. Linear mono-nitriles are identified as ideal wide-liquidus-range solvents that can "softly" solvate lithium ions by weak interactions. In addition, the explainable modules revealed the halogenoid similarity of cyanide as fluorine on the electrolyte properties (e.g. boiling point and dielectric constant). With the further introduction of an ether bond, 3-methoxypropionitrile (MPN) has been eventually determined as a main electrolyte solvent, enabling the battery operation from -60 to 120 °C. Particularly, a LiCoO2/Li cell using the proposed WTE can realize stable cycling with capacity retention reaching 72.3 % after 50 cycles under a high temperature of 100 °C.

Sodiophilic Substrate Induces NaF-Rich Solid Electrolyte Interface for Dendrite-Free Sodium Metal Anode

3D substrate with abundant sodiophilic active sites holds promise for implementing dendrite-free sodium metal anodes and high-performance sodium batteries. However, the heightened electrode/electrolyte side reactions stemming from high specific surface area still hinder electrode structure stability and cycling reversibility, particularly under high current densities. Herein, the solid electrolyte interface (SEI) component is regulated and detrimental side reactions are restrained through the uniform loading of Na-Sn alloy onto a porous 3D nanofiber framework (NaSn-PCNF). The strong interaction between Na-Sn alloy and PF6 - anions facilitates the dissociation of sodium salts and releases more free sodium ions for effective charge transfer. Simultaneously, the modulations of the interfacial electrolyte solvation structure and the construction of a high NaF content SEI layer stabilize the electrode/electrolyte interface. NaSn-PCNF symmetrical battery demonstrates stable cycling for over 600 h with an ultralow overpotential of 24.5 mV under harsh condition of 10 mA cm-2 and 10 mAh cm-2. Moreover, the full cells and pouch cells exhibit accelerated reaction kinetics and splendid capacity retention, providing valuable insights into the development of advanced Na substrates for high-energy sodium metal batteries.

Deciphering the Decomposition Mechanisms of Ether and Fluorinated Ether Electrolytes on Lithium Metal Surfaces: Insights from CMD and AIMD Simulations

The performance of lithium metal batteries can be significantly enhanced by incorporating fluorinated ether-based electrolytes, yet the solid electrolyte interphase (SEI) formation mechanism on lithium metal surfaces remains elusive. This study employs classical and ab initio molecular dynamics simulations to investigate the decomposition mechanisms of lithium bis(fluoromethanesulfonyl)imide (LiFSI) in 1,2-diethoxyethane (DEE) and its fluorinated analogues, F5DEE and F2DEE, when in contact with lithium metal. Our findings indicate that F5DEE-based electrolytes favor the formation of a FSI-rich primary solvation shell around Li+, while F2DEE-based electrolytes yield a solvent-rich environment. The normalized number density at the Li/electrolyte/Li interface shows a depletion of FSI anions in the electrochemical double layer (EDL) structure near the Li anode upon charging, with the distance between the first main peak of the FSI anion and Li anode following the order F5DEE < DEE < F2DEE. Analysis of the electronic projected density of states and charge transfer dynamics unveils the reductive dissociation pathways of FSI anions and fluorinated DEE solvents on the lithium metal surface, taking into account the influence of the EDL structure. DEE is identified as the most reduction-stable solvent, leading to the selective dissociation of FSI anions and the formation of an entirely inorganic SEI. In contrast, F2DEE displays a pronounced reduction tendency, forming an organic-rich SEI due to the solvent-dominated lowest unoccupied molecular orbital at the interface. F5DEE, competing with FSI anions for reduction, results in the formation of an inorganic-rich hybrid SEI with the highest LiF content. The simulation results correlate well with experimental observations and underscore the pivotal role of various fluorinated functional groups in the formation of EDL and SEI near the lithium metal surface.

Theoretical calculations and simulations power the design of inorganic solid-state electrolytes

Using solid-state electrolytes (SSEs) to build batteries helps improve the safety and lifespan of batteries, making it crucial to deeply understand the fundamental physical and chemical properties of SSEs. Theoretical calculations based on modern quantum chemical methods and molecular simulation techniques can explore the relationship between the structure and performance of SSEs at the atomic and molecular levels. In this review, we first comprehensively introduce theoretical methods used to assess the stability of SSEs, including mechanical, phase, and electrochemical stability, and summarize the significant progress achieved through these methods. Next, we outline the methods for calculating ion diffusion properties and discuss the advantages and limitations of these methods by combining the diffusion behaviors and mechanisms of ions in the bulk phase, grain boundaries, and electrode-solid electrolyte interfaces. Finally, we summarize the latest research progress in the discovery of high-quality SSEs through high-throughput screening and machine learning and discuss the application prospects of a new mode that incorporates machine learning into high-throughput screening.

Tailoring Versatile Cathodes and Induced Anodes for Zn-Se Batteries: Anisotropic Orientation of Tin-Based Materials within Bowl-In-Ball Carbon

The advancement of Zn-Se batteries has been hindered by significant challenges, such as the sluggish kinetics of Se cathodes, limited Se loading, and uncontrollable formation of Zn dendrites. In this study, a bidirectional optimization strategy is devised for both cathode and anode to bolster the performance of Zn-Se batteries. A novel bowl-in-ball structured carbon (BIBCs) material is synthesized to serve as a nanoreactor, in which tin-based materials are grown and derived in situ to construct cathodes and anodes. Within the cathode, the multifunctional host material (SnSe@BIBCs) exhibits large adsorption capacity for selenium, and demonstrates supreme catalytic properties and spatially confined characteristics toward the selenium reduction reaction (SeRR). On the anode, Sn@BIBCs displays triple-induced properties, including the zincophilic of the internal metallic Sn, the homogenized spatial electric field from the 3D spatial structure, and the curvature effect of the bowl-shaped carbon. Collectively, these factors induce preferential nucleation of Zn, ensuring its uniform deposition. As a result, the integrated Zn-Se battery system achieves a remarkable specific capacity of up to 603 mAh g-1 and an impressive energy density of 581 W kg-1, highlighting its tremendous potential for practical applications.

Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials

Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as "designer solvents" as they can be mixed to precisely tailor the physiochemical properties. As using machine learning interatomic potentials (MLIPs) to simulate ILs is still relatively unexplored, several questions need to be answered to see if MLIPs can be transformative for ILs. Since ILs are often not pure, but are either mixed together or contain additives, we first demonstrate that a MLIP can be trained to be compositionally transferable; i.e., the MLIP can be applied to mixtures of ions not directly trained on, while only being trained on a few mixtures of the same ions. We also investigated the accuracy of MLIPs for a novel IL, which we experimentally synthesize and characterize. Our MLIP trained on ∼200 DFT frames is in reasonable agreement with our experiments and DFT.

Dipole Moment Influences the Reversibility and Corrosion of Lithium Metal Anodes

Lithium metal batteries (LMBs) must have both long cycle life and calendar life to be commercially viable. However, "trial and error" methodologies remain prevalent in contemporary research endeavors to identify favorable electrolytes. Here, a guiding principle for the selection of solvents for LMBs is proposed, which aims to achieve high Coulombic efficiency while minimizing the corrosion. For the first time, this study reveals that the dipole moment and orientation of solvent molecules have significant impacts on lithium metal reversibility and corrosion. Solvents with high dipole moments are more likely to adsorb onto lithium metal surfaces, which also influence the solid electrolyte interphase. Using this principle, the use of LiNO3 is demonstrated as the sole salt in LiNi0.8Co0.1Mn0.1O2/Li cells can achieve excellent cycling stability. Overall, this work bridges the molecular structure of solvents to the reversibility and corrosion of lithium metal, and these concepts can be extended to other metal-based batteries.

Sequential Selective Dissolution of Coinage Metals in Recyclable Ionic Media

Coinage metals Cu, Ag, and Au are essential for modern electronics and their recycling from waste materials is becoming increasingly important to guarantee the security of their supply. Designing new sustainable and selective procedures that would substitute currently used processes is crucial. Here, we describe an unprecedented approach for the sequential dissolution of single metals from Cu, Ag, and Au mixtures using biomass-derived ionic solvents and green oxidants. First, Cu can be selectively dissolved in the presence of Ag and Au with a choline chloride/urea/H2O2 mixture, followed by the dissolution of Ag in lactic acid/H2O2. Finally, the metallic Au, which is not soluble in either solution above, is dissolved in choline chloride/urea/Oxone. Subsequently, the metals were simply and quantitatively recovered from dissolutions, and the solvents were recycled and reused. The applicability of the developed approach was demonstrated by recovering metals from electronic waste substrates such as printed circuit boards, gold fingers, and solar panels. The dissolution reactions and selectivity were explored with different analytical techniques and DFT calculations. We anticipate our approach will pave a new way for the contemporary and sustainable recycling of multi-metal waste substrates.

Mesophase Behavior of Molecules Containing Three Benzene Rings Connected via Imines and Ester Linkages

Ten new compounds based on the methineazo-azomethine (CH=N-N=CH) and ester linking groups were prepared and investigated for their mesophase behavior and optical stability, and liquid crystals of 4-substituted phenyl methineazo-azomethine phenyl 4-alkoxybenzoates, I n a-e , were investigated. An alkoxy group with a length between 8 and 12 carbons is attached to the phenyl eater wing, while the other terminal ring is substituted in its 4-position with one of the polar NO2, F, Cl, CH3O, and N(CH3)2 groups. The molecular structures of the newly prepared compounds were verified by using 1H NMR, 13C NMR, and elemental analysis. Differential scanning calorimetry and polarized optical microscopy were applied to investigate their mesophase behavior. All members of the prepared homologous series showed excellent thermal mesomorphic stability over wide temperature ranges. The geometrical and thermal properties of the investigated compounds were verified via density functional theory (DFT). The theoretical results revealed that all of the compounds are almost planar. Finally, the experimentally established values of the mesophase data were correlated with the predicted quantum chemical characteristics evaluated by DFT.

Ion association behaviors in the initial stage of calcium carbonate formation: An ab initio study

In this work, we performed static density functional theory calculations and ab initio metadynamics simulations to systematically investigate the association mechanisms and dynamic structures of four kinds of ion pairs that could be formed before the nucleation of CaCO3. For Ca2+-HCO3- and Ca2+-CO32- pairs, the arrangement of ligands around Ca2+ evolves between the six-coordinated octahedral structure and the seven-coordinated pentagonal bipyramidal structure. The formation of ion pairs follows an associative ligand substitution mechanism. Compared with HCO3-, CO32- exhibits a stronger affinity to Ca2+, leading to the formation of a more stable precursor phase in the prenucleation stage, which promotes the subsequent CaCO3 nucleation. In alkaline environments, excessive OH- ions decrease the coordination preference of Ca2+. In this case, the formation of Ca(OH)+-CO32- and Ca(OH)2-CO32- pairs favors the dissociative ligand substitution mechanism. The inhibiting effects of OH- ion on the CaCO3 association can be interpreted from two aspects, i.e., (1) OH- neutralizes positive charges on Ca2+, decreases the electrostatic interactions between Ca2+ and CO32-, and thus hinders the formation of the CaCO3 monomer, and (2) OH- decreases the capacity of Ca2+ for accommodating O, making it easier to separate Ca2+ and CO32- ions. Our findings on the ion association behaviors in the initial stage of CaCO3 formation not only help scientists evaluate the impact of ocean acidification on biomineralization but also provide theoretical support for the discovery and development of more effective approaches to manage undesirable scaling issues.

Organic Solvent Boosts Charge Storage and Charging Dynamics of Conductive MOF Supercapacitors

Conductive metal-organic frameworks (c-MOFs) and ionic liquids (ILs) have emerged as auspicious combinations for high-performance supercapacitors. However, the nanoconfinement from c-MOFs and high viscosity of ILs slow down the charging process. This hindrance can, however, be resolved by adding solvent. Here, constant-potential molecular simulations are performed to scrutinize the solvent impact on charge storage and charging dynamics of MOF-IL-based supercapacitors. Conditions for >100% enhancement in capacity and ≈6 times increase in charging speed are found. These improvements are confirmed by synthesizing near-ideal c-MOFs and developing multiscale models linking molecular simulations to electrochemical measurements. Fundamentally, the findings elucidate that the solvent acts as an "ionophobic agent" to induce a substantial enhancement in charge storage, and as an "ion traffic police" to eliminate convoluted counterion and co-ion motion paths and create two distinct ion transport highways to accelerate charging dynamics. This work paves the way for the optimal design of MOF supercapacitors.

Tuning the Solvation Structure in Water-Based Solution Enables Surface Reconstruction of Layered Oxide Cathodes toward Long Lifespan Sodium-Ion Batteries

Layered oxides of sodium-ion batteries suffer from severe side reactions on the electrode/electrolyte interface, leading to fast capacity degradation. Although surface reconstruction strategies are widely used to solve the above issues, the utilization of the low-cost wet chemical method is extremely challenging for moisture-sensitive Na-based oxide materials. Here, the solvation tuning strategy is proposed to overcome the deterioration of NaNi1/3Mn1/3Fe1/3O2 in water-based solution and conduct the surface reconstruction. When capturing the water molecules by the solvation structure of cations, here is Li+, the structural collapse and degradation of layered oxides in water-based solvents are greatly mitigated. Furthermore, Li(H2O)3EA+ promotes the profitable Li+/Na+ exchange to build a robust surface, which hampers the decomposition of electrolytes and the structural evolution upon cycling. Accordingly, the lifespan of Li-reinforced materials is prolonged to three times that of the pristine one. This work represents a step forward in understanding the surface reconstruction operated in a water-based solution for high-performance sodium layered oxide cathodes.

Sodium Triflate Water-in-Salt Electrolytes in Advanced Battery Applications: A First-Principles-Based Molecular Dynamics Study

Offering a compelling combination of safety and cost-effectiveness, water-in-salt (WiS) electrolytes have emerged as promising frontiers in energy storage technology. Still, there is a strong demand for research and development efforts to make these electrolytes ripe for commercialization. Here, we present a first-principles-based molecular dynamics (MD) study addressing in detail the properties of a sodium triflate WiS electrolyte for Na-ion batteries. We have developed a workflow based on a machine learning (ML) potential derived from ab initio MD simulations. As ML potentials are typically restricted to the interpolation of the data points of the training set and have hardly any predictive properties, we subsequently optimize a classical force field based on physics principles to ensure broad applicability and high performance. Performing and analyzing detailed MD simulations, we identify several very promising properties of the sodium triflate as a WiS electrolyte but also indicate some potential stability challenges associated with its use as a battery electrolyte.

Tuning Na-Ion Diffusion in MXene/Graphene Oxide Heterostructures: An Ab Initio Molecular Dynamics Study

The development of heterostructured anode materials provides an effective approach for enhancing the electrochemical performance of sodium-ion batteries (SIBs). In this work, ab initio molecular dynamics simulations and first-principles calculations are employed to investigate the Na-ion intercalation and diffusion in MXene/graphene oxide heterostructures. The influence of graphene oxidation on interlayer spacing, Na-ion diffusion kinetics, and transport mechanisms is examined at an atomic scale. It has been observed that oxygen functional groups can increase the interspacing between adjacent layers, thereby improving the initial embedding of Na ions. However, overoxidation causes an obstructive effect on the ionic conduction channels. An appropriate oxidation degree enables optimal Na-ion migration kinetics while retaining structural integrity. Our simulation results provide crucial insights into the rational design of high-performance MXene-based anodes for SIBs with excellent capacity and cycling stability.

Charging the Future: Harnessing Nature's Designs for Bioinspired Molecular Electrodes

The transition toward electric-powered devices is anticipated to play a pivotal role in advancing the global net-zero carbon emission agenda aimed at mitigating greenhouse effects. This shift necessitates a parallel focus on the development of energy storage materials capable of supporting intermittent renewable energy sources. While lithium-ion batteries, featuring inorganic electrode materials, exhibit desirable electrochemical characteristics for energy storage and transport, concerns about the toxicity and ethical implications associated with mining transition metals in their electrodes have prompted a search for environmentally safe alternatives. Organic electrodes have emerged as promising and sustainable alternatives for batteries. This review paper will delve into the recent advancements in nature-inspired electrode design aimed at addressing critical challenges such as capacity degradation due to dissolution, low operating voltages, and the intricate molecular-level processes governing macroscopic electrochemical properties.

To Pair or not to Pair? Machine-Learned Explicitly-Correlated Electronic Structure for NaCl in Water

The extent of ion pairing in solution is an important phenomenon to rationalize transport and thermodynamic properties of electrolytes. A fundamental measure of this pairing is the potential of mean force (PMF) between solvated ions. The relative stabilities of the paired and solvent shared states in the PMF and the barrier between them are highly sensitive to the underlying potential energy surface. However, direct application of accurate electronic structure methods is challenging, since long simulations are required. We develop wave function based machine learning potentials with the random phase approximation (RPA) and second order Møller-Plesset (MP2) perturbation theory for the prototypical system of Na and Cl ions in water. We show both methods in agreement, predicting the paired and solvent shared states to have similar energies (within 0.2 kcal/mol). We also provide the same benchmarks for different DFT functionals as well as insight into the PMF based on simple analyses of the interactions in the system.

A Critical Review on Room-Temperature Sodium-Sulfur Batteries: From Research Advances to Practical Perspectives

Room-temperature sodium-sulfur (RT-Na/S) batteries are promising alternatives for next-generation energy storage systems with high energy density and high power density. However, some notorious issues are hampering the practical application of RT-Na/S batteries. Besides, the working mechanism of RT-Na/S batteries under practical conditions such as high sulfur loading, lean electrolyte, and low capacity ratio between the negative and positive electrode (N/P ratio), is of essential importance for practical applications, yet the significance of these parameters has long been disregarded. Herein, it is comprehensively reviewed recent advances on Na metal anode, S cathode, electrolyte, and separator engineering for RT-Na/S batteries. The discrepancies between laboratory research and practical conditions are elaborately discussed, endeavors toward practical applications are highlighted, and suggestions for the practical values of the crucial parameters are rationally proposed. Furthermore, an empirical equation to estimate the actual energy density of RT-Na/S pouch cells under practical conditions is rationally proposed for the first time, making it possible to evaluate the gravimetric energy density of the cells under practical conditions. This review aims to reemphasize the vital importance of the crucial parameters for RT-Na/S batteries to bridge the gaps between laboratory research and practical applications.

Smart Solid-State Interphases Enable High-Safety and High-Energy Practical Lithium Batteries

With the electrochemical performance of batteries approaching the bottleneck gradually, it is increasingly urgent to solve the safety issue. Herein, all-in-one strategy is ingeniously developed to design smart, safe, and simple (3S) practical pouch-type LiNi0.8Co0.1Mn0.1O2||Graphite@SiO (NCM811||Gr@SiO) cell, taking full advantage of liquid and solid-state electrolytes. Even under the harsh thermal abuse and high voltage condition (100 °C, 3-4.5 V), the pouch-type 3S NCM811||Gr@SiO cell can present superior capacity retention of 84.6% after 250 cycles (based pouch cell: 47.8% after 250 cycles). More surprisingly, the designed 3S NCM811||Gr@SiO cell can efficiently improve self-generated heat T1 by 45 °C, increase TR triggering temperature T2 by 40 °C, and decrease the TR highest T3 by 118 °C. These superior electrochemical and safety performances of practical 3S pouch-type cells are attributed to the robust and stable anion-induced electrode-electrolyte interphases and local solid-state electrolyte protection layer. All the fundamental findings break the conventional battery design guidelines and open up a new direction to develop practical high-performance batteries.

Kinetic Evaluation on Lithium Polysulfide in Weakly Solvating Electrolyte toward Practical Lithium-Sulfur Batteries

Lithium-sulfur (Li-S) batteries are highly considered as next-generation energy storage techniques. Weakly solvating electrolyte with low lithium polysulfide (LiPS) solvating power promises Li anode protection and improved cycling stability. However, the cathodic LiPS kinetics is inevitably deteriorated, resulting in severe cathodic polarization and limited energy density. Herein, the LiPS kinetic degradation mechanism in weakly solvating electrolytes is disclosed to construct high-energy-density Li-S batteries. Activation polarization instead of concentration or ohmic polarization is identified as the dominant kinetic limitation, which originates from higher charge-transfer activation energy and a changed rate-determining step. To solve the kinetic issue, a titanium nitride (TiN) electrocatalyst is introduced and corresponding Li-S batteries exhibit reduced polarization, prolonged cycling lifespan, and high actual energy density of 381 Wh kg-1 in 2.5 Ah-level pouch cells. This work clarifies the LiPS reaction mechanism in protective weakly solvating electrolytes and highlights the electrocatalytic regulation strategy toward high-energy-density and long-cycling Li-S batteries.

Evolving better solvate electrolytes for lithium secondary batteries

The overall performance of lithium batteries remains unmatched to this date. Decades of optimisation have resulted in long-lasting batteries with high energy density suitable for mobile applications. However, the electrolytes used at present suffer from low lithium transference numbers, which induces concentration polarisation and reduces efficiency of charging and discharging. Here we show how targeted modifications can be used to systematically evolve anion structural motifs which can yield electrolytes with high transference numbers. Using a multidisciplinary combination of theoretical and experimental approaches, we screened a large number of anions. Thus, we identified anions which reach lithium transference numbers around 0.9, surpassing conventional electrolytes. Specifically, we find that nitrile groups have a coordination tendency similar to SO2 and are capable of inducing the formation of Li+ rich clusters. In the bigger picture, we identified a balanced anion/solvent coordination tendency as one of the key design parameters.

Rescuing zinc anode-electrolyte interface: mechanisms, theoretical simulations and in situ characterizations

The research interest in aqueous zinc-ion batteries (AZIBs) has been surging due to the advantages of safety, abundance, and high electrochemical performance. However, some technique issues, such as dendrites, hydrogen evolution reaction, and corrosion, severely prohibit the development of AZIBs in practical utilizations. The underlying mechanisms regarding electrochemical performance deterioration and structure degradation are too complex to understand, especially when it comes to zinc metal anode-electrolyte interface. Recently, theoretical simulations and in situ characterizations have played a crucial role in AZIBs and are exploited to guide the research on electrolyte engineering and solid electrolyte interphase. Herein, we present a comprehensive review of the current state of the fundamental mechanisms involved in the zinc plating/stripping process and underscore the importance of theoretical simulations and in situ characterizations in mechanism research. Finally, we summarize the challenges and opportunities for AZIBs in practical applications, especially as a stationary energy storage and conversion device in a smart grid.

Principles and Design of Biphasic Self-Stratifying Batteries Toward Next-Generation Energy Storage

Large-scale energy storage devices play pivotal roles in effectively harvesting and utilizing green renewable energies (such as solar and wind energy) with capricious nature. Biphasic self-stratifying batteries (BSBs) have emerged as a promising alternative for grid energy storage owing to their membraneless architecture and innovative battery design philosophy, which holds promise for enhancing the overall performance of the energy storage system and reducing operation and maintenance costs. This minireview aims to provide a timely review of such emerging energy storage technology, including its fundamental design principles, existing categories, and prototype architectures. The challenges and opportunities of this undergoing research topic will also be systematically highlighted and discussed to provide guidance for the subsequent R&D of superior BSBs while conducive to bridging the gap for their future practical application.

Induction of breast cancer cell apoptosis by novel thiouracil-fused heterocyclic compounds through boosting of Bax/Bcl-2 ratio and DFT study

Breast cancer is a common public health disease causing mortality worldwide. Thus, providing novel chemotherapies that tackle breast cancer is of great interest. In this investigation, novel pyrido[2,3-d]pyrimidine derivatives 3,4,(6a-c),(8a,b),9-20 were synthesized and characterized using a variety of spectrum analyses. The geometric and thermal parameters of the novel thiouracil derivatives 3,4,6a,(8a,b),11,12,17,18, 19 were measured using density functional theory (DFT) via DFT/B3LYP/6-31 + G(d,p) basis set. All synthesized compounds were evaluated by MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) method using MCF-7 and MDA-MB-231 breast cancerous cells, compound 17 had the maximum anticancer activity against both breast cancerous cells, recording the lowest half-maximal inhibitory concentration (IC50) values (56.712 μg/mL for MCF-7 cells and 48.743 μg/mL for MDA-MB-231 cells). The results were confirmed in terms of the intrinsic mechanism of apoptosis, where compound 17 had the highest percentage in the case of both cancer cells and recorded Bax (Bcl-2 associated X)/Bcl-2 (B-cell lymphoma 2) ratio 17.5 and 96.667 for MCF-7 and MDA-MB-231 cells, while compound 19 came after 17 in the ability for induction of apoptosis, where the Bax/Bcl-2 ratio was 15.789 and 44.273 for both cancerous cells, respectively. Also, compound 11 recorded a high Bax/Bcl-2 ratio for both cells. The safety of the synthesized compounds was applied on normal WI-38 cells, showing minimum cytotoxic effect with undetectable IC50. Compounds 17, 11, and 19 recorded a significant increase of p53 upregulated modulator of apoptosis (PUMA) expression levels in the cancerous cells. The DFT method was also used to establish a connection between the experimentally determined values of the present investigated compounds and their predicted quantum chemical parameters. It was concluded that Compounds 17, 11, and 19 had anti-breast cancer potential through the induction of apoptotic Bax/Bcl-2 and PUMA expression levels.

Halogen-powered static conversion chemistry

Halogen-powered static conversion batteries (HSCBs) thrive in energy storage applications. They fall into the category of secondary non-flow batteries and operate by reversibly changing the chemical valence of halogens in the electrodes or/and electrolytes to transfer electrons, distinguishing them from the classic rocking-chair batteries. The active halide chemicals developed for these purposes include organic halides, halide salts, halogenated inorganics, organic-inorganic halides and the most widely studied elemental halogens. Aside from this, various redox mechanisms have been discovered based on multi-electron transfer and effective reaction pathways, contributing to improved electrochemical performances and stabilities of HSCBs. In this Review, we discuss the status of HSCBs and their electrochemical mechanism-performance correlations. We first provide a detailed exposition of the fundamental redox mechanisms, thermodynamics, conversion and catalysis chemistry, and mass or electron transfer modes involved in HSCBs. We conclude with a perspective on the challenges faced by the community and opportunities towards practical applications of high-energy halogen cathodes in energy-storage devices.

Minimal Coarse-Grained Models of Polar Solvent for Electrolytes: Stockmayer Versus Dumbbell

This study explores the potential of the dumbbell solvent as a minimal model for understanding electrolyte solutions in polar solvents. Our investigation involves a comparative analysis of the dumbbell model and the Stockmayer model, focusing on ion solvation and ion-ion correlations. We examine electrolytes containing symmetric monovalent salts dissolved in polar solvents while varying the ion density and solvent polarity. Both models predict an augmented solvent coordination number around ions as the solvent polarity increases, with the dumbbell solvent displaying a more pronounced effect. Notably, radial distribution functions (RDFs) between solvent and ions yield differing trends; Stockmayer models exhibit a nonmonotonic relationship due to strong dipole-dipole interactions at higher polarity, while RDFs for ions and dumbbell solvents consistently rise. In response to increased solvent polarity, Stockmayer solvents within the ion's solvation shell undergo continuous dipole orientation shifts, whereas the dumbbell solvent predominantly adopts pointing-away dipole orientations, diminishing pointing-to orientations. This underscores the significance of the interplay between the solvent molecular orientation and dipole rotation. Both models qualitatively predict ion pairing and clustering behaviors across varying solvent dipole strengths and salt concentrations. The Stockmayer solvent generally provides stronger electrostatic screening than the dumbbell solvent due to its neglect of the coupling between molecular orientation and dipole rotation. What's more, at a high dipole moment regime, ion-ion correlations in Stockmayer solvent can become stronger with increasing dipole moment due to stronger solvent-solvent correlations. This study underscores the effectiveness of the dumbbell solvent model in systematically elucidating the fundamental principles governing electrolytes and offers potential applications in the rational design of electrolyte systems.

Ether-Based High-Voltage Lithium Metal Batteries: The Road to Commercialization

Ether-based high-voltage lithium metal batteries (HV-LMBs) are drawing growing interest due to their high compatibility with the Li metal anode. However, the commercialization of ether-based HV-LMBs still faces many challenges, including short cycle life, limited safety, and complex failure mechanisms. In this Review, we discuss recent progress achieved in ether-based electrolytes for HV-LMBs and propose a systematic design principle for the electrolyte based on three important parameters: electrochemical performance, safety, and industrial scalability. Finally, we summarize the challenges for the commercial application of ether-based HV-LMBs and suggest a roadmap for future development.

Electrolyte Dependence of Li+ Transport Mechanisms in Small Molecule Solvents from Classical Molecular Dynamics

As demands on Li-ion battery performance increase, the need for electrolytes with high ionic conductivity and a high Li+ transference number (tLi) becomes crucial to boost power density. Unfortunately, tLi in liquid electrolytes is typically <0.5 due to Li+ migrating via a vehicular mechanism, whereby Li+ diffuses along with its solvation shell, making its diffusivity slower than the counteranion. Designing liquid electrolytes where the Li+ ion diffuses independently of its solvation shell is of significant interest to enhance the transference number. In this work, we elucidate how the properties of the solvent influence the Li+ transport mechanism. Using classical molecular dynamics simulations, we find that a vehicular mechanism can be increasingly preferred with a decreasing solvent viscosity and increasing interaction energy between the solvent and Li+. Thus, a weaker interaction energy can enhance tLi through a solvent-exchange mechanism, ultimately improving Li-ion battery performance. Finally, metadynamics simulations show that in electrolytes where a solvent-exchange mechanism is preferable, the energy barrier to changing the coordination environment of Li+ is much lower than in electrolytes where a vehicular mechanism dominates.

Building Stable Anodes for High-Rate Na-Metal Batteries

Due to low cost and high energy density, sodium metal batteries (SMBs) have attracted growing interest, with great potential to power future electric vehicles (EVs) and mobile electronics, which require rapid charge/discharge capability. However, the development of high-rate SMBs has been impeded by the sluggish Na+ ion kinetics, particularly at the sodium metal anode (SMA). The high-rate operation severely threatens the SMA stability, due to the unstable solid-electrolyte interface (SEI), the Na dendrite growth, and large volume changes during Na plating-stripping cycles, leading to rapid electrochemical performance degradations. This review surveys key challenges faced by high-rate SMAs, and highlights representative stabilization strategies, including the general modification of SMB components (including the host, Na metal surface, electrolyte, separator, and cathode), and emerging solutions with the development of solid-state SMBs and liquid metal anodes; the working principle, performance, and application of these strategies are elaborated, to reduce the Na nucleation energy barriers and promote Na+ ion transfer kinetics for stable high-rate Na metal anodes. This review will inspire further efforts to stabilize SMAs and other metal (e.g., Li, K, Mg, Zn) anodes, promoting high-rate applications of high-energy metal batteries towards a more sustainable society.

Fluorine Chemistry in Rechargeable Batteries: Challenges, Progress, and Perspectives

The renewable energy industry demands rechargeable batteries that can be manufactured at low cost using abundant resources while offering high energy density, good safety, wide operating temperature windows, and long lifespans. Utilizing fluorine chemistry to redesign battery configurations/components is considered a critical strategy to fulfill these requirements due to the natural abundance, robust bond strength, and extraordinary electronegativity of fluorine and the high free energy of fluoride formation, which enables the fluorinated components with cost effectiveness, nonflammability, and intrinsic stability. In particular, fluorinated materials and electrode|electrolyte interphases have been demonstrated to significantly affect reaction reversibility/kinetics, safety, and temperature tolerance of rechargeable batteries. However, the underlining principles governing material design and the mechanistic insights of interphases at the atomic level have been largely overlooked. This review covers a wide range of topics from the exploration of fluorine-containing electrodes, fluorinated electrolyte constituents, and other fluorinated battery components for metal-ion shuttle batteries to constructing fluoride-ion batteries, dual-ion batteries, and other new chemistries. In doing so, this review aims to provide a comprehensive understanding of the structure-property interactions, the features of fluorinated interphases, and cutting-edge techniques for elucidating the role of fluorine chemistry in rechargeable batteries. Further, we present current challenges and promising strategies for employing fluorine chemistry, aiming to advance the electrochemical performance, wide temperature operation, and safety attributes of rechargeable batteries.

In situ Nafion-nanofilm oriented (002) Zn electrodeposition for long-term zinc-ion batteries

Dendrite growth and parasitic reactions of a Zn metal anode in aqueous media hinder the development of up-and-coming Zn-ion batteries. Optimizing the crystal growth after Zn nucleation is promising to enable stable cyclic performance of the anode, but directly regulating specific crystal plane growth for homogenized Zn electrodeposition remains highly challenging. Herein, a perfluoropolymer (Nafion) is introduced into an aqueous electrolyte to activate a thermodynamically ultrastable Zn/electrolyte interface for long-term Zn-ion batteries. The low adsorption energy (-2.09 eV) of Nafion molecules on Zn metal ensures the in situ formation of a Nafion-nanofilm during the first charge process. This ultrathin artificial solid electrolyte interface with zincophilic -SO3- groups guides the directional Zn2+ electrodeposition along the (002) crystal surface even at high current density, yielding a dendrite-free Zn anode. The synergic Zn/electrolyte interphase electrochemistry contributes an average coulombic efficiency of 99.71% after 4500 cycles for Zn‖Cu cells, and Zn‖Zn cells achieve an ultralong lifespan of over 7000 h at 5 mA cm-2. Besides, Zn‖MnO2 cells operate well over 3000 cycles. Even at -40 °C, Zn‖Zn cells achieve stable Zn2+ plating/stripping for 1200 h.

Remarkable water-mediated proton conductivity of two porous zirconium(IV)/hafnium(IV) metal-organic frameworks bearing porphyrinlcarboxylate ligands

Obtaining crystalline materials with high structural stability as well as super proton conductivity is a challenging task in the field of energy and material chemistry. Therefore, two highly stable metal-organic frameworks (MOFs) with macro-ring structures and carboxylate groups, Zr-TCPP (1) and Hf-TCPP (2) assembled from low-toxicity as well as highly coordination-capable Zr(IV)/Hf(IV) cations and the multifunctional linkage, meso-tetra(4-carboxyphenyl)porphine (TCPP) have attracted our strong interest. Note that TCPP as a large-size rigid ligand with high symmetry and multiple coordination sites contributes to the formation of the two stable MOFs. Moreover, the pores with large sizes in the two MOFs favor the entry of more guest water molecules and thus result in high H2O-assisted proton conductivity. First, their distinguished structural stabilities covering water, thermal and chemical stabilities were verified by various determination approaches. Second, the dependence of the proton conductivity of the two MOFs on temperature and relative humidity (RH) is explored in depth. Impressively, MOFs 1 and 2 demonstrated the optimal proton conductivities of 4.5 × 10-4 and 0.78 × 10-3 S·cm-1 at 100 °C/98 % RH, respectively. Logically, based on the structural information, gas adsorption/desorption features, and activation energy values, their proton conduction mechanism was deduced and highlighted.

Ligand-channel-enabled ultrafast Li-ion conduction

Li-ion batteries (LIBs) for electric vehicles and aviation demand high energy density, fast charging and a wide operating temperature range, which are virtually impossible because they require electrolytes to simultaneously have high ionic conductivity, low solvation energy and low melting point and form an anion-derived inorganic interphase1-5. Here we report guidelines for designing such electrolytes by using small-sized solvents with low solvation energy. The tiny solvent in the secondary solvation sheath pulls out the Li+ in the primary solvation sheath to form a fast ion-conduction ligand channel to enhance Li+ transport, while the small-sized solvent with low solvation energy also allows the anion to enter the first Li+ solvation shell to form an inorganic-rich interphase. The electrolyte-design concept is demonstrated by using fluoroacetonitrile (FAN) solvent. The electrolyte of 1.3 M lithium bis(fluorosulfonyl)imide (LiFSI) in FAN exhibits ultrahigh ionic conductivity of 40.3 mS cm-1 at 25 °C and 11.9 mS cm-1 even at -70 °C, thus enabling 4.5-V graphite||LiNi0.8Mn0.1Co0.1O2 pouch cells (1.2 Ah, 2.85 mAh cm-2) to achieve high reversibility (0.62 Ah) when the cells are charged and discharged even at -65 °C. The electrolyte with small-sized solvents enables LIBs to simultaneously achieve high energy density, fast charging and a wide operating temperature range, which is unattainable for the current electrolyte design but is highly desired for extreme LIBs. This mechanism is generalizable and can be expanded to other metal-ion battery electrolytes.

Possible mechanisms and simulation modeling of FLASH radiotherapy

FLASH radiotherapy (FLASH-RT) has great potential to improve patient outcomes. It delivers radiation doses at an ultra-high dose rate (UHDR: ≥ 40 Gy/s) in a single instant or a few pulses. Much higher irradiation doses can be administered to tumors with FLASH-RT than with conventional dose rate (0.01-0.40 Gy/s) radiotherapy. UHDR irradiation can suppress toxicity in normal tissues while sustaining antitumor efficiency, which is referred to as the FLASH effect. However, the mechanisms underlying the effects of the FLASH remain unclear. To clarify these mechanisms, the development of simulation models that can contribute to treatment planning for FLASH-RT is still underway. Previous studies indicated that transient oxygen depletion or augmented reactions between secondary reactive species produced by irradiation may be involved in this process. To discuss the possible mechanisms of the FLASH effect and its clinical potential, we summarized the physicochemical, chemical, and biological perspectives as well as the development of simulation modeling for FLASH-RT.

Localized Medium Concentration Electrolyte with Fast Kinetics for Lithium Metal Batteries

Localized high-concentration electrolyte is widely acknowledged as a cutting-edge electrolyte for the lithium metal anode. However, the high fluorine content, either from high-concentration salts or from highly fluorinated diluents, results in significantly higher production costs and an increased environmental burden. Here, we have developed a novel electrolyte termed "Localized Medium-Concentration Electrolyte" (LMCE) to effectively address these issues. This LMCE is designed and produced by diluting a medium concentration (0.5 M-1.5 M) electrolyte which is incompatible with lithium metal anode before diluting. It has ultralow concentration (0.1 M) and demonstrates remarkable compatibility with lithium metal anode. Surprisingly, our LMCE, despite having an ultralow concentration (0.1 M), exhibits excellent kinetics in Li/Cu, Li/Li, LiFePO4 /Li, and NCM811/Li batteries. Additionally, LMCE effectively inhibits the corrosion of the Al current collector caused by LiTFSI salt under high voltage (>4 V) conditions. This groundbreaking LMCE design transforms the seemingly "incompatible" into the "compatible", opening up new avenues for exploring various electrolyte formulations, including all liquid electrolyte-based batteries.