Rational design of advanced electrolytes to improve the high-voltage capability has been attracting wide attention as one critical solution to enable next-generation high-energy-density batteries. However, the limited understanding of electrolyte antioxidant chemistry as well as the lack of valid quantization approaches have resulted in knowledge gap, which hinders the formulation of new electrolytes. Herein, we construct a standard curve based on representative solvation structures to quantify the oxidation stability of ether-based electrolytes, which reveals the linear correlation between the oxidation potential and the atomic charge of the least oxidation-resistant solvent. Dictating by the regularity between solvation composition and oxidation potential, a (Trifluoromethyl)cyclohexane-based localized high-concentration electrolyte dominated by anion-less solvation structures was designed to optimize the cycling performance of 4.5 V 30-µm-Li||3.8-mAh cm-2-LiCoO2 batteries, which maintained 80% capacity retention even after 440 cycles. The consistency of experimental and computational results validates the proposed principles, offering a fundamental guideline to evaluate and design aggressive electrochemical systems.
Rechargeable all-solid-state lithium metal batteries (ASSLMBs) utilizing inorganic solid-state electrolytes (SSEs) are promising for electric vehicles and large-scale grid energy storage. However, the Li dendrite growth in SSEs still constrains the practical utility of ASSLMBs. To achieve a high dendrite-suppression capability, SSEs must be chemically stable with Li, possess fast Li transfer kinetics, and exhibit high interface energy. Herein, a class of low-cost, eco-friendly, and sustainable oxyhalide-nitride solid electrolytes (ONSEs), denoted as LixNyIz-qLiOH (where x = 3y + z, 0 ≤ q ≤ 0.75), is designed to fulfill all the requirements. As-prepared ONSEs demonstrate chemically stable against Li and high interface energy (>43.08 meV Å-2), effectively restraining Li dendrite growth and the self-degradation at electrode interfaces. Furthermore, improved thermodynamic oxidation stability of ONSEs (>3 V vs Li+/Li, 0.45 V for pure Li3N), arising from the increased ionicity of LiâN bonds, contributes to the stability in ASSLMBs. As a proof-of-concept, the optimized ONSEs possess high ionic conductivity of 0.52 mS cm-1 and achieve long-term cycling of Li||Li symmetric cell for over 500 h. When coupled with the Li3InCl6 SSE for high-voltage cathodes, the bilayer oxyhalide-nitride/Li3InCl6 electrolyte imparts 90% capacity retention over 500 cycles for Li||1 mAh cm-2 LiCoO2 cells.
Rechargeable lithium-ion batteries (LIBs) have prospered a rechargeable world, predominantly relying on various metal oxide cathode materials for their abilities to reversibly de-/intercalate lithium-ion, while also serving as lithium sources for batteries. Despite the success of metal oxide, issues including low energy density have raised doubts about their suitability for next-generation lithium batteries. This has sparked interest in metal chlorides, a neglected cathode material family. Metal chlorides show promise with factors like energy density, diffusion coefficient, and compressibility. Unfortunately, challenges like high solubility hamper their utilization. In this review, we highlight the opportunities for metal chlorides in the post-lithium-ion era. Subsequently, we summarize their dissolution challenges. Furthermore, we discuss recent advancements, encompassing liquid-state electrolyte engineering, solid-state electrolytes (SSEs) cooperation, and LiCl-based cathodes. Finally, we provide an outlook on future research directions of metal chlorides, emphasizing electrode fabrication, electrolyte design, the application of SSEs, and the exploration of conversion reactions.
Deuterium/Tritium (D/T) handling in defined proportions are pivotal to maintain steady-state operation for fusion reactors. However, the hydrogen isotope effect in metal-hydrogen systems always disturbs precise D/T ratio control. Here, we reveal the dominance of kinetic isotope effect during desorption. To reconcile the thermodynamic stability and isotope effect, we demonstrate a quantitative indicator of Tgap and further a local coordination design strategy that comprises thermodynamic destabilization with vibration enhancement of interstitial isotopes for isotope engineering. Based on theoretical screening analysis, an optimized Ti-Pd co-doped Zr0.8Ti0.2Co0.8Pd0.2 alloy is designed and prepared. Compared to ZrCo alloy, the optimal alloy enables consistent isotope delivery together with a three-fold lower Tgap, a five-fold lower energy barrier difference, a one-third lower isotopic composition deviation during desorption and an over two-fold higher cycling capacity. This work provides insights into the interaction between alloy and hydrogen isotopes, thus opening up feasible approaches to support high-performance fusion reactors.
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.
The growth of disorganized lithium dendrites and weak solid electrolyte interphase greatly impede the practical application of lithium metal batteries. Herein, we designed and synthesized a new kind of stable polyimide covalent organic frameworks (COFs), which have a high density of well-aligned lithiophilic quinoxaline and phthalimide units anchored within the uniform one-dimensional channels. The COFs can serve as an artificial solid electrolyte interphase on lithium metal anode, effectively guiding the uniform deposition of lithium ions and inhibiting the growth of lithium dendrites. The unsymmetrical Li||COF-Cu battery exhibits a Coulombic efficiency of 99 % at a current density of 0.5â mA cm-2 , which can be well retained up to 400â cycles. Meanwhile, the Li-COF||LFP full cell shows a Coulombic efficiency over 99 % at a charge of 0.3â C. And its capacity can be well maintained up to 91 % even after 150â cycles. Therefore, the significant electrochemical cycling stability illustrates the feasibility of employing COFs in solving the disordered deposition of lithium ions in lithium metal batteries.
The utilization of layered oxides as cathode materials has significantly contributed to the advancement of the lithium-ion batteries (LIBs) with high energy density and reliability. However, the structural and interfacial instability triggered by side reactions when charged to high voltage has plagued their practical applications. Here, this work reports a novel multifunctional additive, id est, 7-Anilino-3-diethylamino-6-methyl fluoran (ADMF), which exhibits unique characteristics such as preferential adsorption, oxygen scavenging, and electropolymerization protection for high-voltage cathodes. The ADMF demonstrates the capability to ameliorate the growth of cathode-electrolyte interphase (CEI), effectively diminishing the dissolution of transition metal (TM) ions, reducing the interface impedance, and facilitating the Li+ transport. As a result, ADMF additive with side reaction-blocking ability significantly enhances the cycling stability of MCMB||NCM811 full-cells at 4.4 V and MCMB||LCO full-cells at 4.55 V, as evidenced by the 80% retention over 600 cycles and 87% retention after 750 cycles, respectively. These findings highlight the potential of the additive design strategy to modulate the CEI chemistry, representing a new paradigm with profound implications for the development of next-generation high-voltage LIBs.
The current benchmark study is focused on determining the most precise theoretical method for optimizing the geometry of transition metal-dinitrogen complexes. To accomplish this goal, seven density functional (DF) methods from five distinct classes of density functional theory (DFT) have been selected, including B3LYP-D3(BJ), BP86-D3(BJ), PBE0-D3(BJ), ωB97X-D, M06, M06-L, and TPSSh-D3(BJ). These DFs will be utilized with the Karlsruhe basis set (def2-SVP). To carry out this benchmark study, a total of forty-two structurally diverse transition metal-dinitrogen compounds with experimentally known X-ray data have been selected from the Cambridge Crystallographic Data Centre (CCDC). Based on a comparison of the theoretical data with experimental values (X-ray) of the selected transition metal-dinitrogen compounds, statistical parameters such as root-mean-square deviation (RMSD) and N-N and M-N bond lengths are obtained to evaluate the performance of the seven chosen DFs. According to the obtained results, among all DFT methods used in the study, Minnesota functionals (M06 and M06-L) and TPSSh-D3(BJ) show good performance, with lower RMSD values. This suggests that these three methods are the most reliable for optimizing the geometry of transition metal-dinitrogen complexes. Based on the absolute errors of the N-N and M-N bond lengths relative to the X-ray data, further analysis is conducted, and it is determined that M06-L is the best functional for optimizing the geometry of transition metal-dinitrogen compounds. Additionally, the influence of using a high-level basis set (def2-TZVP) compared to def2-SVP on the calculated RMSD among the seven chosen methods is found to be negligible.
Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions of reaction yield and stereoselectivity, whose extrapolative ability is corroborated by additional scaffold-based data splittings and experimental verifications with new catalysts. Because of the embedding of local environment, the model allows the atomic level of interpretation of the steric and electronic influence on the overall synthetic performance, which serves as a useful guide for the molecular engineering towards the target synthetic function. This model offers an extrapolative and interpretable approach for reaction performance prediction, pointing out the importance of chemical knowledge-constrained reaction modelling for synthetic purpose.
Electronics , Engineering , Knowledge , Knowledge Bases , Learning
The arylation of sterically hindered amines represents one of the long-standing challenges in synthetic chemistry. Herein, we report a highly efficient Ni-catalysed arylation of sterically hindered primary and secondary amines with aryl chlorides or phenol derivatives enabled by an unsymmetric N-heterocyclic carbene (NHC) ligand. The protocol provides general, efficient, and scalable access to various sterically demanding anilines in excellent yields under mild conditions. A wide range of functional groups and heterocycles are compatible (>50 examples), including those present in biologically relevant molecules. Computational studies suggest that the unsymmetric bulky and flexible NHC ligand was critical to balance the oxidative addition and reductive elimination elementary steps, thus promoting this challenging transformation.
Challenging enantio- and diastereoselective cobalt-catalyzed C-H alkylation has been realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics of a related transformation as the knowledge source, the designed machine learning (ML) model took advantage of delta learning and enabled accurate and extrapolative enantioselectivity predictions. Powered by the knowledge transfer model, the virtual screening of a broad scope of 360 chiral carboxylic acids led to the discovery of a new catalyst featuring an intriguing furyl moiety. Further experiments verified that the predicted chiral carboxylic acid can achieve excellent stereochemical control for the target C-H alkylation, which supported the expedient synthesis for a large library of substituted indoles with C-central and C-N axial chirality. The reported machine learning approach provides a powerful data engine to accelerate the discovery of molecular catalysis by harnessing the hidden value of the available structure-performance statistics.
The concerted mechanism of thermal BBr3 -mediated [2+2] carbonyl-olefin cycloaddition is intriguing considering the conflict against the Woodward-Hoffmann rule. In this work, we report a mechanistic study of the titled reaction using density functional theory calculations. The concerted [2+2] cycloaddition mechanism is operative even for the truncated model system of 2-methyl-2-butene and butanone. Intrinsic reaction coordinate analysis and potential surface mapping showed the asynchronous nature of the apparent concerted [2+2] cycloaddition, resulting from the proximity of carbocation-oxyanion in the transient zwitterionic structure. Solvent effects can change the reaction to a stepwise mechanism, highlighting the importance of zwitterion stability. Building upon this finding of the asynchronous concerted or stepwise [2+2] cycloaddition mechanism, two mechanistic classifications were elaborated based on substrates containing varied substituents and Lewis acids, with a focus on manipulating the stability of positive carbocation and negative oxyanion in the transient zwitterionic species.
Elevating the charging cut-off voltage is one of the efficient approaches to boost the energy density of Li-ion batteries (LIBs). However, this method is limited by the occurrence of severe parasitic reactions at the electrolyte/electrode interfaces. Herein, to address this issue, we design a non-flammable fluorinated sulfonate electrolyte by multifunctional solvent molecule design, which enables the formation of an inorganic-rich cathode electrolyte interphase (CEI) on high-voltage cathodes and a hybrid organic/inorganic solid electrolyte interphase (SEI) on the graphite anode. The electrolyte, consisting of 1.9 M LiFSI in a 1:2 v/v mixture of 2,2,2-trifluoroethyl trifluoromethanesulfonate and 2,2,2-trifluoroethyl methanesulfonate, endows 4.55 V-charged graphite||LiCoO2 and 4.6 V-charged graphite||NCM811 batteries with capacity retentions of 89% over 5329 cycles and 85% over 2002 cycles, respectively, thus resulting in energy density increases of 33% and 16% compared to those charged to 4.3 V. This work demonstrates a practical strategy for upgrading the commercial LIBs.
Despite the availability and accuracy of modern spectroscopic characterization, the utilization of spectral information in chemical machine learning is still primitive. Here, we report an optical character recognition-based automatic process to utilize spectral information as molecular descriptors, which directly transforms experimental spectrum images to readable vectors. We demonstrate its machine learning application in the reaction yield dataset of Pd-catalyzed Buchwald-Hartwig cross-coupling with aryl halides. In addition, we also show that the predicted spectrum can serve as an alternative encoding source to support the model training.
The activation of dinitrogen (N2) and direct incorporation of its N atom into C-H bonds to create aliphatic C-N compounds remains unresolved. Incompatible conditions between dinitrogen reduction and C-H functionalization make this process extremely challenging. Herein, we report the first example of dinitrogen insertion into an aliphatic Csp3-H bond on the ligand scaffold of a 1,3-propane-bridged [N2N]2--type dititanium complex. Mechanistic investigations on the behaviors of dinuclear and mononuclear Ti complexes indicated the intramolecular synergistic effect of two Ti centers at a C-N bond-forming step. Computational studies revealed the critical isomerization between the inactive side-on N2 complex and the active nitridyl complex, which is responsible for the Csp3-H amination. This strategy maps an efficient route toward the future synthesis of aliphatic amines directly from N2.
Although great progress has been made in new electrolytes for lithium metal batteries (LMBs), the intrinsic relationship between electrolyte composition and cell performance remains unclear due to the lack of valid quantization method. Here, we proposed the concept of negative center of electrostatic potential (NCESP) and Mayer bond order (MBO) to describe solvent capability, which highly relate to solvation structure and oxidation potential, respectively. Based on established principles, the selected electrolyte with 1.7â M LiFSI in methoxytrimethylsilane (MOTMS)/ (trifluoromethyl)trimethylsilane (TFMTMS) shows unique hyperconjugation nature to stabilize both Li anode and high-voltage cathode. The 4.6â V 30â µm Li||4.5â mAh cm-2 lithium cobalt oxide (LCO) (low N/P ratio of 1.3) cell with our electrolyte shows stable cycling with 91 % capacity retention over 200â cycles. The bottom-up design concept of electrolyte opens up a general strategy for advancing high-voltage LMBs.
The development of conductive covalent organic frameworks (COFs) with high stability is desirable for the practical applications in optoelectronics and energy storage. Herein, we developed a new kind of Janus dione-based COF, which is fully sp2 carbon-conjugated through the connection by olefin units. The electrical conductivity and carrier mobility reached up to 10-3 S cm-1 and 7.8 cm2 V-1 s-1, respectively. In addition, these COFs are strongly robust against various harsh conditions. The well-ordered two-dimensional crystalline structures, excellent porosity, high conductivity, and abundant redox-active carbonyl units render these COFs serviceable as high-performance cathode materials in lithium-ion batteries. It is worth noting that TFPPy-ICTO-COF exhibits a capacity of up to 338 mAh g-1 at a discharge rate of 0.1 C, which sets a new capacity record among COF-based lithium-ion batteries. Its capacity retention was as high as 100% even after 1000 cycles, demonstrating the remarkable stability of these Janus dione-based COF materials. This work not only expands the diversity of olefin-linked COFs but also makes a new breakthrough in energy storage.
The intermediacy of alkoxy radicals in cerium-catalyzed C-H functionalization via H-atom abstraction has been unambiguously confirmed. Catalytically relevant Ce(IV)-alkoxide complexes have been synthesized and characterized by X-ray diffraction. Operando electron paramagnetic resonance and transient absorption spectroscopy experiments on isolated pentachloro Ce(IV) alkoxides identified alkoxy radicals as the sole heteroatom-centered radical species generated via ligand-to-metal charge transfer (LMCT) excitation. Alkoxy-radical-mediated hydrogen atom transfer (HAT) has been verified via kinetic analysis, density functional theory (DFT) calculations, and reactions under strictly chloride-free conditions. These experimental findings unambiguously establish the critical role of alkoxy radicals in Ce-LMCT catalysis and definitively preclude the involvement of chlorine radical. This study has also reinforced the necessity of a high relative ratio of alcohol vs Ce for the selective alkoxy-radical-mediated HAT, as seemingly trivial changes in the relative ratio of alcohol vs Ce can lead to drastically different mechanistic pathways. Importantly, the previously proposed chlorine radical-alcohol complex, postulated to explain alkoxy-radical-enabled selectivities in this system, has been examined under scrutiny and ruled out by regioselectivity studies, transient absorption experiments, and high-level calculations. Moreover, the peculiar selectivity of alkoxy radical generation in the LMCT homolysis of Ce(IV) heteroleptic complexes has been analyzed and back-electron transfer (BET) may have regulated the efficiency and selectivity for the formation of ligand-centered radicals.
Chlorine , Hydrogen , Hydrogen/chemistry , Kinetics , Ligands , Metals , Ethanol , Catalysis
Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data-driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles.
Electrolyte engineering advances Li metal batteries (LMBs) with high Coulombic efficiency (CE) by constructing LiF-rich solid electrolyte interphase (SEI). However, the low conductivity of LiF disturbs Li+ diffusion across SEI, thus inducing Li+ transfer-driven dendritic deposition. In this work, we establish a mechanistic model to decipher how the SEI affects Li plating in high-fluorine electrolytes. The presented theory depicts a linear correlation between the capacity loss and current density to identify the slope k (determined by Li+ mobility of SEI components) as an indicator for describing the homogeneity of Li+ flux across SEI, while the intercept dictates the maximum CE that electrolytes can achieve. This model inspires the design of an efficient electrolyte that generates dual-halide SEI to homogenize Li+ distribution and Li deposition. The model-driven protocol offers a promising energetic analysis to evaluate the compatibility of electrolytes to Li anode, thus guiding the design of promising electrolytes for LMBs.
