Single Lithium Ion Conducting "Binderlyte" for High-Performing Lithium Metal Batteries.

Rechargeable lithium metal batteries (LMBs) are deemed as a viable solution to improve the power and/or energy density of the contemporary lithium-ion batteries (LIBs). However, poor Li-ion diffusivity within high-energy cathodes causes sluggish kinetics of the corresponding redox reactions particularly at high C-rates, thereby largely impeding the performance of rechargeable LMBs. In this work, a dual-functional single Li-ion conducting polysalt is proposed as both catholyte and binding agent (coined "Binderlyte") for rechargeable LMBs. The designed Binderlyte is thermally and electrochemically stable, allowing its use for high-energy cathodes like Li(Ni1/3 Mn1/3 Co1/3 )O2 (NMC111). The implementation of designer Binderlyte endows the Li° || NMC111 cell with superior cycling stability and capacity retention even at an extremely high C-rate of 10C. In particular, the soft and flexible nature of the Binderlyte allows the thick NMC cathode to operate at extremely low porosity (20 vol%) with almost no capacity decay. This work may provide a paradigm shift on the design of innovative polymeric materials, which are essential for developing high-performing rechargeable LMBs.

Stable non-corrosive sulfonimide salt for 4-V-class lithium metal batteries.

Rechargeable lithium metal (Li0) batteries (RLMBs) are considered attractive for improving Li-ion batteries. Lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) has been extensively used as a conducting salt for RLMBs due to its advantageous stability and innocuity. However, LiTFSI-based electrolytes are corrosive towards aluminium (Al0) current collectors at low potentials (>3.8 V versus Li/Li+), thereby excluding their application in 4-V-class RLMBs. Herein, we report on a non-corrosive sulfonimide salt, lithium (difluoromethanesulfonyl)(trifluoromethanesulfonyl)imide (LiDFTFSI), that remarkably suppresses the anodic dissolution of the Al0 current collector at high potentials (>4.2 V versus Li/Li+) and significantly improves the cycling performance of Li(Ni1/3Mn1/3Co1/3)O2 (NMC111) cells. In addition, this sulfonimide salt results in the growth of an advantageous solid electrolyte interphase on the Li0 electrode. The replacement of either LiTFSI or LiPF6 with LiDFTFSI endows a Li0||NMC111 cell with superior cycling stability and capacity retention (87% at cycle 200), demonstrating the decisive role of the salt anion in dictating the electrochemical performance of RLMBs.

Suppressed Mobility of Negative Charges in Polymer Electrolytes with an Ether-Functionalized Anion.

Suppressing the mobility of anionic species in polymer electrolytes (PEs) is essential for mitigating the concentration gradient and internal cell polarization, and thereby improving the stability and cycle life of rechargeable alkali metal batteries. Now, an ether-functionalized anion (EFA) is used as a counter-charge in a lithium salt. As the salt component in PEs, it achieves low anionic diffusivity but sufficient Li-ion conductivity. The ethylene oxide unit in EFA endows nanosized self-agglomeration of anions and trapping interactions between the anions and its structurally homologous matrix, poly(ethylene oxide), thus suppressing the mobility of negative charges. In contrast to previous strategies of using anion traps or tethering anions to a polymer/inorganic backbone, this work offers a facile and elegant methodology on accessing selective and efficient Li-ion transport in PEs and related electrolyte materials (for example, composites and hybrid electrolytes).

Enhanced Lithium-Ion Conductivity of Polymer Electrolytes by Selective Introduction of Hydrogen into the Anion.

The anion chemistry of lithium salts plays a pivotal role in dictating the physicochemical and electrochemical performance of solid polymer electrolytes (SPEs), thus affecting the cyclability of all-solid-state lithium metal batteries (ASSLMBs). The bis(trifluoromethanesulfonyl)imide anion (TFSI- ) has long been studied as the most promising candidate for SPEs; however, the Li-ion conductivities of the TFSI-based SPEs still remain low (Li-ion transference number: ca. 0.2). In this work, we report new hydrogen-containing anions, conceived based on theoretical considerations, as an electrolyte salt for SPEs. SPEs comprising hydrogen-containing anions achieve higher Li-ion conductivities than TFSI-based ones, and those anions are electrochemically stable for various kinds of ASSLMBs (Li-LiFePO4 , Li-S, and Li-O2 batteries). This opens up a new avenue for designing safe and high-performance ASSLMBs in the future.

Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.

Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all the molecules involved in a query reaction, compared to a reaction of reference. Thus, a dataset of >100 reactions has been collected for different substrates and internal electrophiles, under different reaction conditions, with a wide range of yields (0-98%). The best PTML model found using General Linear Regression (GLR) has R = 0.88 in training and R = 0.83 in external validation series for 10 000 pairs of query and reference reactions. The PTML model has a final R = 0.95 for all reactions using multiple reactions of reference. We also report a comparative study of linear versus nonlinear PTML models based on artificial neural network (ANN) algorithms. PTML-ANN models (LNN, MLP, RBF) with R ≈ 0.1-0.8 do not outperform the first PMTL model. This result confirms the validity of the linearity of the model. Next, we carried out an experimental and theoretical study of nonreported Parham reactions to illustrate the practical use of the PTML model. A 500 000-point simulation and a Hammett analysis of the reactivity space of Parham reactions are also reported.