Single-ion conducting electrolytes stand as promising alternatives to state-of-The-Art electrolytes in lithium batteries, although a single-ion conducting material with high Li+ conductivity, stability in contact with lithium, and suitable mechanical properties has been slow to emerge. Here, we describe the synthesis of a series of single-ion conducting network polymers from the reaction of tetrakis(4-(chloromethyl)-2,3,5,6-Tetrafluorophenyl)borate with oligoethylene glycoxide linkers Li2O[(CH2CH2)O]n (n = 1, 2, 3, 9, and 22). Polymers with the longest linkers (n = 9 and 22; ANP-9 and ANP-10, respectively) form materials with conductivities of â 10-6 S cm-1 at 100 °C. With the addition of 65 wt % propylene carbonate (PC), all the network polymers in the series exhibit high conductivities at ambient temperatures, with the n = 1 material (ANP-6) achieving a bulk ionic conductivity of 2.5 × 10-4 S cm-1 at 25 °C. More conductive single-ion conducting gels could be prepared by using the less coordinating pentanediol dilithium salt as a linker (ANP-11; σ = 3.5 × 10-4 S cm-1 at 25 °C), although this material exhibited a surprisingly high interfacial resistance in contact with a lithium electrode. In contrast, the gel formed with ANP-6 is notably stable in contact with metallic lithium electrodes, displays a lithium-ion transference number of unity, and boasts a wide electrochemical stability window of greater than 4.5 V. Temperature-dependent ac impedance analysis reveals that the ionic conductivity of this material-and likely the other gels in the series-matches closely to a Vogel-Tamman-Fulcher temperature model.

Abstract: Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well-established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal-organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high-throughput, non-destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.