Synergistic theoretical and experimental study on the ion dynamics of bis(trifluoromethanesulfonyl)imide-based alkali metal salts for solid polymer electrolytes

25038 | Phys. Chem. Chem. Phys., 2023, 25, 25038–25054 This journal is © the Owner Societies 2023 Cite this: Phys. Chem. Chem. Phys., 2023, 25, 25038 Synergistic theoretical and experimental study on the ion dynamics of bis(trifluoromethanesulfonyl)imide-based alkali metal salts for solid polymer electrolytes† Brigette Althea Fortuin, abc Jon Otegi, b Juan Miguel Lo ´pez del Amo, a Sergio Rodriguez Pen ˜a, ab Leire Meabe, a Hegoi Manzano, * b Marı ´a Martı ´nez-Iban ˜ez * a and Javier Carrasco * ad Model validation of a well-known class of solid polymer electrolyte (SPE) is utilized to predict the ionic structure and ion dynamics of alternative alkali metal ions, leading to advancements in Na-, K-, and Cs-based SPEs for solid-state alkali metal batteries. A comprehensive study based on molecular dynamics (MD) is conducted to simulate ion coordination and the ion transport properties of poly(ethylene oxide) (PEO) with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt across various LiTFSI concentrations. Through validation of the MD simulation results with experimental techniques, we gain a deeper understanding of the ionic structure and dynamics in the PEO/LiTFSI system. This computational approach is then extended to predict ion coordination and transport properties of alternative alkali metal ions. The ionic structure in PEO/LiTFSI is significantly influenced by the LiTFSI concentration, resulting in different lithium-ion transport mechanisms for highly concentrated or diluted systems. Substituting lithium with sodium, potassium, and cesium reveals a weaker cation-PEO coordination for the larger cesium-ion. However, sodium-ion based SPEs exhibit the highest cation transport number, indicating the crucial interplay between salt dissociation and cation-PEO coordination for achieving optimal performance in alkali metal SPEs. 1. Introduction The ever-increasing global energy demand tied with increasing environmental concerns 1 regarding climate change has progressively led towards the incorporation of renewable energy sources, such as wind and solar energy, within national grids. However, considering the intermittent nature of natural energy sources, energy storage is vital towards the realization of a sustainable and efficient energy producing sector. The current market leader for energy storage systems, the lithium-ion battery (LIB), shows a high potential towards mitigating energy fluctuations from renewable energy sources within the grid. 2 Considering the rapid expansion of the energy storage market in addition to the rising cost and scarcity of lithium, 3,4 the need for developing alternative battery technologies to LIBs, is essential. Furthermore, demand for higher energy density, increased sustainability, abundant and economically viable energy storage systems have led to a surge in the exploration for alternative battery technologies beyond LIBs in recent years. 5–7 Post-lithium-ion batteries (post-LIBs), such as lithium–metal batteries (LMBs), 8–11 sodium-ion batteries (NIBs), 4,12,13 potassium-ion batteries (KIBs), 14–17 and cesium-ion batteries (CIBs) 18–20 are gaining momentum. Numerous advantages of these post-LIB technologies include higher redox potentials; Li + /Li (3.04 V vs. SHE), Na + /Na (2.71 V vs. SHE), K + /K (2.93 V vs. SHE), and Cs + /Cs (3.03 V vs. SHE), along with superior theoretical specific capacities, 21 an abundance of both sodium and potassium in the Earth’s crust posing an attractively low cost alternative to lithium, and the higher diffusion coefficients (low diffusion barrier) of cesium-based electrodes resulting in hindered dendrite formation for cesium-ion batteries. 20,22 However, despite a Centre for Cooperative Research on Alternative Energies (CIC energiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, 01510 Vitoria-Gasteiz, Spain. E-mail: jcarra[email protected], [email protected] b Department of Physics, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain. E-mail: [email protected] c ALISTORE-European Research Institute, CNRS FR 3104, Hub de l’Energie, Rue Baudelocque, 80039 Amiens Cedex, France d IKERBASQUE, Basque Foundation for Science, Plaza Euskadi 5, 48009 Bilbao, Spain †Electronic supplementary information (ESI) available: Supporting tables and figures including force field parameters, chemical structures, lithium-ion speciation analysis, MD-based ionic conductivity convergence tests, MSD, DSC, EIS, Raman spectroscopy, MAS-NMR spectroscopy, and activation energies of the transport mechanisms for the different investigated systems. See DOI: https:// doi.org/10.1039/d3cp02989a Received 26th June 2023, Accepted 31st August 2023 DOI: 10.1039/d3cp02989a rsc.li/pccp PCCP PAPER Open Access Article. Published on 12 September 2023. Downloaded on 1/19/2024 4:45:05 PM. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. View Article Online View Journal | View Issue This journal is © the Owner Societies 2023 Phys. Chem. Chem. Phys., 2023, 25, 25038–25054 | 25039 its promise, alkali metal batteries face high reactivity, with electrode and electrolyte components spontaneously reacting with most polar aprotic electrolyte solvents, resulting in unstable solid electrolyte interphases, which may affect cycle performance. 21,23 The limited stability and lifetime challenges are further reduced by failure mechanisms, such as dendritic growth on the anode and the current collector, internal shortcircuits and electrolyte decomposition, resulting in electrolyte loss. 21,23,24 Addressing these challenges, the successful development of alternative all-solid-state alkali metal battery technologies necessitates the advancement of alkali metal salts, considering their compatibility with various polymer matrices and alkali metalbased electrodes. Therefore, a fundamental understanding of ion coordination as well as ion transport mechanisms is crucial for assessing the overall performance of these SPEs. Atomistic modelling of polymer electrolytes is an effective approach to accelerate the search towards new alkali metal salts and alkali metal-based electrolyte components, especially considering the recent advances in computing power. 8,25–29 The growing utilization of modelling techniques is driven by several factors, including reduced environmental impact compared to experimental processes, faster simulation times compared to traditional experimental methods, and the capability to acquire information that may not be readily attainable through experimental characterization techniques. 8,30–34 Yet, a key element for the successful integration of computational-based methods in materials development is comprehensive model validation against well-defined, high-quality experimental data, which is often lacking but crucially needed in the field. Herein, we present a comprehensive study of the well-known PEO/LiTFSI SPE as a function of LiTFSI concentration. Our approach involves predicting ion coordination and ion transport using atomistic molecular dynamics (MD) simulations. A key aspect of our study is the validation of the model, where we have taken particular care to combine three different techniques: Raman, magic angle spinning nuclear magnetic resonance (MASNMR), and electrochemical impedance spectroscopies. This combination of well-defined experiments is crucial for validating our theoretical models and simulations. Notably, such a synergistic model validation study has been lacking in the literature on the PEO/LiTFSI SPEs, and our work fills this important gap. Furthermore, our validated theoretical approach provides valuable insight that extends beyond the PEO/LiTFSI system. Specifically, it allows us to predict similar properties for alternative alkali metal-based systems,suchasPEO/XTFSI(X=Na,K,andCs),broadeningthescopeof our study. These insights offer valuable guidance for designing new salts for SPEs, thus contributing to the advancement of the field. 2. Theoretical and experimental methods 2.1. Theoretical methods and computational details Classical MD simulations were conducted on PEO n /LiTFSI for four different EO/Li + ratios (n= 6, 16, 20 and 32). To ensure the robustness of our results and eliminate any potential bias arising from the choice of MD software, we employed two of the most widely used codes in the literature: Gromacs 35 and LAMMPS. 36 Convergence tests were meticulously conducted to validate the consistency of results obtained from both codes. These tests, based on the total ionic conductivity as a function of temperature, enabled us to identify the optimal simulation time and box size for the studied systems (cf. Fig. S1–S3, ESI†). Based on these tests, a simulation time of 100 ns and a medium-sized box containing 40 ion pairs and 40 polymer chains were deemed sufficient to achieve accurate and comparable outcomes. Considering that LAMMPS generally exhibits slower simulation performance compared to Gromacs, 37,38 we selected Gromacs to investigate the influence of salt concentration on Li-containing systems. This choice was particularly advantageous for highly diluted systems, where longer MD simulations are needed to obtain suitable statistical averages, making Gromacs the preferred option due to its higher speed. In contrast, LAMMPS was exclusively utilized to study one intermediate concentration (n=20)whilealsoexploringtheroleofdifferent alkali metals (Li + ,Na + ,K + ,andCs + ). By adopting this approach, we aimed to ensure the validity and reliability of our findings while investigating the diverse aspects of our research. Specifically, we considered the following computational setups. For the simulations performed using Gromacs, 35 the simulation boxes consisted of 40 PEO chains with 24 EO repeating units in each chain (M w =B1056 g mol 1 ), and 160, 60, 48 and 30 LiTFSI ion pairs, for n= 6, 16, 20, and 32, respectively. Initial boxes were generated randomly as implemented in Gromacs. With LAMMPS 36 we examined the ion coordination and transport properties for PEO 20 /XTFSI systems, whereX=Li,Na,K,orCs.Inthiscase,thesimulationboxes consisted of 40 PEO chains with 20 EO repeating units in each chain (M w =B880 g mol 1 ), and 40 XTFSI ion pairs with initial configurations containing randomly positioned molecules, generated using Packmol. 39 Prior to the MD production simulations, an efficient and multistep protocol was applied to prepare and equilibrate the systems. It is worth noting that due to the implementation of Gromacs programming, the initial simulation box needs to be relatively large, resulting in low densities, e.g.,1.64910 1 gcm 3 for PEO 32 /LiTFSI. To ensure the appropriate geometry of the system, the first step in this case involves an energy minimization process where the positions of the molecules are adjusted to achieve a state of minimum energy. Accordingly, an initial NPT structural compression step was carried out using the Berendsen thermostat and the Parrinello–Rahman barostat (with a relaxation time of 5 ps for all cases). This compression was performed at 267 1C (10 K) under a pressure of 98.99 atm (100 bar) to obtain densities closer to experimental values, i.e., simulated (experimental): 1.352 (1.234) g cm 3 (PEO 6 /LiTFSI), 1.193 (1.188) g cm 3 (PEO 16 /LiTFSI), 1.159 (1.179) g cm 3 (PEO 20 /LiTFSI), and 1.167 (1.164) g cm 3 (PEO 32 /LiTFSI). Subsequently, a gradual heating process to 327 1C (600 K) at 1 atm and equilibration in an NVT ensemble were conducted to prevent the formation of possible metastable configurations. Paper PCCP Open Access Article. Published on 12 September 2023. Downloaded on 1/19/2024 4:45:05 PM. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. View Article Online 25040 | Phys. Chem. Chem. Phys., 2023, 25, 25038–25054 This journal is © the Owner Societies 2023 The temperature increase followed an exponential pattern based on the time constant (1 ps). Afterwards, the systems underwent a cooling NPT procedure to reach the simulation temperature of 70 1C (343 K) and pressure of 1 atm. The final configuration was further equilibrated for 1 ns under similar conditions. Finally, the production MD simulations were performed in the NVT ensemble for 200 ns (PEO n /LiTFSI) or 100 ns (PEO 20 /XTFSI) to ensure a fully diffusive regime. All-atom optimized potentials for liquid simulations (OPLS-AA) force field 34,40–44 were utilized to describe the energy potentials of PEO, Li + ,Na + ,K + ,Cs + , and TFSI  , including the force field parameters (bond stretching, bond-angle, dihedral angle, and Lennard-Jones potential). The force field parameters used in this study are available in the ESI†(Tables S1, S2 and Fig. S4). The trajectories obtained by the MD simulations were analyzed using Travis analyzer. 45,46 We utilized the default atomic charges from the OPLS-AA force field for the PEO atoms. However, to enhance the accuracy and suitable description of our systems, we conducted an optimization of the TFSI  anion’s structure in the gas-phase and computed its corresponding atomic charges using density functional theory (DFT) calculations. Subsequently, we incorporated these recomputed charges to update the original OPLSAA force field. The DFT calculations were performed with the Fritz Haber Institute ab initio molecular simulations (FHI-AIMS) software, 47,48 incorporating the Becke’s three parameters (B3) exchange functional with the Lee–Yang–Parr (LYP) nonlocal correlation functional (B3LYP) 49,50 adopted with the ‘‘tier2’’ standard basis set in the FHI-AIMS code. The partial charges of TFSI  were calculated using the electrostatic potential (ESP) method 26,27,30,34,51–54 and scaled to 0.7. The charges of Li + , Na + ,K + , and Cs + were also scaled to +0.7 each to maintain charge neutrality. This scaling factor is used to account for the effects of polarization, which are not considered in conventional classical MD simulations since the charges are treated as unchangeable point charges. The value of 0.7 is often used in the literature for simulating similar PEO-based polymer electrolytes. 26,27,52–54 The cut-off for van der Waals forces and the real space of Ewald summation was selected to be 10 Å, with the fast smooth particle mesh Ewald (PME) electrostatics 55,56 to treat Coulomb interactions in periodic systems. Considering the computed MD trajectories, the diffusion coefficient of each species was deduced from its mean square displacement (MSD), using the Einstein relation: 57 D¼lim ðt!1Þ 1 6tjritðÞri0ðÞj 2 (1) where Dis the diffusion coefficient, tis the time elapsed, r i (t)is the displacement travelled of species iat time t, and r i (0) refers to the displacement of species iat the origin. Based on eqn (1), once the diffusion coefficients were procured, the Nernst– Einstein relation 57 in eqn (2) was used to deduce the ionic conductivity of each ionic species: si¼qi2ci kBTDi(2) where s i is defined as the ionic conductivity, q i refers to the unscaled charge of 1 for the studied cation and TFSI  ion, respectively, c i the concentration, D i the diffusion coefficient of species i,Trepresents the temperature of the system, and k B represents the Boltzmann constant. The cation transport numbers based on MD simulations were calculated using eqn (3): tMD Xþ¼sXþ stotal (3) where tMD Xþrepresents the cation transport number, s X + the ionic conductivity, and s total the total ionic conductivity, determined from the sum of the cation and anion conductivities, for the respective cation species (X + =Li + ,Na + ,K + ,orCs + ) with the TFSI  anion. 2.2. Materials PEO (M w =510 6 g mol 1 ), LiTFSI (99.9%), and acetonitrile (ACN) were purchased from Sigma Aldrich. Prior to use, LiTFSI was dried overnight at 100 1C under vacuum. An argon-filled glovebox was utilized to conduct all the procedures related to the moisture or oxygen sensitive materials (MBraun, H 2 O and O 2 o0.5 ppm). 2.3. Preparation of polymer electrolyte membranes We used the solvent-casting preparation method to fabricate five different PEO n /LiTFSI formulations, with ethylene oxide (EO)/Li + ratios of 6, 16, 20, 32, and 64. The PEO-systems were dissolved in ACN and subjected to a two-step drying process to remove any residual solvent: (i) the solution was left to dry under ventilation at 35 1C for 24 h, and (ii) it was further dried under dynamic vacuum at 50 1C for an additional 24 h. Subsequently, PEO-based SPEs were prepared using the hot pressing method at 60 1C and 3 tons, with varying processing times. For the measurement of ionic conductivity, SPEs with a target thickness of 300 mm and a diameter of 4 mm were used. Meanwhile, lithium symmetric cells were constructed with SPEs having a thickness of 100 mm and a diameter of 16 mm. 2.4. Differential scanning calorimetry (DSC) A DSC Discovery 2500 (TA Instrument) instrument was used to study the phase transition behaviour of the SPEs from 80 to 100 1C, with heating and cooling rates of 10 K min 1 . Following this, the SPE samples (ca. 10 mg) were sealed in Al crucibles in an argon-filled glovebox. Thermal properties, including the values for the glass transition temperature (T g , midpoint of the heat capacity change), melting temperature (T m , maximum of the endothermic peak), and melting enthalpy (DH m , area below the endothermic peak), were deduced from the second heating scan. The calculation of the crystalline fraction (w C )of the polymer electrolytes was performed using eqn (4): wC¼DHm DHmPEO fSPE 100% (4) PCCP Paper Open Access Article. Published on 12 September 2023. Downloaded on 1/19/2024 4:45:05 PM. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. View Article Online This journal is © the Owner Societies 2023 Phys. Chem. Chem. Phys., 2023, 25, 25038–25054 | 25041 where DH m the melting enthalpy of the electrolyte, DH mPEO the melting enthalpy for 100% crystalline polymer matrix (205 J g 1 for PEO 58 ), and f SPE the polymer weight fraction of the SPE. 2.5. Ionic conductivity and lithium transference number CR2032 type coin cells were assembled in an argon-filled glovebox to measure both the ionic conductivity and the lithium-ion transference number for the respective studied systems. The ionic conductivity was measured by using two stainless steel (SS) blocking electrodes, with the configuration SS|SPE|SS and a SPE area of 0.1257 cm 2 , based on electrochemical impedance spectroscopy (EIS), performed on a Multichannel VMP3 (Biologic, Claix France) with a signal amplitude of 10 mV over the frequency range 10 1 –10 6 Hz, at 40 and 70 1C. A stabilization period of 8 h was employed for the measurement at 40 1C, and 2 h for the measurement at 70 1C. The following equation was employed to calculate the ionic conductivity, eqn (5): sDC ¼t Rbulk A(5) where s DC corresponds to the direct current ionic conductivity, tthe thickness of the electrolyte (ca. of 0.3 mm), Arepresents the area of the electrolyte of 0.1257 cm 2 , and R bulk represents the bulk resistance of the polymer electrolyte measured by EIS. Lithium-ion symmetric cells were assembled using a Li|SPE|Li configuration. The lithium-ion transference number was measured following the procedures as described by Bruce and Vincent et al., 59 measured at 40 and 70 1C. CR2032 type coin cells were employed, sandwiching the SPEs between two lithium discs, with a lithium–metal area of 1.54 cm 2 . Prior to EIS measurements, the cells were left to stabilize at 40 1C for 8 h, and at 70 1C for 6 h. Eqn (6) was employed to calculate the lithium-ion transference number, TEIS Liþ: TEIS Liþ¼Iss DVIiRi;Liþ  IiDVIssRss;Liþ (6) where I i and I ss represent the initial and steady-state current, respectively; R i,Li + and R ss,Li + represent the initial and steadystate lithium interfacial resistance, respectively; and DVrepresents the total polarization voltage (10 mV). 2.6. Raman spectroscopy Raman spectra were collected using a Renishaw inVia confocal Raman spectrometer (serial number 16H981) with an incident laser with a wavelength of 532 nm (laser spot size: 0.8 m; spatial resolution: 0.4 m). The Raman spectra, collected in the range of 3000–300 cm 1 , were recorded at 25, 40, and 70 1C with increasing temperature, and a stabilization time of 2 h (40 1C) and 1 h (70 1C). To increase the signal-to-noise ratio (S/N), each plotted spectrum is the average of 10 accumulations of approximately 3 minutes each. The spectra shown has been normalized from 0 to 1. The samples were placed in a sealed, air-tight cell built with a Raman-inactive glass window and assembled in an argon-filled glovebox to prevent contamination from ambient atmosphere (e.g., air, water). 2.7. MAS-NMR spectroscopy 7 Li MAS-NMR spectroscopy was employed to determine the chemical environments of lithium-ion upon concentration changes within the PEO matrix. The experiments were recorded using a WB 500 MHz Bruker Advance III spectrometer equipped with a 2.5 mm probe and was conducted on LiTFSI powder, concentrated PEO 6 /LiTFSI and diluted PEO 32 /LiTFSI SPEs. All samples were spun at a magic angle with a MAS frequency of 20 kHz. The 7 Li MAS-NMR spectra were recorded by using a single pulse experiment (3 ms pulse), with its chemical shift referenced to a 0.1 M solution of LiCl. The temperaturevariation NMR spectra were recorded by varying the temperature, from 40 to 80 1C, with a stabilization time of approximately 1 h in between measurements. DMFIT software was used to analyze the spectra. 60 3. Results and discussion Ion coordination, lithium-ion speciation, and ion transport properties were assessed through MD simulations, focusing on particle density changes around lithium cation or TFSI  anions within the PEO polymer matrix. The MD simulations were conducted at 70 1C, as classical MD simulations are limited to studying molecular kinetics and cannot properly simulate the thermodynamic properties of semicrystalline regions that might be present at lower temperatures. Moreover, the modelling of the PEO 64 /LiTFSI system was excluded since longer time and length scales are required to suitably account for the diffusivity of ionic species within PEO at such low concentrations. 53,54,61,62 The computational framework was validated at 70 1C using experimental techniques, including DSC to analyze polymer chain dynamics, Raman, and NMR spectroscopies for probing the ion coordination environments, and EIS for examining ion transport properties. The validation of the computational framework enabled the prediction of various properties of alternative alkali metal-ions. MD simulations were performed on PEO 20 /XTFSI, investigating the effects of substituting lithium with sodium, potassium, or cesium cations on ionic transport mechanisms. The analysis included ion coordination, ion conductivity, and ion transport, which are critical factors influencing the performance of SPEs in all-solid-state batteries. 3.1. Model validation of PEO n /LiTFSI SPEs 3.1.1. Modelling lithium-ion solvation and mobility. The local ion coordination environment of PEO n /LiTFSI systems was analyzed by calculating the radial distribution function (RDF) and the corresponding coordination numbers (CNs) at different LiTFSI salt concentration, offering insights into the extent of salt dissociation. RDFs describe the probability of finding a particle at a specific distance from a reference particle, indicating the degree of association between two species of interest. The CN is then obtained by integrating the probability curve, representing the number of reference Paper PCCP Open Access Article. Published on 12 September 2023. Downloaded on 1/19/2024 4:45:05 PM. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. View Article Online 25042 | Phys. Chem. Chem. Phys., 2023, 25, 25038–25054 This journal is © the Owner Societies 2023 particles found within the coordination sphere range of the species of interest. Fig. 1(a) and (b) displays both the RDFs and CNs of interaction of the reference particle, lithium, with either PEO, denoted as Li + O  (PEO), or TFSI  ions, denoted as Li + O  (TFSI  ), via their respective oxygen atoms. Comparing the RDFs of the lithium-polymer (Li + O  (PEO)), and lithium-TFSI  (Li + O  (TFSI  ) interactions, Li + O  (PEO) possesses a significantly higher RDF peak intensity and an average CN of 5 compared to the latter Li + O  (TFSI  ). At first glance, based on the CNs, nearly complete LiTFSI dissociation is observed for most of the investigated systems, attributed to the preferential solvation of the oxygen dense EO units from PEO. 28,30,34,54,63 The incomplete LiTFSI dissociation observed at highly concentrated PEO 6 /LiTFSI strongly suggests the formation of ion pairs. This phenomenon can be attributed to two primary factors. Firstly, the increased probability of electrostatic interactions leads to a higher likelihood of ion cluster formation. Secondly, the reduced number of EO coordination sites available for lithium-ion solvation plays a significant role. This scenario becomes evident at such high salt concentration, where the EO/Li + ratio is around 6, causing the coordination between ether oxygen atoms and Li ions to approach saturation. Consequently, it is natural for some Li ions to coordinate with TFSI  oxygen atoms instead. Advancing the prediction to a more in-depth analysis regarding the lithium-ion coordination environment, lithium-ion molecular speciation analysis is performed as a function of LiTFSI salt concentration. Based on RDF analysis, the local minimum of the first RDF peak, for both Li + O  (PEO) and Li + O  (TFSI  ) coordination types, is observed at 3 Å (cf. Fig. 1(a) and (b)). This local minimum corresponds to the edge of the first solvation shell for Li + -ions, therefore, it is selected as the cut-off distance for the lithium-ion molecular speciation analysis. The effects of LiTFSI salt concentration upon the types of ionic species, such as polymer–Li + , polymer–polymer, and various ion–ion interaction interdependencies are plotted in Fig. 1(c) as Fig. 1 RDFs (solid line) and CNs (dashed line) for PEO n /LiTFSI SPEs, indicating the Li + –O  interaction between lithium-ions and (a) PEO, or (b) TFSI  ions. (c) Lithium-ion molecular speciation analysis with lithium-ion as the reference species. The different types of lithium-ion speciation are represented by different colours: isolated Li + (blue), Li + O  (PEO) (green), LiþOðTFSI PEO Þ(purple), and Li + O  (TFSI  ) (red). RDFs and lithium-ion molecular speciation analyses were calculated from MD simulations conducted at 70 1C. PCCP Paper Open Access Article. Published on 12 September 2023. Downloaded on 1/19/2024 4:45:05 PM. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. View Article Online This journal is © the Owner Societies 2023 Phys. Chem. Chem. Phys., 2023, 25, 25038–25054 | 25043 percentage probability. The lithium-ion molecular speciation remains consistent regardless of the EO/Li + ratio, in good agreement with RDF analyses and previous literature findings. 26,52–54 Predominantly, lithium ions interact with PEO via its oxygen atoms, denoted in the following as Li + O  (PEO). The occurrence of Li + O  (PEO) speciation increases as the LiTFSI salt concentration decreases, observed in both PEO 20 /LiTFSI and PEO 32 /LiTFSI (99%), and similarly in PEO 16 /LiTFSI (98%). However, there is a significant decrease in this speciation for highly concentrated PEO 6 /LiTFSI (77%) (cf. Table S3, ESI†). Additionally, a second type of lithium-ion molecular speciation, where lithium ions interact with the oxygen atoms from both TFSI  anions and PEO chains, is exclusively present in PEO 6 / LiTFSI (23%), denoted as LiþOTFSI PEO  . The relatively higher LiTFSI salt concentration and lower PEO concentration in PEO 6 /LiTFSI limit the available PEO oxygen solvation sites, which hinders LiTFSI dissociation and may promote ion cluster formation, 26,52 since lithium ions are preferentially solvated by the oxygen atoms from PEO, 64–67 there is a higher probability of LiþOðTFSI PEO Þspeciation in this case. The study of ion transport incorporates MSD analysis, a useful tool for examining the displacement of particles over time. MSD analysis allows computation of diffusion coefficients (eqn (1)) and lithium-ion transport numbers (eqn (3)). As shown in Fig. 2(a), it is evident that TFSI  anions exhibit greater displacement than lithium ions regardless of the EO/Li + ratio of the SPEs. Upon closer inspection, it is evident that higher ion mobilities are observed for PEO 20 /LiTFSI, closely followed by PEO 16 / LiTFSI. In contrast, for PEO 6 /LiTFSI, the effects of ion pair formation become evident through notably lower Li + and TFSI  ion mobilities in this highly concentrated system, as revealed by RDF analyses. When comparing the MSD of lithium ions to the oxygen atoms from PEO, as depicted in Fig. S5 (ESI†), it becomes apparent that lithium-ion mobility is primarily limited by polymer chain movement, as the MSD peaks for the respective atoms consistently correlate. In moderately and highly diluted PEO n /LiTFSI systems, the MSD curves for lithium-ion mobility are lower compared to the displacement of oxygen atoms from PEO. Remarkably, for the highly concentrated PEO 6 /LiTFSI, the lithium-ion transport appears to be nearly equivalent to PEO motion, suggesting the presence of a distinct lithium-ion transport mechanism in this case. Self-diffusion coefficients as a function of LiTFSI concentration, plotted in Fig. 2(b), tend to be higher for TFSI  compared to Li + ions, irrespective of the LiTFSI concentration. Discernably, more dilute EO/Li + ratios of 20 : 1 and 32 : 1 display higher Li + and TFSI  ion self-diffusion coefficients, substantiated by RDF analyses which indicate nearly complete LiTFSI dissociation for these ratios. Fig. 2(b) additionally displays the lithium-ion transport number tMD Liþ. The tMD Liþtrend remains relatively consistent, except for PEO 6 /LiTFSI, which shows the lowest value of 0.21. For the other studied PEO n /LiTFSI systems, tMD Liþonly slightly increases, with PEO 16 /LiTFSI (0.31), PEO 20 /LiTFSI (0.31), and PEO 32 /LiTFSI (0.30) displaying comparable values (cf. Table S4, ESI†). Interestingly, the modelled ion pair effects predicted for PEO 6 /LiTFSI do not seem to significantly alter the ion transport effects. The lithium-ion transport mechanism can be deduced from the analysis shown in Fig. S6(a) and (b) (ESI†), where a reference lithium-ion is selected to demonstrate lithium-ion diffusion concerning the oxygen atoms from PEO, referred to as the O index, or the oxygen atoms from the TFSI  anions, denoted as the TFSI index. The MD trajectory involved 24 EO monomers to represent 40 PEO chains in the system, which included either 160 (PEO 6 /LiTFSI) or 30 (PEO 32 /LiTFSI) additional Li-TFSI ion pairs. Each single PEO chain is visualized separated by delineated dash lines in Fig. S6 (ESI†), and similarly, each TFSI  ion is separated accordingly. It is important to emphasize that while we focus on a single Li + ion in the analysis displayed in Fig. S6 (ESI†) to illustrate the diffusion mechanism, the same mechanism applies when considering other Li + ions as reference. Essentially, the lithium-ion transport mechanism at high concentrations is inferred from the Fig. 2 (a) MSD functions for lithium- (solid line) and TFSI  ions (dashed line) as a function of LiTFSI salt concentration. (b) Self-diffusion coefficients for the studied EO/Li + ratios, for Li + (solid column) and TFSI  (striped column) as well as lithium-ion transport numbers, tMD Liþ  (solid line). MSDs, selfdiffusion coefficients, and lithium-ion transport numbers were calculated from MD simulations conducted at 70 1C. Paper PCCP Open Access Article. Published on 12 September 2023. Downloaded on 1/19/2024 4:45:05 PM. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. View Article Online 25044 | Phys. Chem. Chem. Phys., 2023, 25, 25038–25054 This journal is © the Owner Societies 2023 collective data presented in Fig. 1(c) and Table S3 (ESI†), and the data used in Fig. S6 (ESI†) serves as a representative example. It is observed that the lithium-ion dissociates from TFSI  ions at the beginning of the simulation, moves towards a PEO chain, and interacts with oxygen atoms from both the TFSI  ions and PEO. Eventually, the lithium-ion diffuses along a single polymer chain while remaining coordinated with TFSI  ions for the remainder of the simulation time. This verifies that lithium-ion diffusion for concentrated PEO 6 /LiTFSI occurs via diffusion along a single polymer chain. In the case of PEO 32 / LiTFSI, the lithium-ion barely interacts with TFSI  ions, clearly jumping from one PEO chain to another, confirming that the lithium-ion transport mechanism for moderately dilute PEO n /LiTFSI systems occurs via an ion-hopping mechanism. 3.1.2. Experimental validation of lithium-ion coordination and dynamics. Polymer chain flexibility plays a crucial role in understanding ion dynamics, which can be explored by studying the crystalline-amorphous phase transition using DSC. The DSC traces for the studied SPEs (Fig. S7, ESI†) exhibit an increase in the glass transition temperature, T g , as the LiTFSI content rises (cf. Table S5, ESI†). This can be linked to the higher LiTFSI concentration, which increases the number of lithium ions coordinated to PEO, impacting the heat capacity near T g . Additionally, the increased LiTFSI concentration also raises the number of charge carriers, which slows down the segmental motion of the polymer chains. 68,69 Consequently, PEO 6 /LiTFSI possesses the highest T g of 26 1C, indicating slower polymer segmental motion and slower ion dynamics, as predicted by the MD simulations. In the case of highly diluted PEO 64 /LiTFSI, the absence of a T g can be ascribed to several factors: the exceptionally low LiTFSI content and significantly high PEO content result in a highly crystalline SPE (56%); additionally, the T g dependency on the temperature sweep rate 70 may also contribute to the absence of a T g signal. The studied SPEs with EO/Li + ratios of 16 and 20 display comparable T g values of 35 1C, however, the slightly more diluted PEO 32 /LiTFSI exhibits the lowest T g (43 1C), indicating improved polymer chain flexibility. Lower temperatures are required to expedite polymer motion in this case, leading to enhanced polymer mobility and, consequently, faster ion dynamics and Li + -ion mobility, as supported by the MSD analysis from the MD simulations. The melting transition temperature, T m , supplies information regarding the temperature range at which the SPE is molten and its long-range structure transitions to a disordered amorphous system (cf. Fig. S7 and Table S5, ESI†). Contrary to the trend for the T g , the T m increases slightly as LiTFSI content decreases, from T m =49to621C, for PEO 16 /LiTFSI and PEO 64 / LiTFSI, respectively, which indicates that the lithium salt promotes the amorphization of the crystalline domains resulting in lower melting temperatures. Additionally, the enthalpy of the melting transition, DH m , has been used to estimate the crystallinity degree, w c , of the studied PEO-based SPEs, which represents the fraction of the polymer that is in a relatively ordered state. Based on the data presented in Table S5 (ESI†), a clear trend emerges, showing an increase in w c with decreasing LiTFSI concentration. This observation can be attributed to the increase in the proportion of semi-crystalline PEO and the corresponding decrease in the amount of LiTFSI salt. This trend confirms the amorphization effect of polar LiTFSI within the PEO matrix. Proceeding with the validation of the model, Raman and MAS-NMR spectroscopies were utilized to evaluate the accuracy of the computational predictions concerning the impact of salt concentration on the lithium-ion coordination environment. Raman spectra were obtained for various EO/Li + ratios at temperatures of 25 1C(cf. Fig. S8(a), ESI†), 40 1C(cf. Fig. S8(b), ESI†), and 70 1C (Fig. 3(a)). Peak deconvolution was then performed to quantify the extent of LiTFSI association as a function of LiTFSI concentration and temperature, with results shown for PEO 6 /LiTFSI in Fig. 3(b) (cf. Table S6, ESI†). For further insight, we conducted MAS-NMR measurements on two distinct SPE systems: a highly concentrated PEO 6 /LiTFSI and a highly diluted PEO 32 /LiTFSI (see Fig. 4 and Table S7, ESI†). These measurements allowed us to discern notable dissimilarities in ion dynamics as a function of concentration and temperature. Analysis of the Raman spectra reveals distinct vibrational peaks for the S–N–S vibration at 70 1C. Coordinated LiTFSI ion pairs form, as evidenced by the signal at 747 cm 1 , while free ion pairs appear at 740 cm 1 , consistent with previous studies. 30,34,71–74 Upon a decrease in LiTFSI salt concentration, from highly concentrated PEO 6 /LiTFSI to highly diluted PEO 64 / LiTFSI, a clear downshift is observed from 743 to 741 cm 1 , corresponding to coordination that changes from partially coordinated Li + –TFSI  to the free TFSI  form. These experimental results align with the predictions from the MD simulations, confirming the accuracy of the computational model. Essentially, the increased dissociation of LiTFSI at lower LiTFSI salt concentrations is evident from the Raman trends, further validating the simulated coordination environment. The quantification of the effect of both LiTFSI concentration and temperature changes upon the lithium-ion coordination environment was attained by performing peak deconvolution of the studied PEO n /LiTFSI SPEs at various temperatures of 25 1C, 40 1C and 70 1C(cf. Table S6 and Fig. S8, ESI†). The temperature effect proves to produce significant changes in the quantification of the two different coordination environments principally detected for LiTFSI. 71,72 It is important to highlight that the Raman spectra collected at 25 1C in this study was performed prior to a preheating treatment step, 71 for which non-preheated and preheated SPEs’ spectra may show considerable differences in the S–N–S vibration window for more concentrated PEO 6 /LiTFSI, assigned to the small amount of ion pairs present in the amorphous phase. 71 Examining the effect of concentration changes, mainly PEO 6 /LiTFSI and PEO 16 /LiTFSI display notable differences, with the remaining SPEs showing comparable results, i.e., complete LiTFSI dissociation. A moderate contribution from coordinated LiTFSI is seen for PEO 6 /LiTFSI at 40 1C (15%), significantly increased at 70 1C (23%). Clearly, the local ionic structure of highly concentrated PEO 6 /LiTFSI is PCCP Paper Open Access Article. Published on 12 September 2023. Downloaded on 1/19/2024 4:45:05 PM. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. View Article Online This journal is © the Owner Societies 2023 Phys. Chem. Chem. Phys., 2023, 25, 25038–25054 | 25045 affected by the melting transition of the SPE. 71,72 Conversely, in the case of PEO 16 /LiTFSI, a negligible decrease in the coordinated LiTFSI form is observed, transitioning from 7% at 40 1C to 4% at 70 1C. These results suggest that more dilute PEO n / LiTFSI systems (n46) tend to retain their ionic structure despite changes in temperature. MAS-NMR spectroscopy is a well-established technique to probe both ion and polymer dynamics at short-range order length scales (1–2 Å) for a given nucleus. This technique offers detailed information about chemical environments and their temporal fluctuations. Thus, we used 7 Li solid-state NMR to gain a deeper understanding of the changes in the local lithium-ion structure and dynamics 75,76 as a function of lithium salt concentration at various temperatures. The line broadening observed in the 7 Li MAS-NMR resonances can be correlated to the rates and amplitudes of local fluctuations in the Li + environments, arising from dynamic averaging of local dipolar and quadrupolar interactions. Slower polymer and lithium-ion dynamics result in broader NMR signals, whilst faster dynamics lead to narrower signals. 75,77 In Fig. 4, the variable temperature 7 Li MAS-NMR spectra of PEO 32 /LiTFSI exhibit a transition from a twoto a one-component system, with both signals becoming narrower as the temperature increases. The broader component eventually disappears in the temperature range of 60–70 1C. In contrast, the variable temperature 7 Li MAS-NMR spectrum of PEO 6 /LiTFSI remains consistent with a predominantly two-component system, where the broader component becomes narrower and increases in quantity with rising temperature. The deconvolution of the 7 Li MAS-NMR spectra from Fig. 4 was performed to quantify the respective contributions from each component (cf. Table S7, ESI†). A two-component system is observed for PEO 32 /LiTFSI, comprising a minor contribution of faster lithium-ion dynamics as a narrower component at 40 1C (46%), increasing consistently up to 60 1C (63%), and subsequently increasing significantly as the temperature is increased to 70 1C (100%), notably transpiring at the melting phase transition temperature (T m =561C). Considering that lithium-ions are preferentially solvated by PEO, as predicted by the RDF and MSD analyses from MD simulations, lithium-ion mobility is interlinked with PEO segmental motion, hence, two modes of lithium-ion transport are regarded: (1) lithium-ion transport via ion hopping between the coordination sites between different PEO chains or segments, or (2) lithium-ion transport without a change of coordination site and via segmental motion of the polymer. 30,54,78,79 Since PEO 32 /LiTFSI is a relatively dilute SPE system, and LiTFSI is entirely dissociated within the PEO matrix at 70 1C, the faster lithium-ion dynamics (narrower 7 Li MAS-NMR signals in Fig. 4) are evidently attributed to the Li + –PEO coordination, as detected by MD simulations and confirmed by Raman spectroscopy. Reviewing the Raman peak deconvolution results at 40 1C, LiTFSI is once again determined to be completely dissociated, inferring that the two-component lithium-ion dynamics deduced by NMR are solely attributed to lithium-ions coordinated to PEO, occurring by either one of the two lithium-ion transport modes. Based on MD simulations (cf. Fig. S6(b), ESI†) and similar MD simulations performed on PEO/LiTFSI systems from literature, 54,78 lithium-ion mobility between different coordination sites between different PEO chains is faster than lithium-ion mobility arising by the lithium-ion coordination via PEO segmental motion. Therefore, the narrower component representing faster lithium-ion dynamics, occurs distinctly via lithium-ion transport between different PEO coordination sites and different polymer chains for PEO 32 /LiTFSI at 70 1C. Continuing with this reasoning, it is interesting to note an opposite trend for PEO 6 /LiTFSI, with the minor contribution of faster lithium-ion dynamics from the two-component system proceeding to decrease with increasing temperature. However, considering the significantly lower number of PEO coordination sites present due to the high LiTFSI salt concentration, additional lithium-ion transport modes may prevail. Based on the lithium-ion transport mechanism deduced in Fig. S6(a) Fig. 3 Raman spectra for the 730 (720)–760 cm 1 vibrational window illustrating the (a) S–N–S vibration for PEO n /LiTFSI SPEs at 70 1C, with spectra of neat LiTFSI and neat PEO at 25 1C. The grey arrow represents the downshift observed upon decreased LiTFSI concentration. Raman spectra of (b) PEO 6 /LiTFSI at 40 (bottom) and 70 1C (top). The solid black line represents recorded spectra, the solid green line with symbols corresponds to the cumulative fit of Voigt character performed by peak deconvolution analysis, with the contribution from the coordinated character (orange) and the free TFSI  ion character (purple). The two vertical dotted lines correspond to the coordinated contact ion pair position at 747 cm 1 (orange) and the free ion pair position at 740 cm 1 (purple), respectively. Paper PCCP Open Access Article. Published on 12 September 2023. Downloaded on 1/19/2024 4:45:05 PM. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. View Article Online 25046 | Phys. Chem. Chem. Phys., 2023, 25, 25038–25054 This journal is © the Owner Societies 2023 (ESI†) and a similar study performed for PEO/LiTFSI systems, 54 at high LiTFSI concentrations, lithium-ions preferentially coordinate with PEO due to the presence of ion pairs and ion clusters. However, despite this preference, the coordination of lithium ions to TFSI  anions enhances lithium-ion transport. Notably, ion pairs and ion clusters offer the most stable structure for transporting lithium cations, resulting in higher lithium-ion mobilities compared to lithium ions solely coordinated to PEO. 54 Consequently, the limited polymer segmental motion induced by the high LiTFSI concentration leads to the faster lithium-ion dynamics observed as a minority. At 40 1C, this minority constitutes 47%, decreasing to 32% at 70 1C. These faster lithium-ion dynamics are noticeably distinguished as lithium ions in the form of ion pairs and ion clusters. This observation finds further supported from MD simulations and Raman spectroscopy, where the minority of the lithium ions is found to be associated with TFSI  anions. Considering that temperatures higher than the T m of the PEO 6 /LiTFSI SPE lead to a greater presence of amorphous regions, facilitating predominantly favoured lithium-PEO coordination, there is an observed increase in slower lithium-ion dynamics (reaching 68% at 70 1C). This observation is further confirmed by Raman spectroscopy, which indicates the presence of dissociated LiTFSI species, and MD simulations, which show Li + –PEO coordination. Ascertaining the accuracy of the model’s ability to predict the effects of concentration on the lithium-ion transport, studying the temperature dependence of the ionic conductivity may convey information about the primary charge carriers and their transport properties within the studied systems. The Arrhenius plot in Fig. 5(a) displays the ionic conductivity of the studied PEO n /LiTFSI systems at 40 1C and 70 1C. The semicrystalline nature of PEO is apparent since the ionic conductivities at 40 1C, a lower temperature than the melting phase transition temperatures (cf. Table S5, ESI†), are significantly lower compared to the ionic conductivities at 70 1C, at which completely amorphous behaviour of the SPEs are observed. Observing the effect of LiTFSI concentration, at temperatures lower than the melting phase transition, i.e.,401C, both the crystallinity degree and the extent of LiTFSI concentration are factors which may affect the ionic conductivity. An increased crystallinity degree signifies an increased fraction of ordered polymer chains, decreasing ionic conductivity, whilst an upsurge in LiTFSI concentration may produce increased ion–ion interdependent interactions, generally restricting ion mobility to a greater magnitude. This rationale follows for more concentrated SPEs, where PEO 6 /LiTFSI possesses lower ionic conductivity (4.2 10 6 7.7 10 7 Scm 1 ) compared to highly diluted PEO 64 /LiTFSI (1.6 10 5 26 10 6 Scm 1 ). Regarding moderately dilute systems, a trend of increasing Fig. 4 7 Li MAS-NMR spectra for (a) PEO 6 /LiTFSI and (b) PEO 32 /LiTFSI at 40 1C, 50 1C, 60 1C, 70 1C, and 80 1C. The solid black line represents the recorded spectra, the dashed red lines represent the peak fitting, whilst the solid purple and cyan lines in (a), and the solid green and blue lines in (b), both respectively represent two distinct lithium-ion dynamics. PCCP Paper Open Access Article. Published on 12 September 2023. Downloaded on 1/19/2024 4:45:05 PM. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. View Article Online This journal is © the Owner Societies 2023 Phys. Chem. Chem. 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