Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning

Wang, Feng; Cheng, Jun

doi:10.1039/d2sc04025e

Cited by 12 publications

(14 citation statements)

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“…TFSI – or BETI – would decompose spontaneously after localization of the excess electron, and thus a restraining potential is applied to help maintain the geometry of the TFSI – and BETI – structure during the computation of redox potentials. It should be noted that restraining potential is applied for chemical definition, which is not a computational artifact. ,, The computed reduction potentials of TFSI – are almost the same in water solutions at all concentrations of Li-salts (Table. ), which indicates the reduction potentials of anions of Li-salts are not the main reason for the unusual stability of WiSE.…”

Section: Resultsmentioning

confidence: 85%

“…It should be noted that it is not energies of molecular orbitals but the redox potentials of solvated species that determine the ESW of electrolytes. In this work, due to their nonideal nature of concentrated aqueous electrolytes, a computational Li reference electrode is used to resolve the uncertainty of potential energy reference under PBC, which is a similar approach to computational standard hydrogen electrode ,, and Ag/AgCl electrode and has been established in previous works . The one-electron reduction potentials of TFSI – in water solutions with different concentrations (or BETI – in HME) are computed by inserting an excess electron in simulation cell (Figure a) using HSE06 functional, and the redox levels vs Li + /Li(s), together with the corresponding vertical energy levels and band positions, are plotted in Figure b.…”

Section: Resultsmentioning

confidence: 99%

“…The redox potentials in electrolytes can not be computed with classical force fields, and concentration effects are hard to be accounted for with implicit solvation models. ,− AIMD-based free energy calculations are used in this work to compute the redox potentials of TFSI – and HER potentials of water (Figure c). One-electron reduction potentials of TFSI – (BETI – in HME) and HER potentials of water are calculated with free energy perturbation (FEP) theory and thermodynamic integration (TI) method, in which 30–100 ps of AIMD trajectories are generated to achieve statistical convergence of thermodynamic integrals. − In FEP theory, a fictitious mapping Hamiltonian H η = (1 – η) H R + ηH P is constructed by linear combination of Hamiltonian of reactant H R and product H P through the Kirkwood coupling parameter η. − The free energy difference Δ A can be rigorously obtained by TI, normalΔ A = prefix∫ 0 1 false⟨ normalΔ E false⟩ η nobreak0em0.25em⁡ normald η , in which the ⟨Δ E ⟩ η is the ensemble average of vertical energy gaps along AIMD trajectories.…”

Section: Methods Sectionmentioning

confidence: 99%

“…For comparing among computed values and against experiments, a computational Li reference electrode 35…”

Section: ■ Introductionmentioning

confidence: 99%

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Switching of Redox Levels Leads to High Reductive Stability in Water-in-Salt Electrolytes

2023

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Developing nonflammable electrolytes with wide electrochemical windows is of great importance for energy storage devices. Water-in-salt electrolytes (WiSE) have attracted great interests due to their widely opened electrochemical windows and high stability. Previous theoretical investigations have revealed changes in solvation shell of water molecules result in opening of HOMO–LUMO gaps of water, leading to the formation of an anion-derived solid-electrolyte-interphase (SEI) in WiSE. However, how solvation structures affect electrochemical windows at atomic level is still a puzzle, which hinders optimization and design of aqueous electrolytes. Herein, machine learning molecular dynamics and free energy calculation method are applied to compute redox potentials of anions of Li-salts and water of aqueous electrolytes at a range of salt concentrations. Furthermore, an analysis based on local solvation structures is employed to demonstrate the structure–property relations. Our calculation shows that the hydrogen evolution reaction is impeded in WiSE due to switching of the order of redox levels of the anion and H2O, leading to formation of SEI and high reductive stability. Level switching is caused by the special solvation environments of isolated water molecules. Our work provides new insight into the electrochemistry of aqueous electrolytes which would benefit the electrolyte design in energy storage devices.

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Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 99%

Section: Methods Sectionmentioning

confidence: 99%

“…For comparing among computed values and against experiments, a computational Li reference electrode 35…”

Section: ■ Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Switching of Redox Levels Leads to High Reductive Stability in Water-in-Salt Electrolytes

2023

Self Cite

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“…Similarly, compared to the ex situ process, more LiF components are generated on the Li surface of the in situ prepared cells after cycling, and the LiF peak area accounts for 88.94%, in contrast to the C–F peak area that hinders ion transport is only 11.06%. Such results are mainly attributed to the increase in the effective solid–solid contact area, which contributes to the TFSI – decomposition and the dehydrofluorination of difluoromethyl to generate relatively more LiF. , The corresponding Li surface morphology of the in situ and ex situ cells after cycling (Figure S15) also verifies the results of enhanced interface stability to the Li electrodes.…”

Section: Resultsmentioning

confidence: 61%

Quasi-Solid Polymer Electrolyte with Multiple Lithium-Ion Transport Pathways by In Situ Thermal-Initiating Polymerization

Lin

Zheng

et al. 2023

ACS Appl. Mater. Interfaces

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Solid polymer electrolytes (SPEs) are considered to be attractive candidates for rechargeable batteries on account of their high safety and flexible processability. However, the restricted polymer segmental dynamics limit the Li+ conduction of SPEs. Herein, a composite electrolyte membrane was prepared via in situ thermal-initiating polymerization of diethylene glycol diacrylate (DEGDA) in a poly(vinylidene fluoride) frameworks (PVDF FMs) electrospun in advance. As a quasi-solid polymer electrolyte (QSPE), it provides multiple transport highways for Li+ built by the CO or C–O or CO/C–O groups in poly(diethylene glycol) diacrylate (PDEGDA), respectively, proved by density functional theory calculations together with the high-resolution 7Li solid-state nuclear magnetic resonance spectra. Since the interaction between Li+ and CO is weaker than that between Li+ and C–O, Li+ tends to move along CO dominating paths in PDEGDA/PVDF FMs QSPEs, even skipping back to CO nodes from the original C–O dominating way. Multiple transport patterns facilitate Li+ migration within PDEGDA/PVDF FMs QSPEs, contributing to the ionic conductivity of 1.41 × 10–4 S cm–1 at 25 °C and the Li+ transference number of 0.454. Ascribing to the wetting capability of the monomer to the electrodes in use, compatible electrolyte/electrode interfaces with low interface resistance and compact cells were acquired by the in situ polymerization. Protective lithiated oligomers (RCOOLi) and LiF are enriched at the Li anode surface, promoting a lasting stable Li plating/stripping over 2000 h. By applying the QSPEs in LiFePO4 cell, a capacity of 157.7 mAh g–1 with almost 100% coulombic efficiency during 200 cycles is achieved at 25 °C.

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Is Unsupervised Dimensionality Reduction Sufficient to Decode the Complexities of Electrochemical Impedance Spectra?

Makogon,

Kanoufi,

Shkirskiy

2024

ChemElectroChem

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As electrochemical research undergoes rapid technological progression, the acquisition of substantial amounts of electrochemical impedance spectra (EIS) becomes increasingly feasible. Yet, this advancement introduces intricate challenges in data processing, automation, and interpretation. This paper delves into the sufficiency of unsupervised machine learning (ML) and in particular dimensionality reduction methods in decoding EIS complexities, examining its strengths, limitations, and potential pathways for optimization. As we navigated the intricacies of non‐linear dimensionality reduction, spotlighting t‐distributed stochastic neighbor embedding (t‐SNE) and uniform manifold approximation and projection (UMAP) algorithms, a pattern emerged: these techniques excel at categorizing divergent impedance spectra but show limitations when faced with analogous circuit configurations, especially those substituting a capacitor with a constant phase element. This observation not only underscores a limitation but also accentuates that unsupervised ML approaches, alone, may not fully unravel the nuances of EIS spectra. In the concluding section of our manuscript, we discuss the implications of this finding from a practical standpoint, particularly for electrochemists seeking to apply these methods in their work.

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Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning

Abstract: Developing electrolytes with excellent electrochemical stability is critical for next-generation rechargeable batteries. Super-concentrated electrolytes (SCE) have attracted great interests due to high electrochemical performances and stability. Previous studies have revealed...

Cited by 12 publications

References 83 publications

Switching of Redox Levels Leads to High Reductive Stability in Water-in-Salt Electrolytes

Switching of Redox Levels Leads to High Reductive Stability in Water-in-Salt Electrolytes

Quasi-Solid Polymer Electrolyte with Multiple Lithium-Ion Transport Pathways by In Situ Thermal-Initiating Polymerization

Is Unsupervised Dimensionality Reduction Sufficient to Decode the Complexities of Electrochemical Impedance Spectra?

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