Analysis of degradation products in electrolyte for rechargeable lithium-ion battery through high mass accuracy MSn and multivariate statistical technique

Significance of the Topic

Rechargeable lithium-ion batteries power a wide range of portable electronics and electric vehicles due to their high energy density and voltage. During repeated charge–discharge cycles, electrolyte decomposition products form and degrade battery performance, making their identification essential for improving electrolyte formulations and extending battery life.

Objectives and Study Overview

This work aimed to detect and characterize degradation products in the electrolyte of a lithium-ion battery subjected to accelerated cycling. By comparing fresh electrolyte (Electrolyte A) with electrolyte extracted after 30 charge–discharge cycles at 60 °C (Electrolyte B), the study sought to identify unique ionic species generated upon cycling.

Methodology and Instrumentation

The analysis combined high-mass-accuracy multistage mass spectrometry (MSⁿ) with multivariate statistics. Key steps included:

• Sample preparation: 1:1 (v/v) ethylene carbonate : diethyl carbonate with 1 M LiPF₆, diluted 1 : 10 in methanol.
• Data acquisition: LCMS-IT-TOF in positive ESI mode (m/z 80–1000).
• Data processing: Peak alignment and profiling using Profiling Solution.
• Statistical analysis: Orthogonal partial least squares discriminant analysis (OPLS-DA) to highlight ions unique to cycled electrolyte.
• Structural identification: Formula Predictor for chemical formula assignment and MS²/MS³ fragmentation to confirm structures.

Main Results and Discussion

OPLS-DA separated fresh and cycled samples into distinct clusters. The S-plot revealed 15 ions exclusive to the cycled electrolyte. Extracted ion chromatograms confirmed their absence in fresh electrolyte. Representative compound peak #2 (m/z 284.0982, (M+NH₄)⁺) was assigned C₉H₁₈O₉, consistent with a linear polycarbonate structure, and its MS²/MS³ fragments supported the proposed structure. Another example, peak #13 (m/z 283.0336) was identified as C₃H₇O₄P, a phosphate derivative. Overall, the degradation products comprised mainly carbonate oligomers and phosphate species formed from LiPF₆ decomposition.

Benefits and Practical Applications of the Method

• Comprehensive profiling of minor degradation products in complex electrolytes.
• High confidence in formula assignment through high-resolution MSⁿ and statistical validation.
• Improved understanding of electrolyte aging mechanisms, guiding electrolyte reformulation and additive development.
• Potential use in quality control and accelerated aging studies for battery production.

Future Trends and Potential Applications

Future research may integrate real-time MS monitoring of battery electrolyte during cycling, apply advanced machine-learning models to predict degradation pathways, and extend the approach to next-generation battery chemistries. Coupling with chromatography for isomer separation and expanding statistical tools could further enhance the sensitivity and specificity of degradation analysis.

Conclusion

This study demonstrated that high-mass-accuracy MSⁿ combined with OPLS-DA can effectively identify and characterize electrolyte degradation products in lithium-ion batteries. Fifteen unique ions were detected in the cycled electrolyte, revealing carbonate and phosphate species responsible for performance loss. The methodology offers a powerful tool for electrolyte optimization and battery life extension.

Reference

No additional literature references were provided in the original text.