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 rely on stable electrolyte composition to maintain high energy density and cycle life. Degradation of organic solvents and salts leads to capacity fade and performance loss.

Objectives and study overview

This work aims to characterize degradation products formed in Li ion battery electrolyte under repeated charge/discharge cycles using combined high accuracy MSn and multivariate statistical analysis. Fresh and cycled electrolyte samples were compared to pinpoint unique compounds generated during cycling.

Methodology and instrumentation used

Sample preparation involved dilution of fresh and 30 cycle aged electrolyte at 60°C in a mixture of ethylene carbonate and diethyl carbonate with LiPF6. High mass accuracy MSn data were acquired by LCMS IT TOF. Orthogonal partial least squares discriminant analysis was used to identify distinguishing ions. Putative formulas were predicted by software tools and validated by MS2 and MS3 fragmentation patterns.

Instrumentation used

• LCMS IT TOF system with ESI positive mode
• Shim pack FC ODS analytical column
• Profiling Solution software for peak alignment
• SIMCA P plus for OPLS DA
• Formula Predictor for chemical composition estimation

Main results and discussion

OPLS DA score plots separated fresh and cycled samples, revealing 15 unique ions in aged electrolyte. Extracted ion chromatograms confirmed absence of these species in fresh samples. Structural elucidation identified major degradation products as polycarbonates and organophosphates, exemplified by a C9H18O9 carbonate and a C3H7O4P phosphate ester. Mass accuracy of predictions was within a few ppm.

Benefits and practical applications

This approach enables sensitive detection and identification of battery electrolyte decomposition products, supporting quality control and development of more durable electrolytes. Clear profiling of degradation pathways can guide formulation improvements and predictive maintenance.

Future trends and potential applications

Integration of high throughput MSn with advanced chemometric techniques and spectral databases may allow real time monitoring of battery health. Extension to other battery chemistries and coupling with in situ sampling could further advance diagnostics.

Conclusion

Combining high resolution MSn and OPLS DA provided a robust workflow for detailed characterization of Li ion electrolyte degradation. Fifteen key degradation products were identified, highlighting carbonate and phosphate species as primary by products under cycling stress.

References

No external literature was cited in the original work.