Introduction
With the ubiquity of portable devices like phones and laptops, lithium-ion (Li-ion) batteries are abundant in everyday life. While this technology is well known, it is constantly improving. Further, the methods used for analytical monitoring to ensure batteries can work safely and efficiently are continually evolving as well. Measuring residual solvents in battery materials is important to optimize their performance, as high concentrations of unwanted compounds can cause adverse and unintended side reactions. These issues can affect the electrochemical reactions inside the batteries and alter their energy storage and delivery capabilities.
Using GC-MS headspace analysis is a simpler alternative to many current methods using liquid extraction to detect residual solvents. There are multiple headspace techniques commonly used to detect trace-level solvents in samples, including Static Headspace (SHS) and Dynamic Headspace (DHS). However, complexity, analysis time, temperature, and detection limits can vary depending on the technique, which makes it critical to select the best method for battery electrode analysis.
Static vs. Dynamic Headspace
Static Headspace (SHS)
This is an equilibrium-based technique in which a sample is incubated in a sealed vial at a controlled temperature, allowing analytes to partition between the sample and the headspace. An aliquot of the headspace is then introduced into the GC-MS for analysis. Because this approach relies on equilibrium, it can result in higher detection limits and limited recovery of less volatile, higher-boiling compounds. It often requires higher incubation temperatures and longer incubation times.
Dynamic Headspace (DHS)
This is a non-equilibrium technique in which the sample headspace is continuously purged, and analytes are trapped onto a sorbent-filled tube. DHS enables high analyte enrichment, exhaustive extraction, and low detection limits at lower incubation temperatures. The analytes are then thermally desorbed and transferred to the GC-MS system for analysis.
Key Differences
While both static and dynamic headspace can be used to examine residual solvents, some key differences are summarized below:
DHS provides lower detection limits – achieved through an active, continuous extraction during DHS versus a single aliquot taken in SHS
DHS enables comprehensive extraction of multiple analytes – DHS utilizes constant purging of the headspace and trapping onto sorbent-filled tubes, which captures a wider range of compounds, while SHS is limited by the headspace equilibrium in the vial
DHS increases recovery of low volatility, high boiling point compounds – continuous trapping in DHS, even at a lower incubation temperature, leads to more exhaustive extraction, whereas SHS often necessitates a higher incubation temperature and detects fewer compounds
Case Study: Residual NMP Extraction from Lithium-Ion Battery Cathodes (AppNote 308)
An important analyte in Li-ion batteries is N-methyl-2-pyrrolidone (NMP), a solvent that is used to help coat cathode slurries onto current collectors. While this solvent is mostly removed by drying during processing, it is important to accurately measure any remaining NMP, as its presence can negatively affect battery power performance and cell capacity over time. However, headspace analyses become more challenging if the compound has a high boiling point and lower vapor pressure, which makes it less readily extracted via SHS analysis. With a boiling point of 202 °C, NMP is one such chemical, and DHS provides a route for improved capture and detection of this analyte.
AppNote 308 demonstrates the advantages of using DHS over SHS when analyzing the NMP in cathodes for Li-ion batteries.Â
The figure above shows that DHS provides a greater analyte response for NMP, and DHS can extract multiple additional compounds. These results were also achieved at a lower incubation temperature of 100 °C, compared to 140 °C used for SHS. The DHS calibration curve results demonstrated a limit of detection (LOD) of 8.1 ng for NMP and a limit of quantitation (LOQ) of 26.9 ng, enabling quantification of NMP in commercial samples of both lithium iron phosphate (LFP) and lithium nickel manganese cobalt oxide (NMC) cathodes.
Conclusion
The experimental results in AppNote 308 exhibit the effectiveness of DHS over SHS for a solvent-free extraction of NMP. Â Combining the low detection limits of DHS with automated analysis could provide a platform for monitoring NMP across electrode batches to ensure consistent removal of NMP during manufacturing. The capability of DHS to establish high recoveries and detect a broad range of compounds can give comprehensive insight into molecules present within the electrodes. With a greater understanding of chemicals in batteries that could influence electrochemical reactions, factors that may negatively impact overall battery performance can be more readily recognized and mitigated.
Learn More about GERSTEL Applications for Dynamic Headspace:
