News from LabRulezGCMS Library - Week 46, 2024

Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 11th November 2024? Check out new documents from the field of the gas phase, especially GC and GC/MS techniques!

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This week we bring you applications by Agilent Technologies, Thermo Fisher Scientific, and Shimadzu!

1. Agilent Technologies: Solvents and Additives Analysis in Lithium Battery Electrolytes Using the Agilent 8850 GC System and Applying It to Real Samples

• Application

Abstract

The electrolyte is a key component in lithium-ion batteries, playing a vital role in transferring and conducting current between the positive and negative electrodes. The selection and optimization of electrolyte components are important in improving battery performance. Therefore, the analysis of electrolyte composition is an essential task in the lithium battery industry. This application note introduces an analytical method for determining carbonate solvents and additives in lithium battery electrolytes based on the Agilent 8850 gas chromatography (GC) system with a flame-ionization detector (FID). In this study, standard samples were used for method evaluation, and excellent performance results in terms of linearity, reproducibility, and detection limits were obtained. Real samples were run to investigate the impact of high acid and high salt samples on the performance of the entire GC system. Compared to undiluted samples, diluted samples can significantly extend the lifespan of the liner and column.

Introduction

The lithium battery industry is a rapidly growing sector, largely driven by the increasing demand for electric vehicles and renewable energy storage systems. Lithium batteries are known for their high energy density and long cycle life. One of the four key materials in lithium batteries, the electrolyte, has main components including organic solvents, lithium salts, and a small amount of additives. The solvents in the electrolyte, such as ethylene carbonate (EC) or dimethyl carbonate (DMC), determine its ionic conductivity and stability. An optimal blend of solvents can enhance the battery's efficiency and lifespan. Additives are also crucial, as they can improve the electrolyte's performance and safety. For instance, they can suppress the formation of harmful byproducts, enhance the stability of the electrolyte, and improve the interface between the electrolyte and electrode. Therefore, analyzing and identifying the composition of carbonate compounds and additives in the electrolyte of lithium batteries is of great significance for
performance study and quality control in the lithium battery industry.

An earlier Agilent application note demonstrated the use of the Agilent 5977B single quadrupole gas chromatography/mass selective detector (GC/MSD) system to measure carbonate solvents and additives in the electrolyte. The note detailed the accurate quantification of target compounds and the qualitative analysis of unknown additives or impurities.¹ In 2023, another application note was published describing the determination of carbonate and additive compounds in the electrolyte with the Agilent 8860 GC/FID system.² Compared with GC/MSD, the GC/FID configuration is easier to use and more cost-effective for users. It is very suitable for laboratories that only need to quantify target compounds and do not need to detect unknown substances. This application note was developed based on the latest small-size, high-performance 8850 GC/FID system. With an effective analytical method established, the impact of running a large number of real samples on the overall GC system performance was examined, particularly the effects of the consumables on the GC inlet and the lifespan of the column.

Conclusion

This application note introduces an analytical method for determining carbonate solvent and some additive compounds in lithium battery electrolytes based on an Agilent 8850 GC system with FID. The performance results for the linearity, repeatability, and detection limit of 13 target compounds
indicate the outstanding sensitivity and reliability of this system. By running undiluted real samples, the impact of high-salt, high-acid samples on the
entire system was explored. This study also demonstrates that even after more than 400 runs, the whole system can still maintain excellent stability after
the real samples have been diluted 100x. Injecting diluted samples can ensure that the system operates at its best performance and greatly extend
the life of consumables. In summary, this method using the 8850 GC system with FID provides an easy-to-use, cost-effective, and stable platform for
electrolyte analysis.

2. Agilent Technologies: LUMA Multichannel Vacuum Ultraviolet Detector on an Agilent 8890 GC

• Application

Introduction

The LUMA multichannel vacuum ultraviolet (VUV) detector is a unique gas chromatography (GC) detector that covers a wide range of applications such as petrochemical, environmental, food, and pharmaceutical analysis. The LUMA detector is considered a universal detector with high sensitivity and a large linear range. Similar to other UV-Vis methodologies, Beer's Law is the detector's guiding principle of quantitation, making the LUMA a concentration-based detector.

The LUMA operates across the electromagnetic spectrum, from the VUV region at 118 nm to the visible light region at 1,050 nm. Light is generated by a specialized deuterium lamp that is housed within the detector box. This light is then collimated by a fixed mirror and directed through a heated flow cell, where it interacts with the analytes. A photodiode array captures the transmitted light and converts it into an electrical signal. For ease of use, this signal is divided into 12 bands, spanning wavelengths from 118 to 1,050 nm, based on discrete energy levels, as shown in Figure 1.

In this application brief, the LUMA detector was paired with an Agilent 8890 GC and Agilent J&W DB-1 column to demonstrate the analysis capabilities of the system.

Conclusion

The LUMA multichannel VUV detector and Agilent 8890 GC equipped with an Agilent J&W DB-1 column together yield a wide range of linearity, high area repeatability, excellent peak resolution, and Gaussian peak shape. With the combination of the VUV spectral data and peak retention time, users can confidently identify the analytes in the sample.

3. Thermo Fisher Scientific: Supporting exposomics research with the Orbitrap Exploris GC 240 mass spectrometer

• Case study

“Our objective is to characterize the environment on a larger scale and to better understand the exposures that occur in human populations to mixtures of thousands of chemicals.”

– Associate Professor Douglas Walker, Emory University, Gangarosa Department of Environmental Health, Atlanta, GA, USA

What is exposomics?

The exposome, which is defined as the cumulative effect of environmental exposures and corresponding biological responses, helps to provide a comprehensive measure for evaluating non-genetic causes of disease. Exposomics is the study of the exposome that seeks to understand and quantify the totality of a human's lifetime exposure to various environmental factors, including chemical, physical, and biological agents. In essence, it involves assessing and analyzing the complex interactions between environmental exposures and an individual's genetic makeup, lifestyle, and other factors to understand their impact on human health and disease. By examining the cumulative effects of multiple exposures over time, exposomics provides valuable insights into the underlying mechanisms of disease development and can inform strategies for prevention and intervention.

Summary

The ability to measure the complexity of the exposome has been limited by the available analytical technology to detect complex exposures patterns at varying concentrations. By using untargeted approaches with high-resolution mass spectrometry, it is possible to detect and identify ongoing exposures that may have not been expected or characterized. High-resolution, full scan mass spectrometry using Orbitrap technology provides a solution for:

• Detection and quantification of compounds using an untargeted full scan acquisition that is both highly selective and sensitive.
• Identification and elucidation of the chemical composition of unknown compounds.
• Retrospective analysis of samples long after data acquisition.

The combined capability of both the Orbitrap Exploris GC 240 mass spectrometer and Compound Discoverer software with spectral libraries is an excellent tool to support exposomics research.

4. Shimadzu: Evaluation of Aroma Characteristics Using the Smart Aroma Database - Simple Calculation of OAV

• Application

User Benefits

• The Smart Aroma Database’s semi-quantitative function (SQF) can be used to calculate approximate quantitative values.
• The SQF can calculate the Odor Activity Value, which evaluates the contribution of aromatic compounds to the overall aroma.
• The SQF can be used only by preparing two types of designated sensitivity-correcting reagents

Introduction

Aroma is an important factor in determining the palatability of foods and beverages. Aromas are made up of many compounds, and the concentration of compounds at which are perceived as an aroma varies based on compounds. To determine the extent to which the constituent compounds contribute to an aroma, it is crucial to establish if they are present in concentrations that exceed the sensory threshold.

The Smart Aroma Database features a semi-quantitative function (SQF) that enables users to calculate approximate quantitative values (semi-quantitative concentrations) of an identified compounds without using a standard sample, so using the SQF enables more efficient workflows during
quantitative analysis of aroma characteristics.

One indicator for evaluating the contribution of a particular aromatic compound to the overall aroma is the Odor Activity Value or OAV, which is calculated by dividing the aroma compound concentration by its sensory threshold value. However, calculating the OAV can be labor-intensive because it involves the following three steps: (1) identifying compounds using qualitative analysis, (2) generating a calibration curve and performing quantitative analysis, and (3) setting the sensory thresholds. Therefore, this study investigated the feasibility of efficiently evaluating aroma characteristics using the Smart Aroma Database’s SQF.

Conclusion

The results of this study demonstrate that using the Smart Aroma Database enables efficient calculation of OAV. The Smart Aroma Database supports the quick acquisition of data required for aroma analysis