News from LabRulezLCMS Library - Week 47, 2024

Our Library never stops expanding. What are the most recent contributions to LabRulezLCMS Library in the week of 18th November 2024? Check out new documents from the field of liquid phase, especially HPLC and LC/MS techniques!

👉 SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT LCMS AND RELATED TECHNIQUES

👉 Need info about different analytical techniques? Peek into LabRulezGCMS or LabRulezICPMS libraries.

This week we bring you applications, presentations, and posters by Metrohm, Thermo Fisher Scientific, Shimadzu, Waters Corporation, and Agilent Technologies!

1. Agilent Technologies: Why Use HILIC and HIC Chromatography?

• Presentation

Agenda

• What is HILIC and when should you consider it?
• How to approach HILIC method development
  • Agilent HILIC column options
  • Mobile phase considerations
• Common HILIC application areas
• Tips and tricks for successful HILIC column use and care
  • Column equilibration
  • Sample solvent compatibility
  • Inert HILIC solution for metal-sensitive compounds
• What is HIC and its separation mechanism?
• Requirements for HIC
  • Factors affecting selectivity
• Common HIC application areas
  • Post-translational modification (PTM) analysis
  • Separation of NIST mab (RM 8671)
  • Monitoring oxidation in mAbs

Summary

When to consider a HILIC column:

• Are your analytes unretained with RPLC?
• Are your analytes at least somewhat soluble in acetonitrile?
• Are you using MS detection?
• Do your analytes interact with metals in the LC system?

Keep the sample solvent in mind for HILIC analyses: prepare the sample in as much acetonitrile as possible, and keep injection volumes as small as possible.

• Most common support issue with HILIC methods

2. Shimadzu: Simultaneous Quantitation of 32 PFAS in Baby Food by LC-MS/MS

• Application

User Benefits

• A simple QuEChERS extraction procedure has been employed for quantifying the PFAS with normal dSPE cleanup.
• This method complies with all acceptance criteria (as per AOAC SMPR 2023.003) without using ISTDs.
• Method with shorter run time can contribute to achieving high throughputs in testing laboratories.

Introduction

PFAS (Per- and polyfluoroalkyl substances) are a class of highly persistent chemicals widely known as "forever chemicals" due to their strong resistance to environmental degradation (Figure 1). Found in products like firefighting foams, non-stick cookware, and water-resistant fabrics, PFAS are now recognized as major environmental and health concerns. These substances accumulate in soil, water, and living organisms, including humans, where they may cause serious health impacts over time.

Given their potential for long-term contamination, the detection and quantification of PFAS are critical. Advanced analytical methods, particularly LC-MS/MS with Multiple Reaction Monitoring (MRM), offer the specificity and sensitivity needed to accurately measure PFAS concentrations. However, one of the primary challenges in PFAS analysis is minimizing background contamination from PFAS residues within the LC system and during sample collection and preparation. Effective strategies to reduce these sources of contamination are essential for reliable results in PFAS quantification.

n this application news, we describe a single laboratory validation study with a rapid extraction of trace level concentrations of 32 PFAS (Table 1) in infant formula (baby food) involving the use of the QuEChERS as an extraction technique followed by mass spectrometric analysis using a Shimadzu LCMS-8060NX triple quadrupole mass spectrometer coupled to a Shimadzu Nexera X3 liquid chromatograph (Figure 2).

Conclusion

• A simple, sensitive, and rapid LC-MS/MS method has been developed to quantify 32 PFAS compounds in infant formula.
• This method utilizes a QuEChERS-based extraction technique for efficient sample preparation.
• With Shimadzu’s LC-MS/MS, the method demonstrated high sensitivity and reproducibility, achieving low relative standard deviations (RSDr and RSDR) in accordance with AOAC SMPR standards at trace levels.
• This approach offers a reliable solution for multi-PFAS analysis in infant formula, making it highly applicable for testing laboratories focused on food safety.

3. Thermo Fisher Scientific / RAFA: PFAS analysis strategy story – direct injection, DLLME, LC-MS/MS, LC- Orbitrap / GC-Orbitrap

• Poster / RAFA

Abstract

Purpose: This work summarizes key components of PFAS analysis. The choice between direct injection and automated sample preparation depends on the instrument's dynamic range, detection limits, and matrix complexity. Complex matrices like waste/industrial water, soil, food, and biological samples require specific preparation techniques to mitigate matrix effects and ensure accurate quantification. Techniques such as Dispersive Liquid-Liquid Microextraction (DLLME) can significantly improve concentration factors and reduce matrix interference, enhancing overall method performance. Additionally, the choice of the
analyzer is a critical consideration.

Methods: Direct injection of water sample was performed on Thermo Scientific TSQ Altis Plus triple quadrupole mass spectrometer. For those sample prepared with automated dispersive liquid-liquid microextraction (DLLME), acquisition was performed on a Thermo Scientific Orbitrap Exploris MX high resolution mass spectrometer.

Results: The methods presented achieves high-level sensitivity for PFAS analysis in drinking water, detecting compounds in the low ng/L range. Using DLLME, 56 PFAS compounds were quantified to low part per trillion levels from just 15 mL of sample. With a FAPAS®-Drinking Water proficiency test, we validated the
three strategies for PFAS analysis: direct injection on TSQ Altis Plus and the DLLME extract injected on both Orbitrap Exploris MX and Thermo Scientific Orbitrap Exploris GC.

Conclusions

• Compared to traditional SPE workflows, direct injection improves sample throughput in laboratories by significantly reducing the required sample preparation.
• The DLLME method is a promising technique for extracting and pre-concentrating PFAS from drinking water and various other matrices. Its reduced sample volume facilitates easier handling, transportation, and storage.
• Both methods lower costs and environmental impact due to minimal solvent usage and the elimination of filters or SPE cartridges.
• The ease of use and robustness of both LCMS methods are based on fixed configuration including SOP with detailed hardware and consumables, a complete acquisition and processing method with customized view settings and reports, and all data handling performed with Chromeleon CDS 7.3.2.

4. Waters Corporation / HPLC: SCALABILITY OF SOLID-CORE PARTICLES FOR CHROMATOGRAPHIC ANALYSES

• Poster / HPLC

NTRODUCTION

Liquid Chromatography plays a crucial role in many areas of science. It is widely used for quality control of pharmaceuticals and biologics, research and medical purposes (1). Over the last several decades the gold standard of analysis for these areas of science has been High-Performance Liquid chromatography (HPLC). Typical HPLC systems operate within a 6000 psi pressure limit (2). Consequently, they are commonly paired with columns packed with 3 - 5 μm particles. Although smaller particle-packed columns allow faster separations, their use in HPLC is restricted to shorter lengths due to elevated backpressures (3). Additionally, established methods which are often used in a quality control setting may restrict alterations to particle size or column configuration.

Improved column efficiency on an HPLC system can be achieved using solid-core particles. Solid-core particles have a non-porous “solid” center and a porous outer layer(4). Columns packed with solid-core particles offer distinct advantages over those packed with fully porous particles, including higher efficiency and reduced operating backpressure. Waters offers a line of solid-core particle columns known as CORTECS Columns.

By utilizing CORTECS Columns, researchers can increase separation efficiency, achieve faster analysis times, reduced solvent waste and decreased column back
pressure. In this poster, we delve into the significance of larger solid-core particles for scalability. We compare columns packed with 5 μm CORTECS Prototype Material, CORTECS UPLC μm) Material and CORTECS HPLC (2.7 μm) C18 Material with columns packed with fully porous particles. Additionally, we explore how CORTECS Columns differ from similar offerings on the market.

CONCLUSION

• 5 μm CORTECS Prototype Columns have 28% lower back pressure in comparison to 3.5 μm XBridge BEH C18 Columns.
• 5 μm CORTECS Prototype Columns have 14% higher optimal efficiency in comparison to 3.5 μm XBridge BEH C18 Columns.
• CORTECS Columns are scalable across all 3 particle sizes shown.
• In comparison, Vendor H and A columns are not scalable across all 3 particle sizes due to the significantly lower retention in the 4 and 5 μm particle size columns, shown in Figure 3.

5. Metrohm: Differential capacity analysis (DCA) for battery research with INTELLO

• Application

Introducing dQ/dE plots, applications, and more

Differential capacity analysis (DCA) is a powerful diagnostic technique widely used in battery research and development to gain detailed insights into the electrochemical behavior of batteries. DCA allows researchers to identify key electrochemical processes, phase transitions, and degradation mechanisms occurring within the battery during charge and discharge cycles. This technique is particularly valuable for characterizing complex systems such as lithium-ion batteries, where multiple electrochemical reactions may overlap.

This Application Note explores the principles and practical applications of differential capacity analysis, highlighting its role in improving battery performance.
This application also reveals how INTELLO, with its dedicated suite of battery-oriented commands and its ability to untether during long measurements, is an excellent choice for battery researchers looking to maximize their time and efficiency when conducting DCA.

INTRODUCTION

Within the INTELLO battery cycling environment, it is possible to sample the differential capacity (defined as dQ/dE = |Qn+1-Qn|/(En+1-En), where n is the index of the data point, Q is the value of the charge/discharge capacity, and E is the measured working electrode potential) and plot by default this value versus the measured voltage of the cell. Note that a general introduction to the cycling environment in INTELLO is provided in AN-BAT-014 while this Application Note specifically focuses on the dQ/dE plot and what can be derived from it.