Dimethyl carbonate (DMC) plays a critical role in lithium-ion battery electrolytes, where solvent purity directly impacts performance, safety, and lifetime. Even trace-level impurities can contribute to degradation of solid electrolyte interfaces, consumption of lithium ions, or gas formation—effects that compromise both efficiency and safety.As battery materials continue to evolve, analytical approaches must provide both high sensitivity and high-confidence identification. A recent LECO application study demonstrates how combining two GC-MS platforms enables a more complete characterization of DMC impurities than either approach alone.<h3>Why Impurity Characterization in DMC Is Challenging</h3>Battery-grade solvents demand detection of contaminants across a wide concentration range, starting from major byproducts to trace-level species. In DMC, impurities may include oxygenated compounds, hydrocarbons, and nitriles, many of which arise from synthesis or handling processes.These compounds can co-elute or exist at levels below typical detection thresholds, making comprehensive analysis difficult with a single analytical technique. In addition, electron ionization (EI) spectra do not always provide a clear molecular ion, complicating compound confirmation.<h3>A Complementary Analytical Strategy: Sensitivity + Confidence</h3>The application note demonstrates a combined workflow using the approach:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1097">Pegasus BTX (GC-TOFMS)</a>&nbsp;for high-sensitivity detection</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/38">Pegasus HRT (high-resolution GC-MS)</a>&nbsp;for accurate mass confirmation</li></ul>The Pegasus BTX enables detection of impurities at below parts-per-billion levels, ensuring that even low-abundance analytes are captured.Following detection, the Pegasus HRT provides high-resolution, accurate mass data in both EI and positive chemical ionization (PCI) modes. PCI is particularly valuable when EI spectra lack a clear molecular ion, allowing confirmation through common adduct species such as protonated molecular ions [M+H]⁺.Because both ionization modes can be accessed without hardware changes, the workflow supports efficient, multi-dimensional confirmation of analyte identity.<h3>Observations</h3>Using this combined approach, multiple classes of impurities were identified in the DMC sample. These included:<ul><li>Oxygenated reaction byproducts</li><li>Alcohols, carbonates, and ethers</li><li>Hydrocarbons and aromatic species</li><li>Additional low-level analytes detected with high library match scores</li></ul>Importantly, the high sensitivity of the Pegasus BTX revealed a broader set of low-concentration compounds, while the Pegasus HRT increased confidence in compound identification through accurate mass measurements and confirmation of molecular formulas. &nbsp;The study also highlights cases where co-eluting compounds were successfully deconvoluted, allowing individual components to be identified even without complete chromatographic separation.<h3>Why This Matters for Battery Materials Analysis</h3>This work illustrates a key principle for advanced materials characterization: detection and identification are distinct challenges that often require complementary solutions.<ul><li>Detection alone&nbsp;is insufficient without confident identification</li><li>High-resolution confirmation&nbsp;is limited if low-level species are not first detected</li></ul>By combining these capabilities, laboratories can gain deeper insight into solvent purity, production processes, and potential performance risks in battery systems.<h3>Explore the Complete Study</h3>This short overview highlights only a portion of the findings. The full application note provides:<ul><li>Detailed acquisition parameters and workflows</li><li>Chromatographic and spectral data examples</li><li>Confirmed impurity lists with retention times and similarity scores</li><li>Examples demonstrating EI/PCI complementarity and accurate mass confirmation</li></ul>To better understand how complementary GC-MS techniques can improve confidence in battery solvent analysis, download the full application note: <a target="_blank" rel="noopener noreferrer" href="https://www.leco.com/documents/203-821-731/">“Battery Solvent Characterization: Identifying Impurities in Dimethyl Carbonate”.</a>

 Confident Identification of Battery Solvent Impurities Using Complementary GC-MS Techniques

The international standard ISO/IEC 17025 [1] clause 7.7.2 with regards to ensuring the validity of results, requires a laboratory to participate in proficiency testing (PT) and/or to participate in interlaboratory comparisons (ILCs) other than PT. However, no guidance as to what these other ILCs might be, or indeed how they might compare in relation to PT, is provided. This leaflet aims to outline other ILCs available, pointing out some of the limitations as compared to PT.&nbsp;There are three major types of ILCs in which a group of laboratories analyses identical portions from a homogeneous, stable test sample, with each type being characterized by its intended purpose [2,3]:<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Interlaboratory_comparisons_other_than_proficiency_testing_2_d9263807fb.jpg" alt="Eurachem: Interlaboratory comparisons other than proficiency testing" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Interlaboratory_comparisons_other_than_proficiency_testing_2_d9263807fb.jpg 245w," sizes="100vw" width="1507" height="607">A key point is that PT is an ILC that is designed to specifically evaluate the performance of participants, providing them with an infrastructure to monitor and assess the validity of their results. This is not the key purpose of the two other ILCs, and thus depending on their intended purpose they have a number of limitations that laboratories need to be aware of when using them rather than PT. Only proficiency testing should have been conducted in full compliance to the competency standard ISO/IEC 17043 [4].&nbsp;<h4>Evaluation of measurement procedure performance&nbsp;</h4>These ILCs, are designed to assess how a particular measurement procedure performs and whether it is fit for its intended purpose. These ILCs are often referred to as a ‘method performance study’ or a ‘collaborative study’ [2]. Some limitations of using such ILCs for monitoring laboratory performance as an alternative to PT are:&nbsp;<ul><li>All participants use the same measurement procedure i.e. the measurement procedure that is being validated. Unless the measurement procedure is being routinely used by the participating laboratory such an ILC will not assess the routine performance of the laboratory using their own routine measurement procedure. Nor does it enable them to compare their performance with other laboratories using alternative measurement procedures.&nbsp;</li><li>The measurement procedure is likely to be new, so participants may not have established stable procedures and quality control, so performance may not reflect routine use.&nbsp;</li><li>The report from such an ILC would not provide information on the performance of the participant, if the statistical model used makes the assumption that all laboratories perform with equal variability.</li><li>The design of the study may require the participants to assess multiple samples and/or to undertake multiple replicate analysis on the test materials. This may not be representative of the routine measurements or tests undertaken by the laboratory.&nbsp;</li><li>The conclusion of the ILC might be that the measurement procedure being assessed is not valid.&nbsp;</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Interlaboratory_comparisons_other_than_proficiency_testing_3_a53ecd2f5d.jpg" alt="Eurachem: Interlaboratory comparisons other than proficiency testing" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Interlaboratory_comparisons_other_than_proficiency_testing_3_a53ecd2f5d.jpg 157w," sizes="100vw" width="570" height="566"><h4>Characterisation of candidate reference materials&nbsp;</h4>These are ILCs that assign a property value to a candidate reference material, usually with a stated measurement uncertainty. These are often referred to as a ‘material certification study’ [2]. Limitations of using such ILCs as an alternative to PT are:&nbsp;<ul><li>The analysis of the candidate reference material is likely to require additional care in exercising the routine measurement procedure, such as substantially more replication, and/or may require quite different reporting formats (e.g. all valid replicates instead of a mean result). Thus, such an ILC will not necessarily provide an evaluation of the performance of the laboratory based on the routine use of their measurement procedures.&nbsp;</li><li>The report from such an ILC may include the results from the participating laboratories but would not provide information on the performance of each laboratory.&nbsp;</li><li>Participating laboratories may be required to assess multiple samples and/or to undertake multiple replicate analysis on the candidate reference materials. Thus this may not be representative of the routine measurements or tests undertaken by the laboratory.&nbsp;</li></ul><h4>Other ILCs&nbsp;</h4>Some other ILCs, whilst not being conducted in full compliance of the requirements of ISO/IEC 17043, may be designed to evaluate participant performance. These would include small ILCs as defined by EA‑2/21 [5] or split sample ILCs i.e. where a portion of a single sample is sent to two or three laboratories. Whilst these ILCs are specifically designed to evaluate participant performance, they may not provide a comprehensive comparison that a PT might provide. In such cases the laboratory needs to evaluate carefully the competence of the organization managing the ILC and the suitability of the service externally provided (ISO/IEC 17025 clause 6.6).

Interlaboratory comparisons other than proficiency testing

RI Calibration in NIST26 Chromatogram and Applying to Calculating RI in Samples

Article

Product

Application

Academy

Video

Software

Eurachem is a European network promoting international traceability of chemical measurements and good analytical practice. It provides expert guidance, supports method validation, uncertainty evaluation, proficiency testing, and facilitates discussion on technical and policy issues in analytical quality.

Eurachem

In 1936, the Laboratory Equipment Company introduced the first rapid carbon determinator for the American iron and steel industry.  Today, almost 80 years later, LECO is recognized globally as a leader in the development of high-quality analytical instrumentation, mass spectrometers, comprehensive two-dimensional chromatography, metallography & optical equipment as well as consumables.

LECO

My main interest is the identification of organic compounds by mass spectrometry in organic mixtures.

James Little/Mass Spec Interpretation Services

Syft Technologies

Shimadzu Corporation

Spectroscopy & Separation Science

In the third episode of our six-part series on SIFT-MS automation, series presenter, and global authority in automated SIFT-MS, Dr Mark Perkins describes application of SIFT-MS to multiple headspace extraction (MHE) to give matrix-independent concentration determinations.
Furthermore, Dr Perkins also describes how the speed of SIFT-MS analysis can be leveraged to accelerate method development for both SIFT-MS and conventional methods – for example, through rapid determination of headspace equilibration times.
Automation of analysis is extremely important in modern analytical laboratories. How has it been achieved for SIFT-MS, a direct analysis technique? Join our series presenter, and global authority in automated SIFT-MS, Dr Mark Perkins who describes in six episodes the principles and practice of SIFT-MS automation that make it an excellent fit for routine and R&amp;D laboratories.
<ul>
<li>Episode 1: Why Automate Analysis?</li>
<li>Episode 2: Accelerate Static Headspace Analysis with SIFT-MS</li>
<li>Episode 3: Advanced Headspace Methods with SIFT-MS</li>
<li>Episode 4: Calibration Approaches for Automated SIFT-MS</li>
<li>Episode 5: Continuous Headspace Analysis… and Beyond</li>
<li>Episode 6: SIFT-MS Automation: Past, Present and Future</li>
</ul>
Benefit from Mark’s extensive SIFT-MS automation experience!
<blockquote>
Presenter: Dr Mark Perkins (Senior Applications Chemist, Anatune, United Kingdom)
</blockquote>
<blockquote>
Host: Dr. Vaughan Langford (Principal Scientist / Consultant)
</blockquote>

Syft Technologies: The SIFT-MS Automation Series: Principles and Practice in Six Episodes!

Advanced Headspace Methods with SIFT-MS

Shimadzu Cannabis Analysis Series [Session 2]
<ul>
<li>2-in-1: Terpene Profiling &amp; Residual Solvent Analysis</li>
<li>Why is terpene profiling &amp; residual solvent testing important?</li>
<li>How is the testing performed?</li>
<li>How are the samples prepared?</li>
</ul>
This series is ideal for anyone currently working on medicinal cannabis or other cannabis products, looking to expand further as well as those new to this area of research who are eager to learn more.
<h3>Future sessions will aim to cover topics such as:</h3>
<ul>
<li>Pesticides analysis</li>
<li>Heavy metals</li>
<li>Purification strategies</li>
</ul>

Shimadzu: Shimadzu Cannabis Analysis Series - Terpenes & Residual Solvents

Shimadzu Cannabis Analysis Series - Terpenes & Residual Solvents

There is increased concern regarding the authenticity of basmati rice throughout the world. For years, traders have been passing off a lesser quality rice as the world&#39;s finest long-grained, aromatic rice, basmati, in key markets like the US, Canada and the EU.
In this on-demand webinar, we describe a proof of principle method to assess the authenticity of basmati rice using the latest advancements in high resolution GC-MS and informatics technologies. Volatile compounds of interest were extracted from heated dry rice via SPME and headspace. Following a generic GC separation, detection was performed using a SYNAPT G2-Si MS run in HDMSE mode. Collection of a HDMSE dataset offers a high level of specificity owing to the orthogonal nature of the ion mobility separation to the GC separation.
Progenesis QI, the latest OMICS informatics package from Waters, is designed to utilize the 4-dimensional data produced during a SYNAPT G2-Si HDMSE acquisition. Initially, alignment of all injections was performed followed by the unique peak co-detection process resulting in the same number of analyte measurements in every sample and no missing values. Data from all isotopes and adducts of a parent compound were then deconvoluted giving a single robust and accurate quantitative measurement for that parent compound. The peak picking and deconvolution algorithms ensure a high quality compound table where adducts and isotopes are grouped with the parent m/z. Compounds of interest from the rice samples were highlighted using various multivariate statistical techniques such as PCA and identified using elucidation tools and relevant database searches within the software platform.
This workflow has general applicability to many areas of discovery OMICS and food research where biomarkers differentiating between two or multiple groups are required.
<h3>What will I learn?</h3>
<ul>
<li>An experimental workflow to evaluate the authenticity of foodstuffs</li>
<li>A basic workflow for the discovery of volatile biomarkers between multiple groups of samples</li>
<li>The basics of HDMSE data independent acquisition</li>
<li>How Progenesis QI is used to analyze metabolomics data</li>
</ul>
<h3>Who should attend?</h3>
<ul>
<li>Food research scientists</li>
<li>Metabolomics research scientists</li>
<li>Biomarker research scientists</li>
</ul>
<blockquote>
Presenter: Robert Tonge, Ph.D. (Senior Product Manager, OMICS Informatics, Waters Corporation)
</blockquote>
<blockquote>
Dr Robert Tonge is Senior Product Manager for OMICS Informatics at Waters Corporation, Manchester UK. Robert has been at Waters for 4 years in both Product Development and Field Marketing and has approaching 20 years experience in OMICS science. He trained initially in Biochemistry and Toxicology before moving to &#39;Big Pharma&#39; where he worked in Discovery and Translational Research across a wide variety of disease areas from cancer, cardiovascular disease and infection research.
</blockquote>
<blockquote>
Presenter: Gareth Cleland, Ph.D. (Principal Scientist, Food &amp; Environmental Business, Waters Corporation)
</blockquote>
<blockquote>
Dr Gareth Cleland is a Principal Scientist within the Food and Environment Business Operations at Waters Headquarters, USA. Following his Chemistry studies, he started his professional career working as a Development Chemist for a small coatings company in Oxfordshire, UK. He joined Waters Corporation in 2002 and has held various roles within the organization, both UK and US based, over the last 12 years.
</blockquote>

Pixabay/Trung Hieu Dang: Discovery OMICS and Progenesis QI for Food Authenticity Testing

GCMS webinars focusing on HeadSpace - page 9

Advanced Headspace Methods with SIFT-MS

Shimadzu Cannabis Analysis Series - Terpenes & Residual Solvents

Discovery OMICS and Progenesis QI for Food Authenticity Testing

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RI Calibration in NIST26 Chromatogram and Applying to Calculating RI in Samples

Interlaboratory comparisons other than proficiency testing

Confident Identification of Battery Solvent Impurities Using Complementary GC-MS Techniques