<p><strong>🎤 Presenter:</strong> Kevin Hayes (Mount Royal University)</p><p>💾 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/paper/32773"><strong>PDF presentation</strong></a></p><h3><strong>Abstract</strong></h3><p><strong>An elemental analysis was conducted on matched pairs of new and used aircraft engine oils for various aircraft engine types (piston, turboprop, and jet). The analysis aimed to determine what, if any, accumulation or loss of oil and fuel additives may occur with engine use. Losses in elemental loadings from new to used oils imply that there may be a possibility for the element-containing compounds to enter the pneumatic system of bleed air pressurized aircraft at a higher rate than that of oil attrition and potentially impact cabin air quality. Inductively Coupled Plasma Optical Emission Spectroscopy (ICP OES) was employed to complete the elemental analysis, and results describe a greater than twenty percent loss of phosphorus from new to used jet oils and a significant accumulation of lead in the oil of piston aircraft (Range: below LOD to 6821 ± 83 mg kg-1 ; n=2).</strong></p><p>In an effort to speciate and identify compounds of concern, the oil samples were introduced to a flow modulated GCxGC-TOFMS (SepSolv BenchTOF). Data was analyzed utilizing Chromspace and Analyzer Pro XD. The new and used oils were compared, and compounds exclusive to the used oils were screened to determine the possibility of increased risk associated with hazardous accumulations in the used oil. This used oil, which is adulterated by either amendment or contamination, is not fully described to individuals who may interact with the product, and therefore an apparent risk is potentially unmitigated.</p><h3><strong>Video transcription</strong></h3><h4><strong>2. Background: Aircraft Engine Oils</strong></h4><p>Aircraft engine oils are chemically complex and vary depending on:</p><ul><li>Manufacturer</li><li>Aircraft type</li><li>Engine type</li></ul><h5><strong>2.1 Piston Engine Oils</strong></h5><ul><li>Typically mineral oil-based</li><li>Simpler formulations</li><li>Lower temperature and pressure requirements</li></ul><h5><strong>2.2 Jet and Turboprop Oils</strong></h5><ul><li>Often based on pentaerythritol esters</li><li>Contain organophosphate additives</li><li>Designed to withstand high temperatures and pressures</li><li>May include aftermarket additives (sometimes required by FAA airworthiness directives)</li></ul><h4><strong>3. Engine Types and Fuels</strong></h4><p>The study included oils from:</p><ul><li>Jet aircraft</li><li>Turboprop aircraft</li><li>Piston engine aircraft</li></ul><h5><strong>3.1 Fuel Differences</strong></h5><ul><li><strong>Jet and turboprop aircraft</strong><ul><li>Jet A (U.S.)</li><li>Jet A-1 (international)</li><li>Jet B (naphtha-based, cold climates)</li><li>Kerosene-based fuels</li></ul></li><li><strong>Piston aircraft</strong><ul><li>Use aviation gasoline (avgas)</li><li>Notably contains tetraethyl lead (anti-knock additive)</li></ul></li></ul><h5><strong>3.2 Engine Construction Differences</strong></h5><ul><li>Jet/turboprop engines:<ul><li>Contain superalloys (steel alloys with molybdenum, nickel, etc.)</li></ul></li><li>Piston engines:<ul><li>Simpler construction</li><li>No need for high-temperature superalloys</li></ul></li></ul><h4><strong>4. Purpose of the Study</strong></h4><p>The research aimed to determine:</p><ol><li>Whether compounds are lost from oils during use</li><li>Whether oils accumulate new compounds during service</li><li>Whether these changes pose occupational health risks</li><li>Whether lost compounds could enter aircraft cabins via bleed air pathways (jet/turboprop engines)</li></ol><h4><strong>5. Elemental Analysis (ICP-OES)</strong></h4><h5><strong>5.1 Sample Collection</strong></h5><ul><li>Used oils from:<ul><li>University aviation fleet (piston aircraft)</li><li>Airport partners (turboprop)</li><li>European business jet operator (limited jet samples)</li></ul></li></ul><h5><strong>5.2 Analytical Technique</strong></h5><ul><li>Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES)</li></ul><h4><strong>6. Results: Wear Metals</strong></h4><p>Fresh oil:</p><ul><li>No wear metals detected (as expected)</li></ul><p>Used piston oils:</p><ul><li>Increased iron and nickel (expected wear metals)</li><li>Cadmium detected (potential concern)</li></ul><p>Used jet/turboprop oils:</p><ul><li>Lower wear metals overall</li><li>Presence of molybdenum (consistent with superalloys)</li></ul><h4><strong>7. Unexpected Findings</strong></h4><h5><strong>7.1 Sulfur</strong></h5><ul><li>Increased sulfur in piston oils</li><li>Expected due to aviation fuel allowances</li></ul><h5><strong>7.2 Lead (Major Finding)</strong></h5><ul><li>Extremely high levels (&gt;6000 mg/kg)</li><li>Lead content 7–8× higher in oil than in avgas</li><li>Oil actively accumulates lead from combustion</li></ul><p>Source:</p><ul><li>Tetraethyl lead in aviation gasoline</li></ul><h4><strong>8. Phosphorus Trends</strong></h4><h5><strong>8.1 Piston Engines</strong></h5><ul><li>~600 ppm phosphorus detected</li><li>Source unclear (likely additive-related)</li></ul><h5><strong>8.2 Jet and Turboprop Engines</strong></h5><ul><li>Phosphorus levels decreased during use</li><li>Likely linked to organophosphate additive depletion</li><li>Important because:<ul><li>Phosphorus stabilizes oil at high temperatures</li><li>Loss may indicate additive degradation</li><li>Possible availability to bleed air pathway</li></ul></li></ul><p><i>Note: Large jet aircraft often do not undergo traditional oil changes; oil is replenished continuously.</i></p><h4><strong>9. GC×GC Analysis</strong></h4><h5><strong>9.1 Method</strong></h5><ul><li>Oil diluted in hexane</li><li>High split ratio</li><li>Instrumentation:<ul><li>Agilent 7890 GC</li><li>Insight flow modulator</li><li>Benchtop mass spectrometer</li></ul></li><li>Hard ionization (70 eV)</li></ul><h4><strong>10. Organo-Phosphate Speciation</strong></h4><h5><strong>10.1 Jet and Turboprop Oils</strong></h5><p>Detected:</p><ul><li>Tricresyl phosphate (TCP) isomers<ul><li>Tri-meta-cresyl phosphate</li><li>Tri-para-cresyl phosphate</li></ul></li></ul><p>Unknown peaks:</p><ul><li>Likely additional TCP isomers</li></ul><p>Important:</p><ul><li>No confirmation yet of ortho-TCP</li><li>Ortho-TCP is neurotoxic</li><li>Historically responsible for "Jake Leg Syndrome"</li></ul><p>Conclusion:<br>Phosphorus loss likely linked to degradation of TCP molecules.</p><h4><strong>11. Oil Complexity After Use</strong></h4><p>Used oils showed:</p><ul><li>Increased chromatographic complexity</li><li>More peaks in early chromatogram</li><li>Likely degradation/pyrolysis products of pentaerythritol esters</li></ul><p>Work ongoing to identify these products.</p><h4><strong>12. Lead Speciation</strong></h4><h5><strong>12.1 Tetraethyl Lead Behavior</strong></h5><p>Characteristics:</p><ul><li>Early elution (low boiling point)</li><li>Distinctive fragmentation pattern</li><li>Easy to identify</li></ul><p>Finding:</p><ul><li>No tetraethyl lead detected in used oils</li></ul><p>Implication:</p><ul><li>Likely fully degraded during combustion</li><li>Remaining species likely lead oxide</li><li>Still toxic, but less dermally bioavailable than tetraethyl lead</li></ul><h4><strong>13. Phosphorus in Piston Oils</strong></h4><p>Expected:</p><ul><li>Tricresyl phosphate (TCP)</li></ul><p>Observed:</p><ul><li>Triphenyl phosphate</li><li>Possibly tert-butyl phenyl diphenyl phosphate (tentative identification)</li></ul><p>Significance:</p><ul><li>Different compound than expected</li><li>Less hazardous than ortho-TCP</li><li>2D separation critical to avoid misidentification</li></ul><h4><strong>14. Overall Conclusions</strong></h4><ul><li>Aircraft engine oils are chemically dynamic</li><li>Oils change significantly during service</li><li>Accumulation (e.g., lead) and depletion (e.g., phosphorus additives) both occur</li><li>Used oil composition differs from safety data sheet composition</li><li>ICP-OES provides screening-level insight</li><li>GC×GC enables detailed compound-level resolution</li></ul><p>The aviation oil space remains a promising area for continued research.</p><blockquote><p><strong>This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.</strong></p></blockquote>

Analysis of fresh and used aircraft oil: Exposure possibility to pollutants (Kevin Hayes, MDCW 2026)

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 2nd March 2026? Check out new documents from the field of the gas phase, especially GC and GC/MS techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT GCMS AND RELATED TECHNIQUES</strong></span></a></p></blockquote><blockquote><p><strong>👉 Need info about different analytical techniques?</strong> Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library"><strong>LabRulezLCMS</strong></a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library"><strong>LabRulezICPMS</strong></a> libraries.</p></blockquote><p>This week we bring you application notes by <strong>Agilent Technologies, BaySpec </strong>and<strong> Shimadzu </strong>and<strong> </strong>presentation by<strong> MDCW / JEOL!</strong></p><h3><strong>1. Agilent Technologies: Total Hydrocarbon Impurity Analysis in PEM Fuel Cell Grade Hydrogen Using the Agilent 8890 GC-FID System</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_hydrocarbon_impurity_analysis_pem_fuel_cell_8890_gcfid_5994_8935en_agilent_00a26949ef.pdf"><strong>Full PDF for download</strong></a></li></ul><p>High-purity hydrogen is crucial for the optimal and long-term performance of fuel cells.&nbsp;Different than the industry grade hydrogen used for chemicals, refining, and metal processing, where small amounts of impurities are acceptable. Fuel cell hydrogen is required to meet the species-specific impurity limits described in the specifications. Such impurities include hydrocarbons, sulfur-containing compounds, ammonia, permanent gases, halogens, and formaldehyde. Each group of compounds has its own regulation limit. The limits are defined based on their impact on the fuel cell lifetime and performance. ISO 14687 and SAE J2719 are the most recognized standards for regulation of fuel cell hydrogen quality.&nbsp;</p><p>Multiple testing points are implemented throughout the whole supply chain of fuel cell grade hydrogen—from its production, storage, and distribution to its final application. The analytical approaches employed depend on the impurity type and the corresponding limit. Among the varied contaminants, hydrocarbon impurity is controlled based on THC amount (excluding methane because it has a separate specific limit) and the quality limit as specified in ISO 14687 and SAE J2719 (2&nbsp;ppm).&nbsp;</p><p>There are two ways to analyze THCs in hydrogen. One is to separate and detect each hydrocarbon, then add their concentrations to get the THC amount (excluding methane). This analytical approach is recommended by the GB/T 44238-2024 test method.<sup>1</sup> The other way is by eluting all hydrocarbons as a single peak and quantitating the peak according to methane's response factor (RF). The total THC is then obtained by subtracting the methane amount, which is analyzed by a separate method.</p><p>In this application note, THC impurity was tested using the second approach. An Agilent 8890 GC with FID system were used for THC analysis, and the Agilent <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=990+Micro+GC"><strong>990 Micro GC</strong></a> was used for methane measurement. The methane analysis solution is published in application note 5994-8830EN.2 Here, we will showcase THC analysis using the 8890 GC-FID system.&nbsp;</p><h4><strong>Experimental&nbsp;</strong></h4><p>The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890"><strong>8890 GC</strong></a> is configured with one gas sampling valve, a split/splitless (S/SL) inlet, and a flame ionization detector (FID), as shown in Figure 1. The fixed volume of gas sample is injected into the S/SL inlet and part of the sample flows into an uncoated capillary column, then elutes off as a single peak for detection. Table 1 lists experimental conditions for the analysis. The standard gases shown in Table 2 are used to verify system precision, generate methane's RF, and calculate method detection limit (MDL).</p><h4><strong>Conclusion&nbsp;</strong></h4><p>This application note demonstrates THC&nbsp;analysis in fuel cell hydrogen using the Agilent 8890 GC-FID system. The hydrocarbons eluted as a single peak on an uncoated column and were quantitated using methane's RF. The method repeatability and reproducibility were around 2%. Method accuracy was better than 120%. The MDL was 0.97&nbsp;ppm. All results met the analytical requirements of regulating THC in fuel cell grade hydrogen according to SAE&nbsp;J2719 and ISO 14687.&nbsp;</p><p>The applied analytical approach requires methane analysis by a separate method, which can be realized using the Agilent 990 Micro GC or using a coated column followed by FID detection on another channel of the 8890 GC system.</p><h3><strong>2. BaySpec: Rapid Detection of Trace Levels of Deoxynivalenol in Wheat Flour by Portable Miniature Linear Ion Trap Mass Spectrometry</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/Bay_Spec_App_Note_MS_Portability_In_Wheat_Quality_04959a96bd.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Deoxynivalenol (Cas:51481‐10‐8), also known as DON, is a trichothecene mycotoxin that is widely found in grains such as wheat, barley, and rice.&nbsp;&nbsp;The molecular structure of DON is provided in figure 1.&nbsp;&nbsp;DON is linked to two plant pathogens: Fusarium graminearum (wheat and corn), and Fusarium ear blight (corn only).&nbsp; &nbsp; DON inhibits the synthesis of DNA / RNA as well as protein synthesis at the ribosomal level, ultimately resulting in the loss of yield and contamination of seeds.&nbsp;&nbsp;An acute dose of DON can induce vomiting (emesis) in pigs, whereas at lower concentrations in the diet, it reduces growth and feed consumption (anorexia).<sup>1</sup>&nbsp;The U.S. FDA has established DON advisory levels to provide safe food and feed at concentrations of &lt; 10 ppm to &lt; 1 ppm depending on application.<sup>2</sup></p><p>Because DON is difficult to derivatize, this compound is typically analyzed via thin‐layer chromatography (TLC) or high performance liquid chromatography (HPLC).<sup>3</sup> While TLC is simple, fast, and economical, this analytical technique does not provide high accuracy nor consistent results on a day‐to‐day basis.&nbsp;&nbsp;HPLC improves on accuracy and consistency but requires large quantities of costly solvents and produces large volumes of hazardous waste.&nbsp;&nbsp; Depending on sampling requirements and system performance, the annual maintenance costs for a standard HPLC instrument can run ~$10‐20k per year.<sup>4</sup> &nbsp;&nbsp; Furthermore, although HPLC systems can be easy to use when properly configured, a trained professional is typically needed for troubleshooting, maintenance, and developing new methods.</p><p>Two samples were analyzed in a recent case study for an agricultural firm based in Canada.&nbsp;&nbsp;&nbsp;One sample contained 200 mg of wheat powder, while the other sample was 1 mg of the pure DON compound. Measurements were taken on a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2219"><strong>BaySpec Portability<sup>TM</sup> Mass Spectrometer</strong></a> (Figure 2) with both thermal desorption (TD) electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) methods. Samples were tested “dry” either by probing the surface of the sample vials or by spreading the powder onto a clean surface and swabbing. An enlarged image of the sampling probe with a small amount of wheat powder is shown in Figure 3.</p><h3><strong>3. MDCW / JEOL: Structural elucidation using GCxGC-TOFMS and machine learning for unknown metabolites in HeLa cell</strong></h3><ul><li>Presentation</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_7_Masaaki_Ubukata_2_56a67fdf00.pdf"><strong>Full PDF for download</strong></a></li></ul><p>The presentation by Masaaki Ubukata focuses on advancing structural elucidation of unknown metabolites in HeLa cells using comprehensive two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?instrumentationItems=2-13-3-5&amp;mainauthorItems=23"><strong>GC×GC-HRTOFMS</strong></a>) and machine learning. The work addresses a major limitation in metabolomics: while GC-MS offers highly reproducible EI fragmentation and strong library support, only a tiny fraction of known chemical space is represented in standard databases such as NIST (about 0.3% of compounds). To overcome this gap, the team developed an AI-based approach capable of predicting EI mass spectra and retention indices for over 200 million compounds, creating a large in-silico spectral library for unknown identification.</p><p>The workflow integrates accurate mass measurements from GC-HRTOFMS with both electron ionization (EI) and soft ionization data. Molecular formulas are first determined using isotopic patterns and fragment ion accuracy, then candidate structures are narrowed using predicted retention indices and compared against AI-predicted EI spectra ranked by cosine similarity. Model performance has steadily improved, reaching an average cosine similarity of 0.86, with correct structures ranked first in 56% of evaluation cases and within the top 10 in 87% of cases. This demonstrates strong capability for resolving unknown compounds beyond conventional library searches.</p><p>The approach was applied to HeLa cell metabolomics, a well-established human cell model. Using GC×GC separation, more than 800 metabolites were detected. Importantly, the system successfully identified N-methyl-uridine monophosphate (N-methyl UMP), a biologically relevant RNA-modification-related metabolite not registered in the NIST database. After molecular formula determination and AI ranking, N-methyl UMP was identified as the top candidate among 53 structural isomers sharing the same formula, demonstrating the practical strength of combining high-resolution MS, soft ionization, and AI-based spectral prediction.</p><p>In conclusion, the study highlights the synergistic power of three technologies: high-resolution GC-TOFMS, soft ionization techniques, and AI-driven structural analysis. Together, they significantly enhance the identification of unknown metabolites in non-targeted metabolomics and provide a scalable solution for exploring chemical space far beyond existing experimental spectral libraries.</p><h3><strong>4. Shimadzu: Analysis of Aroma Components in Apples Using the Smart Aroma Database</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_01016_en_e989b26b7f.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>User Benefits</strong></h5><ul><li>The Smart Aroma Database enables straightforward qualitative analysis of aroma components.</li><li>The HS-20 NX headspace sampler allows for high-sensitivity and convenient analysis of the aroma components in fresh apples.</li><li>Multivariate analysis facilitates an objective evaluation of aroma component differences between samples.</li></ul><p>The aroma components of fruits are one of the key factors that determine their flavor and taste, serving as a significant criterion for consumers when choosing fruits. These aroma components vary based on the type and variety of the fruit, its ripeness, production area, and cultivation environment. The complex interplay of these factors shapes the unique flavors of individual fruits. Therefore, the analysis of aroma components plays an important role in quality control and the development of new fruit varieties. The compounds that make up the aroma of fruits are diverse, including both volatile and non-volatile components, and their numbers can range from dozens to hundreds. These components, which include alcohols, esters, ketones, and acids, possess various chemical structures and contribute to the aroma of the fruit. Gas chromatography mass spectrometry (GC/MS) offers an effective method for objectively evaluating the relationship between aroma and aroma components. This article describes an example of analyzing the aroma components in apples using a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GCMS-QP2050"><strong>GCMS-QP2050</strong></a> <strong>gas chromatograph mass spectrometer</strong> in combination with an<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=HS20+NX"><strong> HS20 NX headspace sampler</strong></a> (Fig. 1).</p><h4><strong>Multivariate Analysis&nbsp;</strong></h4><p>Three types of apples, with five samples each (for a total of 15 samples), were measured once. The obtained data were analyzed using SIMCA17 multivariate data analysis software (Informatics Co., Ltd.) to perform principal component analysis (PCA) and hierarchical clustering analysis. The PCA score plot (Fig. 2), loading plot (Fig. 3), and the dendrogram from the hierarchical clustering analysis (Fig. 4) are shown.&nbsp;</p><p>Based on the PCA score plot, the contribution rate of the first principal component (horizontal axis) is 85.6 %, and the contribution rate of the second principal component (vertical axis) is 10.2 %, totaling 95.8 %. The clear separation of the plots by apple type indicates that there are differences in the aroma of the three varieties. Additionally, the results of the hierarchical clustering analysis show that Jonagold (A) and (B) have relatively similar compositions of aroma components.&nbsp;</p><p>Table 3 shows the aroma components that are relatively abundant in Jonagold (A) along with the sensory information for each aroma component registered in the Smart Aroma Database.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>We analyzed the aroma components of three types of commercially available apples using a GCMS-QP2050 gas chromatograph mass spectrometer. By utilizing an HS-20 NX headspace sampler, we were able to analyze the aroma components of fresh apples with high sensitivity, and the Smart Aroma Database made the process convenient.&nbsp;</p><p>We consider that scientifically analyzing the differences in aroma components based on factors such as fruit type, variety, ripeness, production area, and cultivation environment is beneficial for quality management and the development of new varieties.</p>

News from LabRulezGCMS Library - Week 10, 2026

<p><strong>Symposium "Advances in Chromatography and Electrophoresis &amp; Chiranal" belongs to the periodical meetings of specialists working in the field of separation methods. The biennial conference is organized by Department of Analytical Chemistry, Faculty of Science, Palacky University in cooperation with Czech Chemical Society and Czech Society for Mass Spectrometry. It is organized as a meeting of the scientists from the universities, academic institutes and experts from the routine analytical laboratories.</strong></p><p><a target="_blank" rel="noopener noreferrer" href="https://www.upol.cz/chiranal/programme/"><strong>The scientific program</strong></a> will deal with all areas of separation science from fundamental to applied reseach in chemistry, physics, biology, medicine, forensic science and related disciplines. Official languages of the conference are English, Czech and Slovak.</p><ul><li><strong>Date: </strong>15. - 18. June 2026</li><li><strong>Place:</strong> Olomouc, Czech Republic</li><li><strong>Contact: </strong>prof. RNDr. Petr Bednář, Ph.D. (Department of Analytical Chemistry, Faculty of Science, Palacky University)<ul><li><strong>e-mail: </strong>petr.bednar@upol.cz</li></ul></li></ul><h3><strong>Registration</strong></h3><blockquote><p class="cke-user_style-important"><strong>👉</strong><a target="_blank" rel="noopener noreferrer" href="https://www.upol.cz/chiranal/registration/"><strong>Register for the conference HERE</strong></a></p></blockquote><ul><li><strong>Conference Fees:</strong> 6.000,- CZK / 250 EUR &nbsp;without VAT</li><li><strong>Deadline for registration is May 31st, 2026.</strong></li><li>You will receive a registration confirmation by email.</li></ul><h3><strong>Abstract Submission</strong></h3><blockquote><p class="cke-user_style-important"><strong>👉</strong><a target="_blank" rel="noopener noreferrer" href="https://www.upol.cz/chiranal/abstract-submission/"><strong>Submit your Abstract HERE</strong></a></p></blockquote><ul><li><a target="_blank" rel="noopener noreferrer" href="https://www.upol.cz/fileadmin/users/381/template.docx"><strong>Download Abstract Template</strong></a></li><li>The <strong>deadline for submitting</strong> abstracts for <strong>oral presentations</strong> and posters is <strong>May 11, 2026</strong>.</li></ul><h3><strong>Preliminary Programme</strong></h3><h4><strong>Monday 15th June 2026, Fort Science</strong></h4><h5><strong>16:15 - 17:15 Plenary Lecture</strong></h5><ul><li>Michael Laemmerhofer</li></ul><h4><strong>Tuesday 16th June 2026</strong></h4><h5><strong>11:30 - 12:00 Enantioselective Ion Pairing Systems, my 50 years of Contribution</strong></h5><ul><li>Wolfgang Lindner&nbsp;</li></ul><h5><strong>16:30 - 17:00 Data science-assisted method development for on-line supercritical fluid extraction – chromatography</strong></h5><ul><li>Kevin Schug (<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/companies/301"><strong>The University of Texas at Arlington</strong></a>)</li></ul><h4><strong>Wednesday 17th June 2026</strong></h4><h5><strong>9:00 – 9:30 The Role of Analytical Chemistry in Solving Contemporary Challenges in Biomethane Plants</strong></h5><ul><li>Justyna Płotka-Wasylka</li></ul><h5><strong>11:30 - 12:00 Rational approaches to MALDI-MS matrix design</strong></h5><ul><li>Hans-Dieter Junker</li></ul><h5><strong>12:50 - 13:10 Using Charge Detection Mass Spectrometry with an Electrostatic Linear Ion Trap for the Analysis of Viral Vectors and Large Protein Complexes</strong></h5><ul><li>Alex Muck (Waters)</li></ul><h4><strong>Thursday 18th June 2026</strong></h4><h5><strong>9:00 – 9:30 Capillary Electrophoresis - Mass Spectrometry for the characterization of proteoforms on the intact level</strong></h5><ul><li>Christian Neusüß</li></ul><h5><strong>9:30 – 10:00 Investigation of acid-base and electromigration properties of biopeptides by capillary electrophoresis</strong></h5><ul><li>Václav Kašička</li></ul><h5><strong>11:30 - 12:00 Open-source capillary electrophoresis - democratizing separation science: Design principles and practical implementations from our laboratory</strong></h5><ul><li>Petr Kubáň</li></ul><h5><strong>12:00 - 12:30 Analysis of biomedical, environmental and pharmaceutical samples by microchip electrophoresis</strong></h5><ul><li>Marián Masár</li></ul><h5><strong>12:30 - 13:00 UV-LED coupling for high-sensitivity glycan analysis in commercial capillary electrophoresis system</strong></h5><ul><li>Jana Lavická</li></ul><h4><strong>Sponsors</strong></h4><ul><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.cz/companies/37"><strong>Altium International s.r.o. 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