In the week of July 13, the following webinars await you in the field of gas / solid phase and especially Raman, FTIR, GC and GC/MS.<blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinars">👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</a></h4>👉 Search and filter in the largest global database with over 5 500 GC, LC, MS and spectroscopy webinars.</blockquote><h5>1. Markes International: Understand the aromas consumers really experience</h5><ul><li>Tu, 14.7.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/6382">Registration</a></li></ul>Join Markes as they unveil their latest innovation and explore a new approach to helping researchers and product developers gain a deeper understanding of aroma evolution.<h5>2. Thermo Fisher Scientific: Helium, PFAS &amp; Recycled Plastics: Addressing Key Challenges in Industrial Laboratories</h5><ul><li>Th, 16.7.2026 08:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/6337">Registration</a></li></ul>Tackle industrial GC challenges. Learn strategies for helium conservation, PFAS testing using TD-GC-MS, and analytical solutions for recycled plastics.<h5>3. C&amp;EN: Microplastics Analysis You Can Defend: From Method to Results</h5><ul><li>Th, 16.7.2026 11:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/6334">Registration</a></li></ul>Strengthen your microplastics analysis. Learn how combining LDIR chemical imaging and pyrolysis-GC/MS improves data quality across complex sample matrices.

Webinars LabRulezGCMS Week 29/2026

This study optimizes the quantification of hydrogen point-source emissions through controlled-release experiments using an 8 kW electrolyzer. A multiplatform AirCore sampling system coupled with a high-resolution GC-PDHID simultaneously measured hydrogen, methane, and carbon dioxide to evaluate how sampling conditions influence emission estimates.Analysis of UAV and ground-based measurements showed that plume coverage, wind variability, and sampling resolution strongly affect emission accuracy. The results also demonstrated that hydrogen plume behavior closely resembles methane, indicating that established methane monitoring strategies can be adapted for hydrogen leak detection and supporting future assessments of hydrogen as an indirect greenhouse gas.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsestair.6c00006">Controlled-Release Experiment to Optimize Emission Quantification of H2 Point Sources</a></h4>Iris M. Westra*, Hubertus A. Scheeren, Mareen J. Penninga, Steven M.A.C. van Heuven, and Harro A.J. MeijerACS EST Air 2026, 3, 6, 1576–1585<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acsestair.6c00006">https://doi.org/10.1021/acsestair.6c00006</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.<div>Currently, more than 90% of hydrogen produced is gray hydrogen, derived primarily from steam methane reforming or coal gasification, both of which are carbon-intensive. <a href="javascript:void(0);">(1)</a> Considering the global hydrogen budget, the primary natural source of atmospheric hydrogen is sunlight-driven production (e.g., via methane oxidation), contributing 38 ± 6 Tg yr–1 (about 55% of the total budget). The largest anthropogenic source of hydrogen is the combined contribution from biofuel burning and wildfires, amounting to 12 ± 4 Tg yr–1. <a href="javascript:void(0);">(1)</a> Hydrogen’s indirect Global Warming Potential (GWP) leads to the lengthening of the lifetime of CH4 and ozone, and to increased levels of stratospheric water vapor (H2O). <a href="javascript:void(0);">(2,3)</a> As a result, the GWP of H2 over a 100-year time scale has been estimated at 11.6 ± 2.8, indicating that H2 is 12 times more potent than CO2. <a href="javascript:void(0);">(4)</a> While this is about half the GWP of CH4 it underscores the significance of hydrogen’s indirect effects on climate when released into the atmosphere. <a href="javascript:void(0);">(5)</a> Until recently, small but climate-relevant hydrogen emissions remained undetected along the entire hydrogen value chain. However, such hydrogen losses during production, transport, storage and consumption could lead to a greater atmospheric hydrogen burden. <a href="javascript:void(0);">(3)</a> To illustrate the impact of hydrogen leakage, the HC2017 scenario presented in Hauglustaine et al. (2022) shows that, for a hydrogen mix of 30% blue (gray+CCS) and 70% green hydrogen (renewables) with leakage rates of 1–3%, the climate benefit is substantially reduced, with hydrogen CO2-equivalent emissions corresponding to 20 to 54% of the avoided CO2 (based on GWP100 and GWP20, respectively). At higher leakage rates (&gt;10% for a 100-year horizon; &gt; 5% for a 20-year horizon), this hydrogen mix results even in a net climate penalty, where CO2-eq emissions exceed the avoided CO2.Thus, far, current loss rate estimates, ranging up to 10%, are solely based on bottom-up emission estimates (i.e., theory-based). <a href="javascript:void(0);">(6−8)</a> A limited amount of suitable measurement equipment and a current lack in actual measurements causes these estimates to have no validation. <a href="javascript:void(0);">(9)</a>Only recently, sensitive new techniques to detect low-level hydrogen emissions along the value chain on an atmospheric scale have become available. In our previous study <a href="javascript:void(0);">(10)</a> we combined an established high-precision analysis method (gas chromatograph─pulsed discharge helium ionization detector; GC-PDHID) with a newly applied sampling method (active AirCore <a href="javascript:void(0);">(11)</a>) deployed from various mobile platforms, to detect significant hydrogen emissions originating from a chemistry park in the Netherlands. <a href="javascript:void(0);">(10)</a> In the meantime, Aerodyne Research Inc. (USA) has developed a laser-spectroscopy-based instrument with which atmospheric hydrogen can be measured continuously, through oxidation to water vapor and a subsequent optical measurement, achieving ∼5 ppb precision (per 5 s). <a href="javascript:void(0);">(12,13)</a> The Norwegian oil company Equinor, in collaboration with Advanced Monitoring Solutions (AdMS) and the Department of Chemistry (University of Oslo) in Norway, have developed a mass spectrometer system capable of measuring small atmospheric hydrogen mole fractions (&lt;1 umol mol–1 i.e., ppm) with a detection limit of ∼10 nmol mol–1 (i.e., ppb) directly from dried ambient air. <a href="javascript:void(0);">(14)</a> Hence, with these new developments in atmospheric hydrogen detection techniques, the hitherto unknown hydrogen emissions along the hydrogen value chain become visible.In the field of emission quantification there is already a wealth of reported experiments for greenhouse gases (GHG), like methane (CH4), <a href="javascript:void(0);">(15)</a> with the aim of making emission quantifications more reliable and accurate. These emission estimates help reduce uncertainties in bottom-up estimates of the greenhouse gas (GHG) budget. <a href="javascript:void(0);">(16)</a>Optimal airborne plume sampling for methane emissions from a point source depends on factors such as the downwind distance, maximum flight time, measurement precision, and atmospheric stability conditions. <a href="javascript:void(0);">(17)</a> Especially during near-source sampling (&lt;500 m downwind), accurate visualization of the plume in both the horizontal (width) and vertical (height) plane is critical for reducing significant discrepancies between measured emissions and inventory estimates. <a href="javascript:void(0);">(18−20)</a> Furthermore, Morales et al. showed that measurement uncertainties increase primarily under conditions of wind speeds below 2 m s–1, wind direction variability greater than 33°, and sampling at downwind distances greater than 75 m. So far, under the most favorable measurement conditions, CH4 emissions could be quantified with an overall uncertainty of at best 30%. <a href="javascript:void(0);">(17)</a>In this study, we use measurements from a controlled-release experiment to optimize the quantification of hydrogen point-source emissions by identifying the most influential parameters contributing to uncertainties in the emission estimates.In the following, we first outline the sampling and analysis techniques, building on Westra et al., <a href="javascript:void(0);">(10)</a> after which we give a site description and further necessary prior information. The observations obtained from ground-based and airborne measurements are analyzed using a Gaussian plume dispersion model combined with a Monte Carlo uncertainty analysis to estimate the emission rate. At the end of the paper the conclusions and outlook on our methodology will be given.<h4>2. Materials and Methods</h4><h5>2.4. Analysis Methods</h5>The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890">Agilent 8890 gas chromatograph</a> pulsed discharged helium ionization detector (GC-PDHID) system setup was designed for the precise and accurate analysis of H2, CO2 and CH4 in dry ambient air. We chose a PDHID for its high sensitivity, reliability, and good linearity from low to high mole fractions for H2 (here typically ranging between 0.5 up to 5 ppm). The system setup enables, via a heart-cutting procedure (forward-flush mode of the O2 and N2 fraction), the simultaneous detection of CO2 and CH4, which can aid in the source apportionment of atmospheric H2.Our system design was partly based on the Agilent setup 7890–0237 “Impurities in Monomers Analyzer by PDHID” and a GC setup by Wang et al. <a href="javascript:void(0);">(24)</a> The PDHID system uses Helium 5.0 (99.9999% Vol. %, Westfalen) as carrier gas, for a combination of a HayeSep Q, (G3591–81004, 6 ft,18” “, 2 mm, 80–100 mesh packed) pre-column, followed by a ShinCarbon (G3591–81187, 2 m, 18”, 2 mm, 50/80 mesh) analytical column for detection of H2, CH4 and CO2. For the sample analysis a 2 mL sample loop is flushed at a rate of 43 ± 2 mL min–1 for 0.01–0.67 min. One chromatogram lasts 15.5 min for the detection cycle of H2, CH4 and CO2, and 7.5 min for H2 only. Retention times for the H2–CH4–CO2-method are 2.8 ± 0.1 min for H2 with a 4.2 ± 1.0 s wide peak width (PW), 7.7 ± 0.1 min for CH4 with a 16.8 ± 1 s PW, and 13.9 ± 0.1 min for CO2 with a 78 ± 1 s PW, and for the single H2-method at 2.3 ± 0.1 min for H2 with a 4.8 ± 1.0 s PW. A more detailed description of the GC analysis is given in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsestair.6c00006/suppl_file/ea6c00006_si_001.pdf">Section S1.1</a>.<h4>3. Results and Discussion</h4><h5>3.2. AirCore Results</h5>The determined emission rates were achieved by applying the Gaussian dispersion model to the found H2 enhancements for each transect, and by taking into account the atmospheric conditions during sampling, following the approach outlined in Westra et al. <a href="javascript:void(0);">(10)</a> Using an 8 kW electrolyzer (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsestair.6c00006#fig4">Figure 4</a>) with a known, continuous release rate of 1.1 ± 0.1 m3 h–1 as a controlled source, we analyzed 14 data sets to determine a mean weighted hydrogen emission rate of 0.94 ± 0.06 m3 h–1. This agrees well within uncertainty with the known source rate. The relative uncertainty (ratio of uncertainty over average) of the emission estimate is 6%, compared to 9% for the known source. The source-based uncertainty was derived from variability in the electrolyzer exhaust monitored over multiple hours. More information with regard to the uncertainty analysis can be found S1.5. All data gathered during the field experiments is given in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsestair.6c00006/suppl_file/ea6c00006_si_001.pdf">Sections S2.1 and S2.4</a>.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_EST_Air_2026_3_6_1576_1585_Figure_4_Emission_rates_estimated_for_six_sampling_days_using_the_Gaussian_dispersion_model_and_the_Air_Core_technique_a84e3bb1b0.jpg" alt="ACS EST Air 2026, 3, 6, 1576–1585: Figure 4. Emission rates estimated for six sampling days using the Gaussian dispersion model and the AirCore technique, measured downwind of the electrolyzer at varying distances. Measurements were conducted either at ground level (cart icon) or via UAV (drone icon). Boxplots represent results from 500 Monte Carlo simulations, displaying the mean and interquartile range. From 28 to 11–2024 onward the, as described, an optimized sampling method was used." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_EST_Air_2026_3_6_1576_1585_Figure_4_Emission_rates_estimated_for_six_sampling_days_using_the_Gaussian_dispersion_model_and_the_Air_Core_technique_a84e3bb1b0.jpg 234w," sizes="100vw" width="1453" height="970">On day 13–11–2024, the quality of the data collected along the first 100 m trajectory was lower due to a sampling flow rate of ∼45 sccm combined with a relatively high rate of displacement. As a result, the full plume width was not captured, and the spatial resolution of the measurements was insufficient. This limitation, consistent with the findings of Mohammadloo et al., <a href="javascript:void(0);">(15)</a> led to the underestimation of the actual emission rate. Particularly for transect 100 m (1), where the plateau-shaped peak approximated a Gaussian peak, led the dispersion model to substantially underestimate the emission rate (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsestair.6c00006/suppl_file/ea6c00006_si_001.pdf">Sections S1.6 and S2.3</a>). Furthermore, the relatively large spread in data points observed throughout the remaining trajectories of 13–11–2024 can be attributed to the sampling time of the experiment, during which the atmospheric variability (i.e., wind speed and direction) impacted the uncertainty. As a result, the Monte Carlo simulations produced a wider range of solutions due to this variability, and thus a greater spread in the values. So, the impact of the atmospheric conditions could be reduced by minimizing sampling time and thereby reducing the atmospheric variability.For day 14–11–2024 (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsestair.6c00006#fig5">Figure 5</a>), an average wind speed of 1.7 ± 1 m s–1 was measured. According to Andersen, <a href="javascript:void(0);">(22)</a> wind speeds below 2 m s–1 lead to ill-defined wind directions, making the plume less likely to follow a stable path and thereby increasing uncertainty in the emission estimate. Hence, it is likely that the lower wind speed on this day contributed to less reliable results in the emission quantification. Moreover, for day 14–11–2024, the wider spread observed in the estimates (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsestair.6c00006#fig4">Figure 4</a>) is attributed to incomplete coverage of the plume width along the 100 m downwind transects.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_EST_Air_2026_3_6_1576_1585_Figure_5_Trajectory_of_ground_based_Air_Core_profile_downwind_of_the_electrolyzer_2f1d50dfd7.jpg" alt="ACS EST Air 2026, 3, 6, 1576–1585: Figure 5. Trajectory of ground-based AirCore profile downwind of the electrolyzer (shown by the “x”) on 14–11–2024, first round (a, b) and second round (c, d), shown on a 300 m scale and the H2 mole fraction (ppb) given in the legend on the right. The total sampling time of the trajectory was 35 min. Map tiles © Mapbox, data © OpenStreetMap contributors (ODbL), modified by the author." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_EST_Air_2026_3_6_1576_1585_Figure_5_Trajectory_of_ground_based_Air_Core_profile_downwind_of_the_electrolyzer_2f1d50dfd7.jpg 184w," sizes="100vw" width="1627" height="1383"></div>

Controlled-Release Experiment to Optimize Emission Quantification of H2 Point Sources

A Rapid, Antigen-Independent Diagnostic Strategy for Chronic Chagas Disease Based on Serum ATR-FTIR Spectroscopy and Machine Learning

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LECO ChromaTOF Identification Grading System

ChromaTOF Identification Grading System assigns confidence scores to GC×GC-HRTOFMS identifications using accurate mass, spectra, fragments, and retention indices for faster, defensible results.

<h3>Bring the power of accurate mass and confidence to compound identification</h3>The LECO Identification Grading System™ (IGS™) is designed to simplify and accelerate data review by clearly indicating how confident you can be in each compound identification. By consolidating all available chemical evidence into a single, intuitive grading metric, IGS enables users to quickly justify identifications, report results with confidence, and make informed decisions on next analytical steps.IGS is tightly integrated with data generated by the Pegasus GC-HRT 4D platform and is especially powerful for non-targeted workflows, where large numbers of unknown or partially known compounds must be evaluated efficiently and objectively.<h3>Key Features</h3>The Identification Grading System leverages the full chemical information content produced by GC×GC-HRTOFMS to strengthen confidence in “unknown unknown” identification.Each compound identification is assigned a grade based on four independent criteria:<ul><li>Presence and accurate mass of the molecular ion</li><li>Presence and strong spectral similarity score from a curated reference library</li><li>Accurate mass agreement of chemically reasonable fragment ion formulae associated with the library hit</li><li>Retention index agreement with the corresponding library entry</li></ul>The resulting numerical grade provides an immediate, transparent indicator of identification confidence—the higher the grade, the stronger the supporting evidence.<h3>Theory of Operation</h3>The full potential of confident identification is realized through the combined capabilities of comprehensive two-dimensional gas chromatography (GC×GC) and high-resolution time-of-flight mass spectrometry (TOFMS).GC×GC separates co-eluting species that would otherwise interfere with spectral interpretation, producing significantly cleaner mass spectra. High-resolution TOFMS delivers accurate, unskewed mass spectra across the entire chromatographic peak, including extremely narrow second-dimension peaks. When combined with advanced deconvolution, this results in exceptionally high-quality spectral information.Developed in response to the challenges highlighted by the U.S. EPA’s Non-Targeted Analysis Collaborative Trial (ENTACT), IGS directly addresses the time-consuming and subjective nature of traditional data review. It provides a systematic, evidence-based approach to filtering large result sets and focusing attention on the most reliable identifications.<h4>Practical Impact in Non-Target Analysis</h4>In GC×GC-TOFMS datasets from the EPA ENTACT challenge, unfiltered deconvolution can yield thousands of tentative identifications. Applying the IGS filter dramatically reduces this number, immediately highlighting the most confident matches and allowing analysts to focus on true unknowns with high priority.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/LECO_Identification_Grading_System_3_9b33a4d091.jpg" alt="LECO: Identification Grading System" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_LECO_Identification_Grading_System_3_9b33a4d091.jpg 245w," sizes="100vw" width="513" height="120">In blind ENTACT samples, IGS correctly identified approximately 80% of known compounds introduced into the sample, demonstrating that the combination of GC×GC separation and high-resolution TOFMS is essential for high-confidence non-target identification.

LECO ChromaTOF® Tile analytical software

ChromaTOF Tile enables rapid, statistically robust GC×GC-TOFMS data comparison using tiled heatmaps, reducing false positives and cutting data mining from days to minutes.

Turn Your Data into ChemistryThe volume of GC×GC-TOFMS data can quickly become overwhelming. Thousands of analytes across multiple tables may require months of pixel-level data mining, often complicated by false positives caused by normal chromatographic variation.ChromaTOF Tile fundamentally changes how GC×GC data are compared and interpreted. Instead of evaluating individual pixels, the software statistically compares tiled regions of chromatograms. Truly significant differences rise immediately to the surface—allowing you to focus on chemical meaning rather than data hunting.<blockquote>“ChromaTOF Tile is an enabling technology that has reduced our peak picking time from 10 days to 10 minutes.”</blockquote><h3>Designed for Sample Group Differentiation</h3>Integrated heatmap tables provide instant visual feedback on relative intensities and variance. ChromaTOF Tile highlights differences between two or more sample classes, including:<ul><li>Biomarkers distinguishing healthy vs. diseased samples</li><li>Off-odor detection in food quality control</li><li>Environmental samples upstream/downstream of contamination sources</li><li>Petroleum samples from different geographic origins</li><li>Any complex GC×GC comparison where class differentiation matters</li></ul><h3>Key Features</h3><ul><li>Stand-alone software designed to work in conjunction with <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/41">ChromaTOF</a></li><li>Rapid statistical comparison of GC×GC data sets</li><li>Exportable tables and plots for efficient reporting</li><li>Clean, library-searchable spectra generated from tiling</li><li>Approximate trend quantification using tile areas</li><li>Reduced false positives from chromatographic misalignment</li></ul><h3>Theory of Operation</h3>The challenge: Comparing large sets of GC×GC chromatograms is slow and alignment-sensitive, often taking weeks or months even with automated tools.The solution: In collaboration with Robert Synovec, ChromaTOF Tile applies Fisher ratios to partition chromatograms into regions (“tiles”). Instead of pixel-by-pixel comparison, corresponding tiles are averaged and statistically evaluated.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/LECO_Chroma_TOF_Tile_analytical_software_816a8a7705.jpg" alt="LECO ChromaTOF® Tile analytical software" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_LECO_Chroma_TOF_Tile_analytical_software_816a8a7705.jpg 245w," sizes="100vw" width="854" height="462">The result is a set of intuitive heatmaps that immediately direct attention to regions of high variance and high analytical relevance—without altering the underlying data.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/LECO_Chroma_TOF_Tile_analytical_software_2_c3c8abd476.jpg" alt="LECO ChromaTOF® Tile analytical software" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_LECO_Chroma_TOF_Tile_analytical_software_2_c3c8abd476.jpg 245w," sizes="100vw" width="1045" height="566">

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A Rapid, Antigen-Independent Diagnostic Strategy for Chronic Chagas Disease Based on Serum ATR-FTIR Spectroscopy and Machine Learning

Controlled-Release Experiment to Optimize Emission Quantification of H2 Point Sources

Webinars LabRulezGCMS Week 29/2026