<p><strong>In the week from December 8, the following webinars await you in the field of atomic and molecular spectroscopy and spectrometry</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. AZoM: Achieving Net-Zero – Precision Monitoring in CCUS and Downstream Chemicals</strong></h5><ul><li>Tu, 9.12.2025 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5693">Registration</a></li></ul><p>Explore how FTIR and mass spectrometry provide real-time monitoring of CO₂ purity and impurities in CCUS pipelines, supporting safety, compliance, and efficient e-fuel production.</p><h5><strong>2. SelectScience: Connecting at-line UV-Vis with in-line Process Raman for Real-Time Protein and Excipient Monitoring</strong></h5><ul><li>Tu, 9.12.2025 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5671">Registration</a></li></ul><p>Join us to explore how UV-Vis and Raman spectroscopy work together to deliver a complete picture of bioprocess performance.</p><h5><strong>3. Agilent Technologies: Determination of Extractable and Leachable Elements Using ICP-MS</strong></h5><ul><li>Tu, 9.12.2025 21:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/3211">Registration</a></li></ul><p>We will share an E&amp;L study showing potential drug contamination under extreme conditions (extractables) and contamination occurring during normal or extended storage (leachables).</p><h5><strong>4. Mettler-Toledo: Thermal Analysis of Phase Change Materials</strong></h5><ul><li>Th, 11.12.2025 08:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/4754">Registration</a></li></ul><p>Join our upcoming webinar to explore effective techniques and practical applications to help you characterize your own phase-change materials (PCMs).</p><h5><strong>5. Agilent Technologies: Tips and Tricks – How to Best Use the Handheld Raman Vaya</strong></h5><ul><li>Th, 11.12.2025 15:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5747">Registration</a></li></ul><p>Join us for the second session on Vaya Method Development Tips and Tricks!</p><h5><strong>6. Anton Paar: Heated Stories – What DSC Reveals About Polymers</strong></h5><ul><li>Th, 11.12.2025 18:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5727">Registration</a></li></ul><p>In this webinar, we deep-dive into the interpretation of DSC data across a range of polymer systems.</p>

Webinars LabRulezICPMS Week 50/2025

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 8th December 2025? 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 </strong>and<strong> Thermo Fisher Scientific </strong>and other document by<strong> Shimadzu!</strong></p><h3><strong>1. Agilent Technologies: High-Precision Determination of Four Greenhouse Gases in the Atmosphere Using the Agilent 8890 GC</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_monitoring_greenhouse_gas_8890_gc_5994_8744en_agilent_0e08f0909f.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Currently, China is actively implementing dual-control measures on total energy consumption and intensity. The Ministry of Ecology and Environment issued "The 14th Five-Year Plan for Ecological and Environmental Monitoring", which specifically highlights the advancement of technologies for direct measurement of carbon emissions. It calls for organizing pilot projects in multiple key industries and enterprises to monitor greenhouse gas emissions such as carbon dioxide and methane, establishing greenhouse gas monitoring networks in key cities, and upgrading the greenhouse gas monitoring capabilities of national atmospheric background stations. As a result, conducting greenhouse gas monitoring within the environmental protection stations of provinces and cities has become a critical task.</p><p>The use of GC for detecting greenhouse gases has a long history. The conventional methods used gas sampling valve for injection, TCD and FID for sequential detection of CO<sub>2</sub> and CH<sub>4</sub> , or combining a methanizer with an FID to detect CO<sub>2</sub> and CH<sub>4</sub> in one analytical channel and detecting N<sub>2</sub>O on ECD in another analytical channel.<sup>2</sup> The analytical precision for CO<sub>2</sub>, CH<sub>4</sub>, and N2 O achieved with such configurations typically ranges from 0.2% to 0.5%, but it is difficult to obtain precision better than 0.1%, which does not meet the current requirements for greenhouse gas monitoring accuracy and precision. Moreover, the traditional methods rarely report analytical precision for SF<sub>6</sub> at ppt concentration levels. To monitor trace levels of CO<sub>2</sub> , CH<sub>4</sub>, N<sub>2</sub>O, and ultratrace levels of SF<sub>6</sub> on the same GC system with high-precision results, it is necessary to optimize multiple factors included in sample injection, separation, and detection. Furthermore, the system configuration must be flexible enough to allow expansion for future analytical needs, such as incorporating trace CO analysis in air. This work demonstrates how the 8890&nbsp;GC achieves high-precision analysis of four key greenhouse gases by optimizing the injection volume, controlling injection reproducibility, and utilizing multichannel separation and high-sensitivity detectors to ensure the system runs stably in long‑term operation.</p><h4><strong>Experimental</strong></h4><h5><strong>Instrumental&nbsp;</strong></h5><p>The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=1&amp;search=8890+GC"><strong>8890 GC</strong></a> is configured as a three-channel/multivalve/multicolumn system, enabling independent analysis of four greenhouse gases—CH<sub>4</sub> , CO<sub>2</sub> , N<sub>2</sub>O, and SF<sub>6</sub> —within 8 minutes using a single injection. The target compounds are analyzed in separate channels, reducing chromatographic peak broadening and thereby improving sensitivity and&nbsp;repeatability.&nbsp;</p><ul><li>For CO<sub>2</sub> analysis, a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=1&amp;search=methanizer"><strong>methanizer</strong></a> is used to convert CO<sub>2</sub> to CH<sub>4</sub> , which is then detected by FID. The system eliminates the need for dehydration through backflushing and venting operation via switching valves, preventing interference from moisture and oxygen in the air and extending the lifespan of the methanizer.&nbsp;</li><li>For N<sub>2</sub>O and SF<sub>6</sub> analysis, the ECD with hidden anode design is used for high‑sensitivity detection and strong anti‑contaminant capability.&nbsp;</li><li>The CH<sub>4</sub> analysis is conducted on a single column with a dedicated large injection loop, significantly enhancing detection sensitivity and repeatability, achieving a detection limit for methane as low as single-digit ppb levels.</li></ul><h4><strong>Conclusion&nbsp;</strong></h4><p>Based on its exceptional pneumatic control and valve-based flow path design, the multivalve/multicolumn Agilent 8890 GC system achieves high-sensitivity and high-precision measurements of four key greenhouse gases. This is accomplished through optimized and precise control of sample flow rate/volume, selection of premium analytical columns, and the use of highly stable and robust FID and ECD. Standard gas samples of CO2 , CH4 , N2 O, and SF6 , with concentrations equivalent to atmospheric levels, were measured continuously 10 times. The measurement precision for these gases was better than 0.05%, 0.1%, 0.1%, and 0.5%, respectively, fully meeting the high-precision monitoring requirements of environmental monitoring stations at various levels for target greenhouse gases. Compared to other monitoring solutions, this approach is based on a stable and reliable gas chromatography platform that is technically easier to master and deploy. It represents a trusted solution developed by Agilent to assist in establishing a greenhouse gas monitoring network in China.</p><h3><strong>2. Shimadzu: GC/MS Mass Spectra Library - Polymer Additives Library Ver.2</strong></h3><ul><li>Other document</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/c146_e506_d4c93c4d60.pdf"><strong>Full PDF for download</strong></a></li></ul><h4><strong>Contains Approximately 5,800 Mass Spectra&nbsp;</strong></h4><p>This library consists of a library*, which contains 5,775 mass spectra for 590 types of polymer additives and GC/MS pyrolysates from the pyrolysis GC/MS analysis of additives, and a library containing the mass spectra for 65 compounds often targeted for analysis, selected based on their usage in the commercial market, and chemical substance regulatory information. In total, this product contains 5,840 mass spectra, which provides strong support for the analysis of polymer additives. In Ver.2, the compound registration has been newly enhanced, for example vulcanizing accelerators utilized in the rubber filed. Approximately 1,000 spectra have been added.&nbsp;</p><p><i>*ADD-MS22B F-Search Additives Library from Frontier Laboratories</i></p><h4><strong>Can Be Used with a Variety of GC/MS Systems&nbsp;</strong></h4><p>The library can be used for a variety of GC/MS applications, including pyrolysis GC/MS, which is widely used for the analysis of additives in polymer materials, and liquid sample injection GC/MS. It supports a wide range of additive analysis for customers.</p><h4><strong>Library Specifications</strong></h4><ul><li><strong>Registered compounds:</strong> 5,775+65&nbsp;</li><li><strong>Registered information:</strong> Mass spectrum, retention index for each analytical condition, compound name, molecular weight, compositional formula, structural formula, classification of additives</li><li><strong>Applicable models:</strong> <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=GCMS-QP2050"><strong>GCMS-QP™ serie</strong></a>s, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=GCMS-TQ8050"><strong>GCMS-TQ™ series</strong></a> (This library does not include MS/MS spectra.)&nbsp;</li><li><strong>Pyrolyzer</strong> (Pyrolysis analysis system): <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=43&amp;search=PY-2020D"><strong>PY-2020D</strong></a>, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=43&amp;search=PY-2020iD"><strong>PY-2020iD</strong></a>, and <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=43&amp;search=EGA%2FPY-3030D"><strong>EGA/PY-3030D</strong></a></li><li><strong>Workstations:</strong> <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=GCMSsolution"><strong>GCMSsolution</strong></a>™ Ver. 4.6 and later, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=LabSolutions+GCMS"><strong>LabSolutions™ GCMS</strong></a>™ Ver. 5.131 and later, LabSolutions DB/CS GCMS Ver. 6.131 and later</li></ul><h3><strong>3. Thermo Fisher Scientific: Trace analysis of epichlorohydrin in drinking water using GC-MS coupled with purge and trap</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_003894_gc_single_quad_isq_7610_environmental_an003894_na_en_70123385ae.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Epichlorohydrin (ECH) is a versatile starting material in the production of drugs and polymers and is also used as an insect fumigant and solvent for organic synthesis reactions. ECH-based polymer pipes are commonly used in the production of drinking water due to their durability and resistance to corrosion. However, ECH is known for its high reactivity and toxicity, which poses significant health risks if it contaminates drinking water. Exposure to ECH can cause respiratory issues, skin irritation, and has been classified as a probable human carcinogen.<sup>1</sup></p><p>Due to these risks, many countries have imposed strict limits on the amount of ECH allowed in drinking water. Recently, Europe set a minimum detection limit (MDL) of 30 parts per trillion (ppt) for ECH in drinking water.<sup>2</sup> Whereas typically required detection limits for most compounds mandated for analysis in Europe can be achieved using methods based on static headspace, the stringent MDL required for ECH requires preconcentration, for example using purge and trap (P&amp;T) technology.<sup>3,4</sup> This technology involves purging the water sample with an inert gas to release volatile organic compounds (VOCs), which are then trapped and concentrated for analysis, ensuring reliable detection at very low concentrations. In the United States, the analysis of VOCs in drinking water is mandated by the Environmental Protection Agency (EPA). The EPA requires the use of P&amp;T technology for drinking water analysis to ensure that even trace amounts of harmful compounds like ECH are detected and managed appropriately.&nbsp;</p><p>The following evaluation describes the use of the ISQ 7610 Single Quadrupole MS system coupled with the Thermo Scientific™ TRACE™ 1610 GC with the Thermo Scientific™ HeSaver-H2 Safer™ split/splitless injector and Teledyne LABS Tekmar Lumin P&amp;T concentrator combined with the AQUATek LVA autosampler for the analysis of ECH in drinking water.</p><h4><strong>Experimental</strong></h4><h5><strong>GC-MS parameters&nbsp;</strong></h5><p>A <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/486"><strong>TRACE 1610 GC</strong></a> was coupled to the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/484"><strong>ISQ 7610 MS</strong></a> equipped with the Thermo Scientific™ NeverVent ™ vacuum probe interlock (VPI) and an ExtractaBrite ion source. A <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1773"><strong>Thermo Scientific™ TraceGOLD™ TG-VMS column</strong></a>, 30 m × 0.25 mm, 1.4 µm film (P/N 26080-3320) was used for compound separation. The HeSaver-H2 Safer SSL injector was utilized to reduce the carrier gas consumption by decoupling the gas used for the chromatographic separation from the gas used to pressurize the inlet and maintain split and purge flows. The critical separations were maintained with a run time of under 15 minutes.</p><p>For this analysis, the ISQ 7610 MS was operated in Selected Ion Monitoring (SIM) mode for increased selectivity, as required for this application. Extended method parameters for the ISQ 7610 MS are shown in Table 2.</p><h5><strong>Instrument control and data processing&nbsp;</strong></h5><p>Data was acquired, processed, and reported using the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/76"><strong>Thermo Scientific™ Chromeleon™ Chromatography Data System (CDS)</strong></a>. This software can control both the GC-MS system and the Tekmar Lumin P&amp;T with the AQUATek LVA. This allows a single software to be utilized for the full workflow, simplifying the instrument operation. This application note is available for download via the Thermo Scientific™ AppsLab Library, which contains all the parameters needed to acquire, process, and report the analytical data for analysis of ECH.5</p><h4><strong>Conclusion&nbsp;</strong></h4><p>This study demonstrates the capability of the Tekmar Lumin P&amp;T with the AQUATek LVA system connected to the ISQ 7610 Single Quadrupole GC-MS to detect and quantify low-level ECH in drinking water samples, in compliance with EPA requirements.&nbsp;</p><ul><li>Utilizing the Tekmar Lumin P&amp;T’s ability to purge with nitrogen, along with using the HeSaver-H₂Safer SSL injector, nearly four times less helium was consumed during the analysis without sacrificing system performance.&nbsp;</li><li>The linearity of the calibration curve from 30 ppt to 5,000 ppt passed method requirements.</li><li>The application proved robust during an extended study with 20 samples of a 1,000 ppt ECH standard injected over a series of 124 injections, obtaining 4.4% precision and 118% accuracy of the recovery.</li></ul>

News from LabRulezGCMS Library - Week 50, 2025

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezLCMS Library in the week of 8th December 2025? Check out new documents from the field of liquid phase, especially HPLC and LC/MS techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT LCMS 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://gcms.labrulez.com/library"><strong>LabRulezGCMS</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 </strong>and<strong> Waters Corporation, </strong>technical note by <strong>Shimadzu </strong>and poster by <strong>Thermo Fisher Scientific / ASMS!</strong></p><h3><strong>1. Agilent Technologies: Robust and Reproducible Monitoring of PFAS in Treated Wastewater by Direct Injection on 6495D LC/MS</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_pfas_wastewater_lctq_5994_8346en_agilent_c2412e7d84.pdf"><strong>Full PDF for download</strong></a></li></ul><p>PFAS is a class of synthetic chemicals that are resistant to heat, water, grease, and oil. Exposure to this class of chemicals is widespread and their ability to move and persist in the environment makes them difficult to clean up. Found in everyday household and workplace products such as drinking water, food packaging, fish caught from compromised environments, and personal care products is an indication of how widespread the use of PFAS and its contamination in the&nbsp;environment.&nbsp;</p><p>Traditional techniques of analysis to enhance sensitivity incorporate the use of solid phase extraction, which allows enrichment of the sample before injection. Increased awareness of emerging contaminants has necessitated the need for higher throughput methods, some bypassing the sample preparation step, to decrease sample analysis time. Direct injection analysis can save time by removing the need to prepare samples but still providing robustness and reproducibility to justify its capabilities and improved&nbsp;efficiency.</p><h4><strong>Experimental</strong></h4><h5><strong>Instrumentation&nbsp;</strong></h5><p><strong>Liquid chromatography system:&nbsp;</strong></p><ul><li>Agilent 1290 Infinity II Bio-LC High‑Speed Pump (G7132A)&nbsp;</li><li>Agilent 1290 Infinity II Bio-LC Multisampler (G7137B)&nbsp;</li><li>Agilent 1290 Infinity II Multicolumn Thermostat (G7116B)&nbsp;</li></ul><p><strong>Mass spectrometry system:&nbsp;</strong></p><ul><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=10&amp;search=6495D"><strong>Agilent 6495D Triple Quadrupole LC/MS (G6495D)&nbsp;</strong></a></li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=10&amp;search=Jet+Stream"><strong>Agilent Jet Stream (AJS) Source</strong></a> A PFC free kit, together with a delay column, was also used.</li></ul><h4><strong>Results and discussion&nbsp;</strong></h4><p>The sensitivity of the method was calculated with ten spikes of water at 1&nbsp;ng/L resulting in the method detection limit. The calculated concentration was prior to dilution of methanol and demonstrates that all but three analytes were equal or less than 1 ng/L detection limits, the analytes with greater than 1&nbsp;ng/L MDL were 3:3 FTCA and 8:2 FTSA (Table 6).</p><h4><strong>Conclusion&nbsp;</strong></h4><p>The increased awareness of PFAS globally has meant that regulations are constantly being adopted and larger amounts of samples at lower limits are required. By incorporating the direct injection technique on the Agilent 6495D LC/MS we get a combination of high sample throughput while providing excellent analytical sensitivity. Also, increasing throughput and reducing consumables cost creates increased value in the instrument. The method displays a clean baseline and sensitivity with the observation of 1 ng/L detection in spiked wastewater. Reproducibility and robustness were measured between the concentrations of 0.5 to 100 ng/L on the 6495D LC/MS. The system was able to detect 38 PFAS between 0.25 to 3.4 ng/L with RSD &lt; 20% over 150&nbsp;direct injections, providing confidence in the data for a complex matrix like&nbsp;wastewater.</p><h3><strong>2. Shimadzu: A sustainable analytical approach for detecting extra virgin olive oil adulteration using Nexera™ UC</strong></h3><ul><li>Technical note</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/c190_e341_c488032e81.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Knowledge of the composition of triacylglycerols (TAGs) in vegetable oils is important for dietary and nutritional reasons, due to the influence of the different properties of each fatty acid on the human organism. Specifically, extravirgin olive oil (EVOO) is a popular food ingredient worldwide, and for this reason, detecting economically motivated adulteration is important to protect consumers’ interest and health[1]. According to the latest report from the EU Food Fraud Network (European Union, 2021), olive oil (OO) tops the list of reported adulterated food products. The official methods to assess OO purity and detect the presence of extraneous vegetable oils include the analysis of fatty acids (FAs), triacylglycerols (TAGs) and sterols (Regulation (EU) No 2568/91 and its amendments) [2].&nbsp;</p><p>A variety of chromatographic techniques has been employed for TAGs analysis and separation. High-performance liquid chromatography (HPLC) methods, which use reversed-phase (RP) and silver-ion (Ag) columns for TAGs separation, have been employed either independently or in combination[3]. However, both these approaches have the drawback of relying on toxic organic solvents. The alternative separation technique employed in TAGs analysis is supercritical fluid chromatography (SFC) coupled with a UV detector at very low wavelengths, due to the weak UV absorption of the mobile phase. In this study, a fast, simple and green methodology was optimized to detect intentionally adulterated olive oil with cheaper seed oils at different levels by means of SFC with UV detection, followed by statistical analysis.</p><h4><strong>2. Experimental</strong></h4><h5><strong>2.2 SFC-PDA Nexera™ UC system&nbsp;</strong></h5><p>All experiments were performed on a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=1&amp;search=Nexera%E2%84%A2+UC"><strong>Nexera™ UC system</strong></a> (Shimadzu Europa, Germany). The system was equipped with a CBM-20A communication bus module, a DGU-20A5R degasser, an LC-30ADSF CO2 pump, one LC-20ADXR dual plunger parallelflow pump, a Sil-30AC autosampler, a CTO-20AC column oven, a SPD-M20A photodiode array (PDA) detector, and a SFC-30A backpressure regulator (BPR). Chromatograms were recorded using Shimadzu <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=1&amp;search=LabSolutions"><strong>LabSolution</strong></a> ver. 5.80 software. Four Ascentis Express C18 columns, provided by KGaA (Darmstadt, Germany), were connected in series for TAGs separation. The analytical conditions used are summarized in Table 1.</p><h4><strong>3. Results and discussion&nbsp;</strong></h4><p>Using four C18 columns packed with superficially porous particles, a highly efficient separation of TAGs with an isocratic elution mode was provided, allowed for complete sample TAG elution within 30 min with the conditions used in this method (Figure 1). Peak identification was carried out according to partition number (PN), the published composition of these common vegetable oils and the relative amount of major and minor peaks, and finally taking into account TAGs retention times. For compounds with identical PN, separation was achieved based on the number of unsaturations. The elution order of TAGs was primarily governed by the hydrophobicity of the solutes. As the PN increases, the non-polar character of TAGs becomes more dominant.</p><h4><strong>Conclusions&nbsp;</strong></h4><p>The Nexera™ UC system provided excellent results for assessing TAGs composition in ten different vegetable oils examined in this study employing an SFC-PDA analytical method using isocratic mode and UV detection at 205 nm. This approach does not require any sample preparation and enables analyses to be completed with less waste and less cost compared to traditional methods, making it feasible for routine analyses, while providing reliable quantitative values.&nbsp;</p><p>Quantification of adulterated EVOO was performed by establishing curves based on peak areas of LLL marker versus seed oils concentrations. Statistical analysis using PCA and one-way ANOVA also visually distinguished adulterated EVOO from pure EVOO, and the result was in accordance with the present quantitative method. This fast and environmental method exhibits a huge potential for quality control and authenticity evaluation of EVOO.</p><h3><strong>3. Thermo Fisher Scientific / ASMS: Uncovering biological differences at scale: high-throughput and in-depth plasma proteomics with the Seer Proteograph ONE workflow and Orbitrap Astral Zoom Mass Spectrometer</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/po_393_uncovering_biological_astral_zoom_seer_po393_asms2025_na_en_f697aef9c0.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Plasma proteomics enables minimally invasive biomarker discovery by capturing disease-related molecular signatures. However, its effectiveness is limited by the wide dynamic range of protein concentrations. To address this, we combined the Seer Proteograph ONE workflow with the Orbitrap Astral Zoom mass spectrometer to enable a high-throughput, high-precision platform that delivers deep plasma profiling and actionable insights for large-scale clinical and translational research (Figure 1).</p><h4><strong>Methods:&nbsp;</strong></h4><p>The Seer Proteograph ONE workflow was used to prepare plasma samples from a small disease cohort with matched healthy controls to evaluate its performance and feasibility in detecting biologically meaningful differences. Samples were then separated and analyzed using the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/398"><strong>Thermo Scientific Vanquish Neo UHPLC system </strong></a>coupled to the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1959"><strong>Orbitrap Astral Zoom mass spectrometer</strong></a>, enabling deep and reproducible proteome coverage. Two analytical approaches—a high-throughput method and a Max-ID workflow—were tested to demonstrate the versatility and throughput for comprehensive biomarker discovery in clinical cohort studies.</p><h4><strong>Results:&nbsp;</strong></h4><p>The Seer Proteograph ONE workflow with Orbitrap Astral Zoom mass spectrometer demonstrated that both high-throughput and Max-ID workflow were able to detect differences in biology in diseased states from healthy controls. The high-throughput workflow enabled rapid, reproducible analysis, while the Max-ID approach delivered deeper proteome coverage. The synergy between the Seer Proteograph workflow and Orbitrap Astral Zoom mass spectrometer supports scalable, high-resolution biomarker discovery, making it suitable for large clinical studies and precision medicine applications.</p><h4><strong>Conclusions&nbsp;</strong><span><strong>&nbsp;</strong></span></h4><ul><li>The Seer Proteograph ONE workflow combined with Orbitrap Astral Zoom mass spectrometer identified &gt;10,800 protein groups using ultradeep profiling, compared to &gt;8,200 with the high-throughput method—demonstrating scalable, reproducible plasma proteomics with clear detection of biological differences for biomarker discovery.&nbsp;</li><li>The Protoeograph ONE workflow combined with the Orbitrap Astral Zoom Mass Spectrometer captures exceptional dynamic range of 7-8 orders of magnitude in plasma proteome&nbsp;</li><li>&gt;1,000 differentially abundant proteins detected with biologically relevant dysregulated pathways such as immune signaling or tissue remodeling underscores the complex biology of lung cancer&nbsp;</li><li>Identification of FDA markers as differentially expressed showcases the workflow’s strength for capturing biologically relevant plasma proteins</li></ul><h3><strong>4. Waters Corporation: Improving Throughput and Analytical Greenness in Pharmaceutical Discovery Using Ultrashort (2.1 x 10 mm) HPLC Columns</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720009074_en_406ea811f9.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>Benefits&nbsp;</strong></h5><ul><li>Up to 60% decrease in analysis time using the ultrashort (2.1 x 10 mm) column</li><li>Lower AMGS metric values indicating improvements in analytical method greenness</li></ul><p>Drug discovery is a fast-paced, high-throughput environment where chemists screen large numbers of samples to determine if a suitable compound has been created. Supporting these efforts from an analytical standpoint using liquid chromatography (LC) or liquid chromatography-mass spectrometry (LC-MS) can present challenges. For many discovery workflows, hundreds if not thousands of samples are analyzed for the determination of drug concentrations in biological matrices, for structural confirmation or for an assessment of purity. Analytically speaking, this means that the number of samples that may be analyzed per unit time, or throughput, is valued more highly than is achieving baseline separation of all components. To maximize throughput, rapid (“ballistic”) gradients can be employed along with short analytical columns.1 Ballistic gradients are designed to run at the highest flow rate possible while using the steepest gradient slope, in terms of percent solvent per column volume (%/VC).2 This allows for the fastest elution off the column possible. Shorter columns are used not only to limit the pressure generated by the column, thereby allowing faster flow rates, but also to reduce the analysis time.&nbsp;</p><p>In this application note, the use of a 2.1 x 10 mm 2.5 µm XSelect™ HSS T3 Column is examined compared to 2.1 x 30 mm and 2.1 x 50 mm columns packed with the same stationary phase. These three columns were used to analyze a full 96-well QuanRecovery™ Plate to simulate the output of a drug discovery environment where multiple samples need to be run as fast as possible. Prior to sample analysis, instrument methods were created to increase throughput by limiting the inter-injection delay. The inter-injection delay time was calculated and recorded. The total analysis time for each column configuration was determined and compared, along with the AMGS. While the loss of efficiency in using a 10 mm column was expected, the shorter column configuration achieved adequate results for a single component sample. Coupling this column with mass spectrometry for peak identification would be sufficient for high-throughput discovery screening where separation quality is not the most important attribute of the analysis, but rather speed is of the highest value.3</p><h4><strong>Experimental</strong></h4><h5><strong>Reversed Phase LC Conditions&nbsp;</strong></h5><ul><li><strong>LC system: </strong><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/191"><strong>ACQUITY™ Premier</strong></a> Binary Solvent Manager with Column Manager, TUV Detector, and<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/554"><strong> Xevo™ TQ Absolute Mass Spectrometer</strong></a></li><li><strong>Columns:&nbsp;</strong><ul><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1864"><strong>XSelect HSS T3</strong></a>, 2.1 x 50 mm, 2.5 µm (p/n: 186006149)&nbsp;</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1864"><strong>XSelect HSS T3</strong></a>, 2.1 x 30 mm, 2.5 µm (p/n: 186006148)&nbsp;</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1864"><strong>XSelect HSS T3</strong></a>, 2.1 x 10 mm, 2.5 µm (p/n: 186011466)</li></ul></li><li><strong>Chromatography software:</strong> <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/635"><strong>MassLynx™ V4.1 Software</strong></a></li></ul><h4><strong>Results and Discussion&nbsp;</strong></h4><h5><strong>Instrument Considerations&nbsp;</strong></h5><p>Prior to sample analysis, the system being used was considered. For Waters™ LC systems, there are a few parameters in the inlet method that can be adjusted to speed up the time between injections, or inter-injection delay. This delay comes from the sequence of steps that the system takes to draw the sample and inject it into the flow path of the LC. The parameters that need adjustment are all located in the sample manager panel of the instrument method or inlet method. Figure 1 shows screenshots of the sample manager panel and where the mentioned parameters are located.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>The use of high-throughput LC separations in pharmaceutical discovery allows new lead compounds to be selected quickly and efficiently. This may be achieved using short columns with ballistic gradients. To support these activities 2.1 x 10 mm columns were developed and used to analyze a full 96-well plate of samples. Comparing the results obtained on the 10 mm column to those produced using 30 and 50 mm columns showed a 57–146% higher throughput. Additionally, the use of the 10 mm column was determined to be substantially “greener” as measured by the analytical method greenness scores, with decreases of 40 and 68% relative to 30 and 50 mm length columns.</p>

News from LabRulezLCMS Library - Week 50, 2025

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 8th December 2025? Check out new documents from the field of spectroscopy/spectrometry and related techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT SPECTROSCOPY/SPECTROMETRY 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://gcms.labrulez.com/library"><strong>LabRulezGCMS</strong></a> libraries.</p></blockquote><p>This week we bring you application notes by <strong>Agilent Technologies</strong>, <strong>LECO, and</strong> <strong>Shimadzu!</strong></p><h3><strong>1. Agilent Technologies: Measurement of Opaque Liquids with UV-Vis and ATR</strong></h3><p><strong>Combining the Agilent Cary 60 UV-Vis spectrophotometer with an ATR fiber-optic probe</strong></p><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_cary_60_atr_opaque_liquids_5994_8652en_agilent_e7b9efab4d.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Today, ATR is considered synonymous with FTIR spectroscopy, being the most widely used sampling technique. However, ATR can also be applied to UV-Vis spectroscopy, offering particular benefits for measuring opaque liquids with a fiber-optic probe.&nbsp;</p><p>Samples such as paints, inks, food colorings, sunscreens, and liquid medications are often highly pigmented, rendering them optically opaque. In some cases, measurement of these types of samples using a standard 10 mm path length cuvette may be possible, but only after several hundred‑fold dilution, requiring significant solvent consumption and introducing the possibility of large dilution errors. In the case of emulsion samples such as paints and sunscreens, dilution is often not possible without the sample "splitting" and separating into its component parts.&nbsp;</p><p>ATR spectroscopy is an innovative sampling technique that overcomes these challenges. In ATR, incident light from the spectrometer is directed into a measurement crystal at an angle. Internal reflections within the crystal generate an evanescent wave at the crystal surface, allowing samples in direct contact with the crystal to interact with the standing wave and absorb some of the light energy.&nbsp;</p><p>In a UV-Vis fiber-optic ATR probe, a commonly used crystal is sapphire, with the incident light directed into the crystal to form an angle of 60° at the crystal surface. The crystal is designed to create three bounces at the surfaces (Figure&nbsp;1). The "effective height" of the wave above the surface is a function of the relative refractive indices of the crystal and the sample medium, the angle of incidence, and the wavelength. The multiple bounces extend the effective path length. For this probe, the effective pathlength at 250&nbsp;nm is approximately 3 μm and increases to approximately 13 μm at 1,000&nbsp;nm.</p><h4><strong>Experimental&nbsp;</strong></h4><p>Example data were collected using a <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=10&amp;search=Cary+60+UV-Vis"><strong>Cary 60 UV-Vis spectrophotometer </strong></a>equipped with a fiber-optic interface and Hellma Katana XP 6, 3-bounce ATR probe (Hellma part number PK1KSSZUEASM210N). Spectra were collected by first measuring a baseline using the clean crystal, followed by sample measurement with the crystal immersed in the sample. The Agilent <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=10&amp;search=Cary+WinUV"><strong>Cary WinUV software</strong></a> was used to collect the spectra as well as to process the measured spectra. All spectra were collected using the measurement conditions shown in Table 1.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>This application note demonstrates how an Agilent Cary 60 UV-Vis spectrophotometer, when combined with a Hellma Katana XP 6 ATR probe, provides a practical and easy-to-use solution for measuring optically opaque liquids that would otherwise require extensive dilution, and for monitoring reactions with absorbance changes too large for conventional methods. The Cary 60 UV-Vis with the ATR probe combination is a powerful tool for a wide range of applications, including routine quality control, R&amp;D studies, and research and teaching laboratories.</p><h3><strong>2. LECO: Determination of Nitrogen/Protein in Meat and Plant-Based Meat Alternatives</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/FP_828_NITROGEN_PROTEIN_MEAT_PLANT_BASED_MEAT_ALTERNATIVES_203_821_680_266d8d12f8.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Protein is one of the most significant nutrient components in food products. The accurate and precise determination of protein not only plays a role in the characterization of nutritional or dietary value in food materials but is also the key to the economic value of these materials. Protein in meat products is most commonly calculated using the measured nitrogen content in the sample and a multiplier or conversion factor (commonly 6.25). Nitrogen determination is performed using either the classical wet chemical method (Kjeldahl) or a combustion method (Dumas). The Kjeldahl method involves sample digestion, distillation, and ammonia determination typically by titration. This method involves time-consuming sample preparation and the use of hazardous materials. The LECO FP828 is a nitrogen determinator that utilizes an automated combustion (Dumas) method and provides accurate and precise results in approximately three minutes. This eliminates involved sample preparation and the use of hazardous materials resulting in a rapid, cost-effective method for the quality control of the production of meat and plant-based protein products.</p><h4><strong>Instrument Model and Configuration&nbsp;</strong></h4><p>The <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=24&amp;search=FP828"><strong>LECO FP828</strong></a> is a combustion Nitrogen/Protein determinator that utilizes a pure Oxygen environment in a vertical quartz furnace, ensuring complete combustion and superior analyte recovery. A thermoelectric cooler removes moisture from the combustion gases before they are collected in a ballast. The combustion gases equilibrate and mix in the ballast before a representative aliquot (3 cm<sup>3</sup> or 10 cm<sup>3</sup> volume) of the gas is extracted and introduced into a flowing stream of inert gas (Helium or Argon) for analysis. The aliquot gas is carried to a thermal conductivity cell (TC) for the detection of Nitrogen (N<sub>2</sub>).</p><p>Thermal conductivity detectors work by detecting changes in the thermal conductivity of the analyte gas compared to a reference/carrier gas. The greater the difference between the thermal conductivity of the carrier gas and the analyte gas, the greater sensitivity of the detector.&nbsp;</p><p>The FP828 supports either the use of Helium or Argon as the instrument's carrier gas. When used as a carrier gas, Helium provides the highest sensitivity, and the best performance at the lower limit of the Nitrogen range.&nbsp;</p><p>The thermal conductivity difference between Argon and Nitrogen is not as great as the thermal conductivity difference between Helium and Nitrogen, therefore the detector is inherently less sensitive when using Argon as a carrier gas.</p><h4><strong>TYPICAL RESULTS&nbsp;</strong></h4><p>Data was generated utilizing a linear, force through origin calibration using ~0.25 g of LECO 502-896 (Lot 1007) LCRM EDTA (9.59% N). The calibration † was verified using ~0.1 g of LECO 502-642 (Lot 1020) LCRM Phenylalanine (8.46% N). A protein factor of 6.25 was used for all samples to calculate the protein content.</p><h3><strong>3. Shimadzu: Analysis of Combustion Ash from Biomass Power Generation by EDXRF</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00716_en_9995b70230.pdf"><strong>Full PDF for download</strong></a></li></ul><h4><strong>User Benefits</strong></h4><ul><li>EDXRF enables analysis of Na and K, which cause fluidization problems (agglomeration) in circulating fluidized bed boilers in biomass power generation.</li><li>Simple qualitative/quantitative analysis of elements is possible with minimal sample preparation.</li><li>Comparative analysis of heterogeneous samples in the as-received condition and in the homogenized condition after crushing is possible.</li></ul><p>In biomass power generation, electricity is generated by using bioresources (biomass) derived from animal and plant sources as a carbon neutral fuel. Biomass power is expected to contribute to reducing total CO2 emissions, and in recent years, the number of woody biomass power plants using wood chips, pellets, and palm kernelshells(PKS) hasincreased.<br>Circulating fluidized bed (CFB) boilers used in woody biomass power generation generate power by circulating and burning biomass fuel together with silica sand, which is as a fluidized bed medium. However, alkali metal elements (Na, K) contained in the fuel adhere to the surface of the silica sand and combustion ash, causing the particles to stick together and form agglomerates (lumps, coarse particles), which are considered to cause boiler damage and fluidization problems (agglomeration) in the fluidized bed 1) .&nbsp;</p><p>In this article, a Qual-Quant analysis of PKS combustion ash and its coarse particles was conducted using an <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=1&amp;search=EDX-8100"><strong>EDX-8100</strong></a> energy dispersive X-ray fluorescence spectrometer (EDXRF). Since adhesion of alkali metal elements was detected on the surface of agglomerated lumps, which are considered to cause the problem of agglomeration, analysis of combustion ash by EDXRF can contribute to operational management of combustion furnaces by monitoring the conditionsin the combustion furnace.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>Based on the results of a qualitative analysis of lumps (coarse particles of bottom ash), which are thought to cause agglomeration (defluidization) of the fluidized bed, in the combustion ash of PKS, it was found that alkali metal elements (Na and K) had adhered to the surface of the particles. Since EDXRF enables Qual-Quant analysis of elements with only simple sample preparation, it is considered a useful technique for monitoring the progress of agglomeration and deterioration of the silica sand in the power generation using PKS or woody biomass. Periodic measurements by EDXRF can also be expected to prevent damage and deterioration in combustion furnaces and equipmentstops due to defluidization.</p>

News from LabRulezICPMS Library - Week 50, 2025

<p><strong>In the week of December 15, the following webinars await you in the field of gas / solid phase and especially Raman, FTIR, GC and GC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><div><div><div><div><div><div><h5><strong>1. Wasson-ECE Instrumentation: O-FIDs / ASTM D5599</strong></h5><ul><li>Tu, 16.12.2025 21:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5530">Registration</a></li></ul><p>Learn about O-FIDs and ASTM D5599 methodology, focusing on accurate oxygenate analysis in fuels with optimized GC detector performance.</p><h5><strong>2. Agilent Technologies: Ask the Expert – Ensuring Purity and Compliance in Glycol Testing</strong></h5><ul><li>Th, 18.12.2025 10:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5712">Registration</a></li></ul><p>Learn how validated GC-FID workflows ensure accurate EG/DEG testing and compliance with new global regulations, including expert guidance on method setup, validation, and troubleshooting.</p><h5><strong>3. Agilent Technologies: Microplastics Identification – Tips &amp; Tricks Workshop</strong></h5><ul><li>Th, 18.12.2025 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/4588">Registration</a></li></ul><p>This workshop demonstrates the workflow for analyzing microplastic samples using the Agilent 8700 Laser Direct Infrared Imaging (LDIR) system.</p></div></div></div></div></div></div>

Webinars LabRulezGCMS Week 51/2025

<p><strong>In the week of December 15, the following webinars await you in the field of liquid phase and especially HPLC and LC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. SCIEX: Turn Your SCIEX System up to (Windows) 11 Using SCIEX OS Software for Acquisition</strong></h5><ul><li>Tu, 16.12.2025 18:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5750">Registration</a></li></ul><p>Join the third webinar in the SCIEX OS series to see how the unified platform boosts productivity, automates batches, streamlines data review, and simplifies system upgrades.</p><h5><strong>2. Agilent Technologies: Analysis of Aqueous Environmental Samples Using UV-Vis Instrumentation</strong></h5><ul><li>Tu, 16.12.2025 21:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/2533">Registration</a></li></ul><p>Discover even more environmental analyses you can perform with the Cary 60 UV-Vis spectrophotometer.</p><h5><strong>3. Shimadzu Corporation: Advancing Separation Science – SFE/SFC for Chiral &amp; Achiral Analysis</strong></h5><ul><li>We, 17.12.2025 07:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5717">Registration</a></li></ul><p>Explore sustainable SFE/SFC workflows with Shimadzu’s Nexera UC system, including automated extraction, advanced chiral/achiral columns, and scalable separations.</p><h5><strong>4. Waters Corporation: Effective GLP-1 Drug Development Online Summit</strong></h5><ul><li>We, 17.12.2025 14:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5718">Registration</a></li></ul><p>Join experts to explore analytical, manufacturing, and QC strategies for next-generation GLP-1 therapeutics, from LC-MS to light scattering and bioanalysis.</p><h5><strong>5. Agilent Technologies: HPLC Holiday Special – Proper Downtime Maintenance for Your Instrument</strong></h5><ul><li>We, 17.12.2025 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5653">Registration</a></li></ul><p>Learn how to properly shut down HPLC instrumentation so it is ready to run immediately when the lab restarts.</p><h5><strong>6. C&amp;EN: Automated Liquid Handling for Bench Scientists – Meet Hamilton’s Most Compact Workstation</strong></h5><ul><li>Fr, 19.12.2025 09:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5695">Registration</a></li></ul><p>Discover how the Microlab Prep workstation reduces repetitive pipetting, freeing time for research design and data analysis.</p><h5><strong>7. C&amp;EN: Effective LC Workflow Strategies in Discovery Chemistry Research</strong></h5><ul><li>Fr, 19.12.2025 09:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5694">Registration</a></li></ul><p>This webinar explores how analytical and preparative LC answers key questions across synthesis and discovery workflows.</p>

Webinars LabRulezLCMS Week 51/2025

<p><strong>In the week from December 15, the following webinars await you in the field of atomic and molecular spectroscopy and spectrometry</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Bruker: Hyperspectral Characterization of Advanced Materials using AFM</strong></h5><ul><li>Tu, 16.12.2025 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5751">Registration</a></li></ul><p>We warmly invite you to join us for this webinar on the hyperspectral characterization of advanced materials using Atomic Force Microscopy (AFM).</p><h5><strong>2. SPECTRO Analytical Instruments: Understanding the Difference Between Type and Control Standards in Elemental Analysis</strong></h5><ul><li>Tu, 16.12.2025 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5730">Registration</a></li></ul><p>Discover how type and control standards enhance calibration, precision, and QC stability in arc/spark OES and XRF analysis, including practical software integration tips.</p><h5><strong>3. Agilent Technologies: Analysis of Aqueous Environmental Samples Using UV-Vis Instrumentation</strong></h5><ul><li>Tu, 16.12.2025 21:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/2533">Registration</a></li></ul><p>Discover even more environmental analyses you can perform with the Cary 60 UV-Vis spectrophotometer.</p><h5><strong>4. Agilent Technologies: Microplastics Identification – Tips &amp; Tricks Workshop</strong></h5><ul><li>Th, 18.12.2025 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/4588">Registration</a></li></ul><p>This workshop demonstrates the workflow for analyzing microplastic samples using the Agilent 8700 Laser Direct Infrared Imaging (LDIR) system.</p>

Webinars LabRulezICPMS Week 51/2025

<p><strong>Persistent chemicals released into marine environments pose long-term and potentially irreversible risks, particularly when discharged as complex mixtures in large volumes. To address this challenge, we introduce a persistence-directed testing approach that combines environmentally relevant biodegradation experiments with nontarget analytical techniques to better characterize persistent chemical burdens.</strong></p><p>Produced waters from two offshore oil platforms in the North Sea were used as case studies. After 60 days of incubation under biotic and abiotic conditions, nontarget analyses using SPME–GC-MS and SPE–LC-HRMS identified over 600 chemicals passing quality control. Persistent compounds accounted for 4% of GC-MS detections and 32–44% of LC-HRMS detections, with several annotated as N-substituted aromatic structures. These results demonstrate the effectiveness of persistence-directed testing for revealing previously unidentified persistent chemicals and underscore the need for improved treatment of offshore produced water discharges.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c08802"><strong>Persistence-Directed Testing of Chemicals Discharged from Offshore Oil Platforms Combined with Nontargeted Analysis</strong></a></h4><p>Mette T. Møller*, Heidi Birch, Karina K. Sjøholm, Stefano Papazian, Aina C. Wennberg, Bénilde Bonnefille, Pia M. Kronsbein, Malcolm A. Kelland, Jonathan W. Martin, and Philipp Mayer</p><p>Environ. Sci. Technol. 2025, 59, 44, 24000–24011</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.est.5c08802">https://doi.org/10.1021/acs.est.5c08802</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><p>In this study, we introduce “Persistence-Directed Testing”, a concept inspired by effect-directed analysis (EDA). In EDA toxic chemicals in complex mixtures from contaminated environments are singled out by combining fractionation procedures, biotesting, and sensitive chemical analytical methods to identify toxicants. <a href="javascript:void(0);">(13,14)</a> In persistence-directed testing, persistent chemicals in complex discharges are singled out and potentially identified by combining biodegradation tests, sensitive chemical analytical techniques, and focused data treatment schemes.</p><p>The biodegradation testing method is an advancement of a recently developed biodegradation kinetic testing method, a modified OECD 309. The method was developed for chemicals in mixtures at low concentrations, including hydrophobic and volatile chemicals. <a href="javascript:void(0);">(15,16)</a> In recent years the method has continuously been advanced from testing composed mixtures <a href="javascript:void(0);">(15)</a> to testing samples of unknown or variable composition, complex reaction products, or biological materials (UVCBs), such as petroleum mixtures and essential oils. <a href="javascript:void(0);">(17,18)</a> In the most recent progression of the method, it was used for testing the biodegradation kinetics of chemicals in complex mixtures being discharged to the environment, using native microorganisms in the receiving environment as inoculum. <a href="javascript:void(0);">(19)</a> With this method, biodegradation kinetics were successfully obtained for more than a hundred chemicals discharged from an offshore oil platform measured with solid-phase microextraction (SPME) gas chromatography-mass spectrometry (GC-MS). <a href="javascript:void(0);">(19)</a></p><p>The novelty of the persistent-directed testing in the present study is the design of the test and data treatment for persistence rather than biodegradation. This was achieved by combining a discharge sample (complex chemical mixture) and a seawater sample (native microorganisms), for determining the number, fraction, and, if possible, also the identity of those chemicals that are not degraded at the end of the test and are thus found persistent. The experiments were analyzed with both SPME GC-MS and nontarget solid-phase extraction (SPE) liquid chromatography-high-resolution mass spectrometry (LC-HRMS), in order to extend the chemical space coverage and achieve a more comprehensive persistence assessment.</p><p>Nontarget analysis (NTA) techniques have developed and advanced rapidly in recent years. <a href="javascript:void(0);">(20,21)</a> It is now possible to detect thousands of chemicals in a sample without predetermining targets. By analyzing the sample with HRMS, the exact mass of both the precursor and molecular fragments of each feature detected can be obtained, <a href="javascript:void(0);">(22)</a> which can be used for identification or annotation at various confidence levels. <a href="javascript:void(0);">(23)</a> Limitations of NTA are that exact identification (confidence level 1) and quantification is difficult and time-consuming. <a href="javascript:void(0);">(21)</a> In recent years, new developments of NTA workflows and software tools, such as in silico structure predictions <a href="javascript:void(0);">(24,25)</a> and feature-based molecular networking (FBMN), <a href="javascript:void(0);">(26,27)</a> allow increasingly more automated and higher-throughput molecular annotations (Level 2), structural elucidation (Level 3), and molecular formula assignments (Level 4). This has been successfully showcased for environmental applications. <a href="javascript:void(0);">(28−30)</a></p><p>In the present study, we combine biodegradation testing with NTA to directly enable the simultaneous persistence assessment of a large number of chemicals, while providing identification information across varying levels of confidence.</p><p>The hypotheses were: 1) environmentally relevant biodegradation tests combined with advanced analytical methods can reveal persistent chemicals in complex discharges, and 2) based on a recent feasibility study, <a href="javascript:void(0);">(19)</a> we expect a much higher fraction of persistent chemicals in produced water within the group of polar substances detectable by LC, compared to nonpolar chemicals detectable by GC. We also anticipate that NTA, combined with advanced computational workflows, can allow to reach a deeper molecular-level understanding of the persistent chemicals discovered in complex discharges.</p><h3><strong>2. Materials and Methods</strong></h3><h4><strong>2.8. Sample Preparation and Chemical Analysis</strong></h4><h5><strong>2.8.1. Solid-Phase Microextraction Coupled to GC-MS</strong></h5><p>After 60 days, test systems were transferred directly from the incubator to the GC autosampler (PAL RSI 85; CTC Analytics AG, Switzerland) without any pretreatment or conservation. Automated SPME, with a 30 μm PDMS fiber (Supelco, USA), was applied to unopened test systems by direct immersion. The SPME was thermally desorbed on the GC column (122-5562UI DB-5 ms Ultra Inert column, 60 m × 0.25 mm × 0.25 μm, Agilent), where the analytes were separated by the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=7890B"><strong>GC (7890B</strong></a>; Agilent Technologies, USA). They were then detected by the MS (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5977A"><strong>5977A MSD</strong></a>; Agilent Technologies, USA), in electron ionization full-scan mode (<i>m</i>/<i>z</i> 50–500) to include a large number of nonpolar chemicals in produced water. The analytical method is described in detail in Møller et al. (2024). <a href="javascript:void(0);">(19)</a> The 1:200 dilution from both platforms was analyzed on day 60. On the following 4 days, the remaining dilutions from both platforms were analyzed, due to the number of samples and the analysis time per sample. Each GC-MS analysis run consisted of triplicate alternating biotic and abiotic test systems at one dilution from one platform, with blanks at the beginning and the end. The biotic and abiotic test systems analyzed directly after each other were used as pairs for calculating the peak area ratios.</p><h5><strong>2.8.3. LC-HRMS Analysis</strong></h5><p>Analyses by LC-HRMS for nontarget qualitative profiling were performed using a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/744"><strong>Vanquish Horizon system</strong></a> interfaced with heated electrospray ionization (HESI) to an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/401"><strong>Orbitrap Exploris 480</strong></a> (Thermo Fisher). Each sample was acquired twice using ionization in positive (HESI+) and negative (HESI−) modes. Clean extracts were injected (25 μL) on a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1952"><strong>C18 Waters Acquity column</strong></a> (UPLC BEH C18, 3.0 mm × 100 mm, 1.7 μm particle) combined with an Acquity precolumn (BEH C18, 2.1 mm × 5 mm, 1.7 μm). Reverse-phase chromatography was performed using LC-MS grade (Optima) mobile phases (A) Milli-Q water with 1 mM ammonium fluoride (NH4F), and (B) 100% MeOH. Spectral acquisition alternated between full-scan MS1 (<i>m</i>/<i>z</i> 90–1000, ∼1.5 Hz, nominal resolution 240,000 at <i>m</i>/<i>z</i> 200) and nontarget data-independent (DIA) MS2 acquisition using four sequential precursor isolation windows of 250 <i>m</i>/<i>z</i> (i.e., <i>m</i>/<i>z</i> 89–341, 339–591, 589–841, 839–1091). Additional details are presented in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c08802/suppl_file/es5c08802_si_001.pdf">SI 3</a>.</p><h3><strong>3. Results and Discussion</strong></h3><h4><strong>3.2. Primary Biodegradation and Persistence Assessment</strong></h4><p>Primary biodegradation and persistence were assessed at a dilution of 1:200 based on peak area ratios (biotic/abiotic) of test systems analyzed (GC) or extracted (LC) on day 60. Features were categorized as persistent, when all triplicate peak area ratios were greater than 0.5, corresponding to less than 50% biodegradation (see <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c08802#sec2_9_2">Section 2.9.2</a>). The results for Platform B are presented in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c08802#fig3">Figure 3</a> (Platform A results and results including the standard error of the mean; see <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c08802/suppl_file/es5c08802_si_001.pdf">SI 11</a>).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Environ_Sci_Technol_2025_59_44_24000_24011_Figure_3_Biodegradation_and_persistence_assessment_of_chemicals_in_produced_water_from_Platform_B_eab3376f2c.jpg" alt="Environ. Sci. Technol. 2025, 59, 44, 24000–24011: Figure 3. Biodegradation and persistence assessment of chemicals in produced water from Platform B at 1:200 dilution from mean (n= 3) peak area ratio. A) Features measured with GC-MS and B) Features measured with LC-HRMS. Green = primary biodegraded, yellow = ambiguous, and red = persistent (see Section 2.9.2)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Environ_Sci_Technol_2025_59_44_24000_24011_Figure_3_Biodegradation_and_persistence_assessment_of_chemicals_in_produced_water_from_Platform_B_eab3376f2c.jpg 136w," sizes="100vw" width="1344" height="1544"></p><p>Assessment of the GC-MS data resulted in 3–4% persistent features for Platform A (6/144 features) and Platform B (10/279 features) (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c08802#fig3">Figure 3</a>A and <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c08802/suppl_file/es5c08802_si_001.pdf">SI 11</a>), consistent with previous GC-MS results from a biodegradation experiment conducted one year earlier with produced water from Platform A, where the persistent fraction was also 4%. <a href="javascript:void(0);">(19)</a> In the present study, additional assessment performed on LC-HRMS data yielded 32% persistent features (61 of 191 features) for Platform A and 44% persistent features (183 of 416 features) for Platform B (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c08802#fig3">Figure 3</a>B and <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c08802/suppl_file/es5c08802_si_001.pdf">SI 11</a>). Although the chemical space covered by the two analytical methods has only minor overlap, <a href="javascript:void(0);">(41)</a> some chemicals might still be detected by both GC-MS and LC-HRMS, as for instance was shown for the potentially persistent chemical <i>N</i>-butylbenzenesulfonamide, in our previous feasibility study. <a href="javascript:void(0);">(19)</a> The large extent of biodegradation (&gt;95%) observed in the GC-MS data set shows that microorganisms in the test systems were active (degrading).<named-content></named-content></p><p>In the GC-MS data set, 20 of the petrochemicals in the reference mixture were identified in the produced water samples from both platforms, and they were all biodegraded (benzene and toluene elute too early to be included). No spectral library matches were found for the few persistent features in the GC-MS data sets, which thus remained unidentified. In comparison, many more persistent features were detected by LC-HRMS, and the high-resolution MS data was leveraged for tentative identification of these chemicals using a combination of different workflows.</p><h4><strong>3.3. Tentative Identification of Chemicals in Produced Water (LC-HRMS)</strong></h4><h5><strong>3.3.1. Suspect Screening of Production Chemicals</strong></h5><p>In total, 55 production chemicals were included in the suspect list (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c08802/suppl_file/es5c08802_si_001.pdf">SI 7</a>), and 7 of these were annotated in a high-concentration sample of the produced water (1:10 dilution) at either Platform A, B, or both (accurate mass deviation &lt;0.005, as described in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c08802#sec2_11_1">Section 2.11.1</a>). The annotated molecular structures are presented in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c08802#fig4">Figure 4</a>A. These 7 production chemicals did not pass the quality control filter at 1:200 dilution since their peaks were below the blank area cutoff at this dilution. Therefore, this study cannot conclude whether these are primary biodegraded or persistent.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Environ_Sci_Technol_2025_59_44_24000_24011_Figure_4_Comprehensive_characterization_of_chemicals_in_produced_water_83eedbbe8e.jpg" alt="Environ. Sci. Technol. 2025, 59, 44, 24000–24011: Figure 4. Comprehensive characterization of chemicals in produced water. Examples of tentative identifications resulting from different data analysis workflows are shown. A) Suspect screening of production chemicals. B) Spectral library matching. C) Feature-Based Molecular Networking (FBMN) combined with in silico structural predictions in SIRIUS (considering maximum top-6 proposed structures). D) Molecular formula computed in SIRIUS. Suspect screening, spectral library matching, and molecular formula assignments were performed for both LC-HRMS HESI+ and HESI– data sets. The FBMN in (C) shows one example of a cluster from HESI+ (top) and one cluster from HESI– (bottom). Molecular structures highlighted in red correspond to chemicals categorized as persistent. Identification confidence levels refer to Schymanski et al. (2014). (23)" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Environ_Sci_Technol_2025_59_44_24000_24011_Figure_4_Comprehensive_characterization_of_chemicals_in_produced_water_83eedbbe8e.jpg 175w," sizes="100vw" width="2092" height="1871"></p><h4><strong>3.6. Persistence-Directed Testing</strong></h4><p>This study introduces persistence-directed testing and validates the hypothesis that environmentally relevant biodegradation tests combined with advanced analytical methods can reveal persistent chemicals in complex discharges. A limitation of this study is that the chemicals categorized as “biodegraded” only cover primary biodegradation and not potential persistent metabolites. More research is needed to assess persistent metabolites, e.g., by analyzing peaks that are significantly more abundant in biotic test systems compared to abiotic test systems after 60 days of incubation. If persistent metabolites are found, backtracking the parent molecule in the discharge can be challenging but is possible to approach using predicted degradation pathways, e.g., from specific mass shifts (e.g., 15.996 Da for oxygen attachment) as suggested by Tisler et al. (2022), <a href="javascript:void(0);">(46)</a> or using dedicated software such as BioTransformer (<a target="_blank" rel="noopener noreferrer" href="https://biotransformer.ca/">https://biotransformer.ca/</a>) or EnviPath (<a target="_blank" rel="noopener noreferrer" href="https://envipath.org/">https://envipath.org/</a>).</p><p>To improve the method, future studies could use sterilized inocula and discharge samples as abiotic controls. Furthermore, as analytical sensitivity increases, future studies can potentially come closer to investigating how the biodegradation process is affected by the extreme dilutions occurring in the ocean, and thereby improving environmental relevance even further.</p><p>The persistence-directed testing approach introduced in this study can be used to assess the extent of the problem of persistent chemicals in various types of discharges. An advantage of the approach is that a complex mixture of chemicals can be assessed in one biodegradation study, while the highly time-consuming work of feature identification can be narrowed down by focusing on persistent features. If persistence-directed testing approaches are adopted more widely and identification of the persistent fraction becomes easier with the very rapid development of the NTA field, it will become more and more difficult to unnoticed discharge large quantities of persistent chemicals. The approach can also lead to the identification of new groups of persistent molecules and an improved understanding of persistence in the environment.</p>

Persistence-Directed Testing of Chemicals Discharged from Offshore Oil Platforms Combined with Nontargeted Analysis

<p><strong>Wastewater influent contains valuable information on population-level chemical exposure, but the complexity of the matrix complicates data interpretation. This study investigates daily discharge patterns of trace organic compounds in influent wastewater to identify recurring trends linked to domestic chemical use.</strong></p><p>Hourly composite samples collected over three 24-hour periods were analyzed using LC-HRMS with targeted and non-targeted screening, yielding over 72,000 features and 402 annotated target compounds. Temporal k-means clustering revealed five distinct daily patterns, including two associated with domestic activities—one correlated with wastewater flow and another showing a characteristic morning peak likely linked to urine excretion. Applying these patterns to non-target data prioritized additional features with similar temporal behavior, demonstrating the value of combining LC-HRMS screening with clustering approaches to elucidate wastewater discharge dynamics and population exposure.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c02486"><strong>Exploring Domestic Discharge Patterns in Wastewater through LC-HRMS Screening and Temporal Clustering</strong></a></h4><p>Inga Haalck*, Martin Krauss, Werner Brack, and Carolin Huber</p><p>Environ. Sci. Technol. 2025, 59, 29, 15375–15384</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.est.5c02486">https://doi.org/10.1021/acs.est.5c02486</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><p>Wastewater influent contains valuable epidemiological information on a community level, which is of interest for new monitoring tools such as wastewater-based epidemiology (WBE) or wastewater-based surveillance (WBS). Here, various information, such as population’s pharmaceutical <a href="javascript:void(0);">(1,2)</a> and illicit drug <a href="javascript:void(0);">(3−5)</a> use, diet, <a href="javascript:void(0);">(6)</a> virus prevalence, <a href="javascript:void(0);">(7)</a> or exposure <a href="javascript:void(0);">(8−10)</a> can be retrieved from wastewater. The approach is based on the analysis of human excretion products (biomarkers), <a href="javascript:void(0);">(11)</a> thus focusing on the urinary and/or fecal fraction of wastewater.</p><p>However, the chemical mixture found in influent wastewater is very complex, including not only domestic contributions, such as human excretion products, but also other inputs, such as industrial effluents or surface runoff. This complexity not only increases analytical challenges, but also complicates data interpretation and the link of results to human consumption and exposure. <a href="javascript:void(0);">(12,13)</a> Especially for compounds not intended for human consumption, distinguishing human urinary metabolites from other transformation products formed prior to exposure or in the sewer system is often challenging, but it would be necessary to fully confirm internal exposure and achieve an accurate exposure assessment through WBE. <a href="javascript:void(0);">(13)</a></p><p>Pinpointing those compounds in wastewater stemming exclusively from domestic activities (e.g., urination and defecation, showering, among others) may serve as a useful first step in separating WBE-relevant features from other contributing sources. The analysis of daily discharge patterns in influent wastewater may enhance the understanding of chemical source profiles, allowing the identification of the link between chemicals and specific activities. Therefore, we hypothesize that compounds originating from the same source would exhibit the same daily discharge pattern and could be grouped accordingly. Using known chemicals with a known source as a reference, this temporal co-occurrence could then provide valuable indications for chemicals with an unknown source. For instance, evaluating the pattern of compounds known to be excreted by humans (e.g., pharmaceutical metabolites) might provide a strong indication of human internal exposure to other temporally co-occurring compounds.</p><p>Temporal trend analysis has already been applied in several studies, such as assessing water contamination dynamics, <a href="javascript:void(0);">(14−17)</a> as early warning system for newly emerging contaminants <a href="javascript:void(0);">(18)</a> or spill detection. <a href="javascript:void(0);">(19)</a> Additionally, evaluating the dynamics of chemicals revealed in several cases valuable information regarding the source and origin of contaminants in surface water <a href="javascript:void(0);">(20)</a> or wastewater. <a href="javascript:void(0);">(21,22)</a></p><p>This study aims to analyze daily discharge patterns in influent wastewater to identify recurring patterns for compounds, particularly those from domestic sources, isolating one contributing factor for human exposure and gaining in-depth insights into discharge dynamics. Hourly composite influent samples from a medium-sized wastewater treatment plant (WWTP) were taken for 24 hours on a typical working day, with 3 days as replicates. With targeted and non-targeted LC-HRMS screening combined with clustering methods, we explored discharge patterns of known compounds, and subsequently prioritized non-targeted features exhibiting the same patterns of interest.</p><h4><strong>2. Methods and Materials</strong></h4><h5><strong>2.3. Sample Extraction and Chemical Analysis</strong></h5><p>The sample extraction procedure included filtration, followed by solid phase extraction (SPE) with a Chromabond HR-X (Macherey-Nagel) sorbent and is fully described in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_001.pdf">Supporting Information</a>, Section S2. A mixture of internal standards (IS) (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_002.xlsx">Supporting Information</a>, Table SX3) was added after reconstitution for quality control of the analysis and data processing to compensate for matrix effect differences among samples and calibration standards for the quantification. The analysis was performed on a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/397"><strong>Vanquish HPLC system</strong></a> (Thermo Scientific) coupled to a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/401"><strong>quadrupole-Orbitrap instrument (Exploris 480</strong></a>, Thermo Scientific) operated in positive and negative electrospray ionization mode (ESI+/ESI-) acquiring MS<sup>1</sup> and MS<sup>2</sup> information using Top-8 data-dependent (dd) acquisition. Further details on the chromatographic system and the settings of the mass spectrometer can be found in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_001.pdf">Supporting Information</a>, Section S3 and Table S3.</p><p>In every sample batch (1 day), procedure blanks (LC–MS grade water) and two spiked LC-grade water samples at two levels (200 ng/L and 5 μg/L) were extracted and measured within the sequence. Additionally, 24-h composite samples of each day spiked with two levels (200 ng/L and 5 μg/L) were included as quality control (QC). The blanks and QC samples were spiked with the same IS mixture as the samples (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_002.xlsx">Supporting Information</a>, Table SX3). The samples were analyzed in a randomized order and solvent blanks were injected every 12 samples to monitor for potential carryover. The QA/QC samples, along with 12 of the hourly samples were measured twice to assess the analytical reproducibility. The results of the QC can be found in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_001.pdf">Supporting Information</a>, Section S4 and Figures S1–S3. Before the first sampling campaign, field blanks (<i>n</i> = 2) were taken at the WWTP with tap water using the same sampling device.</p><h4><strong>3. Results and Discussion</strong></h4><h5><strong>3.2. Temporal Clustering of Target Compounds</strong></h5><p>First, the cluster analysis was tested on both, target compounds and all non-targeted features. The unsupervised clustering of the non-targeted features revealed some difficulties in the interpretation of meaningful patterns due to the absence of a prior relevance-based prioritization of the features in relation to the research question. In addition, compounds with a high potential for adduct or fragment formation or multiple co-occurring congeners (e.g., surfactants) might dominate the clustering result, potentially obscuring more relevant patterns. Thus, clustering of the target compounds, selected to reflect the urban environment, provided a clearer picture.</p><p>The range of variation in signal intensities of the IS was 13–30% in ESI+ (<i>n</i> = 22) and 23–28% in ESI– (<i>n</i> = 3). Therefore, only features with a variation &gt;45% were considered, focusing on temporal trends that exceed potential technical shifts in sensitivity or differences in matrix effects (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_001.pdf">Supporting Information</a>, Figures S2 and S3). Further evaluation of the total ion chromatogram between the hourly samples supported the assumption that, for this case study, there was no significant shift in matrix composition or matrix effects over the course of the day that could bias the cluster analysis. However, this may differ at different sample sites, where additional corrections (e.g., normalization with IS or postcolumn infusion of a reference) may be necessary.</p><p>Different data treatment strategies for clustering of the target compounds were also explored. The evaluation summary and reasoning for choosing to cluster the data set as one 72-h continuous period can be found in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_001.pdf">Supporting Information</a>, Section S7 and Figures S7–S13.</p><h5><strong>3.2.1. Observed Temporal Trends</strong></h5><p>Based on the reasons given in the previous section and <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_001.pdf">Section S7</a>, 95 prioritized target compounds were further analyzed in-depth in the selected cluster analysis. A total of 90 target compounds showed a probability of belonging to their assigned clusters greater than 90%. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c02486#fig2">Figure 2</a>A summarizes the modeled GAMs for each cluster and day, combined with the flow rate measured at the WWTP. The full list of target cluster assignments can be found in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_002.xlsx">Supporting Information</a>, Tables SX1–2. Normalizing signal intensities by flow rate before cluster analysis revealed similar trends to those without normalization but tended to overshadow the temporal patterns of some target compounds (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_001.pdf">Supporting Information</a>, Figure S11). Hence, we tended for the interpretation without normalization.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Environ_Sci_Technol_2025_59_29_15375_15384_Figure_2_A_Temporal_trends_of_the_identified_clusters_bottom_and_influent_flow_rate_top_for_three_replicate_sampling_day_7c04e3bddd.jpg" alt="Environ. Sci. Technol. 2025, 59, 29, 15375–15384: Figure 2. (A) Temporal trends of the identified clusters (bottom) and influent flow rate (top) for three replicate sampling days. The starting point of the sampling is indicated by the gray dashed line. (B) Compound classes of the annotated target compounds in each cluster (DA: daytime activity, MP: morning peak, BG [1–3]: background patterns)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Environ_Sci_Technol_2025_59_29_15375_15384_Figure_2_A_Temporal_trends_of_the_identified_clusters_bottom_and_influent_flow_rate_top_for_three_replicate_sampling_day_7c04e3bddd.jpg 245w," sizes="100vw" width="1701" height="916"></p><h5><strong>3.3. Cluster Assignment of Non-targeted Features</strong></h5><p>In a next step, an assignment of the non-targeted features to the identified clusters was predicted. After filtering based on detection frequency (DF &gt; 85%) and temporal variation (RSD &gt; 45%), the final inclusion list for the cluster prediction contained 22,589 features, including also the annotated 90 target compounds (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_001.pdf">Supporting Information</a>, Figure S15).</p><p>The Euclidean distance of each non-targeted feature to the centroids of the clusters was calculated at each time point (<i>n</i> = 72) and summed up to determine the overall closest cluster for each feature. The prioritization described in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c02486#sec2_6">Section 2.6</a> reduced the feature list by more than 80% to 3,757 features (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_001.pdf">Supporting Information</a>, Figure S16). To illustrate the underlying data and the concept of relative distances applied during both cluster analysis and cluster prediction, <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c02486#fig3">Figure 3</a>A presents the three closest target compounds for the MP and DA cluster. As all prioritized target compounds were still in the feature list, their correct assignment to their original cluster during the cluster prediction was used as a quality control for the method.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Environ_Sci_Technol_2025_59_29_15375_15384_Figure_3_Temporal_patterns_of_to_the_morning_peak_MP_and_daytime_activities_DA_clusters_628da059bd.jpg" alt="Environ. Sci. Technol. 2025, 59, 29, 15375–15384: Figure 3. Temporal patterns of to the morning peak (MP) and daytime activities (DA) clusters: (A) The three closest target compounds highlighted in front of the variation of the target compounds. (B) Non-targeted features assigned to each cluster. The thick lines in (A) and (B) illustrate the GAM predictions averaged across three sampling days (see Figure S14 for other clusters)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Environ_Sci_Technol_2025_59_29_15375_15384_Figure_3_Temporal_patterns_of_to_the_morning_peak_MP_and_daytime_activities_DA_clusters_628da059bd.jpg 139w," sizes="100vw" width="1264" height="1418"></p><p>Excluding the target compounds, a total of 1,133 features were assigned to cluster DA, while 70 features were assigned to the MP cluster. The remaining features were distributed among the background clusters (BG1: 1,868 features, BG2: 585 features, BG3: 11 features).</p><p>As seen in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c02486#fig3">Figure 3</a>B, with the use of the Euclidean distance, non-targeted features were successfully prioritized and matched to the previously established daily patterns (further visualization for all other clusters, see <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.est.5c02486/suppl_file/es5c02486_si_001.pdf">Supporting Information</a>, Figure S17).</p><h5><strong>3.6. Implications and Future Applications</strong></h5><p>Wastewater is a valuable source of epidemiological information; however, its complex matrix exacerbates extracting meaningful results and the link to population exposure. By studying the daily discharge patterns, this study offers insights into chemical discharge profiles of different sources (e.g., domestic vs. road runoff), making this approach valuable for chemical source identification. Additionally, the results demonstrate the advantages of high-time resolution sampling, as by avoiding dilution, we could find more compounds and higher maximum concentrations in the hourly samples compared to the 24-h composite samples.</p><p>We demonstrate the presence of reproducible and recurring daily patterns, pinpointing two patterns associated with domestic use: one representing general daytime activities and another with a closer connection to human metabolism. The majority of compounds in the latter cluster are associated with pharmaceutical use, which facilitated the assessment of whether these compounds had passed through the human body, enabling the association to (morning) urine. We propose this discrimination as a beneficial tool for monitoring approaches such as WBE, providing additional evidence about the origin of a potential biomarker and supporting the interpretation of other compounds related to human metabolism (e.g., endogenous compounds used for population normalization or to confirm the presence of human excretion), and making conclusions more meaningful. However, in this pilot study, only pharmaceuticals represent chemicals intended for consumption, while WBE is currently seeking to include a wider range of chemicals related to unintentional exposure. <a href="javascript:void(0);">(49)</a> To include them, comparing the temporal patterns and cluster assignments of parent compounds and their human-specific metabolites could provide a more precise understanding of their internal exposure profile, helping to confirm that their source is exclusively related to human exposure. <a href="javascript:void(0);">(50)</a></p>

Exploring Domestic Discharge Patterns in Wastewater through LC-HRMS Screening and Temporal Clustering

Sequential versus Simultaneous Quantitative Analysis of Biomarkers in Individual Cells by ICP-MS and Mass Cytometry: A Focus on Immunotherapy

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