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.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.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c08802">Persistence-Directed Testing of Chemicals Discharged from Offshore Oil Platforms Combined with Nontargeted Analysis</a></h4>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 MayerEnviron. Sci. Technol. 2025, 59, 44, 24000–24011<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.est.5c08802">https://doi.org/10.1021/acs.est.5c08802</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.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.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>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.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>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.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.<h3>2. Materials and Methods</h3><h4>2.8. Sample Preparation and Chemical Analysis</h4><h5>2.8.1. Solid-Phase Microextraction Coupled to GC-MS</h5>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">GC (7890B</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">5977A MSD</a>; Agilent Technologies, USA), in electron ionization full-scan mode (m/z 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.<h5>2.8.3. LC-HRMS Analysis</h5>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">Vanquish Horizon system</a> interfaced with heated electrospray ionization (HESI) to an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/401">Orbitrap Exploris 480</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">C18 Waters Acquity column</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 (m/z 90–1000, ∼1.5 Hz, nominal resolution 240,000 at m/z 200) and nontarget data-independent (DIA) MS2 acquisition using four sequential precursor isolation windows of 250 m/z (i.e., m/z 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>.<h3>3. Results and Discussion</h3><h4>3.2. Primary Biodegradation and Persistence Assessment</h4>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>).<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">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 N-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>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.<h4>3.3. Tentative Identification of Chemicals in Produced Water (LC-HRMS)</h4><h5>3.3.1. Suspect Screening of Production Chemicals</h5>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.<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"><h4>3.6. Persistence-Directed Testing</h4>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>).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.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.

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

<h3>Introduction</h3>In automated chromatographic workflows, it is often necessary to ensure that controlled devices perform specific actions once a measurement sequence has finished. The video demonstrates how <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/999">Clarity Chromatography Software</a> &nbsp;allows users to define such actions by configuring a Shutdown Method within the Sequence Options.This functionality enables devices to transition into a safe or standby state automatically after the last sequence row is measured, reducing manual intervention and supporting unattended operation.<h3>Purpose of the Shutdown Method</h3>The Shutdown Method is designed to define post-sequence actions for controlled devices once a sequence has finished.This method can be used to:<ul><li>Gradually lower the flow</li><li>Switch off detector lamps</li><li>Cool down devices</li><li>Bring instruments into standby or shutdown state</li></ul>These actions are executed automatically based on the user-defined settings in the sequence configuration.<h3>Accessing Sequence Options</h3>To configure post-sequence behavior, it is necessary to:<ol><li>Open the Sequence Options</li><li>Navigate to the section labeled “After sequence is finished”</li></ol>This section contains all relevant controls for defining how and when shutdown actions are applied.<h3>Configuration in the “After Sequence Is Finished” Section</h3><h4>1. Selecting the Shutdown Method</h4>In the After sequence is finished section, the user must:<ul><li>Select a prepared Shutdown Method</li><li>Define how the method should be executed</li></ul>The Shutdown Method must already exist and contain the desired device actions (e.g. cooling, lamp switch-off, flow reduction).<h4>2. Differences Between Devices</h4>The necessary settings differ according to the used devices. This means shutdown behavior is device-dependent, and not all instruments require the same configuration.Examples:<ul><li>Some devices only require the method to be sent<ul><li>For example, when the goal is to initiate cooling</li></ul></li><li>Other devices require the method to be run<ul><li>For example, detectors that include an option such as “switch the lamp off after run”</li></ul></li></ul><h4>3. Performing Device Shutdown</h4>In addition to sending or running the shutdown method, the user can enable:<ul><li>Perform Shutdown</li></ul>When this option is checked:<ul><li>A shutdown command is sent directly to the devices</li><li>Each device may react differently to this command</li></ul>Device responses to this command depend on the specific hardware.<h3>Execution Order After Sequence Completion</h3>Once the last row of the sequence is measured, Clarity executes shutdown actions in a defined order:<ol><li>The Shutdown Method is sent</li><li>The Shutdown Method is run (if configured)</li><li>The Perform Shutdown command is sent (if enabled)</li></ol>This ensures that all configured post-sequence actions are applied consistently and automatically.<h3>Practical Implications</h3>The demonstrated configuration allows users to:<ul><li>Automate post-analysis instrument handling</li><li>Reduce the need for manual shutdown procedures</li><li>Ensure devices enter a defined and safe state after sequence completion</li></ul>This is particularly useful for long or unattended sequences where operator presence at the end of the run is not guaranteed.<h3>Conclusion</h3>Clarity provides a flexible mechanism to control device behavior after a sequence has finished through the use of a Shutdown Method and the After sequence is finished settings.By selecting an appropriate shutdown method and defining whether it should be sent, run, or followed by a shutdown command, users can tailor post-sequence behavior to the requirements of their specific devices. The execution is performed automatically once the final sequence row is measured, ensuring consistent and reliable shutdown behavior.

Automated Device Shutdown After Sequence Completion in Clarity Chromatography Software

Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 15th December 2025? Check out new documents from the field of the gas phase, especially GC and GC/MS techniques!<blockquote>👉 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library">SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT GCMS AND RELATED TECHNIQUES</a></blockquote><blockquote>👉 Need info about different analytical techniques? Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library">LabRulezLCMS</a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library">LabRulezICPMS</a> libraries.</blockquote>This week we bring you application notes by Agilent Technologies, Shimadzu and brochure by Thermo Fisher Scientific!<h3>1. Agilent Technologies: Increased Thermal Stability of the DB‑5Q GC Column</h3>Increased signal-to-noise for trace level concentrations of controlled substances<ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_stability_gc_column_5994_8658en_agilent_4c52ae52cf.pdf">Full PDF for download</a></li></ul>The analysis of controlled substances by gas chromatography/mass spectrometry (GC/MS) requires a high level of confidence and thus requires a method that provides optimal data quality. Identification of controlled substances relies heavily on comparison of mass spectra to commercially available spectral libraries, and increased column bleed can cause spectral interference at high temperatures. These interferences can lead to poor library matching probabilities.1-3 The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=J%26W+5Q">Agilent J&amp;W 5Q series</a> of gas chromatography columns provide the most thermally stable columns available, which helps to decrease spectral interference due to column bleed and improves data accuracy.&nbsp;Poor thermal stability will cause an increase in the slope of the baseline as the oven temperature is increased, leading to difficult peak integrations and inaccurate quantitation. Additionally, sloped or elevated baselines can lead to a decreased response and a loss of sensitivity for analytes eluting during the oven ramp or at the method’s upper temperature limits. Several commercially available 5ms type columns were compared to the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=J%26W+DB-5Q">Agilent J&amp;W DB-5Q GC column</a> to examine the impact thermal stability has on response at upper temperature limits.&nbsp;<h4>Experimental&nbsp;</h4>An <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890+GC">Agilent 8890 GC system</a> coupled with an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5977B">Agilent 5977B mass selective detector (GC/MSD)</a> with an inert extractor ion source was used for acquisition of data and MassHunter 10.0 Quantitative and Qualitative Analysis software was used for data analysis.&nbsp;A representative toxicology mixture, the Agilent GC/MS forensic toxicology checkout mixture (part number 5190-0471) was used to monitor analytical performance. Calibration standards were prepared in methanol (MeOH) at concentrations ranging from 0.1 to 3.0&nbsp;μg/mL. An internal standard mix was prepared at 10&nbsp;μg/mL from individual stocks of methamphetamine-d3, diazepam-d5, and hydrocodone-d6 all purchased from Millipore Sigma (St.&nbsp;Louis, MO).<h4>Results and discussion&nbsp;</h4>The testing evaluated the Agilent J&amp;W DB-5Q GC column for the analysis of controlled substances by GC/MS. The increased thermal stability of the DB-5Q is seen in the decreased baseline at higher temperatures, as seen in Figure&nbsp;1, and also provides a decrease in the slope during the oven ramping program.&nbsp;An increase in the slope of the baseline will also decrease the signal-to-noise ratio (S/N) of an analyte but will also make integration more difficult and lead to less accurate quantitative results. Figure 2 demonstrates the comparison of the DB-5Q and the commercially available 5ms type G column, and the impact to chromatography during the oven ramp. The S/N for the analytes on the oven ramp slope are a factor of ten or greater for the DB-5Q in comparison to the 5ms type G column.<h4>Conclusion&nbsp;</h4>The increased thermal stability of the Agilent J&amp;W DB-5Q GC column demonstrated in this application note provides a decreased baseline at higher temperatures, which allows for an increased response for later eluting compounds at trace level concentrations. The baseline of the DB-5Q remains more consistent as the temperature is increased. This consistency allows for more accurate integration and quantitation for analytes eluting during oven ramping, in comparison to other commercially available 5ms-type columns. The robust DB-5Q column phase maintained peak shape for trace level basic compounds even when stressed with a heavy matrix and is an excellent choice for the analysis of controlled substance&nbsp;analysis.<h3>2. Shimadzu: Quantitative Analysis of Anti-Degradant Additive (6PPD) in Tire Rubber Using Pyrolysis-GC-MS</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00952_en_b95b2348e5.pdf">Full PDF for download</a></li></ul><h5>User Benefits</h5><ul><li>The simple analysis procedure required just cutting off a sample fragment and placing it in a sample cup without pretreatment.</li><li>Utilizing a calibration standard that includes a resin solution allows for analysis under conditions that closely resemble those of samples, which improves the reliability of quantitative measurements.</li><li>With Shimadzu’s Polymer Additive Library, it is possible to acquire information on the additives of identified compounds.</li></ul>6PPD (N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine) is an anti-degradant (anti-oxidant) additive that is used in many rubber products. Its chemical structure is shown in Fig. 1. It plays an important role in tires by preventing degradation and extending its lifespan. However, when 6PPD reacts with ozone in the atmosphere, it forms a highly toxic substance called 6PPD-quinone (6PPD-Q).&nbsp;As an automotive tire wears, microscopic particles are released from the tire into the environment. These particles, tire road wear particles (TRWP), contain 6PPD-Q along with many other chemical substances. On rainy days, these particles are washed into rivers and other bodies of water, where they accumulate in the environment as microplastics.&nbsp;There are serious concerns about the negative effects these microplastics have on ecosystems and the environment. 6PPDQ has been shown to induce acute mortality in Salmonidae fish, specifically coho salmon. 1)&nbsp;Currently, there is no commercial alternative that can maintain tire stability and performance with minimal impact on the environment. Research into alternatives requires accurately assessing 6PPD levels in rubber products and objectively determining whether alternative compounds offer equivalent stability and performance while having a reduced impact on the environment.&nbsp;This article describes using a pyrolysis gas chromatograph mass spectrometer (Py-GC-MS) system to measure 6PPD levels in tire rubber.<h4>Analysis Process&nbsp;</h4>ISO/TS 21396describes a method for determining the mass concentration of TRWP in soil and sediment that uses Py-GC-MS. Similar to this method, Py-GC-MS was used for the analysis; however, this article employs optimized analytical conditions. A schematic diagram of the analysis processisshown in Fig. 2. For more information on the analysis conditions and results from the evolved gas analysis (EGA-MS, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=42&amp;search=EGA%2FPY-3030D">EGA/PY-3030D Multi-Shot Pyrolyzer AS-1020E Auto-Shot Sampler</a>, Frontier Laboratories Ltd.) method described in Fig. 2, please see Application News No. 01-00901. The analysis conditions for the thermal desorption (TD)-GC/MS (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GCMS-QP2020+NX">GCMS-QP2020 NX</a>) method used in this article are shown in Table 1. The thermal desorption temperature was chosen based on results obtained by the EGA-MS method.<h4>Qualitative Analysis of Additive&nbsp;</h4>The total ion current (TIC) chromatogram obtained from analyzing the tire rubber is shown in Fig. 4. A spectral library search was also performed using NIST-23 and Shimadzu’s Polymer Additives Library. The library search results for the peak that eluted at around 10.43 minutes are shown in Fig. 5. Based on the results from Shimadzu’s Polymer Additives Library, the peak was estimated to be the anti-degradant Nocrac 6C (a trade name for the chemical 6PPD).<h4>Conclusion&nbsp;</h4>This article presents an analysis of the additive 6PPD in tire rubber utilizing Py-GC-MS. Good results were obtained for calibration curve linearity, calibration standard repeatability, limit of quantification, and limit of detection. Shimadzu’s Polymer Additives Library was used to identify the additive.<h3>3. Thermo Fisher Scientific: Gas chromatography analyzers for the oil &amp; gas industry: upstream to downstream</h3><ul><li>Brochure</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/br_003966_gc_analyzers_trace_1610_industrial_br003966_na_en_44ad1327c6.pdf">Full PDF for download</a></li></ul>Gas chromatography (GC) plays a central role in the oil and gas industry, providing high-accuracy, high-speed analysis of complex hydrocarbon mixtures from upstream exploration through midstream transport to downstream refining and distribution. Across this value chain, GC supports regulatory compliance, product quality control, custody transfer, and process optimization. As the industry adapts to decarbonization goals and stricter environmental regulations, robust and automated analytical solutions have become essential for monitoring hydrocarbons, impurities, and trace components under demanding 24/7 operational conditions.Thermo Scientific™ GC analyzers are engineered to meet these challenges, with the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/486">TRACE™ 1610 GC</a> platform forming the backbone of a comprehensive portfolio of preconfigured and customizable systems. Its modular “Instant Connect” design enables rapid configuration of injectors, detectors, and valves, reducing downtime and simplifying maintenance. The platform supports multi-detector configurations—including FID, TCD, FPD, PDD, MS, and VUV—allowing a single analyzer to address a broad range of applications, from natural gas composition and BTU determination to sulfur species, fixed gases, and detailed hydrocarbon analysis .Dedicated GC analyzer configurations are available for specific industry needs, including natural gas and natural gas liquids (NGA/NGL), refinery gas analysis (RGA), light hydrocarbons (LPG), oxygenates, aromatics (BTEX), permanent gases, and high-purity gas streams. Advanced techniques such as GC-VUV further simplify complex analyses like PIONA group-type characterization of gasoline, delivering comprehensive compositional data in a single run while reducing calibration effort and cost per sample. These systems are designed to comply with major ASTM, ISO, GPA, DIN, EN, and UOP standards, ensuring global regulatory alignment .Complementing the hardware, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/76">Thermo Scientific™ Chromeleon™ Chromatography Data System (CDS)</a> provides an integrated informatics backbone for method control, data processing, reporting, and connectivity to LIMS platforms. This unified approach enhances data integrity, improves workflow efficiency, and enables real-time decision-making across refinery and gas processing operations. Together, Thermo Scientific GC analyzers and integrated software solutions deliver reliable, scalable, and future-ready analytics for the evolving oil and gas industry.

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