In this insightful interview, David Oliva, General Manager of Organomation, sits down with Dr. David Hanigan, Associate Professor at the University of Nevada, Reno, to discuss cutting-edge PFAS research. Dr. Hanigan sheds light on efforts to break the "forever cycle" of PFAS by exploring thermal destruction processes and the challenges of ensuring complete elimination rather than creating more volatile forms. The conversation also touches on the standardized EPA methods, including EPA 1633, and the role of LC-MS in analyzing PFAS across various environmental matrices. Learn how this research aims to improve air and water quality through innovative environmental engineering approaches.<h3>Video Transcription</h3><h5>Interviewer: David, could you introduce yourself and your research focus?</h5>David Hanigan: I’m an associate professor at the University of Nevada, Reno. I’m trained as an environmental engineer, and today most of my work is aquatic and environmental chemistry. A big area is PFAS—per- and polyfluoroalkyl substances. Across drinking water, wastewater, and other media, we’re pretty good at separating and concentrating PFAS, but then what? You’ve created a concentrate and now need destruction. A major focus in my lab is understanding thermal processes—what happens during incineration and how to ensure PFAS aren’t simply transformed into smaller, more volatile PFAS and released. If they are formed, can air-pollution control devices capture and control those releases?<h5>Interviewer: Is your work tied to standardized EPA methodologies?</h5>Dr. Hanigan: We use EPA Method 1633 frequently—though not for gas-phase PFAS. 1633 is designed for aqueous, soil, and solid matrices. For gas-phase work, our setups are closer to OTM-45 and OTM-50 (Other Test Methods). That said, for most gas-phase experiments we measure online: rather than collecting with a canister as in OTM workflows, we route the furnace outlet directly to an FTIR and quantify in real time.<h5>Interviewer: My background is drinking-water methods (533, 537, 1633). How do matrices affect method choice and prep?</h5>Dr. Hanigan: EPA 537 is drinking water only—EPA really doesn’t intend it to be “modified 537 for wastewater.” Methods are matrix-specific. 1633 is broader and explicitly covers multiple matrices (e.g., wastewater and solids), so we use that when applicable.<h5>Interviewer: You recently installed a new LC-MS. How’s it going, and how will you prep samples?</h5>Dr. Hanigan: Good! We brought on a part-time technician/research scientist with extensive LC-MS experience; he’s been going through manufacturer training. It’s great having a new instrument under warranty and service contract. For most PFAS work that falls within a defined method, we expect to use EPA 1633. We also analyze short-chain PFAS combustion byproducts—those may fall outside current EPA methods, so we’ll tailor workflows accordingly.<h5>Interviewer: Anything you’d like readers to keep in mind about PFAS and destruction research?</h5>Dr. Hanigan: Think beyond removal. Concentration without destruction just moves PFAS around. We need to validate that thermal destruction actually minimizes formation of undesirable gas-phase PFAS and that downstream controls capture what’s produced. Our online FTIR approach helps reveal what’s really happening in the stack in real time.<h4>Key takeaways</h4><ul><li>Removal isn’t enough: Concentrated PFAS require verified destruction pathways.</li><li>Thermal processes under scrutiny: Incineration must avoid forming/releasing more volatile PFAS; air-pollution controls are critical.</li><li>Method matters: EPA 537 = drinking water only; EPA 1633 = broader matrices (aqueous/soil/solids). OTM-45/50 guide gas-phase sampling—but Hanigan’s team often measures online via FTIR.</li><li>LC-MS readiness: New platform supports EPA 1633 workflows and exploratory analysis of combustion byproducts outside current methods.</li></ul><blockquote>This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.</blockquote><blockquote><h4>Concentrating on Chromatography Podcast</h4>Dive into the frontiers of chromatography, mass spectrometry, and sample preparation with host David Oliva. Each episode features candid conversations with leading researchers, industry innovators, and passionate scientists who are shaping the future of analytical chemistry. From decoding PFAS detection challenges to exploring the latest in AI-assisted liquid chromatography, this show uncovers practical workflows, sustainability breakthroughs, and the real-world impact of separation science. Whether you’re a chromatographer, lab professional, or researcher you'll discover inspiring content!You can find Concentrating on Chromatography Podcast in podcast apps:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://open.spotify.com/show/1wZg67rCjoGju4yhqxI3tL">Spotify</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podcasts.apple.com/tz/podcast/concentrating-on-chromatography/id1788429185">Apple podcast</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podscan.fm/podcasts/concentrating-on-chromatography">podscan</a></li></ul>and on <a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/@ChromatographyTalk/videos">YouTube channel</a></blockquote>

Breaking the Forever Cycle: PFAS Destruction & EPA Methodologies with Dr. David Hanigan

🎤Monique MelloIn this episode of Concentrating on Chromatography, David sits down with Monique Mello, analytical chemist, educator, and LA-ICP-MS imaging specialist, to explore how laser ablation ICP-MS (LA-ICP-MS) and immuno-mass spectrometry imaging (iMSI) are transforming pathology, environmental science, and translational research.Monique shares her journey from public-health and pathology labs in Brazil to environmental and biomedical research in Australia — and explains why metrology, traceability, and defensible measurements are the foundation of meaningful science.We dive into her work developing multiplexed elemental imaging methods that allow researchers to quantify multiple proteins at once in tissue — revealing interactions that traditional single-marker methods miss. Her studies show how LA-ICP-MS can map dystrophin-glycoprotein complex proteins in muscular dystrophy and track elemental distributions like zinc in Alzheimer’s disease tissue.We also discuss something many labs overlook: sample preparation and immunolabelling can change the chemistry you’re trying to measure. Monique’s research demonstrates how staining steps can redistribute endogenous metals and why rigorous validation is critical for trustworthy data.If you care about chromatography, mass spectrometry, or analytical chemistry that genuinely impacts patients and communities, this episode is for you.<h5>In this conversation, we cover:</h5><ul><li>What LA-ICP-MS imaging is and how it works</li><li>Multiplexed antibody tagging with lanthanides for quantitative tissue imaging</li><li>Why metrology and uncertainty matter more than “pretty data”</li><li>Common analytical failures (and why sample prep causes most of them)</li><li>Elemental mapping in muscular dystrophy and Alzheimer’s research</li><li>How immunolabelling and coverslipping can perturb endogenous metals</li><li>Teaching analytical chemistry for real-world problem solving</li></ul><h3>Video Transcription</h3>Analytical chemistry sits at the intersection of science, decision-making, and trust. Whether applied in environmental monitoring, clinical research, or biological imaging, the reliability of analytical results determines how confidently we can interpret data and act on it.In a recent expert discussion, key themes emerged that resonate strongly across the analytical community—from the fundamentals of metrology to advanced techniques such as laser ablation ICP-MS (LA-ICP-MS). This article highlights the most important insights for scientists working with complex matrices, trace analysis, and high-impact data.<h4>Analytical Chemistry: Evidence Over Opinion</h4>At its core, analytical chemistry is about transforming observations into defensible, quantitative evidence. The use of calibrated methods—such as properly constructed calibration curves—ensures that measured values reflect reality as closely as possible.Reliable data is not just a technical outcome; it is a responsibility. Every result must be:<ul><li>Traceable</li><li>Accurate</li><li>Interpretable</li><li>Fit for decision-making</li></ul>When methods are robust, the sample itself “tells the truth.” The challenge lies in ensuring that nothing in the workflow distorts that message.<h4>Metrology: The Invisible Backbone of Data Quality</h4>Despite its importance, metrology is often overlooked outside specialized laboratories. This is largely because it operates silently—until something goes wrong.Several factors contribute to its underappreciation:<ul><li>Focus on novelty over measurement quality Scientific recognition often prioritizes new findings rather than data reliability.</li><li>Black-box instrumentation Modern instruments appear “plug-and-play,” leading to overconfidence in automatically generated results.</li><li>Confusion between precision and accuracy Repeatability does not guarantee correctness.</li><li>Complexity of uncertainty Concepts like uncertainty budgets and traceability are perceived as abstract or overly statistical.</li><li>Time and cost pressures Proper validation, use of certified reference materials (CRMs), and quality control require additional resources.</li></ul>Improving metrology awareness requires translating these concepts into practical laboratory workflows and emphasizing their direct impact on scientific conclusions.<h4>Where Analytical Methods Most Commonly Fail</h4>Failures in analytical workflows typically originate upstream—well before data processing or interpretation.<h5>1. Sample Preparation (Primary Source of Error)</h5>The majority of analytical errors arise during sample preparation, including:<ul><li>Contamination (reagents, labware, environment)</li><li>Poor recovery (adsorption, volatilization, filtration losses)</li><li>Matrix effects</li><li>Non-representative sampling</li><li>Lack of proper controls</li></ul>Errors introduced here propagate through the entire analytical chain.<h5>2. Data Interpretation</h5>Common issues include:<ul><li>Misuse or misunderstanding of calibration models</li><li>Ignoring instrumental drift</li><li>Overreliance on linearity without validation</li></ul><h5>3. Instrumentation</h5>Although often blamed, instruments are less frequently the root cause. Still, issues such as:<ul><li>Detector saturation</li><li>Carryover</li><li>Misalignment</li><li>Signal drift can significantly impact results.</li></ul><h5>Best Practices for Reliable and Defensible Data</h5>For early-career scientists and experienced analysts alike, several habits are essential:<ul><li>Document everything (lab notebooks, timestamps, workflows)</li><li>Standardize sample preparation procedures</li><li>Use quality controls (blanks, spikes, duplicates, CRMs)</li><li>Validate before analyzing samples</li><li>Avoid reusing calibration curves</li><li>Preserve raw data securely</li><li>Evaluate uncertainty and detection limits</li></ul>Strong documentation and consistent QC transform data from “results” into defensible evidence.<h4>LA-ICP-MS: A Powerful Tool for Biological Imaging</h4>Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) represents a significant advancement in analytical capabilities—particularly for spatially resolved analysis.<h5>Key Advantages</h5><ul><li>Direct analysis of solid samples (no digestion required)</li><li>Preservation of spatial information</li><li>Simultaneous multi-element detection</li><li>Quantitative imaging of biological tissues</li></ul>In biological applications, LA-ICP-MS enables researchers to:<ul><li>Map the distribution of biomolecules or metals in tissues</li><li>Study interactions in situ</li><li>Combine spatial and quantitative information</li></ul>For example, multiplex imaging allows simultaneous observation of multiple targets, providing a more realistic representation of biological systems compared to single-marker approaches.<h4>Multiplex Imaging vs. Single-Marker Analysis</h4>Traditional methods often focus on one analyte at a time. However, biological systems are inherently complex and interactive.Multiplex approaches enable:<ul><li>Co-localization analysis</li><li>Detection of interactions between biomolecules</li><li>Identification of spatial heterogeneity</li></ul>This is particularly critical in disease research, where:<ul><li>Single-marker data may lead to misleading conclusions</li><li>Multi-target analysis reveals underlying biological mechanisms</li></ul><h4>Applications in Disease Research: The Case of Metal Distribution</h4>In neurodegenerative diseases such as Alzheimer’s, total elemental concentration alone is insufficient.For example:<ul><li>Zinc may be present at similar total levels in healthy and diseased tissue</li><li>However, its spatial distribution changes significantly, forming localized clusters</li></ul>Elemental mapping therefore provides:<ul><li>Insight into disease progression</li><li>Potential diagnostic markers</li><li>Support for targeted therapeutic strategies</li></ul><h4>Lessons from Environmental Analysis</h4>Environmental chemistry offers valuable principles applicable across analytical disciplines:<ul><li>Treat complex matrices with caution</li><li>Control contamination rigorously</li><li>Ensure representative sampling</li><li>Validate methods with appropriate standards</li></ul><blockquote>One key takeaway: A linear calibration curve does not guarantee accuracy—traceability does.</blockquote><h4>Sample Preparation: The Most Underrated Investment</h4>Laboratories often prioritize instrumentation over sample preparation, but this is a critical imbalance.Key recommendations:<ul><li>Treat sample preparation as part of the analytical system</li><li>Invest in method development and training</li><li>Routinely verify performance (blanks, drift, recovery)</li><li>Maintain strict adherence to SOPs</li></ul>Poor sample preparation cannot be corrected downstream—even with the most advanced instrumentation.<h4>Scientific Continuity and Data Integrity</h4>A less-discussed challenge in analytical science is the loss of continuity:<ul><li>Staff turnover</li><li>Incomplete documentation</li><li>Fragmented datasets</li></ul>This leads to:<ul><li>Repetition of work</li><li>Loss of knowledge</li><li>Reduced reproducibility</li></ul>Robust documentation and standardized workflows are essential to ensure that research progress is cumulative rather than cyclical.<h4>Mindset for the Next Generation of Analytical Scientists</h4>To produce impactful and trustworthy work, scientists should adopt the following principles:<ul><li>Resilience – experiments fail; learning continues</li><li>Critical thinking – understand every step, not just follow protocols</li><li>Curiosity – always ask “why”</li><li>Impact awareness – consider who benefits from your data</li><li>Respect for fundamentals – QC, documentation, and preparation matter most</li></ul>Analytical chemistry is not just about generating numbers—it is about generating truth.<h4>Conclusion</h4>From metrology and sample preparation to advanced imaging techniques like LA-ICP-MS, the strength of analytical science lies in its ability to produce reliable, interpretable, and actionable data.As analytical challenges grow more complex—especially in fields such as biomedicine and environmental science—the need for robust methodologies and critical thinking becomes even more essential.Ultimately, the value of analytical chemistry is measured not only by precision, but by the confidence we place in the decisions it enables.<blockquote>This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.</blockquote><blockquote><h4>Concentrating on Chromatography Podcast</h4>Dive into the frontiers of chromatography, mass spectrometry, and sample preparation with host David Oliva. Each episode features candid conversations with leading researchers, industry innovators, and passionate scientists who are shaping the future of analytical chemistry. From decoding PFAS detection challenges to exploring the latest in AI-assisted liquid chromatography, this show uncovers practical workflows, sustainability breakthroughs, and the real-world impact of separation science. Whether you’re a chromatographer, lab professional, or researcher you'll discover inspiring content!You can find Concentrating on Chromatography Podcast in podcast apps:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://open.spotify.com/show/1wZg67rCjoGju4yhqxI3tL">Spotify</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podcasts.apple.com/tz/podcast/concentrating-on-chromatography/id1788429185">Apple podcast</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podscan.fm/podcasts/concentrating-on-chromatography">podscan</a></li></ul>and on <a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/@ChromatographyTalk/videos">YouTube channel</a></blockquote>

How LA-ICP-MS Imaging Reveals Disease in Tissue

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Společnost Organomation, založená v roce 1959, navrhuje a vyrábí vysoce kvalitní dusíkové koncentrátory a extrakční systémy. Jejich laboratorní přístroje, známé svou inovativností a odolností, se používají po celém světě pro efektivní přípravu vzorků a jsou doprovázeny silnou zákaznickou podporou.

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