<p><strong>This study systematically compares four widely used lysosome enrichment strategies, including density gradient separation, magnetic bead isolation, filter-based enrichment, and subcellular fractionation. Using the THP-1 monocytic cell line, the methods were evaluated for lysosome yield, purity, membrane integrity, reproducibility, scalability, and contamination through complementary analytical techniques and proteomics.</strong></p><p>The results showed that gradient- and bead-based methods achieved the highest lysosomal purity, while subcellular fractionation yielded larger numbers of lysosomes but with greater contamination and variability. Filter-based enrichment enabled rapid processing but recovered mainly nonintact lysosomes. The findings provide practical guidance for selecting lysosome isolation methods in both research and clinical applications.</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.analchem.5c05792"><strong>Benchmarking Lysosome Enrichment Methods: A Guide for Research and Clinical Translation</strong></a></h4><p>Anniek L. de Jager, Sara Kassem, Louis Alesha, Brigitta A.E. Naber, Inge F. de Laat, Bas de Mooij, Kyra van der Pan, Erik Bos, Roman I. Koning, Jacques J.M. van Dongen*, Cristina Teodosio*, and Paula Díez*</p><p>Anal. Chem. 2026, 98, 6, 4585–4600</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c05792">https://doi.org/10.1021/acs.analchem.5c05792</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><div><p>Since their discovery by De Duve et al. <a href="javascript:void(0);">(1)</a> in 1955, lysosomes have emerged as dynamic organelles critical to cellular homeostasis, regulating processes from nutrient sensing to plasma membrane repair. <a href="javascript:void(0);">(2−5)</a> These acidic, enzyme-packed vesicles <a href="javascript:void(0);">(6,7)</a> are now acknowledged as central players in pathologies far beyond their classical association with lysosomal storage disorders (LSDs). Dysfunctional lysosomes are involved in neurodegenerative diseases (e.g., Alzheimer’s, Parkinson’s), <a href="javascript:void(0);">(8−10)</a> cancer metastasis, <a href="javascript:void(0);">(11−13)</a> and cardiovascular disorders. <a href="javascript:void(0);">(14,15)</a> At the same time, lysosomal proteins like GBA1 and LAMP2 serve as biomarkers and therapeutic targets. <a href="javascript:void(0);">(16−19)</a> Despite their clinical significance, a major challenge in lysosomal research persists since lysosomes constitute less than 5% of cellular volume, <a href="javascript:void(0);">(20,21)</a> requiring precise enrichment for accurate analysis in diagnostic and therapeutic development. <a href="javascript:void(0);">(22,23)</a></p><p>Current lysosome isolation methods, including density gradient-based separation, <a href="javascript:void(0);">(24−28)</a> filter-based size exclusion approaches, <a href="javascript:void(0);">(29)</a> magnetic particle phagocytosis, <a href="javascript:void(0);">(30−33)</a> subcellular fractionation (SCF), <a href="javascript:void(0);">(34)</a> and immunoprecipitation strategies, <a href="javascript:void(0);">(35,36)</a> vary widely in yield, purity, structural integrity, and practicality. For instance, density gradient-based methods, which often use Percoll <a href="javascript:void(0);">(37)</a> or sucrose, <a href="javascript:void(0);">(38)</a> while well-established, user-friendly, and cost-effective, coisolate organelles with similar densities, such as endosomes, peroxisomes, and mitochondria, <a href="javascript:void(0);">(39,40)</a> and can be time-consuming, potentially affecting lysosomal protein integrity. Conversely, filter-based size exclusion methods isolate lysosomes based on their size, <a href="javascript:void(0);">(29)</a> offering shorter processing times and straightforward protocols. Nevertheless, these approaches may result in cross-contamination, as various cellular vesicles share similar sizes. <a href="javascript:void(0);">(20,21)</a> Magnetic bead-based methods (e.g., superparamagnetic iron oxide nanoparticles, SPION) involve the endocytic uptake of magnetic beads, <a href="javascript:void(0);">(35,41)</a> offering specificity but altering lysosomal physiology through forced phagocytosis, which hampers functional assays, such as in cancer drug screening. <a href="javascript:void(0);">(42,43)</a> Finally, SCF, which employs sequential lysis steps to enrich specific organelles, including lysosomes, <a href="javascript:void(0);">(34,44)</a> is a rapid, easy-to-use, and cost-effective method; however, significant cross-contamination is linked to this method. <a href="javascript:void(0);">(34,44)</a> Such trade-offs are rarely addressed in single-modality studies, leaving clinicians and researchers without clear guidance for protocol selection. This gap is particularly critical in translational contexts, where diagnostic laboratories that prioritize high-purity lysosomes for biomarker discovery require protocols different from those of therapeutic teams that need lysosomes for drug efficacy testing.</p><p>Previous comparative studies, such as the proteomic analysis by Singh et al., <a href="javascript:void(0);">(35)</a> have provided valuable insights but primarily focused on yield and purity assessments via proteomic markers, overlooking several key parameters with potentially relevant impacts in translational and clinical applications. Critically, these studies often omit functional and practical metrics: for instance, they typically do not evaluate lysosomal membrane integrity, which is vital for enzyme activity and therapeutic testing, nor do they consider the scalability and feasibility of protocols for contexts where only limited patient samples are available or the potential presence of isolation artifacts. This omission of practical, multimodal assessment represents a critical gap that prevents researchers from selecting a protocol optimized for specific clinical goals.</p><p>Our study seeks to address these limitations by employing a multimodal framework that evaluates four widely used lysosome isolation methods across several metrics that are critical for clinical applications. We systematically assessed the yield and reproducibility of each method, factors that are essential for biobanking and high-throughput diagnostic workflows. We also examined organelle purity, crucial for avoiding false positives in proteomic biomarker screenings, together with lysosomal membrane integrity, an important consideration for functional studies and drug testing. Finally, we considered the scalability of each method, ensuring that our findings are relevant for studies involving rare or precious patient-derived cells together with the cost and processing time of each technique. Thus, by integrating nanoparticle tracking analysis (NTA), spectral flow cytometry, transmission electron microscopy (TEM), and mass spectrometry (MS)-based proteomics, our study revealed distinct advantages for each method depending on the intended application. With this comprehensive benchmark, we bridged a critical gap between fundamental lysosome biology and clinical implementation, guiding researchers and clinicians in the selection of the most appropriate isolation strategies tailored to their diagnostic or therapeutic goals.</p><h3><strong>Experimental Section</strong></h3><h4><strong>Liquid Chromatography–Tandem Mass Spectrometry Analysis (LC–MS/MS)</strong></h4><p>For liquid chromatography–tandem mass spectrometry (LC–MS/MS) studies, a total of 15 μg of proteins were reduced with 10 m<span>M</span> DTT at 45 °C for 30 min, followed by alkylation with 40 m<span>M</span> 2-iodoacetamide (IAA, Sigma-Aldrich) at RT for 30 min. This last reaction was quenched with 20 m<span>M</span> DTT, and protein digestion and peptide recovery were performed using hydrophilic and hydrophobic beads (Sera-Mag Carboxylate-Modified Magnetic Particles, Cytiva, Marlborough, MA/USA) according to the Single-Pot Solid-Phase-enhanced Sample Preparation (SP3) method. <a href="javascript:void(0);">(46,47)</a> In short, samples were incubated twice with beads (1:10 (w/w) protein:bead ratio) in 70% acetonitrile (ACN) for 18 min at RT. After incubation, bead-bound proteins were retained by using a DynaMag-PCR Magnet (Thermo Fisher), and contaminants were washed away three times with 80% ethanol (Sigma-Aldrich). Proteins were digested using a Trypsin/LysC mix (1:25 (w/w) enzyme:protein ratio, Promega, Madison, WI/USA) in 50 m<span>M</span> ammonium bicarbonate at RT overnight. Beads were then washed twice with ACN, after which peptides were eluted in 2% (v/v) dimethyl sulfoxide (DMSO, Thermo Fisher), lyophilized in a freeze-dryer, and stored at −20 °C. Samples were then tagged using the tandem mass tag (TMT) labeling kit (TMTpro 16plex tags, Thermo Fisher) by resuspending the lyophilized peptides in 40 m<span>M</span> HEPES pH 8.4 and labeling with the TMT tags at a 1:5 (w/w) sample:tag ratio for 1 h at RT. The labeling reaction was quenched with 5% hydroxylamine. TMT-labeled lysosome samples were pooled, lyophilized in a freeze-dryer, and stored at −20 °C until LC–MS/MS analysis.</p><h4><strong>Mass Spectrometry Data Acquisition and Analysis</strong></h4><p>MT-labeled peptides were dissolved in solvent A (water/formic acid (FA), 100/0.1 (v/v)) and analyzed by online C18 nano-HPLC MS/MS, coupling an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=3&amp;search=UltiMate+3000"><strong>UltiMate 3000 gradient HPLC system</strong></a> (Thermo Fisher) and an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/401"><strong>Orbitrap Exploris 480 mass spectrometer</strong></a> (Thermo Fisher). A total of 12 fractions were injected onto a precolumn (300 μm × 5 mm; PepMap100 C18 5 μm, Thermo Fisher) equilibrated with solvent A and eluted via a homemade analytical nano-HPLC column (50 cm × 75 μm; ReproSil-Pur C18-AQ 1.9 μm) using solvent B (80/20/0.1 (v/v/v) ACN/water/FA) in a gradient from 5 to 30% in 160 min. The nano-HPLC column was drawn to a tip of ∼5 μm and functioned as an electrospray needle. A Sonation PRSO-V2 column oven was used to maintain the analytical column temperature at 50 °C. The MS1 spectra were recorded in the Orbitrap at a mass-to-charge ratio (<i>m</i>/<i>z</i>) range of 350–1,600, a resolution of 120,000, a maximum injection time of 50 ms, and automatic gain control (AGC) at standard. Dynamic exclusion was applied after <i>n</i> = 1 with an exclusion duration of 45 s and a mass tolerance of 10 ppm. Charge states 2–5 were included. Precursors for MS2 were selected using a TopSpeed method of 3 s at a resolution of 45,000 and fragmented by high-energy collision-induced dissociation (HCD) at a normalized collision energy (NCE) of 36%, with an AGC at 200 and the maximum injection time set to auto. The isolation window for MS/MS was 1.2 Da.</p><p>Raw data files were converted to the peak list by using <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1782"><strong>Proteome Discoverer v2.4</strong></a> (Thermo Fisher), followed by protein identification employing the UniProtKB database (<i>Homo sapiens</i>, 20,596 entries) using Mascot v2.2.07 (Matrix Science Inc., Boston, MA/USA) with the following parameters: precursor and fragment mass tolerances of 10 ppm and 0.02 Da, respectively; TMT16plex on N-term and Lys as fixed modifications; oxidation on Met and acetylation of N-term as variable modifications; and full tryptic digestion (no P rule) with up to 2 missed cleavages. A false discovery rate (FDR) of 1% was set for peptide spectrum matches (PSMs), peptides, and proteins.</p><p>The MS data have been deposited via the PRIDE partner repository to the ProteomeXchange Consortium with the identifier PXD040003.</p><h3><strong>Results</strong></h3><h4><strong>Quantification and Size Distribution of Isolated Vesicle Fractions</strong></h4><p>To assess the yield and size distribution of vesicles isolated by different methods, nanoparticle tracking analysis (NTA) was employed. The SCF procedure yielded the highest median number of vesicles per cell (2,356.3 ± 7,461.7; median ± range), followed by gradient-based (1,630.2 ± 1632.3), filter-based (228.2 ± 265.6), and bead-based isolation (27.8 ± 35.2). Both filter- and bead-based approaches resulted in significantly lower yields compared to SCF (<i>p</i>-value = 0.002 and <i>p</i>-value &lt;0.0001, respectively), and the gradient-based method yielded significantly more vesicles than the bead-based approach (<i>p</i>-value = 0.002; <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05792#fig1">Figure 1</a>A).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_6_4585_4600_Figure_1_Quantification_and_size_analyses_of_isolated_vesicles_2d5c120f05.jpeg" alt="Anal. Chem. 2026, 98, 6, 4585–4600: Figure 1. Quantification and size analyses of isolated vesicles. A) Quantification of isolated vesicles per cell, shown as median with range. Circles represent technical replicates. B) Size distribution of isolated vesicles (in nm), displaying average D10 (red), D50 (blue), and D90 (gray), depicted as median with range. Circles represent technical replicates. C–F) Particle distribution plots showing mean concentration at specific sizes; each line represents a technical replicate. For each method, six technical replicates are shown, except for SCF, which includes 12 replicates. SCF, subcellular fractionation. Statistical significance: p &lt; 0.05 and FDR 5%. For statistical analysis, the nonparametric Kruskal–Wallis test was used and corrected for multiple comparisons using the method of Benjamini and Hochberg." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_6_4585_4600_Figure_1_Quantification_and_size_analyses_of_isolated_vesicles_2d5c120f05.jpeg 126w," sizes="100vw" width="1383" height="1708"></p><p>Median vesicle size (D50) was comparable across all methods: 159.5 ± 17.2 nm (gradient-based), 169.9 ± 82.7 nm (filter-based), 168.9 ± 42.4 nm (bead-based), and 150.0 ± 55.6 nm (SCF). Similarly, decile 10 (D10) values were consistent, ranging from 87.1 ± 87.3 nm (SCF) to 108.2 ± 3.1 nm (gradient-based). However, filter-based isolation led to higher size heterogeneity of the isolated particles, capturing significantly larger vesicles at the upper end (D90:308.5 ± 110.7 nm) compared to SCF (244.9 ± 112.6 nm, <i>p</i>-value = 0.005), while gradient- and bead-based methods showed intermediate D90 values (250.8 ± 31.7 nm and 273.0 ± 45.9 nm, respectively; <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05792#fig1">Figure 1</a>B). Notably, greater intra-assay variation in concentration and size distribution was observed with filter-based and SCF methods compared to the other approaches (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05792#fig1">Figure 1</a>C–F).</p><h4><strong>Morphology of the Isolated Fractions</strong></h4><p>Transmission electron microscopy (TEM) of the isolated fractions confirmed the presence of heterogeneously sized vesicles, in line with the size distributions obtained by NTA. Notably, only the gradient-based method yielded relatively pure vesicle preparations. In contrast, filter-based and SCF methods displayed contamination with cellular debris or protein aggregates, while the bead-based approach was associated with the presence of lipid structures (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05792#fig2">Figure 2</a>).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_6_4585_4600_Figure_2_Transmission_electron_microscopy_TEM_of_isolated_fractions_f14ddbe180.jpeg" alt="Anal. Chem. 2026, 98, 6, 4585–4600: Figure 2. Transmission electron microscopy (TEM) of isolated fractions. Panels A–D show TEM images of the four isolated fractions. Insets display magnified views of the regions outlined in black squares. Black arrows indicate identified representative isolated vesicles, and white arrows denote areas of contamination." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_6_4585_4600_Figure_2_Transmission_electron_microscopy_TEM_of_isolated_fractions_f14ddbe180.jpeg 145w," sizes="100vw" width="1263" height="1361"></p><h3><strong>Conclusion</strong></h3><p>This study provides a systematic benchmarking of four lysosome isolation strategies, revealing inherent trade-offs among yield, integrity, purity, and reproducibility driven by their underlying separation principles. No single method is universally optimal; instead, the method selection must be guided by the intended downstream application. By integrating complementary analytical approaches in a clinically relevant and challenging monocytic model, our work offers a practical framework to support the informed selection of lysosome enrichment strategies, facilitating reliable lysosomal analyses in both research and translational settings.</p></div>

Benchmarking Lysosome Enrichment Methods: A Guide for Research and Clinical Translation

A Method for the Detection of Tire Wear Microplastics in Zebrafish Guts by Laterally Resolved LA-ICP-MS-Based Elemental Fingerprinting and Chemometrics

<p><strong>This leaflet is intended to help participants in quantitative proficiency testing (PT) schemes to better understand the performance assessment made by the PT provider [1-4].</strong></p><h3><strong>Performance assessment parameters&nbsp;</strong></h3><h4><strong>Assigned value&nbsp;</strong></h4><p>In order to assess individual performances, the results reported by participants are compared to the value assigned by the PT provider (<i>x<sub>pt</sub></i>). ISO 13528 [1] suggests five different ways to obtain <i>x<sub>pt</sub></i> (Figure 1). In this context, the design of the PT scheme must reflect the objectives of the scheme, comparing results either to a predetermined <i>x<sub>pt</sub></i> (independent from the participant results), or to a value derived from the reported results. The PT provider is responsible for defining <i>x<sub>pt</sub></i> , taking into account the advantages and disadvantages of the various possibilities when establishing metrological traceability or estimating the uncertainty of the assigned value, <i>u</i>(<i>x<sub>pt</sub></i>).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Figure_1_e5ef5d1f9c.jpg" alt="Eurachem - Figure 1 Options for obtaining the assigned value, Xpt" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Figure_1_e5ef5d1f9c.jpg 245w," sizes="100vw" width="640" height="382"></p><h4><strong>Uncertainty of the assigned value&nbsp;</strong></h4><p>The uncertainty of the assigned value u(xpt) can be estimated in at least five different ways [1], each corresponding to the value assignment strategy illustrated in Figure 1. The different uncertainty evaluation methods are listed in Figure 2. This uncertainty can be reported as a standard uncertainty (<i>u</i> (<i>x<sub>pt</sub></i>)), or as an expanded uncertainty <i>U</i> (<i>x<sub>pt</sub></i>) with a coverage factor k, where <i>U</i> (<i>x<sub>pt</sub></i>) = <i>k . u </i>(<i>x<sub>pt</sub></i>). The choice of k will impact the level of confidence (e.g. approximately 95 % for <i>k</i> = 2).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Figure_2_f41ebc0c61.jpg" alt="Eurachem: Figure 2 Options for obtaining the uncertainity of the assigned value, u(Xpt)" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Figure_2_f41ebc0c61.jpg 245w," sizes="100vw" width="727" height="378"></p><h4><strong>Standard deviation for proficiency assessment&nbsp;</strong></h4><p>Five possibilities are suggested by ISO 13528 to set the standard deviation for proficiency assessment <i>s<sub>pt</sub> </i>(Figure 3). The chosen approach must comply with the objectives of the PT scheme.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Figure_3_f09cb3969e.jpg" alt="Eurachem: Figure 3 Options for obtaining the standard deviation for proficiency assesment" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Figure_3_f09cb3969e.jpg 245w," sizes="100vw" width="638" height="355"></p><p>Two approaches for performance assessments use <i>s<sub>pt</sub></i> as shown in Table 1.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Table_1_Scoring_for_performance_assessment_3dd03aa3fb.jpg" alt="Eurachem: Table 1 - Scoring for performance assessment." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Table_1_Scoring_for_performance_assessment_3dd03aa3fb.jpg 245w," sizes="100vw" width="1390" height="571"></p><h3><strong>Performance assessment&nbsp;</strong></h3><p>All performance assessments are based on the difference between the participant’s results (<i>x<sub>i</sub></i>) and the assigned value (<i>x<sub>pt</sub></i>) divided by a normalisation factor (Table 1).&nbsp;</p><ul><li>The percent difference <i>D%</i> is the normalised difference expressed as a percentage of xpt , for which a prescribed maximum relative permissible error (<i>d<sub>E%</sub></i> = <i>d<sub>E</sub></i> / <i>x<sub>pt</sub></i>) is defined by the PT provider.&nbsp;</li><li>The <i>z</i> score (unitless) compares the participant’s deviation from the assigned value with the standard deviation for proficiency assessment (spt). When the uncertainty of the assigned value is significant (<i>u</i>(<i>x<sub>pt</sub></i>) &gt; 0.3 <i>s<sub>pt</sub></i>), the <i>z</i>’ score should be used instead, to include the contribution of <i>u</i>(<i>x<sub>pt</sub></i>) in the denominator.&nbsp;</li><li>The zeta (ζ) score (unitless) states whether the laboratory’s result agrees with the assigned value within the respective uncertainty. The denominator is the combined (standard) uncertainty of the assigned value <i>u</i>(<i>x<sub>pt</sub></i>) and the (standard) measurement uncertainty reported by the laboratory <i>u(x<sub>i</sub></i>). An unsatisfactory zeta score can either be caused by an inappropriate estimation of the measured quantity value (e.g. concentration, content), or of its measurement uncertainty, or both.&nbsp;</li><li>The <i>E<sub>n</sub></i> score (unitless), unlike the zeta score, uses a combination of expanded uncertainties as the normalisation factor. This combination corresponds to a confidence interval of approximately 95 %. <i>E<sub>n</sub></i> scores are often used in metrology comparisons among calibration laboratories.</li></ul><h3><strong>Interpretation of performance scores&nbsp;</strong></h3><p>According to ISO 13528:2022 clause 9.4.2, the conventional interpretation of the <i>z, z’</i> and ζ scores is as follows:&nbsp;</p><ul><li>A result that gives |score| ≤ 2.0 is considered to be acceptable, indicating satisfactory performance.&nbsp;</li><li>A result that gives 2.0 &lt; |score| &lt; 3.0 is considered to give a warning signal, indicating questionable performance.&nbsp;</li><li>A result that gives |score| ≥ 3.0 is considered to be unacceptable (or action signal), indicating unsatisfactory performance.&nbsp;</li></ul><p>Similarly, |<i>E<sub>n</sub></i>| &lt; 1 and |<i>D%</i>| &lt; <i>d<sub>E%</sub></i> indicate successful (acceptable) performance.</p>

Understanding PT performance assessment

Article

Academy

Scientific article

Science and research

Eurachem is a European network promoting international traceability of chemical measurements and good analytical practice. It provides expert guidance, supports method validation, uncertainty evaluation, proficiency testing, and facilitates discussion on technical and policy issues in analytical quality.

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Thermo Fisher Scientific

PerkinElmer

<div><p><strong>Thermal gravimetric analysis (TGA) is an ideal tool for the characterization of all kinds of organic and inorganic solids and liquids. Thermodynamic transitions, thermal stability, decomposition and chemical reactions can be detected and quantified with high accuracy over a broad temperature range. However, this method does not provide details about the chemistry behind the processes under study</strong>.</p><p>The combination of thermal analysis with infrared spectroscopy bridges this analytical gap. Fourier-Transform Infrared (FT-IR) spectroscopy is a classical tool for the identification and quantification of gases. This technique is based on the interaction of infrared radiation with the vibrating dipole moments of molecules. FT-IR yields a unique IR spectrum for each substance, except for homonuclear diatomic gases and pure metals</p><p>In a simple case, only one type of gas molecule is evolved with an applied temperature increase in the TGA. The evolved gas is flowed into the FT-IR and can be readily identified by searching its spectrum against a gas phase reference library. In a typical decomposition process, evolved fume is a mixture of gases. Different hardware and software approaches to identify the composition of complex gas mixtures will be presented with a thorough discussion of the following topics:</p><ul><li>Mixture analysis using library search</li><li>TGA-IR at reduced pressure&nbsp;</li><li>Spectra manipulation method for deconvoluting complex spectra (mixtures)</li></ul><p>Examples of applications will include the identification of plasticizers in polymers, PVC undercoating analysis, decomposition of&nbsp;PETN explosive, NHF propellant, and others.</p><blockquote><p><strong>Presenter: </strong>Dr. Sergey Shilov (North America Product Manager, Science Business Unit, Bruker Optics)</p><div><p>Dr. Sergey V. Shilov is a Product Manager for North America at Bruker Optics. Sergey joined Bruker in 2001. He is responsible for the support and development of new applications for the Bruker research FTIR systems. He received his Ph.D. in 1992 from the Russian Academy of Sciences in Polymer Physics and his M.S. from St. Petersburg State University (Russia) in Physics in 1986. Alexander Von Humboldt Foundation (Germany) awarded him a research fellowship in 1996. Sergey published 37 papers in peer-reviewed journals. His research interests include time-resolved FTIR spectroscopy, surface science, polymer, and life-science applications.&nbsp;</p></div></blockquote></div>

Bruker: To Evaluate Complex Gas Mixtures evolved during Thermal Decomposition

To Evaluate Complex Gas Mixtures evolved during Thermal Decomposition

Learn how combining TGA with FT-IR enables chemical analysis of evolved gases, including mixture analysis, spectral deconvolution, and practical applications.

Evaluating Complex Gas Mixtures in Thermal Decomposition

<p><strong>In this webinar, PerkinElmer will explore how advancements in materials development, supported by cutting-edge analytical technologies, are opening new opportunities in renewable energy markets like solar energy. Innovations in solar materials like glass, polymers, coatings, paints, and nanomaterials require in-depth characterization techniques and modern methodologies to guarantee high performance and durability.</strong></p><p>In this session, participants will learn how the use of advanced analytical techniques like UV-Vis-NIR Spectroscopy, Differential Scanning Calorimetry (DSC), and Thermogravimetric Analysis (TGA) allows users to shed light on some important aspects of advanced materials in solar panels and ensure high performance throughout their lifecycle.</p><h4><strong>By Attending This Webinar, You Will Learn:</strong></h4><ul><li>How analytical technologies are used to test the chemicals and materials used to improve the efficiency of solar renewable energy</li><li>How total solar reflectance (TSR) is measured using UV-Vis-NIR to develop better glass and coating materials How high-end UV analytical accessories can provide a deeper understanding of material behaviors</li><li>How heat capacity of aged materials can be tested using thermal analysis techniques, such as DSC, for improved polymer and nanomaterial performance</li><li>How thermal analysis instruments can be hyphenated with GC/MS to help develop improved advanced materials' performance</li></ul><blockquote><p><strong>Presenter: </strong>Valentina Paolucci (Sr. Application Scientist, PerkinElmer)</p><p>Valentina works on the Molecular Spectroscopy Portfolio at PerkinElmer. She received her PhD in Chemistry from University of Copenhagen and before joining PerkinElmer, she worked across various industries as Development Leader and Technical Sales Specialist supporting customers with material application and process issues. She has expertise in molecular spectroscopy, microscopy and chromatography for the analytical characterization of nanomaterials, textile and metallurgic materials. &nbsp;</p><p><strong>Presenter:&nbsp;</strong>Kieran Evans (Sr. Applications Scientist, PerkinElmer)</p><p>Kieran has a focus on evolved gas analysis using hyphenated techniques such as TGA-FTIR and TGA-GC/MS. During his time at PerkinElmer, Kieran has worked in a variety of end markets such as polymer, pharmaceutical, and environmental analysis. He has also developed applications for several other techniques including thermal analysis (DSC &amp; TGA) and molecular spectroscopy (FT-IR, FT-NIR, UV/Vis &amp; fluorescence). Kieran obtained a master’s degree in chemistry from the University of Surrey with a focus on spectroscopic identification of natural products and is currently pursuing a PhD in environmental analytical chemistry using TGA-GC/MS to detect tire wear particles in the environment.</p><p><strong>Presenter:&nbsp;</strong>Simone Cailotto (Associate Market Leader, PerkinElmer)</p><p>Simone is the Associate Market Leader for Batteries and Renewable Energies at PerkinElmer. He studied in Venice and obtained his PhD in Chemistry with a thesis focus on the synthesis of innovative materials and their characterization. After his PhD he focused his attention on nanomaterials from renewable sources. Then he moved to PerkinElmer where he is now involved in the applied market segment with a particular focus on renewable energies and batteries.</p></blockquote>

PerkinElmer: From Lab to Light: Characterizing Advanced Materials for Solar Breakthroughs

From Lab to Light: Characterizing Advanced Materials for Solar Breakthroughs

PerkinElmer will explore how advancements in materials development, supported by analytical technologies, are opening new opportunities in renewable energy 

Characterizing Advanced Materials for Solar Breakthroughs

<div><div container-id="cq-colctrl-lt0" comp-id="A1"><div><p><strong>We are pleased to invite you to upcoming Webinar on "Exploring analytical methods &amp; applications in polymer science." This event is free to attend will cover key techniques such as Rheology, FTIR, NIR, Process Raman, XRF, XRD, and TGA, highlighting their roles in understanding polymer properties and optimizing processes.</strong></p><p>Join to learn how these methods can enhance material design, quality control, and performance. Gain insights into the latest advancements and applications in polymer science.</p><h3><strong>Webinar Schedule</strong></h3><h4><strong>Day 3: Hyphenated techniques</strong></h4><p>Thursday, October 2, 2025, | 10:00-11:15 UTC+1 &nbsp; &nbsp;</p><h5><strong>Session 1: Combining Rheometry &amp; Raman-Spectroscopy for simultaneous analysis</strong></h5><ul><li><strong>Speaker:</strong> Dr. Fabian Meyer and Dr. Maximilian Ries</li></ul><p>In this session, Dr. Meyer and Dr. Ries will introduce Rheo-Raman spectroscopy, a powerful technique that combines rheology and Raman spectroscopy. Rheo-Raman allows for the simultaneous measurement of the mechanical properties and molecular structure of materials under deformation. This technique is particularly useful for studying polymers, gels, and other complex fluids. The presenters will discuss the principles behind Rheo-Raman, its instrumentation, and its applications in material science.</p><h5><strong>Session 2: Polymer Extrusion Assisted by Raman Spectroscopy</strong></h5><ul><li><strong>Speaker:</strong> Mr. Shailesh Karavadra</li></ul><p>Mr. Karavadra will present on the integration of Raman spectroscopy with polymer extrusion processes. This hyphenated technique enables real-time monitoring of the molecular composition and structural changes of polymers during extrusion. By coupling Raman spectroscopy with extrusion, researchers can optimize processing conditions and improve the quality of polymer products. The session will cover the setup, benefits, and case studies demonstrating the effectiveness of this approach.</p><h5><strong>Session 3: Thermal Gravimetric Analysis Coupled with Infrared Spectroscopy</strong></h5><ul><li><strong>Speaker:</strong> Dr. Hilmar Geitlinger</li></ul><p>Dr. Geitlinger will explore the combination of Thermal Gravimetric Analysis (TGA) with Infrared (IR) spectroscopy. TGA measures the change in weight of a sample as it is heated, providing insights into thermal stability and composition. When coupled with IR spectroscopy, it allows for the identification of evolved gases and decomposition products. This session will highlight the advantages of TGA-IR, its applications in material characterization, and practical examples from research and industry.</p><blockquote><p><strong>Presenter: </strong>Meyer, Fabian (Teamleiter Applikation Lab, Production Process and Analytics)</p><p><strong>Presenter: </strong>Ries, Maximilian (Business Development Manager EMEA, Spectroscopy)</p><p><strong>Presenter: </strong>Geitlinger, Hilmar (Application Specialist, Spectroscopy)</p><p><strong>Presenter:&nbsp;</strong>Karavadra, Shaileshkumar N. (Sr Manager, Sales Development/Product Specialists, Chemical Analysis Division)</p></blockquote></div></div></div>

Thermo Fisher Scientific: Advanced analytical methods in polymer science webinar series

Advanced analytical methods in polymer science, Part 3

Join Day 3 of our polymer science webinar to discover Rheo-Raman, Raman-assisted extrusion, and TGA-IR techniques for advanced polymer characterization.

<header><h4><strong>How to Use Thermal Analysis to Improve Safety in the Chemical Industry</strong></h4></header><main><div><div><h5><strong>Program</strong></h5><ul><li>Basic concepts of safety analysis: time to maximum rate, criticality of a reaction</li><li>Thermal analysis and reaction calorimetry applications</li><li>Industry applications: differential scanning calorimetry (DSC)</li><li>Industry applications: thermogravimetry (TGA)</li><li>Interactive Q&amp;A session</li></ul></div></div><div><div><div><p>Accident prevention in chemical production plants is a critical topic, and avoiding incidents that could cause casualties or environmental harm is paramount. Thermal runaway reactions are often at the root of such incidents and must be effectively managed. By using thermal analysis and reaction calorimetry, you can easily assess the thermal safety of chemicals and processes. This means that thermal hazards can be identified at an early stage of process development, saving time, costs, and lives.</p><p>Join this live webinar to learn how thermal analysis techniques—DSC and TGA—are used to investigate hazardous compounds and conduct safety studies in the chemical industry. We will present real-life examples of safety investigations using these techniques.</p><p>Don’t miss the live <strong>Q&amp;A session</strong> where you can ask our experts your own questions. Secure your spot today.</p><h4><strong>How to Use Thermal Analysis for Safety Assessment</strong></h4><ul><li><strong>DSC</strong> is typically used to investigate the thermal safety aspects of individual chemicals. Reaction calorimetry is used to provide safety information about processes, for example, to determine the heat flow during the dosing of reactants or during stirring.</li><li><strong>TGA </strong>is useful for determining evaporation, desorption, and vaporization behavior, as well as thermal stability and the decomposition kinetics.</li><li><strong>Evolved gas analysis</strong> techniques are used to gain information about the products evolved during a decomposition reaction. The TGA module is combined with a technique, such as FTIR, MS, or GC-MS.</li><li><strong>TGA/DSC</strong> combines two different thermal analysis techniques to provide complementary information about a sample's thermal behavior.</li></ul><p>Don’t miss this opportunity to gain in-depth knowledge on safety evaluations for the chemical industry.&nbsp;</p></div><div><div data-advanced-option="false" data-has-preview-image="false" data-video-id="" data-component-id="2110920040"><div style="max-width:1px;width:100%;" id="mediaContainerWrapper_2110920040"><div style="height:1px;width:1px;" data-external-url="" id="mediaContainer_2110920040" data-advanced-option="false" data-width="1" data-height="1" data-video-id="">&nbsp;</div></div></div></div></div></div></main><div><div><div data-advanced-option="false" data-has-preview-image="false" data-video-id="" data-component-id="2110920040"><blockquote><div><strong>Presenter: </strong>Dr. Teresa Dennenwaldt</div><p>Dr. Teresa Dennenwaldt obtained her PhD in chemistry in 2013 and completed a postdoc at the Ecole Polytechnique Fédérale de Lausanne, Switzerland in the field of electron microscopy and spectroscopy. In 2018, she joined METTLER TOLEDO as an application specialist for Thermal Analysis. In her current position, she expertly uses, teaches, and supports DSC, TGA, TMA, and DMA instruments at the METTLER TOLEDO head office in Switzerland.</p></blockquote></div></div></div>

Mettler Toledo: Thermal Analysis for Safety Evaluation of Chemical Processes

Thermal Analysis for Safety Evaluation of Chemical Processes

Join this webinar to explore how thermal analysis helps identify thermal hazards, improve process safety, and prevent runaway reactions in chemical production.

<p><strong>In this webinar, you will learn about the analytical techniques and best practices for testing polymer-based advanced materials, from early R&amp;D through to production quality control. Discover how to leverage both macro- and micro-level IR analysis to optimise formulations and enhance material performance. Learn how to maximise the value of your thermal analysis instrumentation to diagnose production issues, evaluate VOCs, and support reverse‑engineering projects.</strong></p><p>This webinar is highly relevant to key industrial markets working with adhesives, sealants, coatings, paints, and 3D‑printing additives. Technologies covered will include <strong>FTIR, TGA</strong>, and hyphenated techniques such as <strong>TG‑IR and TG‑IR‑GC/MS.</strong></p><p>Join the free webinar with certification of attendance and CPD points now. &nbsp;</p><h4><strong>Attend the Webinar to Learn:</strong>&nbsp;</h4><ul><li>Which analytical techniques and best practices are most effective for characterising polymer‑based advanced materials</li><li>How to apply macro‑ and micro‑level IR analysis, alongside thermal analysis, to optimise formulations, enhance performance properties, and diagnose production issues.</li><li>Benefits of hyphenated techniques (TG‑IR and TG‑IR‑GC/MS) for real‑world applications</li></ul><h4><strong>This Webinar is Ideal For:</strong></h4><ul><li>Materials scientists</li><li>Polymer researchers</li><li>Lab managers</li><li>Meet the Webinar Speakers</li></ul><blockquote><p><strong>Speaker: </strong>Nicholas Lancaster (Application scientist, PerkinElmer)</p><p>Nic is an application scientist in the R&amp;D team at PerkinElmer. With a background in materials characterization using FTIR &amp; UV/Vis spectroscopy, thermal analysis as well as chromatography and mass spectrometry he has been a part of PerkinElmer since 2014. He joined PerkinElmer as a Field Application Scientist (FAS) in South Africa, and now works from the UK developing laboratory workflow solutions for a range of end markets including renewable energy, lubricants, and advanced materials.</p><p><strong>Speaker: </strong>Dr Krystelle Mafina (Product leader, PerkinElmer)</p><p>Krystelle is a product leader at PerkinElmer, overseeing the thermal (TEA) analysis business. She holds a PhD in Biomedical Biomaterials Science and is a Chartered Engineer (CEng) and Chartered Scientist (CSci) with a strong background in materials science. A member of the Institute of Materials, Minerals, and Mining (IOM3) since 2009; a committee member since 2019, Krystelle is committed to continuous professional development and was recently made 'Fellow' of the institute. Her blend of scientific knowledge and industry experience makes her a valuable contributor to PerkinElmer and the wider scientific community.</p></blockquote>

Azom: Analysis Masterclass for Polymer-Based Advanced Materials

Analysis Masterclass for Polymer-Based Advanced Materials

Which analytical techniques and best practices are most effective for characterising polymer‑based advanced materials

ICPMS webinars focusing on Thermal Analysis - page 1