<p><strong>In the week from February 9, the following webinars await you in the field of atomic and molecular spectroscopy and spectrometry</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Thermo Fisher Scientific: Process Raman – A Reliable and Versatile Tool for Downstream Process Monitoring and Control for mAb Production</strong></h5><ul><li>Tu, 10.2.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5808">Registration</a></li></ul><p>The presentation highlights how process Raman spectroscopy offers several advantages over traditional in-line UV-Vis analysis for process monitoring and metabolite production control.</p><h5><strong>2. Bruker: Meet Compact-Q DEER Spectrometer – Your Gateway to Quantum Innovation</strong></h5><ul><li>Tu, 10.2.2026 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5757">Registration</a></li></ul><p>Discover how the Compact-Q DEER spectrometer enables high-fidelity qubit characterization and control, combining advanced microwave architecture, AWGs, and cryogen-free operation.</p><h5><strong>3. Agilent Technologies: Novel Advancements in Trace Level Determination and Long-Term Stability of Metals in TMAH and UPW Matrices</strong></h5><ul><li>Tu, 10.2.2026 21:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/3733">Registration</a></li></ul><p>Explore novel approaches for trace-level metal determination and long-term stability assessment in TMAH and ultrapure water matrices.</p><h5><strong>4. AZoM: From First to Best – Introducing the Litesizer DIF Series</strong></h5><ul><li>We, 11.2.2026 15:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5765">Registration</a></li></ul><p>Join this live webinar introducing the new Litesizer DIF series of laser diffraction particle size analyzers.</p><h5><strong>5. Anton Paar: Polymer Rheology – Rotational and Transient Measurements on a Polymer Melt</strong></h5><ul><li>We, 11.2.2026 18:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5786">Registration</a></li></ul><p>Learn how rotational and transient viscosity measurements on rotational rheometers reveal polymer melt flow behavior, from method setup and accessories to data interpretation.</p><h5><strong>6. Milestone: Practical Guidance to Improve the Journey from Sample to Heavy Metals Analysis for Environmental Labs</strong></h5><ul><li>Th, 12.2.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5803">Registration</a></li></ul><p>Improve heavy metals analysis in environmental laboratories with practical tips on reagents, filtration, cleaning, and workflow optimization to enhance data quality and efficiency.</p><h5><strong>7. Agilent Technologies: Moving from R&amp;D and Limited QC on a UV-Vis-NIR Instrument to Large-Scale QC Using an Autosampler</strong></h5><ul><li>Th, 12.2.2026 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/2104">Registration</a></li></ul><p>This presentation covers key aspects of adding and using an autosampler to maximize UV-Vis-NIR QC workflows when scaling from R&amp;D to routine quality control.</p>

Webinars LabRulezICPMS Week 07/2026

<p><strong>LA-ICP-MS imaging, particularly in TOF mode, provides rapid, high-resolution elemental maps of complex geological materials, but quantitative accuracy is often limited by heterogeneous mineralogy. This study introduces a workflow using UMAP for unsupervised dimensionality reduction combined with k-means clustering to segment mineral phases directly from per-pixel elemental data.</strong></p><p>Cluster compositions are matched to known minerals based on stoichiometry, allowing phase-specific normalization and reducing quantification errors by up to 60%. Validated on dawsonite-bearing sandstones, the approach enables consistent phase-resolved geochemical comparisons across depth profiles and establishes UMAP as a practical tool for accurate, interpretable multielement imaging.</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.5c05398"><strong>Mineral Phase-Resolved Quantification in LA-ICP-MS Imaging</strong></a></h4><p>Barbara Umfahrer, Jakub Buday, Pavel Pořízka, Jozef Kaiser, Paolo S. Garofalo*, and Detlef Günther*</p><p>Anal. Chem. 2026, 98, 1, 581–589</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c05398">https://doi.org/10.1021/acs.analchem.5c05398</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><p>Since its introduction in 1985, <a href="javascript:void(0);">(1)</a> Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) has matured into a routine technique for both qualitative and quantitative elemental analysis, finding use in diverse fields such as geology, <a href="javascript:void(0);">(2−4)</a> biology, <a href="javascript:void(0);">(5,6)</a> materials science, <a href="javascript:void(0);">(7)</a> and cultural heritage. <a href="javascript:void(0);">(8,9)</a> Especially in comparison to liquid analysis techniques, the method excels through minimal sample preparation, negligible sample destruction, and low limits of detection, which enable trace element analysis. However, besides the availability of standard reference materials, a longstanding challenge of quantitative LA-ICP-MS is the need for a suitable internal standard to account for variations in ablation rate and transport efficiency, as well as instrumental drift. <a href="javascript:void(0);">(10)</a> Developed for applications in geological matrices, this requirement was elegantly circumvented by Liu et al. <a href="javascript:void(0);">(11)</a> through 100 wt % normalization, which assumes that all detected elements form oxides summing up to 100 wt %. This approach was conceptually inspired by the summed-spectrum normalization calibration procedure introduced by Leach and Hieftje in 2000, which emphasized the use of total signal intensities for robust internal standardization. <a href="javascript:void(0);">(12)</a> The 100 wt % normalization strategy was subsequently extended to the bulk analysis of carbonates. <a href="javascript:void(0);">(13)</a></p><p>Concurrently, the advent of time-of-flight (TOF) mass analyzers and the development of fast-washout laser ablation cells <a href="javascript:void(0);">(14−16)</a> have transformed LA-ICP-MS into a powerful imaging tool, capable of generating spatial maps across a variety of sample matrices at high data acquisition rates. <a href="javascript:void(0);">(17−19)</a> Furthermore, in comparison to, e.g., X-ray fluorescence spectroscopy (XRF) or Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS), LA-ICP-MS offers a high depth resolution of approximately 0.2 μm on glass, enabling precise measurement of fine-scale structures not only laterally but also in depth. <a href="javascript:void(0);">(18)</a> However, as the volume and complexity of these elemental images have grown according to the progressive advancement of the method itself, now consisting of gigabytes of data produced within minutes, manual data interpretation has become increasingly impractical. To uncover meaningful details and patterns, researchers have applied multivariate methods such as Principal Component Analysis (PCA) and k-means clustering to reduce dimensionality and delineate distinct regions within the images. <a href="javascript:void(0);">(20−23)</a> The nonlinear dimensionality reduction technique Uniform Manifold Approximation and Projection (UMAP), <a href="javascript:void(0);">(24)</a> which supports data interpretation with its capability to capture and separate subtle local structures, has first been employed to LA-ICP-MS bulk analysis data in 2024, where it was used to classify pyrite samples and discriminate between Pb and Zn deposit types. <a href="javascript:void(0);">(25,26)</a> In combination with elemental imaging, UMAP has recently been applied to biological tissue data sets as an exploratory analysis tool, <a href="javascript:void(0);">(27)</a> further enhancing the ability to visualize and interpret complex elemental distributions.</p><p>Despite these advances, no existing approach combines the unsupervised segmentation of mineral phases by UMAP with phase-specific normalization. In this study, we introduce a UMAP-driven workflow that (1) automatically segments distinct mineralogical phases within fine-grained geological matrices and (2) applies phase-specific 100 wt % normalization (e.g., oxide <a href="javascript:void(0);">(11)</a> or carbonate <a href="javascript:void(0);">(13)</a>), thereby improving quantification accuracy and enabling phase-specific quantitative interpretation of mineral distributions in complex matrices.</p><h4><strong>Materials and Methods</strong></h4><h5><strong>LA-ICP-TOFMS Imaging</strong></h5><p>LA-ICP-TOFMS analyses were performed using a 193 nm ArF excimer laser (GeoLas C, Lambda Physik, Göttingen, Germany) coupled to an ICP-TOFMS instrument (icpTOF 2R, TOFWERK AG, Thun, Switzerland). Ablation was conducted in the modified parallel flow ablation cell (MPFAC) <a href="javascript:void(0);">(16,34)</a> under a He atmosphere (99.999%, Linde Gas Schweiz AG, Dagmersellen, Switzerland) with Ar (99.996%, Linde Gas Schweiz AG) used as the carrier gas. A piezoelectrically driven xyz translational stage (1 nm resolution, SmarAct GmbH, Oldenburg, Germany) enabled precise sample positioning. Laser, stage movement, and ICP-TOFMS data acquisition were synchronized using a custom-built imaging control system. <a href="javascript:void(0);">(19)</a> The system was optimized each day based on maximum sensitivity while ensuring a <sup>238</sup>U<sup>+</sup>/<sup>232</sup>Th<sup>+</sup> intensity ratio of 1.0 to 1.1 on NIST SRM 610 and a <sup>232</sup>Th<sup>16</sup>O<sup>+</sup>/<sup>232</sup>Th<sup>+</sup> rate of below 0.5%.</p><h5><strong>LIBS Imaging</strong></h5><p>The LIBS analyses were performed on a Firefly system (Lightigo, Czech Republic). The laser (1064 nm, 5 mJ, pulse duration ∼10 ns, diameter 6 mm, operating at 50 Hz) was focused on the sample with a singlet lens (focal length 50 mm) to a 30 μm spot. The spatial resolution of the acquired elemental maps, with a size of about 16 mm × 26 mm, was 20 μm. Plasma radiation is collected by a multifiber configuration with a collimator and an optical fiber (400 μm core diameter), which delivered the light to several Czerny–Turner spectrometers (resolution 0.25–0.4 nm). The spectral range of the spectrometers is 180–850 nm. The radiation is collected 1.5 μs after ablation, with a gate width of 15 μs.</p><p>Data processing was done with Python 3.11.8 in the Spyder environment and included background correction and per-pixel z-score normalization.</p><h4><strong>Results and Discussion</strong></h4><h5><strong>LIBS and XRD Measurements</strong></h5><p>To assess whether carbonate-based normalization <a href="javascript:void(0);">(13)</a> is warranted, first, large-scale LIBS elemental intensity maps of the samples across about a third of the thin sections were acquired. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05398#fig1">Figure 1</a> reveals the results for “Layer 2” of the Poggio del Gatto sandstone for selected elements. A complete mapping of all four thin sections can be found in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05398/suppl_file/ac5c05398_si_001.pdf">Figures S3 and S6</a> in the Supporting Information. The maps reveal that C is strongly enriched in fracture veins as well as in finer-grained domains within the matrix and correlates with Ca, indicating carbonate minerals rather than oxides. Bulk XRD analysis identifies calcite alongside quartz, plagioclase, white mica, and kaolinite. These complementary observations confirm calcite as a major phase and thus justify the need for performing the 100 wt % normalization on a carbonate basis for pixels representing calcite.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_1_581_589_Figure_1_LIBS_elemental_intensity_maps_of_the_data_set_from_Layer_2_of_the_Poggio_del_Gatto_sandstone_mounted_on_a_microscope_slide_73ada1b7b8.jpg" alt="Anal. Chem. 2026, 98, 1, 581–589: Figure 1. LIBS elemental intensity maps of the data set from “Layer 2” of the Poggio del Gatto sandstone mounted on a microscope slide, depicting calcite veins and fine-grained structures within the matrix, underlining the need for carbonate-based normalization for specific pixels. Emission lines for evaluation are shown in the corresponding subfigure title. The epoxy used to fix the thin section on the slide is visible along the margin." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_1_581_589_Figure_1_LIBS_elemental_intensity_maps_of_the_data_set_from_Layer_2_of_the_Poggio_del_Gatto_sandstone_mounted_on_a_microscope_slide_73ada1b7b8.jpg 245w," sizes="100vw" width="2088" height="414"></p><h5><strong>Renormalization Based on Carbonates</strong></h5><p>Applying an oxide-based normalization to carbonate samples will introduce composition-dependent biases that cannot immediately be generalized as a singular numerical value. The difference between the molecular masses of carbonate CO<sub>3</sub><sup>2–</sup> and its oxide equivalent O<sup>2–</sup> yields an intrinsic bias on the order of a factor of about two, depending on the stoichiometry. Once this data is renormalized to a total of 100 wt %, the magnitude of the error becomes highly dependent on the specific elemental composition of the sample. An exemplary calculation illustrating this effect for a representative calcite pixel is provided in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05398/suppl_file/ac5c05398_si_001.pdf">Table S2</a>, yielding a relative error of 79%. In practice, this error varies across the image according to the local phase composition. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05398#fig6">Figure 6</a> illustrates the impact of normalization approaches for layer 2 of the samples as well as the spatial distribution of the resulting relative error. The figure presents Ca and Si elemental maps in two rows, while the first three columns compare different normalization strategies: normalization based on all oxides, normalization based on all carbonates, and a pixel-wise normalization guided by mineral phase identification, as outlined in the workflow proposed in this study. The final column displays the relative error between the bulk oxide- or carbonate-based normalization and the pixel-wise, phase-informed normalization.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_1_581_589_Figure_6_Comparison_of_normalization_strategies_for_Ca_and_Si_LA_ICP_TOFMS_elemental_maps_in_Layer_2_ce3a0c1a5c.jpg" alt="Anal. Chem. 2026, 98, 1, 581–589: Figure 6. Comparison of normalization strategies for Ca and Si LA-ICP-TOFMS elemental maps in “Layer 2” of the Poggio del Gatto sandstone. Rows show Ca (top) and Si (bottom); columns compare (1) oxide-based normalization, (2) carbonate-based normalization, (3) pixel-wise, phase-informed normalization (this study), and (4) relative error between the first two and the phase-informed approach." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_1_581_589_Figure_6_Comparison_of_normalization_strategies_for_Ca_and_Si_LA_ICP_TOFMS_elemental_maps_in_Layer_2_ce3a0c1a5c.jpg 202w," sizes="100vw" width="2088" height="1622"></p><p>For Ca (in this matrix primarily associated with calcite, a dominant carbonate phase in our samples), the relative error depicted quantifies the effect of applying oxide-based normalization across the entire image. This results in a systematic overestimation of the Ca concentration by approximately 60%. Conversely, for Si─in this matrix found mainly in quartz and various plagioclase phases─the error is shown for applying carbonate-based normalization across an oxidic matrix. In such cases, the Si concentrations are underestimated by roughly 10% to 40%, depending on the mineral phase.</p><p>These findings highlight that using a single normalization scheme across heterogeneous samples introduces substantial quantification errors. To mitigate this, the pixel-wise phase segmentation enabled by the proposed UMAP-based workflow offers a significant advantage, especially in fine-grained matrices. In short, phase-aware, pixel-wise normalization combines the strengths of both normalization approaches and proves to be highly beneficial.</p><h4><strong>Conclusion</strong></h4><p>This study demonstrates that phase-specific normalization, guided by unsupervised phase segmentation of LA-ICP-MS images, can substantially improve the accuracy of elemental quantification in fine-grained geological matrices. Rather than relying on a single global assumption (e.g., all oxides or all carbonates), the workflow adapts normalization to the actual mineralogy, minimizing systematic errors and enabling phase-resolved geochemical interpretation.</p><p>The segmentation step, based on UMAP embedding and <i>k</i>-means clustering, is fully unsupervised. However, the subsequent matching of clusters to specific minerals still relies on a limited set of preidentified stoichiometries and is therefore not yet fully unsupervised. Expansion to larger stoichiometric databases could help overcome this limitation and move toward automated mineral classification. A key challenge remains the similarity of cation ratios in certain mineral groups, which may still require complementary data to ensure robust assignments.</p><p>Overall, beyond the specific case of the Poggio del Gatto sandstone presented in this study, this work provides a framework for quantitative elemental imaging of complex geological samples and is readily transferable to other contexts that require spatially resolved composition-aware normalization. This contribution adds a practical implementation to a growing literature that already uses multivariate statistics to unlock the immense information depth of LA-ICP-TOFMS.</p>

Mineral Phase-Resolved Quantification in LA-ICP-MS Imaging

<p><strong>Eurachem and CITAC welcome analysts interested in the evaluation of chemical analysis uncertainty and in how to use this information in results interpretation. The Eurachem Week 2026 will begin with a training course on “Evaluation of the measurement uncertainty from in-house precision and recovery data” scheduled for Sunday, 10 May 2026. A two-days workshop on “Quality in Analytical Measurements: Uncertainty Evaluation and Results Interpretation” open to all interested participants will take place from 11 to 12 May 2026. On 13 May 2026, the Eurachem Working Groups will convene, followed by the Eurachem General Assembly, which will bring together the Executive and national delegates on 14 and 15 May. Both Working Group Meetings and the General Assembly are restricted to their members.</strong></p><p>The results of qualitative and quantitative chemical analyses should have an acceptable uncertainty, which should ideally be reported and considered in results interpretation.</p><p>For many analytical applications, simplified uncertainty evaluations can determine uncertainty values that meet the defined requirements for this parameter. The workshop will focus on the key technical challenges involved in practical evaluations of measurement uncertainty based on precision and recovery data gathered during in-house method validation and quality control. There will be a discussion of how to model the variation of the measurement uncertainty within the analytical range and how to handle divergent recovery values from the analysis of various reference materials. A session on the use of measurement uncertainty in conformity assessment will explore how the risk of false conformity decisions can be managed.</p><blockquote><h5><strong>New Eurachem guidance on using in-house precision and recovery data will be discussed at the workshop.</strong></h5></blockquote><p>Topics will also include the validation and uncertainty evaluation for qualitative analyses with particular attention to current accreditation requirements for these types of analysis. The last session of the workshop will be dedicated to the evaluation of measurement uncertainty arising from sampling and using it to judge the fitness for purpose, and hence validation, of measurement procedures that include sampling. Four sessions of the workshop will include engaging breakout sessions where participants will have the chance to share their views about challenging aspects of the analytical work. Participants are invited to propose posters and oral communications on session topics or any other relevant topic for analytical chemists or stakeholders of the analytical work.</p><h3><strong>Registration</strong></h3><blockquote><p class="cke-user_style-important"><strong>👉</strong><a target="_blank" rel="noopener noreferrer" href="https://conferences.chemistry.pt/register/eurachem2026"><strong>Register here for the workshop</strong></a></p></blockquote><h4><strong>Registration Fees</strong></h4><h5><strong>Early bird (EUR) - Until April 15th</strong></h5><ul><li><strong>Workshop: Regular participant (11 &amp; 12 May 2026): </strong>400 EUR</li><li><strong>Workshop: Student (11 &amp; 12 May 2026): </strong>250 EUR</li><li><strong>Eurachem Working Group<sup>1</sup> (13 May 2026):</strong> 75</li><li><strong>General Assembly<sup>2</sup> (14 &amp; 15 May 2026): </strong>250</li><li><strong>Training On Uncertainty Evaluation<sup>3</sup> (10 May 2026):</strong> 150</li></ul><h5><strong>After April 15th</strong></h5><ul><li><strong>Workshop: Regular participant (11 &amp; 12 May 2026): </strong>450 EUR</li><li><strong>Workshop: Student (11 &amp; 12 May 2026):</strong> 300 EUR</li><li><strong>Eurachem Working Group<sup>1</sup> (13 May 2026): </strong>100</li><li><strong>General Assembly<sup>2</sup> (14 &amp; 15 May 2026):</strong> 300</li><li><strong>Training On Uncertainty Evaluation<sup>3</sup> (10 May 2026): </strong>200</li></ul><blockquote><p>§ - <strong>50 EUR reduction for members of Eurachem</strong>, CITAC, SPQ, RELACRE, ForMEQ and SPMet at the workshop.</p><p>10% reduction for organisations that registered more than one participant.</p><p><sup>1 </sup>- Working group members</p><p><sup>2</sup> - Eurachem members</p><p><sup>3</sup> - 50% reduction for workshop participants</p></blockquote><h3><strong>Workshop</strong></h3><h4><strong>Eurachem/CITAC Workshop on Quality in Analytical Measurements:</strong></h4><h5><strong>Uncertainty Evaluation and Results Interpretation</strong></h5><p>The results of qualitative and quantitative chemical analyses should have an acceptable uncertainty, which should ideally be reported and considered in results interpretation. For many analytical applications, simplified uncertainty evaluations can determine uncertainty values meeting defined requirements for this parameter. The workshop will focus on the key technical challenges involved in practical evaluations of measurement uncertainty based on precision and recovery data gathered during in-house method validation. It will be discussed how to model the variation of the measurement uncertainty within the analytical range and how to handle divergent recovery values from the analysis of various reference materials. A session on the use of measurement uncertainty in conformity assessment will explore how the risk of false conformity decisions can be managed.</p><p>Topics will also include the validation and uncertainty evaluation of qualitative analyses with particular attention to current accreditation requirements for these types of analysis. The last session of the workshop will be dedicated to the evaluation of measurement uncertainty arising from sampling and using it to judge the fitness for purpose, and hence validation, of measurement procedures that include sampling.</p><p>Four sessions of the workshop will include engaging breakout sessions where participants will have the chance to share their views about challenging aspects of the analytical work. Participants are invited to propose posters and oral communications on session topics or any other relevant topic for analytical chemists or stakeholders of the analytical work.</p><h3><strong>Training Course</strong></h3><h4><strong>Evaluation of the measurement uncertainty from in-house precision and recovery data</strong></h4><ul><li>10 May 2026</li></ul><p>A course on the fundamentals of the evaluation of the measurement uncertainty from in-house precision and recovery data will be held on Sunday, 10 May 2026. This training is targeted for analysts with a basic understanding of the measurement uncertainty concept, how uncertainty components propagate, and how the combined uncertainty is expanded. Following the morning presentation of the theory of uncertainty evaluation, the afternoon will be dedicated to applying the theory to real-world cases. Trainees should have basic skills in using Microsoft Excel spreadsheets and bring a laptop with Microsoft Excel to complete the exercises. The interaction between trainers and trainees will be promoted to guarantee learning efficiency.</p><h5><strong>Trainers:</strong></h5><ul><li>Ricardo Bettencourt da Silva, Faculdade de Ciências da Universidade de Lisboa, Portugal (rjsilva@ciencias.ulisboa.pt)</li><li>Stephen L. R. Ellison - UK National Measurement Laboratory (NML) hosted at LGC, United Kingdom</li><li>Vicki Barwick - UK National Measurement Laboratory (NML) hosted at LGC, United Kingdom</li></ul><h4><strong>Agenda</strong></h4><h5><strong>8:00 Registration</strong></h5><h5><strong>9:00 Opening</strong></h5><h5><strong>9:05 Identifying, combining, and expanding uncertainty components: Theory and examples</strong></h5><h5><strong>9:45 Precision uncertainty: Theory and examples</strong></h5><h5><strong>10:30 </strong>Coffee break</h5><h5><strong>10:50 Trueness-related uncertainty: Theory and examples</strong></h5><h5><strong>12:00 Additional questions and discussion</strong></h5><h5><strong>12:30 </strong>Lunch</h5><h5><strong>13:30 Application example 1</strong></h5><h5><strong>15:30 </strong>Coffee break</h5><h5><strong>15:50 Application example 2</strong></h5><h5><strong>17:00 Additional questions and discussion</strong></h5><h5><strong>17:30 Closing</strong></h5><h3><strong>Venue</strong></h3><p>The series of Eurachem events will be held in:</p><ul><li>Meliã Lisboa Aeroporto ****</li><li>Rua C,2 1749-125 Lisboa, Portugal&nbsp;</li></ul>

Eurachem/CITAC Workshop on Quality in Analytical Measurements 2026

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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 Scientific ARL SMS-2300 Robotics-Based Automation for OES or XRF

Thermo Scientific™ ARL™ SMS-2300 enables fast, reproducible, and unattended OES/XRF analysis to optimize steel production and meet the strictest quality standards.

<h4><strong>Meet stringent steel specifications while increasing efficiency and lowering production costs.</strong></h4><p><strong>The Thermo Scientific™ ARL™ SMS-2300 Robotics-Based Automation for OES or XRF is an automated quality control solution to optimize your steel production operation. With the high-performance ARL SMS-2300 system, even complex samples are prepared and analyzed under reproducible conditions without operator intervention, eliminating human variables and subjective factors.</strong><br>👉 <strong>Result</strong>: <strong>Increased output</strong>, <strong>reliable results</strong>, and <strong>reduced costs</strong>.</p><h3>🔧 <strong>ARL SMS-2300 Automation for OES or XRF</strong></h3><p>Provides <strong>ultimate power and flexibility</strong> for <strong>automatic, unattended 24/7 operation</strong> of:</p><ul><li><strong>ARL™ 4460</strong></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1213"><strong>ARL™ iSpark</strong></a><strong> / </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1977"><strong>iSpark Plus OES</strong></a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products?instrumentationItems=100&amp;manufacturerItems=3&amp;search=9900"><strong>ARL™ 9900 XRF</strong></a></li></ul><h3>✅ <strong>Main Features</strong></h3><ul><li><strong>Easy integration</strong> into <strong>fully automatic laboratories</strong></li><li><strong>Installed in ARL QuantoShelter</strong>, close to production process</li><li><strong>Parallel tasking</strong>:<ul><li>Sample preparation</li><li>Sample introduction</li><li>Analysis</li><li><strong>Instrument stand cleaning</strong> performed in hidden time</li></ul></li><li><strong>High throughput</strong> with:<ul><li><strong>Automatic sample preparation</strong></li><li><strong>Haas Mini Mill</strong> + <strong>ARL™ PrepControl™</strong> + <strong>EasyPrep™ software</strong></li></ul></li><li><strong>Direct access</strong> to <strong>Haas Mini Mill</strong> for <strong>faster ferrous sample processing</strong></li><li><strong>Factory-configured and customized</strong></li><li><strong>Compact chassis</strong>, <strong>simple installation</strong>, and <strong>integrated electronics</strong></li><li><strong>High reliability</strong>, <strong>rapid ROI</strong>, and <strong>fast result availability</strong></li><li><strong>Frees lab personnel</strong> from <strong>routine tasks</strong></li><li><strong>ISO 9001 compliant</strong> manufacturing and maintenance</li></ul><h3>🚀 <strong>Advanced Technology</strong></h3><ul><li><strong>Fanuc industrial robot</strong></li><li><strong>OXSAS analytical software</strong></li></ul>

Thermo Scientific ARL SMS-3300 Automation for Single or Twin OES or XRF

Thermo Scientific™ ARL SMS-3300 enables fully automated operation of one or two OES or XRF spectrometers, enhancing lab efficiency in industrial environments.

<p><strong>Drive process control laboratory efficiencies in the metals, mining and cement industries</strong> with the <strong>Thermo Scientific™ ARL™ SMS-3300 Automation for Single or Twin OES or XRF</strong>, a <strong>complete laboratory workflow automation solution</strong>.<br>The automation of <strong>single or multiple spectrometers and preparation machines</strong> with the ARL SMS-3300 system offers:</p><p>👉 <strong>Reduced sample analysis cost</strong>,<br>👉 <strong>Increased sample throughput</strong>,<br>👉 <strong>Better reproducibility</strong>, and<br>👉 <strong>Higher quality results</strong></p><p>for users requiring <strong>more comprehensive and integrated automation solutions</strong> in the <strong>metals, mining and cement industries</strong>.</p><h3>🔧 <strong>ARL SMS-3300 – Automation for Single or Twin OES or XRF</strong></h3><p>Provides <strong>ultimate power and flexibility</strong> for <strong>automatic, unattended, 24/7 operation</strong> of:</p><ul><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1213"><strong>Thermo Scientific™ ARL™ iSpark </strong></a><strong>/</strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1977"><strong> iSpark Plus</strong> </a>(Optical Emission Spectrometers)</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products?instrumentationItems=100&amp;manufacturerItems=3&amp;search=9900"><strong>Thermo Scientific™ ARL™ 9900</strong></a> (X-ray Fluorescence Spectrometer)</li></ul><h3>✅ <strong>Main Features</strong></h3><ul><li>Designed for <strong>easy integration</strong> into <strong>fully automatic laboratories</strong></li><li>Can be <strong>mounted in Thermo Scientific ARL QuantoShelter</strong>, close to the production process</li><li><strong>Parallel task execution</strong>:<ul><li>Sample preparation</li><li>Sample introduction</li><li>Analysis</li><li><strong>Instrument stand cleaning</strong> performed <strong>in hidden time</strong> by the spectrometer, independently of the robot</li></ul></li><li><strong>Direct access to milling machine</strong> for reduced processing times of <strong>ferrous samples</strong></li><li><strong>Higher throughput</strong> with:<ul><li><strong>Automatic sample preparation</strong></li><li><strong>ARL™ PrepControl™ system</strong> + <strong>ARL™ EasyPrep™ software</strong></li></ul></li><li><strong>Factory configured and customized</strong></li><li><strong>Compact chassis</strong> and <strong>simple installation</strong></li><li><strong>Integrated electronics</strong></li><li><strong>Improved analysis reliability and quality</strong></li><li><strong>Very rapid payback</strong></li><li><strong>Faster result availability</strong></li><li><strong>Qualified lab personnel freed from routine tasks</strong></li><li><strong>Manufactured and maintained</strong> according to <strong>ISO 9001-2000 procedures</strong></li></ul><h3>🚀 <strong>Advanced Technology</strong></h3><ul><li><strong>State-of-the-art Fanuc industrial robot</strong></li><li><strong>OXSAS analytical software</strong></li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Scientific_ARL_SMS_3300_Automation_for_Single_or_Twin_OES_or_XRF_fadcbb621a.jpg" alt="Thermo Scientific™ ARL SMS-3300 Automation for Single or Twin OES or XRF" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Scientific_ARL_SMS_3300_Automation_for_Single_or_Twin_OES_or_XRF_fadcbb621a.jpg 245w," sizes="100vw" width="650" height="366"></p>

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