<p><strong>This study introduces a quantitative workflow for multielement single-cell analysis by mass cytometry using metal-loaded, multielement-barcoded polystyrene beads as calibration standards. Online mixing allows bead and cell events to be distinguished by TOF-MS, enabling elemental concentrations in labeled cells to be quantified from bead responses.</strong></p><p>The approach was demonstrated with Lu-, Nd-, and Ir-labeled cancer cells and extended through a polynomial calibration model to elements not directly included in the beads. Agreement with external calibration highlights the method’s potential as a universal quantitative strategy for mass cytometry and single-cell ICP-TOF-MS.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/ancham/article/98/31/22966/5231419/Quantitative-Single-Cell-Multielemental-Analysis#249383980"><strong>Quantitative Single-Cell Multielemental Analysis Using Metal-Loaded Microparticles: toward a Universal Quantification Workflow in Mass Cytometry</strong></a></h4><p>Ángela de la Rosa-Díaz; Carlos López-Portugués; Mario Corte-Rodríguez*; María Montes-Bayón *</p><p>Anal. Chem. (2026) 98 (31): 22966–22976</p><div><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.6c02340">https://doi.org/10.1021/acs.analchem.6c02340</a></div><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><div><div><div><div><p>Understanding the biochemical and functional heterogeneity of individual cells is a central challenge in modern biology, immunology, oncology, and clinical translational research. Flow cytometry, and, more recently, spectral flow cytometry, (<a href="javascript:;">1</a>) have become essential tools for high-dimensional single-cell analysis. However, obtaining robust quantitative information at the single-cell level remains challenging, particularly when comparing measurements across experiments and laboratories. Quantitative approaches have been developed for fluorescence flow cytometry, typically relying on calibration beads and standardized antibody conjugates to improve interexperimental comparability. (<a href="javascript:;">2−4</a>) Nevertheless, the accuracy of these methods remains influenced by fluorophore-specific properties and labeling variability, highlighting the need for alternative quantitative single-cell analytical strategies.</p></div><div><p>Mass cytometry, or cytometry by time-of-flight (CyTOF), which was introduced by the team of Bandura in 2009, has emerged as a technological breakthrough by substituting fluorophores with isotopically pure metal tags, whose signals are detected by TOF elemental mass spectrometry (MS) using an inductively coupled plasma as ion source (ICP–TOF–MS). (<a href="javascript:;">5</a>) This technology has allowed to remove nearly all spectral overlaps and enabled experiments that can routinely include 40–60 parameters, including not only protein biomarkers but also DNA markers and metallodrugs or nanoparticles. (<a href="javascript:;">6, 7</a>) Numerous studies have taken advantage of the highly multiplex capabilities of CyTOF to investigate immune responses, characterize tumor-infiltrating lymphocytes, or follow hematopoietic differentiation with greater resolution than was previously possible. (<a href="javascript:;">8−10</a>) Indeed, the technique has become central to many multiparametric immunology pipelines by facilitating deeper functional analyses, from mapping subtle cell-cycle states (<a href="javascript:;">11</a>) to profiling intracellular cytokine signatures and activation markers (<a href="javascript:;">12</a>) that are often difficult to resolve by fluorescence-based approaches. (<a href="javascript:;">13</a>)</p></div><div><p>Beyond its multiplexing capability, an often overlooked advantage of mass cytometry is its potential for elemental quantification. Unlike fluorescence-based approaches, ICP–MS-based detection enables direct conversion of signal intensities into elemental amounts through established calibration strategies. This principle, successfully applied for single-cell elemental analysis by ICP–MS (SC–ICP–MS), has shown to be suitable in different applications. Thus, quantification of intrinsic (<a href="javascript:;">14−17</a>) or exogenous (<a href="javascript:;">18−20</a>) elements, as well as protein biomarkers following metal-labeling strategies (<a href="javascript:;">21, 22</a>) compare well with the bulk analyses on the same cell suspension. However, despite the efforts made to develop quantitative strategies, mass cytometry remains as a mainly qualitative strategy, where very few approaches have explored its unquestionable quantification potential. (<a href="javascript:;">4, 23−25</a>)</p></div><div><p>CyTOF data quality depends critically on managing instrumental variability. Plasma fluctuations, partial clogging of the sample introduction system or injector and periodic retuning, introduce changes in sensitivity that can obscure biological differences between samples measured at different times or in different instruments. To mitigate these effects, CyTOF applies multielement calibration polymeric beads, such as EQ six-element calibration beads, that are mixed with every sample at a fixed concentration before nebulization. (<a href="javascript:;">26, 27</a>) These beads, containing defined quantities of yttrium, indium, cerium, terbium, lutetium and bismuth, allow computational normalization and provide a reference signal that corrects for shifts in sensitivity over time. More recently, approaches using frozen antibody master mixes, unified acquisition templates, and sophisticated normalization algorithms have been introduced to enable reproducible multibatch studies and harmonize data sets across different research centers. (<a href="javascript:;">27, 28</a>)</p></div><div><p>Since the sought beads that contain multiple elements in a wide mass range (from <sup>89</sup>Y to <sup>209</sup>Bi) are commercially available and show high reproducibility, they could serve as quantitative (single-point) analytical standards (once characterized accordingly). This use would increase their analytical value not only as mere drift correctors but also as calibrants that are present within the sample (matrix-matched). Such a strategy is aligned with recently proposed particle-based calibration approaches in SP/SC–ICP–MS, which exploit transient events of the same nature as the analyte to avoid transport-efficiency-related uncertainties. (<a href="javascript:;">29</a>) In this way, the estimation of parameters like transport efficiencies of the ionic standards is eliminated, reducing the associated uncertainties. Moreover, since the beads are routinely added to all samples in CyTOF, any raw data file could be reprocessed at any time to extract any quantitative information without any additional measurements.</p></div><div><p>Despite their routine use for signal normalization, CyTOF calibration beads have not been systematically explored as quantitative standards for single-cell elemental analyses. Their use for quantification implies several analytical challenges, including the accurate characterization of their elemental content, the translation of bead-derived responses into cellular elemental amounts, and the extension of the calibration strategy to isotopes that are not present in the beads. Addressing these challenges is essential before calibration beads can be used as reliable quantitative reference.</p></div><div><p>An interesting additional feature of plasma-based techniques is their relatively uniform sensitivity across the entire mass range. It has been described that the observed instrumental response in most ICP–MS can be fitted to a simple equation as a function of the <i>m</i>/<i>z</i> of the isotope to be measured for the elements with similar ionization in the plasma, such as the lanthanides. (<a href="javascript:;">30, 31</a>) If the transient signals of the multielemental beads are used to obtain the instrumental responses and these are empirically modeled across the monitored <i>m</i>/<i>z</i> range, a practical route toward multielemental quantification strategy is obtained. This would represent a quantification workflow for multiplex single-cell elemental analysis that repurposes multielement CyTOF calibration beads as generic standards. Such an approach seeks to address the limitations in calibration procedures and, ultimately, to enhance the quantitative applications of high-dimensional single-cell MS that can be applied not only to CyTOF-based analysis but could also be extended to any other SC–ICP–MS analysis using any other TOF mass analyzers.</p><h4><strong>Experimental Section</strong></h4><h5><strong>Instrumentation</strong></h5><div><p>The single-particle ICP–MS measurements were performed by using the <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/1206"><strong>iCAP TQ triple quadrupole ICP–MS system</strong></a> from Thermo Fisher Scientific (Bremen, Germany). All measurements were done using the standard configuration for sample introduction, including the Micro-Mist nebulizer at a sample flow rate of 0.4 mL min<sup>–1</sup> and a cyclonic spray chamber. The sample introduction setup was used together with a T<strong>eledyne Cetac ASX-560 autosampler.</strong> SP-ICP-MS characterizacion of the lanthanide-loaded polystyrene beads was carried out setting a dwell time of 5 ms. Daily optimization of ICP–MS was performed to enhance signal sensitivity, ensuring that the generation of oxide ions and doubly charged species remained below 3% and 5%, respectively.</p></div><div><p>The CyTOF experiments were conducted in a <strong>CyTOF XT</strong> (Standard BioTools, San Francisco, CA, USA). This instrument is an ICP–MS that operates TOF mass analyzer with a shortened mass coverage from <i>m</i>/<i>z</i> 75 to 209. The samples are placed in a refrigerated carousel at 4 <span>°</span>C and injected into the CyTOF with a hydraulic autosampler that is able to handle sample introduction and system washing. The autosampler automatically adds the EQ6 calibration beads (Standard BioTools) to every sample at a 1:10 dilution. After mixing, the bead-containing sample is injected at 30 μL·min<sup>–1</sup> through a pneumatic nebulizer and a spray chamber that is heated at 200 <span>°</span>C. using a constant sample flow of at 10 μL min<sup>–1</sup>. Microparticle imaging was carried out with a <strong>JEOL JEM-2100F transmission electron microscope.</strong></p><h4><strong>Results and Discussion</strong></h4><h5><strong>BiRM: An Interpolated Model for Multielement Quantification</strong></h5><p>Taking advantage of the multielemental composition of the beads uniformly distributed along the CyTOF measuring range, their molar response factors can be estimated and plotted against the <i>m</i>/<i>z</i> of each of the isotopes contained on the beads. This is possible thanks to the high ionization efficiency achievable in the plasma for the elements of the beads that is close to 100%. In that case, previous publications demonstrated that the ICP–MS molar response can be modeled using different theoretical equations. (<a href="javascript:;">30, 31</a>) Consequently, the molar responses (<i>n</i><sub>resp</sub>) for all the 8 isotopes of the 6 elements contained in the beads (typically &gt;5000 events) were calculated as in <a href="javascript:;">eq 3</a> and represented against the mass-to-charge ratio (<i>m</i>/<i>z</i>). The molar response, differently to the previously reported response factor (α<sub>i</sub>), takes into account the atomic mass of the element and isotope, the isotope abundance, and the concentration. This response was fitted empirically to polynomial functions of degree 2, 3, 4, and 5, resulting in the quartic function the one with the best correlation (<i>r</i><sup>2</sup> = 0.99) while avoiding overfitting of the data. The response factors for all the isotopes are represented in <a href="javascript:;">Figure 4</a> (every data point considered for the fitting corresponds to the average intensity obtained for a minimum of 5000 bead events and the corresponding standard deviation), together with the fitted quartic polynomial function of <a href="javascript:;">eq 4</a>. This behavior showing a mass-dependent response with a slight decrease at high <i>m</i>/<i>z</i> is also consistent with published results using ICP–TOF–MS instruments. (<a href="javascript:;">34</a>) The fourth-order polynomial was used as an empirical interpolation of the instrumental response within the calibrated mass range in our specific CyTOF instrument, without physical significance of the coefficients.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_31_22966_22976_Figure_4_Molar_response_factors_for_every_isotope_in_the_beads_b8317c3de7.jpg" alt="Anal. Chem. (2026) 98 (31): 22966–22976: Figure 4. Molar response factors for every isotope in the beads as a function of m/z and the quartic function model (dashed line) that fits such data obtained using CyTOF results. Standard deviation of each data point is also included (only distinguishable in the case of 140Ce)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_31_22966_22976_Figure_4_Molar_response_factors_for_every_isotope_in_the_beads_b8317c3de7.jpg 245w," sizes="100vw" width="996" height="492"></p><p>Using the <i>m</i>/<i>z</i> of the isotope of interest and by interpolation in the curve, it is possible to estimate its molar response on each sample (matrix matched). Out of the obtained molar response and taking the integrated dual counts obtained for such isotope in the sample, inferring its mass in a single cell event is straightforward. <a href="javascript:;">Figure 5</a>A shows the correlation between the predicted (using <a href="javascript:;">eq 4</a>) and the experimental value (using <a href="javascript:;">eq 3</a>) for the molar responses of the isotopes contained in the beads. A good correlation can be observed (<i>R</i><sup>2</sup> = 0.99), with a slope of 0.99, suggesting an excellent ability of the model to calculate the molar sensitivity of these isotopes.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_31_22966_22976_Figure_5_A_Plot_showing_the_correlation_between_the_Bi_RM_predicted_and_the_experimental_molar_response_for_the_multielemental_beads_6acfc9378b.jpg" alt="Anal. Chem. (2026) 98 (31): 22966–22976: Figure 5. (A) Plot showing the correlation between the BiRM-predicted and the experimental molar response for the multielemental beads and (B) the correlation of 8200 individual cell events obtained by BiRM versus calibration with 175Lu standards. Error bars were omitted for clarity. Experimental uncertainties associated with the molar response factors are reported in Table 2." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_31_22966_22976_Figure_5_A_Plot_showing_the_correlation_between_the_Bi_RM_predicted_and_the_experimental_molar_response_for_the_multielemental_beads_6acfc9378b.jpg 116w," sizes="100vw" width="997" height="1336"></p><p>Although it is possible to perform the described calculations using a datasheet like from microsoft excel, Google Sheets, or LibreOffice Calc, the BiRM interpolation and quantification workflow becomes increasingly cumbersome as the number of isotopes, events and biological replicates grows. For this reason, an in-house script has been developed in R, which is provided as <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/ancham/article-supplement/5231419/pdf/ac6c02340_si_001/">Supporting Information</a>. The script, which is fully transparent and open-access, operates directly on the data matrix generated for either cell events or calibration-bead events after standard CyTOF preprocessing (see the Experimental section). By introducing the elemental information required for BiRM calculations as described in the accompanying “Readme” file, the script automatically computes the molar or mass amount of each element per cell. In addition, it generates summary statistics, diagnostic figures and histograms for rapid quality assessment. This implementation ensures reproducibility, minimizes operator-dependent variability and allows users to reprocess previously acquired data sets without any additional experimental steps. A continuously updated version of software will be made available in a public repository, where future improvements, extension and documentation will be deposited. Because the isotopes used in <a href="javascript:;">Figure 5</a>A were also employed to construct the BiRM model, an additional leave-one-out validation was performed to assess its predictive capability. In each iteration, one calibration isotope was excluded from the fitting procedure and its molar response factor was subsequently predicted using a model constructed from the remaining isotopes. The predicted values were then compared with the experimentally determined molar response factors. The leave-one-out validation yielded a mean absolute relative error (MARE) of 14.3%, compared with 8.3% for the all-in model. As expected, prediction errors increased when an isotope was excluded from the model construction. However, the obtained deviations remained within a reasonable range considering the limited number of calibration points available for model fitting.</p><h4><strong>Conclusion</strong></h4><p>This study presents a practical route to quantitative mass cytometry by repurposing the multielement calibration beads that are routinely added to CyTOF samples as in-tube quantitative standards that can be extended not only to CyTOF but also to any other TOF-based ICP–MS instruments. Two complementary strategies were proposed. First, BaQS performs single-point calibration for bead-encoded isotopes to obtain the absolute mass of lanthanide per cell. Second, the BiRM makes use of the multielement bead signature to empirically model the instrumental response across the monitored <i>m</i>/<i>z</i> window, enabling the prediction of response factors for isotopes that are not present in the beads. The results indicate that both strategies provided accurate results with no statistically significant differences with the validated external standards calibration for the quantification of bead-contained and noncontained lanthanides, being BaQS and BiRM simpler, faster, more efficient, and ready to be applied to already acquired data with no additional measurements needed.</p></div></div></div></div></div></div>

Quantitative Single-Cell Multielemental Analysis Using Metal-Loaded Microparticles: toward a Universal Quantification Workflow in Mass Cytometry

<p><strong>In the week from August 31, 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>5 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Technology Networks: Modern Methods for Faster Microbial Analysis</strong></h5><ul><li>Tu, 1.9.2026 11:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6454">Registration</a></li></ul><p>Struggling to choose the right technique for microbial testing? Learn how MALDI-TOF, FTIR, and automated DNA/RNA electrophoresis accelerate identification.</p><h5><strong>2. Agilent Technologies: ICP-MS for Hybrid Photoelectrode Characterization</strong></h5><ul><li>Tu, 1.9.2026 21:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6411">Registration</a></li></ul><p>Quantify catalyst loading on hybrid photoelectrodes and discover how the Agilent 7850 ICP-MS supports solar fuel research.</p><h5><strong>3. SciY/Mestrelab Research: From Legacy Workflows to Paperless Labs: Track Every Sample, Process, and Decision</strong></h5><ul><li>Th, 3.9.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6425">Registration</a></li></ul><p>Transform your lab from paper to digital. Learn how WebLab and LIMS workflows automate sample tracking and support laboratory compliance.</p><h5><strong>4. Agilent Technologies: Learn to Select and Apply the Proper Internal Standard in ICP-OES</strong></h5><ul><li>Th, 3.9.2026 20:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6412">Registration</a></li></ul><p>Master the use of internal standards in ICP-OES and learn how to select suitable standards to correct matrix effects and signal drift.</p>

Webinars LabRulezICPMS Week 36/2026

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 31st August 2026? Check out new documents from the field of spectroscopy/spectrometry and related techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT SPECTROSCOPY/SPECTROMETRY RELATED TECHNIQUES</strong></span></a></p></blockquote><blockquote><p><strong>👉 Need info about different analytical techniques?</strong> Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library"><strong>LabRulezLCMS</strong></a> or <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library"><strong>LabRulezGCMS</strong></a> libraries.</p></blockquote><p>This week we bring you brochures by <strong>Agilent Technologies </strong>and<strong> Waters Corporation </strong>and application notes by <strong>Shimadzu </strong>and<strong> Thermo Fisher Scientific!</strong></p><h3><strong>1. Agilent Technologies: Agilent OpenLab ICP-MS Software</strong></h3><p><strong>Simplify every step of ICP-MS analysis</strong></p><p><strong>For Agilent </strong><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=7800"><strong>7800</strong></a><strong>, </strong><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=7850"><strong>7850</strong></a><strong>, and </strong><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=7900"><strong>7900</strong></a><strong> ICP-MS systems, and Agilent </strong><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=8900"><strong>8900</strong></a><strong> and </strong><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=9500"><strong>9500 ICP-QQQ</strong></a><strong> systems.</strong></p><ul><li>Brochure</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/br_openlab_icp_ms_software_5994_9288en_agilent_05f01dcdef.pdf"><strong>Full PDF for download</strong></a><strong>&nbsp;</strong></li></ul><p>The brochure introduces <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=OpenLab+ICP-MS+Software"><strong>Agilent OpenLab ICP-MS Software</strong></a>, designed to simplify routine operation and data handling for <strong>Agilent 7800, 7850, and 7900 ICP-MS systems</strong> and the <strong>8900</strong> and <strong>9500</strong> <strong>ICP-QQQ systems</strong>. The software supports the complete analytical workflow—from checking instrument readiness and creating batches to acquisition, data review, reporting, and compliance. A central dashboard provides an overview of system status, maintenance requirements, instrument health, and utilization, while Smart EMF and post-run performance checks help users identify when maintenance may be required. Guided Diagnostics, Meter Monitor, and time-stamped system records also support troubleshooting by tracking parameters such as vacuum levels, gas flows, and temperatures.</p><p>Method and batch setup are designed to reduce manual configuration. Batches can be created from preset methods, templates, existing batches, Method Wizard, or Method Advisor, and legacy Agilent ICP-MS MassHunter batches can be opened directly in OpenLab ICP-MS. For the <strong>9500 ICP-QQQ</strong>, Method Advisor provides an element-focused approach in which users select target elements and calibration information while the software applies recommended acquisition masses and cell-gas conditions. Preset methods are available for applications including <strong>EPA 200.8/EPA 6020, USP &lt;232&gt;, ICH Q3D, and Chinese Pharmacopoeia requirements</strong>, while an IntelliQuant Screening preset supports semiquantitative screening of unknown samples. Routine instrument tasks can also be automated through autotuning, startup checks, batch queues, end-of-run verification, rinse functions, and automatic shutdown.</p><p>For data evaluation, OpenLab ICP-MS provides a real-time workspace where users can review analyte concentrations, internal-standard recoveries, stability plots, spectra or chromatograms, calibration information, QC charts, and IntelliQuant results in a single view. <strong>IntelliQuant</strong> acquires a complete mass spectrum and displays semiquantitative elemental concentrations using a color-coded periodic table, while <strong>IntelliQuant Star Rating</strong> assigns a zero-to-five-star data-quality score based on factors such as potential interferences, calibration quality, background, detection limits, and precision. Reporting options include whole-batch reports, elemental impurity reports for USP &lt;232&gt;/&lt;233&gt; and ICH Q3D, customizable reports, structured CSV export, and direct data transfer to LIMS.</p><p>The software can be expanded for specialized applications, including <strong>LC-ICP-MS and GC-ICP-MS</strong>, advanced QC workflows, single-particle ICP-MS and FFF-ICP-MS, ADS 2 automation, semiconductor analysis, and peripheral-device control. Supported analytical approaches include external calibration, standard additions, semiquantitative calibration, isotope ratios, and isotope dilution. For regulated laboratories, OpenLab ICP-MS can be combined with scalable OpenLab compliance solutions providing electronic signatures, user access control, configurable roles, system activity logs, and audit trails, supporting workflows subject to <strong>21 CFR Part 11, EU Annex 11, and equivalent regulations</strong>. Overall, the platform is positioned as a unified environment for routine and advanced ICP-MS workflows, combining instrument control, method setup, diagnostics, data-quality assessment, reporting, and compliance support.</p><h3><strong>2. Shimadzu: Identification of Virgin PET and Recycled PET by X-ray Fluorescence Spectrometry</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/an_01_01204_en_f3f20ff020.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>User benefits</strong></h5><ul><li>EDX enables the identification of recycled PET.&nbsp;</li><li>Measurements are quick and simple, require no cumbersome pretreatment, and can be automated using a multi-sample turret.&nbsp;</li><li>The instrument offers excellent cost-performance and supports the detection of a wide range of elements.</li></ul><p>Establishing a sustainable circular economy is a major societal challenge, and recycling plastic materials such as PET (polyethylene terephthalate) plays a particularly important role in reducing environmental impact. Large numbers of PET bottles and molded products are used every day in the food and beverage sectors. To recycle these materials safely and reliably, technologies are required that can accurately identify their origin. In practice, however, appearance and conventional physical properties alone are insufficient to distinguish virgin material from recycled material, creating a need for a more reliable identification method.&nbsp;</p><p>For rapid identification in industrial settings, a method requiring little or no pretreatment is highly desirable. In some cases, nondestructive analysis is also needed to enable subsequent application of other analytical techniques. This article presents a practical approach in which metal catalysts present in PET are detected by EDX (Energy-Dispersive X-ray Spectroscopy) and used to distinguish material-recycled PET from virgin PET.</p><h4><strong>Principle and Overview&nbsp;</strong></h4><p>Metal catalysts such as antimony (Sb) and germanium (Ge) are used in the synthesis of PET resin. These catalyst metals have been reported to be useful indicators of material origin. 1),2) For example, Sb and Ge are frequently detected together in material-recycled PET, whereas only one catalyst metal is detected in chemically recycled PET and virgin PET. This likely reflects the fact that material-recycled PET is derived from a mixture of feedstocks using different catalysts.&nbsp;</p><p>ICP-AES (ICP optical emission spectrometry) and ICP-MS (ICP mass spectrometry), which are commonly applied to catalyst metal analysis, provide high sensitivity and accuracy. However, they require pretreatment to convert the sample into a solution, thereby lengthening the analytical workflow. By contrast, EDX can measure solid samples directly, making it well-suited to applications requiring rapid results, such as incoming material inspection and in-line analysis in molding plants. Because EDX is non-destructive, it is also suitable for multimodal analytical workflows.&nbsp;</p><p>On the other hand, some commercially available handheld EDX instruments do not support elements such as Sb and Ge. At the same time, higher-end systems may not offer the specifications or price range needed for industrial deployment. The <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=1&amp;search=EDX-7200"><strong>EDX-7200</strong></a> supports a broad range of elements with excellent cost performance. This article describes the analysis of Sb and Ge in PET using this instrument.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>EDX enables rapid and straightforward elemental analysis without cumbersome sample pretreatment. Previous studies have shown that the presence of Sb and Ge can be used to identify recycled PET. Building on this concept, this article presents a practical analytical approach based on EDX. Using the EDX-7200, which combines broad elemental coverage with excellent cost performance, the proposed method provides a practical tool for rapid on-site identification, quality control, and traceability management. When combined with physical property data and other information, this approach is expected to contribute to the safety assessment and quality assurance of recycled materials, thereby supporting a more advanced circular economy.</p><h3><strong>3. Thermo Fisher Scientific: Optimizing lithium-ion battery recycling operations using handheld XRF analysis</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/lithium_ion_battery_recycling_handheld_xrf_analysis_an1120_en_4c00477b78.pdf"><strong>Full PDF for download</strong></a></li></ul><p>The shift to a low-carbon economy and the adoption of electric vehicles will drive an exponential increase in the demand for lithium-ion batteries. As a corollary to this, the International Energy Agency expects the demand for some key commodities to increase significantly over the next two decades: the demand for Copper (Cu) is anticipated to increase by more than 40%; for nickel (Ni) and cobalt (Co), by 60-70% each; and for lithium (Li), by nearly 90%.¹ This will translate into increased mining activities and potentially generate larger amounts of deleterious waste.&nbsp;</p><p>To reduce the environmental impact and limit reliance on scarce commodities associated with economical and geopolitical implications, several countries have implemented regulations regarding producers’ responsibilities.² These regulations contain both incentives and accountability guidelines for lithium-ion battery and electric vehicles manufacturers. Their goal is to facilitate the emergence of a circular economy in which recycling will increasingly contribute to the supply of the strategic raw materials.&nbsp;</p><p>Lithium-ion batteries are made of multiple components, the most valuable being the cathode that contains between 40 and 70% ³, ⁴ of the total value of the battery, depending on its exact chemistry. As of 2023, lithium nickel cobalt manganese oxides (NCM) account for 66% of lithium-ion battery cathode active materials for electric vehicles, followed by lithium iron phosphates (LFP) which account for 24%, and lithium nickel cobalt aluminum oxide (NCA) accounts for the remaining 10%.³</p><h4><strong>Handheld X-ray Fluorescence Analysis&nbsp;</strong></h4><p>Handheld X-ray fluorescence analysis (HHXRF) is an elemental analysis technique that has proven to be cost-effective in recycling areas such as scrap metal and automotive catalytic converters. HHXRF can measure elements from magnesium (Mg) to uranium (U) in various types of materials such as metals and alloys; non-metallic inorganic materials such as ceramics; or ores or plastics. The <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/1993"><strong>Thermo Scientific™ Niton™ XL2</strong></a> and <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/1992"><strong>Thermo Scientific™ Niton™ XL5 Plus Handheld XRF Analyzers </strong></a>deliver accurate elemental analysis in real time, with little or no sample preparation, across multiple steps of the lithium-ion battery recycling workflow. Although handheld XRF does not detect lithium, it can measure most elements from the periodic table including nickel or cobalt which often command much higher value than the lithium in a lithium-ion battery.⁴</p><h4><strong>Conclusion&nbsp;</strong></h4><p>There is no ideal process to recycle lithium-ion batteries that simultaneously incorporates low environmental impact, high metal recovery yield, and economic viability. Because of the variety of technologies and materials used, recycling lithium-ion batteries is a complex journey with multiple possible paths. HHXRF helps recyclers by generating lab-quality data in real time, allowing them to optimize their processes and make fast decisions that generate significant benefits:&nbsp;</p><ul><li>Unwanted materials containing heavy metals such as lead or cadmium can be prevented from entering subsequent steps in the recycling workflow.&nbsp;</li><li>Materials can be accurately sorted and adequate processes for recovery selected depending on the material type (e.g., LFP vs. MCM).&nbsp;</li><li>The economic value of incoming and outgoing material can be more accurately estimated.</li></ul><h3><strong>4. Waters Corporation: Waters Aura Systems</strong></h3><ul><li>Brochure</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/720009268_7b9ad02cb4.pdf"><strong>Full PDF for download</strong></a></li></ul><p>The presentation introduces the <strong>Waters Aura Systems</strong>, a family of high-throughput, fluidics-free instruments for the characterization and identification of visible and subvisible particles. All Aura platforms provide particle counting, identification, morphology assessment, and flexible sample volumes ranging from microliters to milliliters. The portfolio includes configurations tailored to different applications: <strong>AuraBMI</strong> for biologics particle analysis, <strong>Aura</strong> for particle analysis with identification, <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2437"><strong>AuraPTx</strong></a> for protein formulation development, <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2438"><strong>AuraGT</strong></a> for gene therapy, <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2439"><strong>AuraCL</strong></a> for cell therapy, and <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2190"><strong>Aura+</strong></a> as the most comprehensive platform. The systems are designed for automation-ready operation, straightforward method transfer, and continuity from research through QC.</p><p>A key feature of the Aura platform is the combination of <strong>Backgrounded Membrane Imaging (BMI), Fluorescence Membrane Microscopy (FMM), and Side Illumination Membrane Imaging (SIMI)</strong>. BMI captures particles on a membrane and subtracts the membrane background to improve particle contrast, while FMM uses fluorescent dyes or conjugated antibodies to distinguish specific particle types. This enables differentiation of protein aggregates, cells, viral capsids, degraded excipients, plastics, fibers, and other contaminants that may look similar based on morphology alone. SIMI adds further information for identifying unlabeled inorganic particles such as glass, fibers, and Dynabeads. Together, these techniques provide more specific particle identification than morphology-based classification alone.</p><p>The systems are particularly suited to <strong>biologics, gene therapy, and cell therapy development</strong>. For gene therapy, Aura can evaluate AAV stability, distinguish capsid-related particles, and monitor DNA leakage using SYBR Gold fluorescence, with measurements possible from as little as <strong>5 µL of sample</strong>. For cell therapy, the system can differentiate single cells, cell aggregates, protein aggregates, and extrinsic particles. The platform also supports flexible sample volumes and 24- or 96-well membrane formats, allowing the same general approach to be used from sample-limited early development through larger-volume and QC workflows. The brochure reports excellent quantitative linearity across serial dilutions and emphasizes the ability to preserve sample for additional orthogonal analyses.</p><p>From a technical perspective, Aura Systems provide <strong>100% sampling efficiency</strong>, a particle detection and quantitation range above <strong>1 µm</strong>, and can handle concentrations above <strong>3,000,000 particles/mL</strong> for 1.6 µm particles. Brightfield membrane imaging takes approximately <strong>one minute per sample</strong>, while fluorescence measurements require about <strong>15–30 seconds per sample</strong>. The instruments are compatible with robotic automation and offer different fluorescence-channel configurations depending on the selected Aura model. Overall, the platform is positioned as a comprehensive solution for obtaining quantitative particle counts together with information on particle identity and composition across the development and quality-control lifecycle.</p>

News from LabRulezICPMS Library - Week 36, 2026

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Mestrelab Mnova ElViS software

By integrating Mnova ElViS into your analytical workflows, you can enhance the efficiency and accuracy of your spectroscopic data analysis across a wide range of techniques.

<h4><strong>Visualize, process, analyze and report various electronic and vibrational spectroscopic techniques</strong></h4><p>Carry out the analysis of various optical spectroscopy data including ultraviolet and visible (UV/Vis), near and mid infrared (NIR/MIR), Raman, fluorescence, and other spectroscopic methods operating in the whole region of wavelength from 100 nm to 100 µm.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li>Spectroscopic processing and analysis of ultraviolet and visible (UV/Vis), near and mid infrared (NIR/MIR), Raman, fluorescence methods.</li><li>Vendor and Operating System agnostic, supports most data file formats.</li><li>Wide range of processing functions, including a dynamic processing template.</li><li>Manual and automatic peak picking analysis.</li><li>Data Analysis of stacked/arrayed spectra.&nbsp;</li></ul><h4><strong>Features:</strong></h4><h5><strong data-start="1039" data-end="1108">PLS-Based Baseline Correction Tool (IarPLS)</strong></h5><p>To improve the processing of electronic and vibrational spectroscopy data, Mnova 15 introduces an enhanced PLS-based baseline correction tool, IarPLS (penalized-least-squares based). It now features an automatic button to compute the near-optimal asymmetry parameter.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_PLS_Based_Baseline_Correction_Tool_Iar_PLS_f3180f6927.webp" alt="Mestrelab Research: Mnova EIVis - PLS-Based Baseline Correction Tool (IarPLS)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_PLS_Based_Baseline_Correction_Tool_Iar_PLS_f3180f6927.webp 245w," sizes="100vw" width="1500" height="845"></p><h5><strong>Normalization Processing Modes</strong></h5><p>Three Normalization Processing modes, namely Largest Peak, Integral and Vector Length are available in Mnova 15. These three features aim to simplify the baseline correction process, streamline your workflow, and provide a rapid method for normalizing data to address quantitative issues.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Normalization_Processing_Modes_675cb7c407.webp" alt="Mestrelab Research: Mnova EIVis - Normalization Processing Modes" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Normalization_Processing_Modes_675cb7c407.webp 245w," sizes="100vw" width="1500" height="844"></p><h5><strong>Electronic and Vibrational Spectroscopies supported file formats</strong></h5><p>Mnova ElViS supports the most frequently used data file formats for these analytical techniques, including import and export of single and multi-column matrix-like spectra (i.e., ASCII XY and XYY, as .txt or .csv files). Supported formats include SPC (Galactic), JCAMP-DX, OPUS (Bruker), SPA and SPG (Omnic), Perkin Elmer's .sp files, and ASCII (XY and XYY).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Electronic_and_Vibrational_Spectroscopies_supported_file_formats_bb6afc3eab.webp" alt="Mestrelab Research: Mnova EIVis - Electronic and Vibrational Spectroscopies supported file formats" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Electronic_and_Vibrational_Spectroscopies_supported_file_formats_bb6afc3eab.webp 245w," sizes="100vw" width="1500" height="910"></p><h5><strong>Manual &amp; automatic spectral analysis</strong></h5><p>Mnova ElViS provides both manual and automatic peak picking, offering flexibility and convenience in your spectral analysis. It also features advanced tools for the analysis of stacked or arrayed spectra, ensuring thorough data evaluation and interpretation for all your spectral needs.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Manual_and_automatic_spectral_analysis_e24a19a151.webp" alt="Mestrelab Research: Mnova EIVis - Manual &amp; automatic spectral analysis" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Manual_and_automatic_spectral_analysis_e24a19a151.webp 240w," sizes="100vw" width="940" height="611"></p><h5><strong>Main processing features</strong></h5><p>Mnova ElViS comes with a range of useful processing features, including Multiplicative Scatter Correction, Probabilistic Quotient Normalization, Baseline correction (both automatic IarPLS and tailored Multipoint Baseline Correction), several smoothing techniques (such as Savitzky-Golay, Nonlocal Means, Exponential), normalization and Standard Normal Variate (SNV), derivatives, and units conversion between absorbance and transmittance.<br><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Main_processing_features_bc6eb64b99.webp" alt="Mestrelab Research: Mnova EIVis - Main processing features" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Main_processing_features_bc6eb64b99.webp 245w," sizes="100vw" width="1074" height="581"></p>

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