<h3>Lunch &amp; learn tips &amp; tricks series 2026 – part 1</h3>In modern analytical laboratories, efficiency, consistency, and data confidence are essential. Whether you are operating an ICP-OES or ICP-MS system, the way you use your software can significantly influence productivity and analytical performance. In the first session of our Lunch &amp; Learn Tips &amp; Tricks Series, we focused on one of the most common themes raised by users: how to navigate <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1489">Qtegra </a>software more effectively and optimize everyday workflows.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Fisher_Scientific_Mastering_Qtegra_Software_Practical_Strategies_to_Improve_Your_Daily_Lab_Workflow_3d35454782.jpg" alt="Thermo Fisher Scientific: Mastering Qtegra Software: Practical Strategies to Improve Your Daily Lab Workflow" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Fisher_Scientific_Mastering_Qtegra_Software_Practical_Strategies_to_Improve_Your_Daily_Lab_Workflow_3d35454782.jpg 245w," sizes="100vw" width="1024" height="512">This session explored three key areas in detail: understanding the structure of Qtegra and the Configurator, using the Dashboard as your operational control center, and building smarter, more efficient LabBooks. Below is a structured recap of the key insights shared during Part 1.<h3>Understanding the structure of Qtegra</h3>One of the most valuable features of Qtegra software is its consistency across multiple instrument platforms. Whether you are working with the <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products?category=227&amp;instrumentationItems=84&amp;manufacturerItems=3&amp;search=icap+pro">iCAP PRO ICP-OES</a>, the <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products?category=232&amp;instrumentationItems=83&amp;manufacturerItems=3">iCAP MX ICP-MS</a>, the iCAP 7000 OES, or Qnova ICP-MS systems, the interface and workflow logic remain familiar. This consistency reduces the learning curve when transitioning between instruments and supports standardised training across laboratories.Qtegra consists of two primary components: the Qtegra application itself and the Configurator.<ul><li>The Qtegra application is where you directly interact with your instrument. It is used to create LabBooks, control the instrument, execute analyses, and review results.</li><li>The Configurator, in contrast, defines how your system behaves at a deeper level. It controls system-level settings that underpin daily operation. Within the Configurator, users can manage features such as Fast Uptake setup, drain sensor configuration, wavelength libraries for ICP-OES systems, the Reaction Database for ICP-MS systems, and user access permissions.</li></ul>Understanding the distinction between these two components helps users troubleshoot more effectively and ensures that system configuration aligns with laboratory requirements.<h3>Using the dashboard as your central hub</h3>When Qtegra is opened, the first screen presented is the Dashboard. The Dashboard functions as the central hub for instrument monitoring and control. From this location, users can view instrument status, monitor live operating parameters, access tuning tools, review performance checks, and identify error or interlock notifications.<ul><li>For ICP-OES users, the Dashboard provides access to tune sets, source settings, AutoPeak alignment, autotuning functions, and live views such as Plasma TV. These tools allow users to confirm that the plasma and optical system are performing as expected before analysis begins.</li><li>For ICP-MS users, the Instrument tab within the Dashboard provides access to autotune routines, performance report summaries, gas flow settings, nebuliser adjustments, operating mode selection such as Standard, KED, or CCT modes, IntelliLens control, and real-time data streams.</li></ul>The Dashboard should be used as a structured checkpoint before running any analysis. Confirming that all parameters are within acceptable ranges reduces the likelihood of interruptions and supports consistent performance throughout the run.<h3>The get ready workflow: structured instrument preparation</h3><ul><li>The Get Ready function, which is launched directly from the Dashboard, provides a guided preparation workflow. This feature is designed to ensure that the instrument is fully configured and operating correctly for the selected method before analysis begins.</li><li>Within Get Ready, users can access scheduling tools that automate warmups, standby routines, shutdowns, and maintenance checks. These tools are particularly beneficial for laboratories operating multiple shifts or managing variable workloads.</li></ul>For ICP-MS systems, the Get Ready workflow includes automated performance checks, autotune routines, and controlled warm-up conditions. These routines verify that the instrument is operating within its expected performance range.By following the structured steps within Get Ready, laboratories can reduce the risk of missed preparation tasks and maintain day-to-day analytical consistency.<h3>Building smarter LabBooks</h3>The LabBook forms the foundation of every analytical sequence in Qtegra. A carefully constructed LabBook improves efficiency, reduces setup errors, and simplifies data review.Users can create a LabBook from scratch or begin with a template. Templates are particularly useful for laboratories that integrate with LIMS systems or import sample lists via CSV files, as they promote standardisation and consistency.<h4>Defining analytes</h4><ul><li>For ICP-OES methods, users must select appropriate wavelengths and evaluate potential spectral interferences.&nbsp;</li><li>For ICP-MS methods, users select the required masses, consider isobaric and polyatomic interferences, and use the Reaction Database to support reaction mode selection.</li></ul>Careful analyte selection at this stage helps prevent complications later in the analytical workflow.<h4>Configuring acquisition parameters</h4>Acquisition parameters define how the instrument collects data.<ul><li>For ICP-OES systems, this includes selecting viewing modes, choosing wavelength ranges such as iFR or eUV, and defining integration times.</li><li>For ICP-MS systems, this includes selecting operating modes such as Standard, KED, or CCT, configuring Reaction Finder options, enabling Intelligent Matrix Handling, and defining digital trigger settings.</li></ul>Thoughtful parameter configuration ensures that data quality objectives are met without unnecessary reanalysis.<h4>Using intelligent uptake and rinse</h4>The Intelligent Uptake and Rinse feature detects when the sample or rinse solution reaches the nebuliser. This ensures consistent timing across samples and improves overall throughput. In high-throughput environments, this feature contributes to both efficiency and reproducibility.<h4>Calibration and quantification strategy</h4>Calibration design is central to reliable quantification. Qtegra allows users to manage global standards, configure dilution tools, select calibration fit types, assign internal standards, and establish quality control samples.For ICP-OES systems, users can define standard ranges and switch points. For SemiQuant workflows, Relative Sensitivity Factors can be applied.Establishing these parameters during LabBook creation ensures that data validation is straightforward once analysis is complete.<h4>Autosampler and sample list setup</h4>For laboratories using the <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1715">iSC-65 autosampler</a>, features such as StepAhead, custom rack definitions, colour-coded sample views, and visual integrity checks support efficient sequence management.When constructing the sample list, users should clearly define sample types, establish a logical sequence order, and configure column layouts appropriately. Setting flags and limits during initial setup simplifies the data review process and supports quality assurance.<h3>Why workflow optimisation matters</h3>Optimising Qtegra usage does not require learning new software features. Instead, it involves using existing tools more strategically. By treating the Dashboard as a pre-run checkpoint, using Get Ready consistently, and constructing LabBooks with long-term efficiency in mind, laboratories can reduce downtime, improve reproducibility, and strengthen confidence in analytical results.<h3>Looking ahead to part 2</h3><blockquote>In Part 2 of the Qtegra Tips &amp; Tricks series, we will focus on post-analysis best practices. Topics will include interpreting signal intensities, reviewing calibration performance, evaluating recoveries and readbacks, applying flags effectively, exporting data, and implementing practical workflow improvements discussed in Part 1.</blockquote>By building a structured approach to both preparation and data review, laboratories can transform routine analysis into a streamlined, reliable, and repeatable process.We look forward to continuing the discussion in Part 2.<h5>Acknowledgement</h5>I would like to thank Matthew Gregory Field Applications Scientist, North EMEA, Trace Elemental Analysis and David Fishwick Field Applications Scientist, North EMEA, Trace Elemental Analysis for the support on this blog and the webinars<h4>Sign up for our webinars live and on demand.</h4>English<ul><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5795">Lunch and Learn Tips and Tricks Series: Qtegra Part 1</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5976">Lunch and Learn Tips and Tricks Series: Qtegra Part 2</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5977">Lunch and Learn Tips and Tricks Series: Trace Elemental Analysis (ICP-OES &amp; ICP-MS)</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5978">Trace Elemental Analysis: Ask the Expert</a></li></ul>Deutsch<ul><li>19. März 2026 <a target="_blank" rel="noopener noreferrer" href="https://event.on24.com/wcc/r/5179134/393E071E3B501943730958BA6203F477">Interne Standards in der ICP-OES: Ein Praxisguide für zuverlässige Qualitätssicherung im Labor</a></li><li>18. Juni 2026 <a target="_blank" rel="noopener noreferrer" href="https://event.on24.com/wcc/r/5179292/AE4C576B6A54D1AC182F7F77573D380F">Fehler reduzieren, Durchsatz steigern: Autoverdünnung mit PrepFast am ICP-MS</a></li><li>17. September 2026 <a target="_blank" rel="noopener noreferrer" href="https://event.on24.com/wcc/r/5179423/B5019C560431BADCA2A76E54E4EFE56F">ICP-OES smarter nutzen: Probendurchsatz erhöhen, Ressourcen sparen</a></li><li>17. Dezember 2026 <a target="_blank" rel="noopener noreferrer" href="https://event.on24.com/wcc/r/5179530/94EEDD1F56138F5EEE2AF24E29E6C12A">Qtegra ISDS Software Unlocked – Live im Expertengespräch</a></li></ul>Visit us on&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://www.linkedin.com/showcase/72003132/admin/dashboard/">LinkedIn:</a>&nbsp;#Qtegra, #ICPMS, #ICPOES, #TraceElementalAnalysis<blockquote><h5>Petra Gerhards</h5>Petra Gerhards, Dipl-Ing, is Regional Marketing Manager of GC and GC-MS for EMEA at Thermo Fisher Scientific. She has more than 29 years of experience in the fields of GC-MS, SPE and LC-MS. Since joining the regional team she has contributed to workflow solutions combining vials and closures with SPE solutions, GC-MS and LC-MS. She works with KOL's on data for regional specific marketing campaigns, organizes in-house seminars and works on customer specific solutions. Her main expertise is in the field of doping and drugs-of-abuse analysis.</blockquote>

Mastering Qtegra Software: Practical Strategies to Improve Your Daily Lab Workflow

<h4>1 - What is fitness for purpose?&nbsp;</h4>The test meets the necessary performance criteria for a clinical binary examination to ensure it is suitable for making accurate and reliable clinical decisions in the intended patient population. Among others, performance criteria include clinical sensitivity and clinical specificity.&nbsp;<h4>2 - How to assess clinical performance?&nbsp;</h4>Assessing the clinical performance of a binary examination, stating results as 'positive' or 'negative,' involves calculating the examination's Clinical Sensitivity and Clinical Specificity. From the physician's point of view, clinical performance is evaluated by using predictive values, i.e., how likely is it that a positive or negative result corresponds to the presence or absence of the condition, which is also influenced by the prevalence of the condition in the population being tested.&nbsp;<h4>3 - What is Clinical Sensitivity?&nbsp;</h4>Clinical sensitivity (SS), also known as the True Positive Rate, measures the proportion of true positives correctly identified by the test. Data from a group of subjects who have been confirmed to have the disease or condition through diagnosis or a gold standard test (D1 in Table 1) are needed to calculate clinical sensitivity.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Table_1_2x2_Contingency_table_for_clinical_diagnosis_241735a171.jpg" alt="Eurachem: Table 1. 2x2 Contingency table for clinical diagnosis." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Table_1_2x2_Contingency_table_for_clinical_diagnosis_241735a171.jpg 245w," sizes="100vw" width="1401" height="461">The formula for clinical sensitivity is:&nbsp;SS = tp / (tp + fn), with:&nbsp;<ul><li>tp, True Positives are the cases where the test correctly identifies the presence of the disease.&nbsp;</li><li>fn, False Negatives are the cases where the test incorrectly identifies the absence of the disease when it is actually present.&nbsp;</li></ul>Table 1 represents the components of clinical sensitivity and specificity. Figure 1 illustrates an example of the overlapping of the number of components. The symbol "∩" represents the intersection of groups. For example, D1∩n denotes the set of negative results obtained for known positive cases, i.e., false negatives.<h4>4 - What is Clinical Specificity?&nbsp;</h4>Clinical Specificity (SP), also known as the True Negative Rate, measures the proportion of true negatives correctly identified by the test. Data from a group of subjects who have been confirmed not to have the disease or condition through a gold standard test (D0 in Table 1) are needed to calculate clinical specificity.The formula for clinical specificity is:&nbsp;SP = tn / (tn + fp), where:&nbsp;<ul><li>tn, True Negatives are the cases where the test correctly identifies the absence of the disease.&nbsp;</li><li>fp, False Positives are the cases where the test incorrectly identifies the presence of the disease when it is actually absent.&nbsp;</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Figure_1_Graphical_representation_of_an_example_of_85ab82a16b.jpg" alt="Eurachem: Figure 1. Graphical representation of an example of tp (D1∩p), tn (D0∩n), fn (D1∩n), and fp (D0∩p). " srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Figure_1_Graphical_representation_of_an_example_of_85ab82a16b.jpg 245w," sizes="100vw" width="1300" height="736"><h4>5 - How to calculate the uncertainty of clinical sensitivity and specificity?</h4>Calculating clinical sensitivity and specificity uncertainty involves statistical methods that estimate each parameter's confidence interval (CI). These intervals provide a range of values within which the true sensitivity or specificity is expected to lie with a certain level of confidence, typically 95 %. Please refer to the AQA:2021 for information about statistical models to compute the 95 % CI [1].<h4>6 - How to define targets?&nbsp;</h4>Target minimum values for clinical sensitivity and specificity should be identified. These values may originate from regulations or criteria based on the intended use of the binary results. A target or minimum value for the lower limit of the 95 % CI for result rate, i.e., SS or SP, (LLSS.95 or LLSP.95) or the upper limit of the 95 % CI for result rate (HLSS.95 or HLSS.95) should also be defined according to the purpose of the analysis. The target or minimum value (LLSStg or LLSPtg) is particularly significant when the impact of false results is critical. For example, for blood components used in transfusions, the screening for infectious diseases should be performed with tests associated with an LLSS.95 close to 100 %.&nbsp;Figure 2 illustrates five different tests, each assessed for clinical sensitivity and its associated uncertainty. These cases reflect different levels of compliance with a target sensitivity of 0.5. Case 1 is non-compliant, Cases 2 to 4 are partially compliant, and Case 5 is compliant. From a simplified statistical power view, the laboratory should always take into account the minimum number of samples when defining its target. This number can be estimated by simulation. For example, for a clinical sensitivity of 90 % the evaluation will require a minimum of 10 samples. It should be emphasized that samples must be representative of the target population for a clinical power evaluation - for example, epidemiologically representative samples of the types and subtypes of a given virological agent are required.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Figure_2_Compliance_assessment_with_clinical_sensitivity_a21ece043c.jpg" alt="Eurachem: Figure 2. Compliance assessment with clinical sensitivity uncertainty using the lower 95% confidence interval limit for five performance assessment scenarios. " srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Figure_2_Compliance_assessment_with_clinical_sensitivity_a21ece043c.jpg 245w," sizes="100vw" width="1500" height="648">

Performance Assessment of Binary Output Examinations in Medical Laboratories

Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 30th March 2026? Check out new documents from the field of spectroscopy/spectrometry and related techniques!<blockquote>👉 <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library">SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT SPECTROSCOPY/SPECTROMETRY RELATED TECHNIQUES</a></blockquote><blockquote>👉 Need info about different analytical techniques? Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library">LabRulezLCMS</a> or <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library">LabRulezGCMS</a> libraries.</blockquote>This week we bring you application notes by Metrohm, Shimadzu, Thermo Fisher Scientific and Waters Corporation!<h3>1. Metrohm: Quantification of methanol in contaminated spirits</h3>Protecting consumers from contaminated beverages<ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_rs_056_download_3b37cb883a.pdf">Full PDF for download</a></li></ul>An alarming global trend highlights the serious harm that can result from ingesting illegal, improperly distilled alcohol. Home-distilled spirits prepared using industrial solvents (i.e., wood alcohol) and presented as legitimate alcoholic beverages often contain methanol. Methanol causes blindness and can lead to death when ingested. This has led to fatal consequences around the world [1–3].&nbsp;The breaking point for the Czech Republic came in September 2012. The sale of hard liquor was temporarily banned after 20 people died from the consumption of spirits with dangerous levels of methanol [2]. After an exhaustive study using various screening tools, the Czech Republic adopted Raman spectroscopy as the method of choice for identifying and quantifying methanol in contaminated spirits. This Application Note demonstrates how Raman spectroscopy can be employed as an efficient and rapid screening method for samples of rum contaminated with methanol.<h4>EXPERIMENT&nbsp;</h4>This example study measures commercially available coconut rum that is spiked with methanol in concentrations between 0.33% and 5.36%. The <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=220&amp;search=iRaman">iRaman NxG 785H</a> with a fiber-optic probe is used to collect Raman spectra of the mixtures (Figure 3). Table 1 lists the relevant equipment and instrument settings used for this application study. The peak at around 1000 cm-1 (highlighted by the inlay of Figure 3) visibly increases with increasing concentration of methanol, becoming significant at approximately 1%.<h4>CONCLUSION</h4>These results validate that Raman spectroscopy can be used for rapid, quantitative screening of dangerous adulterants in alcoholic beverages. This technique can be expanded to investigate adulteration in other media such as food, petroleum, and pharmaceutical drugs [4].<h3>2. Shimadzu: Evaluation of Organic Impurities in Lithium Carbonate by TOC Analysis</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_01098_en_ec9b3e73fc.pdf">Full PDF for download</a></li></ul><h4>User Benefits</h4><ul><li>TOC analysis enables effective control of organic impurities in lithium carbonate.</li><li>Using the combustion tube kit for high-salt samples can extend the lifespan of the combustion tube and catalyst, reducing maintenance frequency.</li><li>With the <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=1&amp;search=ASI-L+autosampler">ASI-L autosampler</a>, multiple samples can be measured automatically, improving throughput and efficiency</li></ul>Lithium carbonate (Li2CO3) is an important material used in a wide range of applications in the lithium-ion battery industry. For example, it is used as a lithium source for cathode active material synthesis and electrolyte salt production, as a raw material for oxide-based solid electrolytes, and as a form of lithium commonly recovered in recycling processes.&nbsp;Because impurities in lithium carbonate can affect downstream processes and final products, appropriate control is essential. In particular, organic impurities require especially careful attention because they may originate from so many sources, such as solvents and processing aids used during purification, residual organics from manufacturing steps, or contamination introduced during handling and storage.&nbsp;Total organic carbon (TOC) analysis offers an effective way to manage such organic impurities in raw materials. This article presents an example of using a <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=1&amp;search=TOC-L">Shimadzu TOC-L</a> total organic carbon analyzer (Fig. 1) to evaluate the TOC content of lithium carbonate.<h4>TOC Analysis&nbsp;</h4><h5>TOC Analysis Methods&nbsp;</h5>Two commonly used methods are available for TOC analysis:&nbsp;(1) TC - IC method: Total carbon (TC) and inorganic carbon (IC) are measured separately, and then TOC is calculated as the difference (TOC = TC – IC).(2) NPOC method: TOC is measured as non-purgeable organic carbon (NPOC). After acidifying the sample to pH &lt; 3, IC is subsequently removed by sparging and the remaining carbon is measured as TC, which is regarded as TOC. The acidification and sparging steps are performed automatically by the instrument.&nbsp;For lithium carbonate solutions, the amount of carbonatederived IC is typically very high. As a result, the TC–IC method tends to yield calculated TOC values with a larger relative error. Therefore, the NPOC method isrecommended.&nbsp;In lithium carbonate solutions prepared as described above, IC is expected to be present mainly as bicarbonate (HCO3 –) and carbonic acid (H2CO3). If IC is not fully removed during NPOC pretreatment, the TOC result may be overestimated. Therefore, thorough IC removal is critical for samples with high IC content. For actual samples, sparging parameters, such as sparge gas flow rate and sparge time settings, can be adjusted.&nbsp;<h5>Combustion Tube for High-Salt Samples&nbsp;</h5>The prepared lithium carbonate solution contains a large amount of salts. When a large number of salt-containing samples are analyzed, salts may accumulate inside the combustion tube, which can lead to catalyst clogging, reduced sensitivity, and poorer repeatability.For this analysis, the optional combustion tube for high-salt samples was used, shown in Fig. 2. The high-salt combustion tube has a larger diameter than the standard combustion tube and contains larger catalyst particles to help mitigate saltrelated clogging. Thus, the frequency of combustion tube replacement can be significantly reduced.<h4>Conclusion&nbsp;</h4>In this article, TOC analysis was performed for a 10 g/L lithium carbonate solution using a Shimadzu TOC-L total organic carbon analyzer. By measuring TOC in the prepared solution, the TOC content in the lithium carbonate solid was calculated. The results showed good recovery rates in the spike-recovery test and consistent performance was also confirmed during long-term continuous analysis. These findings demonstrate that the TOC-L is a reliable solution for measuring TOC in lithium carbonate and for effective management of organic impurities.&nbsp;When applying the NPOC method to lithium salt-containing solutions, careful selection of the acid is essential because salts formed during acidification can affect both data quality and instrument condition. For this example, sulfuric acid is recommended.In addition, using the optional combustion tube kit for high-salt samples can extend the lifespan of the combustion tube and catalyst, thereby reducing maintenance frequency. This option is especially helpful for samples with high salt concentrations.&nbsp;In addition to lithium carbonate, the TOC-L can also be applied to TOC analysis of lithium hydroxide solutions, determination of water-extractable TOC in recycled black mass, and analytical needs in lithium refining processes based on direct lithium extraction (DLE). With its broad applicability across various upstream materials, recycling streams, and refining processes, the TOC-L supports both quality control and process optimization operations throughout the lithium-ion battery value chain.<h3>3. Thermo Fisher Scientific: Evaluating Silicon using Raman Microscopy</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/vib_silicon_analysis_with_raman_microscope_app_note_mcs_an1232_en_11_2024_1887815_web_50639c9148.pdf">Full PDF for download</a></li></ul>Raman spectroscopy is a form of vibrational spectroscopy that probes chemical bonds and molecular structure and is sensitive to small changes in chemical and structural environments. The Raman spectrum of a material is a molecular “fingerprint” that can be used for identification of unknown materials and for characterizing and qualifying expected materials. Since intermolecular effects and lattice vibrations are also detected, it can be used to evaluate crystallinity. Mechanical phenomena such as strain can also be detected from peak shifts in Raman spectra. Raman imaging allows visualization of the distribution of components or changes in physical state across the sample.<h4>Silicon in aluminum&nbsp;</h4>Silicon is used in alloys with aluminum to increase the strength of lightweight aluminum-based parts. Addition of silicon to aluminum also alters the viscosity of melted aluminum, improving the castability. These alloys typically contain 3% to 25% silicon. While silicon and aluminum can form a eutectic mixture, it is common to have finely dispersed silicon particles throughout the material.¹ The size and distribution of silicon particles can affect the strength of the aluminum.&nbsp;One common example of an aluminum product, aluminum foil, is made up of almost pure aluminum (approximately 98.5%) but it does contain small amounts of silicon and iron.² Raman imaging of a piece of aluminum foil reveals small particles of silicon (Figure 1). A <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2237">Thermo Scientific™ DXR3xi Raman Imaging Microscope </a>was used with a 532 nm laser and a 100X&nbsp;metallurgical objective to collect the Raman imaging data. An area of 408 μm x 208 μm was imaged using an image pixel size of 1 μm. The Raman image, based on the 520 cm-1 peak of silicon, shows the spatial distribution of the particles and image analysis provides size and shape information. In this case, 81&nbsp;silicon particles were identified with a size (area) range of 6-151 μm². This analytical approach may be extrapolated to other types of Si/Al alloy parts.<h4>Conclusion&nbsp;</h4>The importance of silicon as a technologically significant material cannot be overstated. It has properties that make it useful in numerous different types of products. Raman micro-spectroscopy has proven itself an important tool for the assessment of molecular structure and physical states of silicon. Raman images provide a way to observe the spatial distribution of silicon and silicon-containing components, structural variations such as crystallinity, and physical effects such as strain. This note showed a few representative examples to illustrate some of the advantages offered by Raman microscopic analysis of silicon. The coverage is not intended to be comprehensive as there are numerous other areas where the Raman analysis of silicon is currently employed and other opportunities where it could be used. The Thermo Scientific DXR3 Raman Microscope and the Thermo Scientific DXR3xi Raman Imaging Microscope have both been shown to be excellent choices for the Raman analysis of silicon samples.<h3>4. Waters Corporation: High-Throughput Detection of Degraded Polysorbate in Biological Formulations with FMM</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720009114_en_99c7483d1a.pdf">Full PDF for download</a></li></ul>The FDA requires that all biologic formulations be free of visible particles and has defined allowable levels of subvisible particles (SVPs) larger than 10 µm to ensure the potency, efficiency, and safety of these drug.1 While most protein drug particle analysis focuses on particles formed due to formulation instability, SVPs formed through the degradation of formulation excipients must also be considered.&nbsp;Polysorbates 20 (PS20) and 80 (PS80), best known as Tween-20 and -80, are excipients used in &gt;70% of marketed parenteral biological drugs to improve product stability and shelf life.2 However, when these formulations are stored for long periods of time (&gt;6 months) at low temperatures (4 °C), visible and subvisible particles are formed due to the enzymatic hydrolysis of the polysorbates by host cell proteins (HCPs) such as esterases and lipases.3,4 PS20 in particular has been found to be extremely prone to degrading into fatty acid particles.5 Lauric, myristic and palmitic acids are the most common fatty acid degradation products, and there is a direct correlation between low fatty acid solubility and particle formation in common formulation buffers.3–7 However, high-throughput, sensitive, and specific analysis of polysorbate particles has been difficult due to their complex chemistry and their low concentrations (&lt;&lt;0.5%) in high protein concentration (&gt;100–200 mg/mL) formulation environments, making it a perennial needle in a haystack problem.In this application note, we introduce the Aura™ polysorbate degradation assay, a specific and quantitative assay that detects free fatty acid particles (FFAs) formed during polysorbate degradation on Aura PTx System. The assay analyzes 96 samples using anywhere from 5 µL–10 mL of sample in a few hours. <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2190">Aura™ Systems</a> use backgrounded membrane imaging (BMI) and fluorescence membrane microscopy (FMM) to count, size and ID particles from 1 µm to 5 mm. Fluorescent labels utilized by FMM are selected based on thier ability to interact with particles of a certain nature, allowing for the generation of key information related to identity of the particles.<h4>Results and Discussion&nbsp;</h4><h5>Morphological Appearance of Different Fatty Acid Particles&nbsp;</h5>Aura systems image every particle, enables subvisible particle size distributions analysis, and makes morphological differentiation possible using built-in image analysis filters. When we analyze different free fatty acid solutions with Aura PTx System, we find that each forms particles with different morphological characteristics. Lauric acid particles are large and irregular clusters, myristic acid particles are small and oval shaped, and palmitic acid particles form fibril-like particles and small circular clusters (Figure 2a-c). These unique morphological characteristics suggest the presence of free fatty acid particles that can then be confirmed with FMM using labeled fluorescence.<h4>Conclusion&nbsp;</h4>Aura PTx System and <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2190">Aura+ System</a> can easily detect the major degradation components of PS20 in proteincontaining samples at any stage of the drug manufacturing process. The method only requires 5 µL of sample, is specific and sensitive, and can analyze 96 samples in just a few hours, far outperforming other techniques. Aura PTx System and Aura+ System can also identify and differentiate the key degraded particulates from polysorbate formulation by their distinguishable shape, appearance, and specific labeling with BODIPY FL C16. The Aura polysorbate assay can detect FFAs at concentrations relative to lauric acid above 9.38 µM (&gt;2183 counts/mL) and quantitate above 18.75 µM (&gt;2976 counts/mL).

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<div><div id="inner_content_body_text" data-content-id="394785"><h4>Customer Talk with MIT Professor Thomas J. Wallin</h4>The combination of rheology and Raman spectroscopy offers unique opportunities for simultaneously measuring both the physical and chemical properties of a system. In this webinar, discover how the Anton Paar integrated Rheo+Raman system allows users to gain a deeper understanding of material properties in a matter of minutes.The session opens with an introduction from Sunny Sidhartha, who will present an overview of the Anton Paar integrated Rheo+Raman system.This is followed by an exclusive Customer Talk with Professor Thomas J. Wallin from the Department of Materials Science and Engineering at the Massachusetts Institute of Technology (MIT) in the US.Professor Wallin will share how he uses Anton Paar’s Rheo+Raman system to monitor the UV curing of 3D printing polymers in real time. By combining the compact Cora 5001 Raman spectrometer with an MCR rheometer, he is able to replicate in situ conditions during manufacturing.This enables a deeper understanding of the process-structure-property relationship in 3D-printed polymers, supports advanced formulation development, and helps identify optimal print parameters and quality control workflows for industrial applications.Get access to the free “Polymer Processing e-Book,” which is available as a free webinar handout.<h5>Attend This Webinar to:</h5><ul><li>Learn about new research in the field of 3D printing polymers using soft materials</li><li>Participate in a Q&amp;A session with a subject matter expert</li><li>Understand the principles and advantages of Raman spectroscopy</li><li>Explore rheological applications that benefit from in situ, real-time Raman spectroscopy</li><li>Discover how the combined system works and how easy it is to operate</li></ul><h5>Attend This Webinar If You:</h5><ul><li>Study curing, structure, and performance in real time</li><li>Combine flow behavior with chemical insights</li><li>Develop advanced formulations with deeper material understanding</li><li>Optimize manufacturing by monitoring transitions in situ</li><li>Adopt faster, multifunctional characterization methods</li><li>Refine 3D printing and curing workflows</li></ul><blockquote>Presenter: Sunny Sidhartha (Anton Paar GmbH, Global Market Development - Raman spectrometers)Sunny Sidhartha is a technical sales and business development professional with over 15 years of experience across various industries such as NIR spectroscopy, oil and gas, and manufacturing. He holds a degree in mechanical engineering with a focus on metallurgy and welding from Punjab Technical University, India, and a business degree from the European School of Management and Technology, Berlin. At Anton Paar, he is currently responsible for global market development for the Cora 5001, the benchtop Raman analyzer.Presenter: Professor Thomas J. Wallin (Department of Material Science, Massachusetts Institute of Technology)Professor Wallin earned a BS in physics and chemistry from the College of William and Mary, Virginia, in 2010, and an MS and a PhD in materials science and engineering from Cornell University, which he completed in 2018. Before joining MIT, he was a research scientist in soft wearable technologies at Meta’s Reality Labs Research, which focuses on immersive technologies.He has extensive expertise in additive manufacturing, particularly the use of soft materials in 3D-printed devices. At MIT, his research focuses on advancing soft wearable devices. His group combines materials, chemistry, advanced manufacturing, and mechanical design to develop new technologies that expand the complexity and functionality of soft machines.</blockquote></div></div>

AZOM: Rheo+Raman: Deeper Polymer Insights from Research to QC

Rheo+Raman: Deeper Polymer Insights from Research to QC

Discover how MIT uses Anton Paar’s Rheo+Raman system to study 3D printing polymers in real time, combining rheology and Raman spectroscopy for material insight.

ICPMS webinars focusing on Material analysis - page 1

Rheo+Raman: Deeper Polymer Insights from Research to QC

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