Alloys are formed when a metal is combined with another element to strengthen the base metal’s properties. Basically, an alloy is just a modified metal. Superalloys are similar, but these specific high-performance materials are held to a higher standard. They are typically nickel-based, cobalt-based, titanium-based, or iron-based.&nbsp;To be classified as a superalloy, the alloy must be able to reliably function under extremely high temperatures, possess superior strength and surface stability, and must be resistant to thermal creep deformation. Strong corrosion and oxidation resistance are also features of superalloys, but it’s important to note that they are not impervious to oxidation over time if protective measures are not taken. Unfortunately, even when protective measures are taken, contaminants like sulfur threaten to render that protection useless.<h4>Why is Protection from Oxidation Important?</h4>Oxidation negatively affects a superalloy by weakening its strength, which over time can lead to embrittlement and cracking. Since high-performance materials like superalloys are mainly used and relied on in industries like aerospace and nuclear energy, it is crucial that these materials perform as intended. Material failures could be the difference between life and death, in some circumstances.<h4>Selective Oxidation</h4>To make superalloys even more indestructible and shield against oxidation, selective oxidation is performed. With selective oxidation, superalloys are designed to oxidize in a way that creates a protective layer of oxidized material that “covers” the alloy, keeping the alloy’s inner structure from oxygen invasion. However, this means that in order to keep protecting the superalloy’s infrastructure, the protective oxidized layer needs to remain stable and intact.<h4>Sulfur: A Dangerous Threat</h4>The presence of sulfur, even at trace levels, can interfere with the selective oxidation process. Sulfur degrades that protective layer, leaving the superalloy vulnerable to negative oxidation and shortening its useful lifespan. Therefore, the accurate determination of sulfur content, down to the tenth of a microgram, is paramount for ensuring optimal performance and longevity in superalloys.<h4>A Combustion Technique for Ultimate Confidence</h4>Historically, the only way to accurately determine ultra-low levels of sulfur was with<a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1842"> glow discharge mass spectrometry (GD-MS)</a>, due to its high sensitivity. Now, even combustion methods can be used when paired with the right instruments, like<a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1879"> LECO’s CS844ES.</a>&nbsp;<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/LECO_rada_844_Analyza_uhliku_a_siry_spalovaci_metodou_18532af2ef.jpg" alt="LECO: Superalloys and Sulfur: How Even Trace Amounts Can Be Detrimental: LECO 844 Series Combustion Analysis for Carbon and Sulfur." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_LECO_rada_844_Analyza_uhliku_a_siry_spalovaci_metodou_18532af2ef.jpg 139w," sizes="100vw" width="710" height="792">The CS844ES uses high-frequency combustion and intelligent carrier gas management with infrared detection to reach a detection limit of approximately 0.1μg, guaranteeing even trace amounts of sulfur will be detected and ensuring confident quality control of superalloys. The sensitivity, ease-of-use, and speed of this instrument make it a reliable tool for any lab. Plus, the cost of combustion instrumentation is significantly lower than GD-MS, making it a practical choice as well.<h4>More About Sulfur and Superalloys</h4>Since superalloys make up fundamental parts of aerospace equipment, medical devices, and more, knowing that they will perform as they are supposed to is paramount. Feeling confident in sulfur level results can be achieved through specialized combustion techniques, avoiding costly GD-MS.&nbsp;To learn more about LECO’s CS844ES, <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/CS_844_844_ES_S_STEEL_NICKEL_SUPERALLOYS_203_821_540_9f6feba049.pdf">download this app note</a> that details how the instrument performed when testing steel, nickel, and superalloys. You can also <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1879">visit our website</a> to read more about the instrument and<a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/209_214_5c92613a24.pdf"> download a brochure.</a> To learn more about how LECO serves the high-performance materials industry, visit our<a target="_blank" rel="noopener noreferrer" href="https://www.leco.com/applications/high-performance-materials/"> High Performance Materials Resource Hub.</a>

Superalloys and Sulfur: How Even Trace Amounts Can Be Detrimental

The standard uncertainty arising from random effects is often derived from repeated experiments and is quantified in terms of the standard deviation s of the measured values of the quantity. If it is the standard uncertainty arising from random effects on a single measurement that is required, then the observed standard deviation s is used without change. However, for a result which is the mean x of n measurements the standard uncertainty decreases as n increases, so that:<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Using_repeated_measurements_to_improve_the_standard_uncertainty_equation_1_19f8f07f8f.jpg" alt="Eurachem: Using repeated measurements to improve the standard uncertainty - Equation 1" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Using_repeated_measurements_to_improve_the_standard_uncertainty_equation_1_19f8f07f8f.jpg 245w," sizes="100vw" width="485" height="138">Example 1 shows how Eq (1) applies when estimating the uncertainty of a mean value, but not when considering the uncertainty of individual observations.<blockquote>Example 1 A volumetric pipette is calibrated with 12 measurements; the mean and the standard deviation are calculated. Eq. 1 applies to estimate the standard uncertainty of the mean value. However, when using the pipette to deliver one aliquot, Eq. 1 does not apply and the standard uncertainty due to random variation on this single measurement is the standard deviation s</blockquote>For Eq (1) to be valid, it is a requirement that all the measurements are independent and from a stable test sample under the same measurement conditions. The measurement conditions for all the measurements could be, for example, 1) repeatability conditions, 2) intermediate precision (within laboratory reproducibility) conditions or 3) reproducibility conditions&nbsp;It is very important to be aware that the standard uncertainty given by Eq. 1 only gives the estimated uncertainty due to random variation under the conditions of measurement under which the observations were taken, and is only strictly valid for independent observations.&nbsp;It can be hard to decide whether observations are sufficiently independent for Eq. 1 to apply, as there is no simple, general, rule. The following paragraphs therefore give examples of different circumstances to help identify circumstances when Eq. 1 can safely be used.<h3>Example where Equation (1) applies&nbsp;</h3><h4>Measurement of inhomogeneous samples&nbsp;</h4>If inhomogeneity for test samples is a major part of the uncertainty the analyst may choose to measure more test portions from each test sample in order to reduce the standard uncertainty. If all these measurements are made under repeatability conditions, i.e. the same measurement conditions following the whole procedure, including repeated random sampling from the test sample, the standard deviation of the mean, given by Eq. (1), should be used to estimate the uncertainty arising from variation under repeatability conditions.&nbsp;<h3>Examples where Equation (1) does not apply&nbsp;</h3>The following paragraphs give two examples where neither the standard deviation nor the standard deviation of the mean can be directly applied without further analysis of the data.&nbsp;<h4>Measurements taken in groups&nbsp;</h4>One example is when we have data from internal quality control from a measurement procedure that includes a calibration before analysis each day. We want to calculate the standard uncertainty of the mean value used to set the central line in the QC chart. The data consist of duplicate QC measurements performed on a stable test sample each day over a longer period of time (say, p days), giving a total of 2p observations; that is, p groups of two measurements. Because each pair of duplicate measurements has a single calibration error in common; the duplicates within the data set are not strictly independent and Eq. (1) cannot be directly used for all 2p observations. The uncertainty of the mean can most easily be calculated by taking the standard deviation of the p means for each day and dividing that standard deviation by √p . Analysis of variance can also be useful in similar cases. Similar principles apply to other kinds of grouping, including grouping by operator, instrument etc.&nbsp;<h4>Measurement when the test item or measurement system is not stable over time</h4>Another common example is time dependent data. The time dependence could be due to instrument drift or to actual change in concentration by time. In such cases the error for a given observation is partly random and in part ‘carried over’ from the previous observation. Again the errors affecting each observation are not independent because some part of the error is common to successive observations; Eq. (1) can not be used and more complex statistical techniques, that allow for correlation, must be used to process the data.&nbsp;For further guidance on the treatment of correlated data in uncertainty evaluation, see Eurolab Technical Report 1/2006: Guide to the Evaluation of Measurement Uncertainty for Quantitative Test Results, Appendix A.5

Using repeated measurements to improve the standard uncertainty

Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 27th July 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 Agilent Technologies, Shimadzu and Thermo Fisher Scientific!<h3>1. Agilent Technologies: High-Precision Determination of Phosphorus and Potassium in Fertilizers by ICP-OES</h3>Accurate nutrient analysis of fertilizers using an <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=10&amp;search=5800">Agilent 5800 ICP-OES</a> and standard bracketing<ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_fertilizers_standard_bracketing_5800_icp_oes_5994_9311en_agilent_83cbca87fd.pdf">Full PDF for download</a>&nbsp;</li></ul>Accurate characterization of fertilizer nutrient content is critical for optimizing plant growth, maximizing yield, and ensuring sustainable agricultural practices. Fertilizers must comply with strict regulatory specifications, particularly for declared NPK (nitrogen–phosphorus–potassium) ratios, which define the relative nutrient content supplied to crops. Even small deviations from the specified ratio can impact plant yield, regulatory compliance, customer confidence in the product, as well as its commercial value.&nbsp;Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) is a well-established technique for multi-element analysis of complex samples, so is ideally suited to meet the precision and accuracy requirements of fertilizer testing.1,2 However, applications that require very tight tolerances, such as NPK verification, benefit from enhanced calibration approaches as even small deviations from reported values can have large financial implications for the manufacturer.Using Agilent ICP-OES instruments, standard bracketing calibration based on a standard–sample–standard measurement sequence, minimizes the impact of sample-to-sample variability and matrix effects.3,4 This approach further improves analytical precision and accuracy compared to conventional external calibration, making it a particularly valuable method for analyzing fertilizers with tightly controlled specifications.&nbsp;In this study, an Agilent 5800 Vertical Dual View (VDV) ICP-OES, equipped with an Agilent SPS 4 autosampler and controlled using Agilent ICP Expert Pro software,5 was used to determine P and K concentrations and corresponding NPK ratios in a standard reference material (SRM) and two commercially available fertilizers. The standard bracketing method was adapted from ISO methods 11494 and 114956,7 and adjusted for fertilizer analysis. The performance of the method, when combined with real-time data processing tools, was evaluated for accuracy and precision.<h4>Experimental&nbsp;</h4><h5>Instrumentation&nbsp;</h5>The <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=10&amp;search=5800">Agilent 5800 VDV ICP-OES </a>was configured with the <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=10&amp;search=SPS+4+autosampler">Agilent SPS 4 autosampler</a>, which provided automated sample delivery to the instrument (Figure 1). The 5800 ICP-OES was equipped with an Agilent SeaSpray nebulizer, double-pass cyclonic spray chamber, and one-piece VDV torch with a 1.8 mm internal diameter (ID) injector. All components were controlled by Agilent ICP Expert Pro* software. Instrument operating parameters are listed in Table 1.<h4>Conclusion&nbsp;</h4>This study demonstrates the effectiveness of the Agilent 5800 VDV ICP-OES standard bracketing method for high-precision determination of phosphorus and potassium in fertilizers. The method delivered excellent performance, with relative standard deviations below 0.1% and excellent agreement with expected NPK values, with recoveries ranging from 99 to 102%. The combination of gravimetrically prepared standards, internal standard correction, and bracketing calibration minimizes analytical variability and ensures reliable results for fertilizer quality control.&nbsp;In addition, Agilent ICP Expert Pro software streamlines analysis through real-time calculations and automated quality control tools, improving laboratory efficiency and reducing the risk of reporting errors. This approach provides a robust and practical solution for high throughput fertilizer testing laboratories, enabling precise NPK verification that strengthens regulatory compliance, reduces the risk of mislabeling, and supports accurate product pricing.<h3>2. Shimadzu: Multifaceted Evaluation of Frozen Tempura Batter Coating Focusing on Food Texture</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00987_en_f919b540e7.pdf">Full PDF for download</a></li></ul>The “deliciousness” of foods consists of multiple factors, including taste, smell, and visual appeal. However, texture, sometimes called “mouthfeel,” is also one extremely important factor. Mouthfeel refers to the physical sensations experienced when food is placed in the mouth or chewed, and is expressed by various onomatopoeic words such as “crisp” and “chewy.” Among these, the packaging of some foods uses onomatopoeic terms to emphasize the appeal of their distinctive textures, and this has become an important “selling point” for consumers when deciding which product to select.&nbsp;In the development of food products, the method of evaluating texture has become a key point. Sensory methods have long been used, but because sensory evaluations depend on the human senses, they are easily influenced by differences between individuals conducting the evaluation and their physical condition.Thus, the difficulty in ensuring repeatability is an issue. As an additional issue, technical training for the panelists who conduct evaluations also requires time and labor. To compensate for these problems, evaluation methods based on measurement of physical properties with instruments may be used. This approach eliminatesthe human error that had been a problem with sensory evaluations, enabling objective evaluations.&nbsp;Food texture is also complexly influenced by the interactions of various factors, such as the ingredients contained in the food, their amounts, and the cooking method. Since differences in these factors may appear in the structure and surface condition of foods, observation and measurement of the internalstructure and surface condition of foods make it possible to analyze and understand the factors that contribute to texture, allowing more efficient food product development from the physical viewpoint. This article introduces an example of an analysis of the differencesin texture that occur due to differences in the heating method from the aspects of evaluation of physical properties and structural and surface observation, using frozen tempura asthe testsample.<h4>Experiment&nbsp;</h4>In this experiment, four instruments were used for evaluation of mechanical properties and structural and surface observation. Table 1 shows the names of the instruments, the purpose of the experiment, and the corresponding sectionsin this article. The test samples used here were commercially-available frozen tempura, which features “moistness” when prepared by microwave oven cooking and “crispness” after microwave oven + oven cooking. The “Moist” type is prepared by heating for 1 min in a microwave oven, and the “Crisp” type is prepared by additional heating in an oven after microwave heating. Since the heating time in the oven is adjusted depending on the sample size required by each instrument, the specific sample preparation procedure and the flow of the tests will be described in the sections concerning each instrument.<h5>Evaluation of Coating Surface Roughness by Laser Microscope&nbsp;</h5>The laser microscope was used in high-magnification measurements of the roughness of the sample surface and color observation (color images). It is possible to grasp the differences in the batter coatings of the “Moist” and “Crisp” types from changes in roughness and also changes in color. Fig. 11 shows the appearance of the OLS5500 laser microscope used here. In this instrument, optical resolution is enhanced by using a singlewavelength 405 nm laser beam, and topographical information can be acquired by reflective confocal laser microscopy. Color images of the same field can also be obtained by using a white LED.In this experiment, the samples were prepared by the following procedure in order to observe the same areas of the “Moist” and “Crisp” samples. First, the entire kabocha tempura was heated for 1 min in the microwave oven, after which part of the sample was cut off and observed on the stage (Fig. 12). Following this, the observed sample was heated in the oven and placed on the stage again, and the “Crisp” sample was observed (Fig. 13). To observe the shape of a wide area of the sample, an alignment (“tile-stitching”) function was used. The image acquisition condition was the ultra-high speed mode using a 20x objective lens.<h4>Conclusion&nbsp;</h4>A sensory evaluation and evaluation of physical properties using a texture analyzer, and structural observations from the macro to the micro level using an X-ray CT, laser microscope, and SEM were conducted for frozen tempura with different food textures depending on the heating conditions. The impression of the food texture, or “mouthfeel,” obtained in the sensory evaluation was quantified in the form of physical property values by using the texture analyzer, and the factors that formed the grounds for those numerical values were then evaluated based on the results of various types of observation. As a result, it was suggested that differences in food texture correspond to changes in the food structure and oil content.&nbsp;In this experiment, different samples were examined with four types of instruments. However, if physical property tests can be carried out with the samples as-is after observation of the same areas, a more direct and high accuracy analysis is considered to be possible. Moreover, in this experiment, the food texture was changed by using different heating methods, but in cases where the ingredients (flour, oil) or the deep-frying method is changed, there is a possibility that different factors from those in this study may contribute to the texture. As demonstrated in this experiment, although “food texture” is often grasped sensorily, as suggested by the term “mouthfeel,” this property can be visualized scientifically in terms of the structure and physical properties of the food and the condition of the oil content by combining multifaceted evaluations using multiple instruments. This technique is expected to become an effective approach for reproducing and controlling the target texture in food product development and quality design.<h3>3. Thermo Fisher Scientific: Bauxite analysis with Niton XRF analyzers</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/bauxite_analysis_with_niton_xrf_analyzers_an1795_en_93b319a76e.pdf">Full PDF for download</a></li></ul>Globally, bauxite is the primary source of aluminum (Al) ore and anchors a major industry in many countries, including China, Australia, and Brazil. As such, grading Al concentration is of paramount interest in the mine as is the need to determine concentrations of penalty elements. The desired goal is to conduct all the analysis in a way that maximizes productivity.&nbsp;<h4>Application&nbsp;</h4>Analysis of aluminum (Al), silicon (Si), and iron (Fe) presents unique challenges. Accurate analysis of all three is critical for ore grading and understanding their influence on the processing and production of the primary metal of interest. Relying on lab-analysis only in bauxite mining operation often results in costly time delays and reduced productivity while waiting hour if not days and weeks for the results.&nbsp;With the ruggedized, dust- and splash-proof <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1997">Thermo Scientific™ Niton™ XL3t GOLDD+ XRL Analyzer</a>, near lab-quality analysis of Al, Si and Fe can be generated on sites, enabling quick decisions in at remote mines. Indeed, the Niton XL3t GOLDD+ analyzer provides rapid and accurate elemental analyses of bauxite ore enabling to map chemical concentrations in realtime, identify excavation hotspots and detect penalty elements.&nbsp;<h4>Handheld XRF analyzer&nbsp;</h4>Thermo Scientific Niton Handheld XRF analyzers, including the Niton XL3 GOLDD+ analyzer, easily analyze elements from magnesium (Mg) to uranium (U) to fill all exploration and mining needs. These instruments make it easy to perform trend analysis by averaging readings in real-time or by downloading results later to a PC. They deliver fast, accurate elemental analysis for intensive metals exploration and production whether base metals, precious metals, or even rare earth elements.<h4>Comments&nbsp;</h4>The correlation coefficients and repeatability data for the key elements in bauxite analysis demonstrate the excellent accuracy and precision of the handheld Niton XL3 950 GOLDD+ analyzer, indicating that the Niton XL3 GOLDD+ instrument is ideal for fast mine or field analysis. The instant chemical information it provides allows the user to make critical decisions with minimal downtime, keeping projects running and productive. Additionally, they provide information that minimizes the expense and time of shipping samples for analysis and increases savings. The Thermo Scientific Niton XRF analyzer is a great addition to any process in the life of the mine.

News from LabRulezICPMS Library - Week 31, 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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In 1936, the Laboratory Equipment Company introduced the first rapid carbon determinator for the American iron and steel industry.  Today, almost 80 years later, LECO is recognized globally as a leader in the development of high-quality analytical instrumentation, mass spectrometers, comprehensive two-dimensional chromatography, metallography & optical equipment as well as consumables.

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Thermo Scientific™ OXSAS™ Optical Emission Analytical Software

Thermo Scientific™ OXSAS™ software for OES enables fast, accurate metals analysis, method and calibration development, automation, and easy batch processing.

Perform rapid high quality metals analyses with a customizable software solution for optical emission spectrometers. Flexible and powerful Thermo Scientific™ OXSAS™ Optical Emission Analytical Software runs a variety of analyses, including the development of new analytical methods and calibrations as well as rapid high-quality routine elemental analysis.&nbsp;From simple tasks to complex jobs, the OXSAS software platform can perform many operations in a single click and is designed to evolve to meet your needs throughout your instrument's lifetime.<h3>📋 Template-Driven Analysis</h3><ul><li>Simple, comprehensive analysis of unknown samples.</li><li>Required analytical parameters and results processing details pre-defined in the routine analysis shortcut.</li><li>One click creation of a batch with all samples requiring analysis.</li><li>Automatic Method Choice selects the best analytical parameters set without user intervention.</li></ul><h3>⚙️ Fast Calibration</h3><ul><li>Multiple analyte curve display, instant base curve calculation, templates for editing and measuring of calibration standards.</li><li>Calibration curve determination using multi-variable regression with additive and multiplicative correction models.</li></ul><h3>🖥️ Familiar User Interface</h3><ul><li>Comprehensive post-treatment of results, including export and Microsoft™ Excel support.</li><li>Integrated Microsoft SQL Server Express relational database stores setup and analyses data.</li><li>Export and import facilities allow calibration standards, methods or grades to be easily transferred between instruments.</li><li>Full network and serial transmission capabilities.</li><li>Common software platform with the <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products?manufacturerItems=3&amp;search=WDXRF">WDXRF Thermo Scientific series</a> instrument range.</li><li>Supports Thermo Scientific SMS system for full automation of sample analysis.</li><li>Monitoring and maintenance tools to maximize uptime.</li><li>Measurement Uncertainty and Conformance Probability on unknown samples analysis.</li><li>SCT Manager: status and history overview of instrument and method analytical capability.</li><li>Comprehensive, on-line contextual Help.</li><li>Real, on-line integrated SPC.</li><li>Key to Metals: comprehensive steel and nonferrous metals database with cross-reference.</li></ul><h3>🔌Instrument Compatibility</h3>Supports the Spark option of the:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1977">Thermo Scientific™ ARL iSpark™ Plus</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1213">ARL iSpark™</a></li><li>Thermo Scientific™ ARL 4460™ Optical Emission Spectrometer</li></ul>Allows complex calculations such as:<ul><li>element soluble/insoluble</li><li>element inclusions (number, size and chemical composition)</li><li>with no additional analysis time</li></ul><h3>🧩 Options for OXSAS Software</h3><ul><li>Charge correction: calculates material weights to correct out-of-spec charges.</li><li>SPC-Full: full graphical extension for on-line Statistical Process Control.</li><li>ARLcom: result transmission over network or serial via TCP/IP or to files/printers.</li><li>Remote Sample Definition: sample registration from external applications anywhere on the network.</li></ul><h3>✅ Compatible with:</h3><ul><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1977">ARL iSpark Plus Optical Emission Spectrometer</a></li><li>ARL iSpark Plus Fire Assay Analyzer</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1213">ARL iSpark Optical Emission Spectrometer</a></li><li>ARL 4460 Optical Emission Spectrometer</li><li>Thermo Scientific™ ARL™ Fire Assay Analyzer</li></ul>

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News from LabRulezICPMS Library - Week 31, 2026

Using repeated measurements to improve the standard uncertainty

Superalloys and Sulfur: How Even Trace Amounts Can Be Detrimental