Although modern agriculture needs to be based on accurate real-time data on soil, plant, and environmental conditions, affordable, sufficiently sensitive, and robust monitoring devices for this purpose are currently missing. According to scientists, low-cost sensors based on new smart materials and printing technologies offer a suitable solution. The results of multidisciplinary research thus far and proposals for the development of these devices have been presented by researchers from Czechia, Great Britain, Spain, and the USA in a review article in the prestigious journal <a target="_blank" rel="noopener noreferrer" href="https://www.nature.com/articles/s41467-026-68778-6#Abs1">Nature Communications</a>.“Sensor technologies for monitoring plant health are still in their infancy. Only simple sensors capable of monitoring basic parameters such as pH, soil moisture, or temperature are commonly available on the market. However, if farmers are to face current challenges, including climate change, and ensure sufficient food for humanity, they need much more effective tools that will enable them to monitor, for example, various pathogens, including bacteria and fungi. In our opinion, a suitable solution is a combination of new smart materials and printing for the production of inexpensive, durable, and scalable materials,” said the article’s first author, David Panáček from CATRIN, who worked on the study during his internship at Imperial College London.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/CATRIN_Soucasne_senzory_nestaci_Vedci_navrhuji_novou_cestu_ke_sledovani_zdravi_rostlin_b4014952a3.jpg" alt="CATRIN: David Panáček" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_CATRIN_Soucasne_senzory_nestaci_Vedci_navrhuji_novou_cestu_ke_sledovani_zdravi_rostlin_b4014952a3.jpg 208w," sizes="100vw" width="1536" height="1152">Sensors for monitoring plant health, for example, lag behind similar devices used in human medicine. According to the authors, advances in materials science, which will enable the development of scalable sensor technologies, play a key role in this potential shift. “Printing techniques, including inkjet printing, screen printing, aerosol printing, 3D printing, and direct laser writing, offer flexible ways to produce flexible sensors, large-area sensors, and sensors integrated directly into plants. The review article summarizes recent advances in printable low-dimensional materials for agricultural sensors, analyzes their physico-chemical properties in relation to sensor performance, and discusses key challenges and future opportunities requiring interdisciplinary integration,” said one of the corresponding authors, Michal Otyepka.The research brought together materials scientists, chemists, biologists, sensing experts, and printing technology specialists. Materials research and the use of graphene-based smart materials were led by scientists from CATRIN at Palacký University and VŠB-Technical University of Ostrava. Plant biologists from CATRIN contributed their knowledge of plant development, their responses to stress conditions, and the monitoring of their properties. Researchers from Imperial College London took care of the electronics, while representatives from<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/companies/36"> CEITEC – VUT</a> focused on printing technologies. The team also included leading experts in sensing from the Catalan Institute of Nanoscience and Nanotechnology in Barcelona, and Joseph Wang of the University of California San Diego, the “father of sensing and electrochemistry,” also contributed his extensive knowledge.“This is a groundbreaking study on this topic, which has managed to attract some truly big names in their respective fields. Last but not least, the study demonstrates that interdisciplinarity at CATRIN gives rise to completely new and promising research topics, such as plant sensing and its use for studying plant-environment interactions and smart agriculture,” added another corresponding author, Nuria De Diego.

Current sensors are not good enough. Scientists propose a new way to monitor plant health

In the week from February 16, the following webinars await you in the field of atomic and molecular spectroscopy and spectrometry<blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinars">👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</a></h4>👉 Search and filter in the largest global database with over 5 000 GC, LC, MS and spectroscopy webinars.</blockquote><h5>1. Thermo Fisher Scientific: The Perfect Pair for GMP Compliance – IonicX and TruScan for Raw Material Identification and Verification</h5><ul><li>Tu, 17.2.2026 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5835">Registration</a></li></ul>Learn how the IonicX™ Portable XRF Analyzer enables rapid, at-line identification of ionic salts in GMP manufacturing, delivering real-time PASS/FAIL results and streamlined raw material verification.<h5>2. Milestone: Reliable Mercury Analysis in Environmental Samples – Tools, Techniques, and Best Practices</h5><ul><li>Th, 19.2.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5804">Registration</a></li></ul>Episode 3 focuses on reliable mercury analysis in environmental samples, demonstrating how direct analysis simplifies workflows, reduces contamination risks, and delivers accurate results without digestion.<h5>3. Agilent Technologies: Learn How to Confirm Your Data on ICP-OES – Are You Getting the Right Result?</h5><ul><li>Th, 19.2.2026 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/2785">Registration</a></li></ul>This webinar discusses accuracy and precision in ICP-OES analysis and practical strategies to ensure reliable and defensible results.

Webinars LabRulezICPMS Week 08/2026

Metalloproteins play essential roles in marine phytoplankton, yet their metalloproteomes remain poorly characterized. This study combines protein modeling, quantitative proteomics, and ICP-MS measurements to assess metal requirements across several phytoplankton species, including chlorophytes, a haptophyte, and a cyanobacterium.Results show strong agreement between proteome-inferred metal demands and measured cellular metal quotas, while providing deeper insight into biological functions. Mg-containing proteins were most abundant overall, with Zn, Fe, and Mn as dominant trace cofactors. Distinct differences between prokaryotic and eukaryotic phytoplankton were observed, and strain-level analysis demonstrated how metalloproteomes reflect environmental adaptation and metal limitation strategies.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233">Unlocking Phytoplankton Metallomes with Comparative Analysis of Metal Quotas, Quantitative Proteomics, and Inferred Metalloproteomes</a></h4>Qiong Zhang*, Jiayou Ge, Fengjie Liu, Shabaz Mohammed, Kedong Yin, and Rosalind E. M. RickabyEnviron. Sci. Technol. 2025, 59, 50, 27367–27377<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.est.5c11233">https://doi.org/10.1021/acs.est.5c11233</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.Many important biological processes of phytoplankton, including photosynthesis, carbon fixation, and nitrogen reduction, rely on enzymes that have metals as cofactors. <a href="javascript:void(0);">(1)</a> It has been estimated that half of all enzymes must be associated with a particular metal to function. <a href="javascript:void(0);">(2)</a> The availability of metals in the environment has changed over geological time and, given imperfections in the selectivity of metal transport systems, proteins have evolved to make the best use of metals that are more available. <a href="javascript:void(0);">(3−5)</a> Depending on their evolutionary histories, different groups of marine phytoplankton may have differences in their metalloprotein compositions and metal transport strategies. The evolving trace metal availability in the environment, thus, may provide a selective pressure to promote the proliferation of different phytoplankton groups in the oceanic environment, affecting the phytoplankton community structure with knock-on effects on the marine food web and carbon sequestration. <a href="javascript:void(0);">(5)</a> It is crucial to understand the utilization of trace metals by different groups of marine phytoplankton that inhabit contrasting niches in the modern ocean, not only by assessing the accumulation of trace metals in cells but also examining the functions of trace metals in different biological pathways.In previous studies, the utilization or requirements of trace metals by marine phytoplankton were mostly determined by measuring the elemental stoichiometry in cells (metallome or quota of trace metals). <a href="javascript:void(0);">(6−8)</a> In the field, attempts have also been made to investigate the elements in phytoplankton <a href="javascript:void(0);">(9,10)</a> which have then been compared with profiles in the abiotic environment to understand their relationships. <a href="javascript:void(0);">(11)</a> The discovery of various metal storage proteins and the plasticity in elemental quotas under different environmental conditions raise the question of whether intracellular trace metal quotas reflect true differences in trace metal usage by phytoplankton. <a href="javascript:void(0);">(12,13)</a> In the past few decades, more and more studies have focused on metal–protein partnerships, using approaches such as genome-wide analysis to identify homologues of known metalloproteins and other deduced metal-binding motifs encoded within sequenced genomes. <a href="javascript:void(0);">(5,14)</a> However, this approach has limitations in estimating metal abundance in cells because not all genes predicted in the genome would be translated and expressed as proteins under all environments, and a single gene for a metalloprotein could be highly expressed, such that a translation from genetic presence to a required concentration is not linear and could be environment-dependent. To obtain in situ metal abundance in metalloproteins in cells, native separation of proteins by HPLC or PAGE followed by the detection of metals using ICP-MS <a href="javascript:void(0);">(15,16)</a> has been used. This approach also has limitations in that, without bioinformatics, it cannot identify the function of proteins within which the metals are found, and it may also depend on the composition of the media to which the organism has been exposed and in which it has been growing. An ideal way to solve the problem would be to separate and purify each of the proteins and then identify both the associated metal (e.g., by ICP-MS) and the protein sequence (e.g., by mass spectrometry). However, this approach is constrained by the resolution of chromatographic or electrophoretic techniques, making it challenging to establish the metal–protein associations unambiguously; many proteins often co-occur in metal-containing fractions. Additionally, this method requires huge volumes of biomass and is both time-consuming and complex, complicating the reconstruction of the expressed metalloproteome in cells. The techniques and challenges for metalloproteomics have been discussed in detail in previously published review articles (and the references therein). <a href="javascript:void(0);">(17)</a>In this study, we determined the trace metal utilization (the metallome) of different phytoplankton from their expressed proteome via a protein modeling and homologue identification approach, and then compared the resulting metallome with the metal quota data measured by the traditional inductively coupled plasma mass spectrometry (ICP-MS). This approach allows the building of a link between the truly used trace elements and their biological functions in phytoplankton. We aim to fill the gap between an estimation of metals encoded in the genome and metal analysis by ICP-MS in phytoplankton, which may be more broadly applicable to infer cellular metallomes.<h3>Materials and Methods</h3><h4>ICP-MS Analysis</h4>All preparation of samples for ICP-MS measurement was undertaken using trace metal clean methods. All plasticware used in this study, including culture flasks, pipette tips, and centrifuge tubes, was precleaned in 10% quartz-distilled HCl and thoroughly rinsed with 18 MΩ cm water in a clean laboratory. Trace elements in the filtered lysates of G. huxleyi (OA1, OA4, OA8, OA15, OA16, OA23, and RCC 1242) and Synechocystis (FACHB898) strains were measured using an ICP-MS. The detailed ICP-MS method and instrumental settings are described in Zhang et al. (2018). <a href="javascript:void(0);">(31)</a><h4>Protein Mass Spectrometry</h4>The peptides were resuspended in 5% formic acid and 5% DMSO prior to mass spectrometry. They were separated on an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=3&amp;search=Ultimate+3000+UHPLC">Ultimate 3000 UHPLC </a>system (Thermo Fisher Scientific) and electrosprayed directly into a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/559">Q-Exactive Mass Spectrometer</a> (Thermo Fisher Scientific) through an EASY-Spray nanoelectrospray ion source (Thermo Fisher Scientific). The peptides were trapped on a C18 PepMap100 precolumn (300 μm i.d. × 5 mm, 100 Å, Thermo Fisher Scientific) using solvent A (0.1% formic acid in water) at a pressure of 500 bar. The peptides were separated on an in-house packed analytical column (75 μm i.d. × 50 cm packed with ReproSil-Pur 120 C18-AQ, 1.9 μm, 120 Å, Dr. Maisch GmbH) using a linear gradient (length: 65 min, 15% to 35% solvent B [0.1% formic acid in acetonitrile], flow rate: 200 nL/min). The raw data were acquired on the mass spectrometer in a data-dependent mode (DDA). Full scan MS spectra were acquired in the Orbitrap (scan range 350–1500 m/z, resolution 70000, AGC target 3e6, maximum injection time 50 ms). After the MS scans, the 10 most intense peaks were selected for HCD fragmentation at 30% of normalized collision energy. HCD spectra were also acquired in the Orbitrap (resolution 17500, AGC target 5e4, maximum injection time 120 ms, isolation window 1.5 m/z) with the first fixed mass at 180 m/z. Charge exclusion was selected for unassigned and 1+ ions. The dynamic exclusion was set to 20 s.<h3>Results and Discussion</h3><h4>Trace Elements Determined from the Expressed Proteomes of Different Phytoplankton</h4>Based on the expressed proteome sequences, we predicted metal-binding proteins and compared their distribution among various phytoplankton species (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig2">Figure 2</a>). The inference revealed 28 different metals in G. huxleyi, 31 different metals in C. reinhardtii, and 30 different metals in Synechocystis. Although only 6 different metals were identified in O. tauri proteins, it has to be noted that the analysis for O. tauri was only based on the 40 most abundant proteins reported in this organism <a href="javascript:void(0);">(21)</a> and, hence, the listed elements, including Mg, Fe, Mn, Ca, Zn, and Cu, may represent the most essential metals for the species.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Environ_Sci_Technol_2025_59_50_27367_27377_Figure_2_The_number_of_metal_binding_proteins_identified_in_the_expressed_proteome_of_the_haptophyte_G_9b8ac0f004.jpeg" alt="Environ. Sci. Technol. 2025, 59, 50, 27367–27377: Figure 2. The number of metal-binding proteins identified in the expressed proteome of the haptophyte G. huxleyi (a), the chlorophyte C. reinhardtii (b) and O. tauri(c), and the cyanobacteria Synechocystis (d). Pie charts show the percentages of metalloproteins containing specific metals. Original proteome data for G. huxleyi are from Mckew et al. (2013) (24) for C. reinhardtii are from Hsieh et al. (2013) (77) for O.tauriare from Martin et al. (2012) (21) for Synechocystisare from Chen et al. (2018). (22) *Noted that for O. tauri, only the 40 most abundant proteins were included in this analysis. ND: no metal binding was found in the proteins." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Environ_Sci_Technol_2025_59_50_27367_27377_Figure_2_The_number_of_metal_binding_proteins_identified_in_the_expressed_proteome_of_the_haptophyte_G_9b8ac0f004.jpeg 123w," sizes="100vw" width="934" height="1181">The number of proteins that bind with different metals varies dramatically in phytoplankton. For metalloproteins, there are variations in the binding affinities of different metals to organic molecules. For example, the affinities of divalent metals to proteins have a tendency to follow a universal order of preference, namely the Irving-Williams series (Mg2+ and Ca2+ [weakest binding] &lt; Mn2+ &lt; Fe2+ &lt; Co2+ &lt; Ni2+ &lt; Cu2+ &gt; Zn2+). <a href="javascript:void(0);">(43)</a> Cells can simultaneously contain proteins having high-affinity-binding metals, such as Cu and Zn, and others that require uncompetitive metals, such as Mg and Ca. One of the least competitive metals for binding with biological chelators, Mg, is even found to be the most widely distributed metal in the metalloproteomes of various phytoplankton species in this study, supporting the observations of high concentrations in the protein fraction of cells (at millimolar concentrations <a href="javascript:void(0);">(31)</a>).In all phytoplankton species in our analysis, the Mg-containing proteins are found to have the highest proportion in all metalloproteins identified. About 30% of the metalloproteins in different strains of phytoplankton are found to contain Mg or depend on it for function (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig2">Figure 2</a>), suggesting the important role of Mg in phytoplankton cells. It is generally coordinated in protein complexes by ATP or ADP and has also been found to be largely distributed in the chloroplasts or thylakoids in G. huxleyi and Synechocystis. <a href="javascript:void(0);">(22,24)</a> The essential roles of Mg in plants have been reported in various studies. Mg2+ is known to be the central atom of the chlorophyll molecule, and its levels in the chloroplast regulate the activity of key photosynthetic enzymes. <a href="javascript:void(0);">(44)</a> It was reported that approximately 15–35% of total Mg is in the chloroplasts of a plant, <a href="javascript:void(0);">(45)</a> and it also serves as an osmotically active ion in the regulation of cells <a href="javascript:void(0);">(46)</a><h4>Comparison Between Metallome Measured Directly and Reconstructed from Proteins</h4>A large proportion of metals, such as Fe, Mn, and Cu, are found in membrane proteins,and may contribute significantly to total cellular quotas. <a href="javascript:void(0);">(54,55)</a> However, for method development purpose, we endeavored to remove the cell membranes from our samples to focus mostly on the analysis of intracellular metal quotas and metalloproteins in the cytosol fraction in this study. We note that our analysis includes some membrane-bound proteins likely derived from organelles or surface membranes disrupted during sonication.The derivation of cellular metal composition, as determined by two different methods, is compared in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig3">Figure 3</a>. The abundance of metal in the proteins is determined as the relative abundance of the protein multiplied by the number of metal ions needed for the specific protein. The abundance of the proteins was normalized to ensure that the total intensities from each replicate measurement for the same species were similar. Since the proteomic data used here were generated from different studies using different instruments, we cannot quantify or compare the absolute biomass of proteins used for each species. Therefore, we cannot compare the absolute trace metal abundance between species solely from metal inferences of expressed proteomes. Nevertheless, we can estimate the relative abundance of different metals in the same species. For the elements that are both measured by the ICP-MS and identified in the proteome, there are generally good correlations (r2 = 0.81 in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig3">Figure 3</a>a and r2 = 0.74 in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig3">Figure 3</a>b) between the two approaches. This indicates that the metal compositions determined from the protein modeling may be reliable and, therefore, can reflect the metal usage in different phytoplankton. This would provide a level of assurance to further investigate the metal binding associated with various proteins, thereby deepening our understanding of the roles that different metals play in phytoplankton.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Environ_Sci_Technol_2025_59_50_27367_27377_Figure_3_Relationship_between_the_abundance_of_metals_determined_by_ICP_MS_analysis_e7b259e376.jpeg" alt="Environ. Sci. Technol. 2025, 59, 50, 27367–27377: Figure 3. Relationship between the abundance of metals determined by ICP-MS analysis (normalized to C, mmol/mol C) and metal abundance determined from the metalloproteome analysis (the unit is arbitrary). To account for strain variations, the ICP-MS data for the cellular metal content of G. huxleyi are average values from 7 G. huxleyi strains isolated from different areas of the ocean, and the proteome-derived metal composition data are average values calculated from 2 of those G. huxleyi strains (OA1 and OA16) isolated previously, and data from Mckew et al., 2013. (24) The ICP-MS data for Synechocystis are from Synechocystis sp. FACHB898 measured in this study, and the proteome data are calculated from Chen et al., 2018. (22) Noted that the abundance of metals from each proteome was calculated by using the metal stoichiometry multiplied by the relative abundance of proteins, which provides only a relative relationship of different trace metals, not absolute abundance. The symbols represent the mean values, and the error bars indicate standard deviations." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Environ_Sci_Technol_2025_59_50_27367_27377_Figure_3_Relationship_between_the_abundance_of_metals_determined_by_ICP_MS_analysis_e7b259e376.jpeg 245w," sizes="100vw" width="1384" height="641">Compared to the other metals investigated in this study, Ca and Mg appear to have a much higher relative abundance in the ICP-MS analysis than in the proteomic analysis of G. huxleyi and Synechocystis (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig3">Figure 3</a>). These two elements are essential macronutrients for phytoplankton and plants. Ca is the most prominent secondary messenger that plays a crucial role in response to extracellular stimuli in all eukaryotes. Ca2+ fluxes are transported via Ca channels and transporters, such as Ca-ATPase, regulating a series of important biological functions, including mineral uptake and utilization, K+ homeostasis adjustment, and regulatory network activity in response to P or N deficiency. <a href="javascript:void(0);">(56)</a> Ca is not always incorporated into protein structures. The Ca abundance determined from the proteins in this study, therefore, is much lower than the actual Ca needed intracellularly. Similarly, the intracellular Mg2+ and K+ abundances should also be higher than those determined from the protein structures. Free Mg2+ and K+ are known to regulate cation–anion balance and serve as osmotically active ions in the regulation of cells <a href="javascript:void(0);">(46)</a> but free metal ion concentrations cannot be determined through the approach used in this study. Thus, it is not surprising to see much higher relative abundances of Ca and Mg in ICP-MS analysis than in proteomic analysis (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig3">Figure 3</a>).

Unlocking Phytoplankton Metallomes with Comparative Analysis of Metal Quotas, Quantitative Proteomics, and Inferred Metalloproteomes

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The Czech Advanced Technology and Research Institute (CATRIN) at Palacký University is a cutting-edge scientific hub dedicated to advancing research in the fields of nanotechnology, biotechnology, and biomedicine. At its core, CATRIN boasts outstanding scientific teams, featuring international researchers. We emphasize interdisciplinarity, foster global collaborations, and work to translate our research findings into practical applications.

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

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

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

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

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

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

Thermo Scientific ARL SMS-3500 Automation for Twin OES or XRF

The ARL SMS-3500 automates two OES or XRF spectrometers with smart sample flow management for fast, efficient, and continuous production control.

Drive process control laboratory efficiencies in the metals and mining industries with the Thermo Scientific™ ARL™ SMS-3500 Automation for Twin OES or XRF, a complete laboratory workflow automation solution. Primary metals producers' laboratories typically run two fully duplicate and separate spectrometry lines to assure fast response times when several urgent samples must be processed simultaneously. 👉 The ARL SMS-3500 system is an affordable alternative.The automation of multiple spectrometers and preparation machines with the ARL SMS-3500 system offers:<ul><li>Reduced sample analysis cost</li><li>Increased sample throughput</li><li>Better reproducibility</li><li>Higher quality results</li></ul>for users requiring more comprehensive and integrated automation solutions in the metals and mining industries.The ARL SMS-3500 Automation for Twin OES or XRF simplifies sample handling by providing:<ul><li>a single introduction location for all samples</li><li>a magazine storage function to free the introduction 👉 This reduces response times, increases sample processing cadence, and improves the availability of automatic sample preparation in highly critical production control environments.</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Scientific_ARL_SMS_3500_Automation_for_Twin_OES_or_XRF_2_0553f2880b.jpg" alt="Thermo Scientific™ ARL™ SMS-3500 Automation for Twin OES or XRF - 2.jpg" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Scientific_ARL_SMS_3500_Automation_for_Twin_OES_or_XRF_2_0553f2880b.jpg 217w," sizes="100vw" width="650" height="469"><h3>✅ Main Features</h3><ul><li>No need for time-consuming electro-mechanical transfer systems – the SMS-3500 robot moves samples between components within seconds</li><li>Simplified operation and reduced system complexity for easier maintenance and servicing</li><li>Single interface screen for all sample tracking and automation functions</li><li>Unified registration and introduction point for all production samples, with automated routing to the appropriate system components</li><li>Support for two sample preparation machines:<ul><li>Reduces response times</li><li>Increases processing cadence</li><li>Improves automatic sample preparation availability</li></ul></li><li>Operator workload is reduced – the system manages samples by priority and availability of resources<ul><li>Includes internal sample storage to free the introduction point immediately</li></ul></li><li>Each instrument can be used offline for manual tasks without interrupting automated operation</li><li>Uses the same analytical software for XRF and OES</li><li>Automation is based on proven ARL SMS software</li></ul><h3>🔧 Recommended for:</h3><ul><li>Iron and steel applications</li><li>High alloy, stainless steel, copper, brass, and bronze applications</li><li>Aluminum or fire assay applications</li></ul>

Thermo Scientific™ ARL™ iSpark Plus Fire Assay Analyzer

Thermo Scientific™ ARL™ iSpark Plus Fire Assay Analyzer – fast and accurate spark-OES system for direct precious metals analysis in lead buttons without cupellation.

<h4>Efficient and Clean Alternative to Traditional Fire Assay</h4>Traditional fire assay analysis is labor-intensive, expensive and environmentally harmful. The Thermo Scientific™ ARL™ iSpark Plus Fire Assay Analyzer provides a simple and clean alternative to traditional cupellation and analysis stages by using spark optical emission spectrometry (OES) to determine precious metal concentrations directly in lead buttons.With this system, you can achieve simultaneous quantitative determination of all precious metals in fire assay lead buttons in less than a minute. The ARL iSpark Plus is a high-end spark OES spectrometer specially optimized for lead button analysis.<h3>Applications and Detection Capabilities</h3>The analyzer is ideal for various types of samples in:<ul><li>Gold and platinum mining (plant control, exploration, metal accounting)</li><li>Precious metals refining and recycling</li><li>Contract laboratories</li></ul>Thanks to:<ul><li>1-meter focal length Paschen-Runge optics</li><li>High-performance photomultiplier tubes (PMTs)</li><li>Advanced signal processing technologies</li></ul>…it offers excellent detection limits for trace levels of gold, silver, and platinum group elements in lead buttons.<h4>Key Benefits and Technical Highlights</h4><ul><li>Unmatched hardware for long-term stability and reliability</li><li>Exceptional performance in detection limits, precision, accuracy and speed</li><li>Minimal training required for operators</li><li>Automation-ready: streamlined sample prep and high throughput</li><li>Factory-calibrated for fast commissioning</li><li>Compatible with automated fire assay labs</li></ul><h4>Advanced Technology Inside</h4><ul><li>Current Controlled Source (CCS): Flexible spark discharge control for high sensitivity, reproducibility and small sample geometry</li><li>Time Resolved Spectroscopy (TRS): Signal acquisition at optimal timing for improved sensitivity and precision</li><li>1-meter Paschen-Runge vacuum polychromator for excellent resolution</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2028">Thermo Scientific™ OXSAS™ software</a>: Powerful and intuitive interface</li><li>Temperature-controlled cast iron cabinet: ±0.1°C at 38 °C</li><li>Argon-flushed, water-cooled table with closed-loop coolant system</li></ul>

Products focused on Optical Emission Spectroscopy (OES) distributed by Thermo Fisher Scientific (EMEA) - page 1

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