<p><strong>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 </strong><a target="_blank" rel="noopener noreferrer" href="https://www.nature.com/articles/s41467-026-68778-6#Abs1"><i><strong>Nature Communications</strong></i></a><strong>.</strong></p><p>“<i>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</i>,” said the article’s first author, <strong>David Panáček</strong> from CATRIN, who worked on the study during his internship at Imperial College London.</p><p><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"></p><p>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. “<i>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</i>,” said one of the corresponding authors, <strong>Michal Otyepka</strong>.</p><p>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"><strong> CEITEC – VUT</strong></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.</p><p>“<i>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,</i>” added another corresponding author, <strong>Nuria De Diego</strong>.</p>

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

<p><strong>In the week from February 16, the following webinars await you in the field of atomic and molecular spectroscopy and spectrometry</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Thermo Fisher Scientific: The Perfect Pair for GMP Compliance – IonicX and TruScan for Raw Material Identification and Verification</strong></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><p>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.</p><h5><strong>2. Milestone: Reliable Mercury Analysis in Environmental Samples – Tools, Techniques, and Best Practices</strong></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><p>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.</p><h5><strong>3. Agilent Technologies: Learn How to Confirm Your Data on ICP-OES – Are You Getting the Right Result?</strong></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><p>This webinar discusses accuracy and precision in ICP-OES analysis and practical strategies to ensure reliable and defensible results.</p>

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<p><strong>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.</strong></p><p>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.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233"><strong>Unlocking Phytoplankton Metallomes with Comparative Analysis of Metal Quotas, Quantitative Proteomics, and Inferred Metalloproteomes</strong></a></h4><p>Qiong Zhang*, Jiayou Ge, Fengjie Liu, Shabaz Mohammed, Kedong Yin, and Rosalind E. M. Rickaby</p><p>Environ. Sci. Technol. 2025, 59, 50, 27367–27377</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.est.5c11233">https://doi.org/10.1021/acs.est.5c11233</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><p>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.</p><p>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 <i>in situ</i> 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 (<i>e.g.,</i> by ICP-MS) and the protein sequence (<i>e.g.,</i> 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></p><p>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.</p><h3><strong>Materials and Methods</strong></h3><h4><strong>ICP-MS Analysis</strong></h4><p>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 <i>G. huxleyi</i> (OA1, OA4, OA8, OA15, OA16, OA23, and RCC 1242) and <i>Synechocystis</i> (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></p><h4><strong>Protein Mass Spectrometry</strong></h4><p>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"><strong>Ultimate 3000 UHPLC </strong></a>system (Thermo Fisher Scientific) and electrosprayed directly into a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/559"><strong>Q-Exactive Mass Spectrometer</strong></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 <i>m</i>/<i>z</i>, 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 <i>m</i>/<i>z</i>) with the first fixed mass at 180 <i>m</i>/<i>z</i>. Charge exclusion was selected for unassigned and 1+ ions. The dynamic exclusion was set to 20 s.</p><h3><strong>Results and Discussion</strong></h3><h4><strong>Trace Elements Determined from the Expressed Proteomes of Different Phytoplankton</strong></h4><p>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 <i>G. huxleyi</i>, 31 different metals in <i>C. reinhardtii</i>, and 30 different metals in <i>Synechocystis</i>. Although only 6 different metals were identified in <i>O. tauri</i> proteins, it has to be noted that the analysis for <i>O. tauri</i> 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.</p><p><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"></p><p>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 (Mg<sup>2+</sup> and Ca<sup>2+</sup> [weakest binding] &lt; Mn<sup>2+</sup> &lt; Fe<sup>2+</sup> &lt; Co<sup>2+</sup> &lt; Ni<sup>2+</sup> &lt; Cu<sup>2+</sup> &gt; Zn<sup>2+</sup>). <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>).</p><p>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 <i>G. huxleyi</i> and <i>Synechocystis</i>. <a href="javascript:void(0);">(22,24)</a> The essential roles of Mg in plants have been reported in various studies. Mg<sup>2+</sup> 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></p><h4><strong>Comparison Between Metallome Measured Directly and Reconstructed from Proteins</strong></h4><p>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.</p><p>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 (<i>r</i><sup>2</sup> = 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 <i>r</i><sup>2</sup> = 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.</p><p><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"></p><p>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 <i>G. huxleyi</i> and <i>Synechocystis</i> (<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. Ca<sup>2+</sup> 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<sup>+</sup> 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 Mg<sup>2+</sup> and K<sup>+</sup> abundances should also be higher than those determined from the protein structures. Free Mg<sup>2+</sup> and K<sup>+</sup> 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>).</p>

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

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Like Mnova, it remains <strong>vendor-neutral</strong>, adapting to your unique requirements. Elevate your analytical data processes—automate with Mnova Gears!</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Gears_Design_your_data_workflows_and_let_Mgears_do_the_rest_5771003702.jpg" alt="Mestrelab Research: Mnova Gears - Design your data workflows and let Mgears do the rest!"></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-03-25/3nwsl4"><strong>Visit Mestrelab Research Mnova Gears product page to learn more!</strong></a></h4></blockquote><h4><strong>What’s new in Mnova Gears 2.5?</strong></h4><p><strong>Mnova Gears 2.5 has been released with Mnova 15 introducing a range of new capabilities:</strong></p><ul><li>New file copy option that copies the input files to the output folder without the need to open, process, or analyze them.</li><li>Ability to add chromatogram blind regions.</li><li>Improved data selection and auto-detection.</li><li>Capability to manage input data organized in multiple subfolders and generate sorted reports for each subfolder.</li><li>Heat map data visualization.</li><li>And other improvements that you can check in the complete changelog.</li></ul><p><strong>Upgrade to Mgears 2.5 now to explore these features and to unlock the full potential of data automation.</strong></p><p>Also be sure you check our newest Mgears brick designed for <a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-03-25/3nwskt"><strong>Affinity Selection Mass Spectrometry</strong></a> analyses.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Gears_Increase_your_efficiency_using_automated_analysis_workflows_c9d3fb13e6.jpg" alt="Mestrelab Research: Mnova Gears - Increase your efficiency using automated analysis workflows!"></p><h3><strong>Mnova Gears products and how they relate to one another!</strong></h3><h4><a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-03-25/3nwsl4"><strong>Mnova Gears</strong></a></h4><ul><li>Automation of multiple sample analysis</li><li>Batch and real time mode to generate documents for analysis</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2020-10-22/25htxg"><strong>Mnova MyGears</strong></a></h4><ul><li>Automation of single sample analysis</li><li>Automation workflows on an open Mnova document</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-04-10/3p2gsd"><strong>Mnova Gears Bricks</strong></a></h4><ul><li>Build automation workflows using the following Gears bricks</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Gears_1_b99a72ab8c.jpg" alt="Mestrelab Research: Mnova Gears products and how they relate to one another."></p><blockquote><h4><strong>Want to run a 45-day FREE trial?</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-04-10/3p2gsh"><strong>CONTACT AN EXPERT</strong></a></h4></blockquote><h3><strong>Intuitive 5-Step workflow!</strong></h3><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Gears_Intuitive_5_Step_workflow_9191e59a96.jpg" alt="Mestrelab Research: Mnova Gears - Intuitive 5-Step workflow."></p><h4><strong>Mnova Gears Benefits</strong></h4><ul><li><strong>Automate</strong> routine <strong>processes and workflows</strong>, saving valuable specialist time.</li><li>Create and manage <strong>end-to-end workflows</strong> seamlessly within a single tool.</li><li>Streamline <strong>multiple analytical workflows</strong> on a common, user-friendly platform.</li><li><strong>Customize workflows</strong> by selecting components tailored to specific needs.</li><li>Enjoy <strong>flexibility and configurability</strong>, allowing seamless integration with existing infrastructure.</li><li>Collaborate with <strong>different systems in an unified environment</strong> due to vendor independence.</li><li>Leverage the familiarity with the <strong>Mnova interface</strong> to deploy and adopt <strong>automation</strong> solutions by your team <strong>without an extensive learning curve</strong>.</li><li>Benefit from <strong>expedited product support and development</strong>, consolidating all your automation analyses in one software suite.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Gears_Mnova_Gears_Benefits_3186347cb7.jpg" alt="Mestrelab Research: Mnova Gears - Mnova Gears Benefits"></p><h4><strong>Mnova Gears Features</strong></h4><h5><strong>Powerful Automation Capabilities for Comprehensive Data Tasks</strong></h5><ul><li>A user-friendly platform equipped with all essential controls for customizing data processes, covering input data, processing and analysis methods, design, and output generation.</li><li>A wide array of default features and options is available to maximize adaptation to customer needs.</li><li>Acceptance of templates and scripts for further customization; you can even construct your own automation bricks using scripting.</li><li>Details of your customization are stored in a settings file, easily shareable and importable.</li></ul><h5><strong>Automatic Reporting and Archiving</strong></h5><ul><li>Diverse output formats, including CSV, PDF, HTML, JSON, and Mnova formats.</li><li>Seamless adaptation to data organization standards by specifying requirements for correct data archiving (in a folder, database, ELN, etc.).</li><li>Easy workflows to automatically update historic data to new standards, ensuring consistency across years and sites.</li><li>Continuous support and updates: Contact the support team for specific import/export format requests.</li></ul><h5><strong>A User-Friendly Result Viewer with Graphical Dashboards for Rapid and Easy Data Review</strong></h5><ul><li>Effortless exploration of results with the Mgears Results Viewer, featuring graphical dashboards.</li><li>At-a-glance and by-exception reviews for quick and effective data analysis.</li><li>Smooth navigation through sample lists or well plates, facilitating the review of data and analyzed spectra.</li><li>Identification of samples not meeting specifications using graphics and warning flags, saving time on extensive scrutiny.</li><li>Swift recalculations and result updates.</li></ul><h4><strong>Multi-Technique and Multi-Application Analytical Workflows</strong></h4><ul><li>Multiple techniques supported: NMR, LC-MS, GC-MS, Chromatography, etc.</li><li>Mnova Gears has already proven its prowess with a broad range of applications spanning from discovery, R&amp;D, to manufacturing and quality control.</li><li>Available applications include structure verification, purity determination, mixture analysis, chemical reaction optimization, and affinity selection mass spectrometry analysis. Explore the <a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-04-10/3p2gsd"><strong>full list of applications</strong></a> for comprehensive coverage.</li></ul><h3><strong>Markets</strong></h3><h5><strong>Academic, Government &amp; Industrial</strong></h5><h4><strong>PRACTICAL APPLICATIONS</strong></h4><ul><li>Quality Control (NMR, Mass Spectrometry, etc)</li><li>Natural Product Dereplication by NMR</li><li>Reaction Monitoring by NMR</li><li>Quantitative NMR</li><li>Targeted mixtures analysis by NMR</li><li>Multi-technique reporting</li><li>Protein-ligand Interaction</li><li>Product ID confirmation</li><li>Structure verification</li><li>DNA Profiling by LCMS</li><li>Ligand-based drug screenings</li><li>Reaction Optimization (HTE)</li><li>Determination of solubility, lipophilicity</li><li>System Suitability testing (NMR, LCMS)</li><li>Compound separation and purification workflows</li></ul>

Mestrelab Mbook 4.0 

Mbook, a powerful Electronic Laboratory Notebook (ELN), is designed specifically for chemists, providing a streamlined solution for managing experimental data.

<p class="cke-user_style-important"><strong>Designed by chemists, for chemists</strong></p><p>In today's chemistry labs, efficiency, data security, and seamless collaboration are essential for success. <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/main-product/mbook-chemistry?utm_source=adv&amp;utm_medium=labrulez&amp;utm_campaign=mar&amp;utm_term=mbook&amp;utm_content=blogpost"><strong>Mbook</strong></a>, a powerful Electronic Laboratory Notebook (ELN), is designed specifically for chemists, providing a streamlined solution for managing experimental data, analytical results, team coordination, stockroom inventory, and overall workflow efficiency.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mbook_Header_Image_e5fa76cd18.jpg" alt="Mestrelab Research: Mbook 4.0" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mbook_Header_Image_e5fa76cd18.jpg 242w," sizes="100vw" width="1406" height="906"></p><h4><strong>Why choose Mbook?</strong></h4><p>Mbook 4.0 is more than just an ELN for recording your reactions, projects, or experiments—it offers advanced capabilities to optimize your laboratory management, including:</p><ul><li>Experiment witnessing for enhanced supervision and confidence</li><li>COSHH assessments to simplify chemical safety management</li><li>Comprehensive inventory management with a Compound DB of over 2,500 preloaded compounds</li><li>Customizable Stockroom with PDF exportable options</li><li>Bottle consumption and tracking for accurate inventory control</li><li>Customizable Reports for actionable insights</li><li>Integrated raw data processing and analysis powered by Mnova 15.1</li><li>In-app messaging system for seamless communication</li><li>Advanced experiment write-up section for detailed documentation<br>…and much more!</li></ul><h4><strong>What’s New in Mbook 4.0?</strong></h4><p>Mbook continues to evolve with cutting-edge features and improvements. <strong>Mbook 4.0 is now available</strong>, bringing enhanced usability, performance upgrades, and deeper integration with advanced analytical tools. Explore its powerful features today!<br>Start optimizing your lab workflow today! <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/mbook-trial"><strong>Click here </strong></a>to begin your free trial of Mbook.</p>

Products from category GC, LC and MS data processing focused on Software from SciY/Mestrelab Research - page 1

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