<h4>Introduction</h4>Since the very first release of <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1081">Mnova</a>, we have been (and still are!) very fortunate to include in the software the prediction of NMR spectra provided by Modgraph Consultants. It is really a privilege to offer prediction capabilities developed by the pioneers and leaders in the field for so many years. Nowadays, when Machine and Deep Learning techniques are so popular, it is worth remembering that the predictions commercialized by Modgraph have used Neural Networks (in addition to other methods, vide infra) for more than 25 years already.Modgraph pioneered the concept of combining several NMR prediction methods. For example, in the case of 13C Prediction, Modgraph combines a Neural Network prediction with the so-called <a target="_blank" rel="noopener noreferrer" href="http://www.modgraph.co.uk/product_nmr_HOSE.htm">HOSE prediction</a>, both developed by Professor Wolfgang Robien of the University of Vienna and recognized as to offer the most reliable C13 NMR predictions for many years. They have implemented a procedure that selects the “Best” C13 prediction for each atom from the two prediction methods (Neural Network + HOSE).Based on the idea that different predictors can be combined to form a potentially better predictor, we have added more predictors since Mnova version 14 which are combined (using a Bayesian approach) with two main objectives: (1) Improve overall prediction accuracy (2) Reduce the number of prediction outliers.<h4>The need of an ensemble NMR Prediction</h4>Leaving aside slow quantum mechanical (QM) calculations, usually by the gauge-invariant atomic orbital (GIAO), all fast NMR prediction methods use in one form or other databases of assigned data. In a nutshell, we can consider the following fast prediction approaches:<ul><li>Increments methods (look-up tables of fragments)</li><li>HOSE-code methods</li><li>Machine Learning methods</li></ul>The first two methods perform quite accurately as long as the predicted atom is well represented in the internal data base.However, no matter how large the database used, it is going to be extremely tiny compared to the actual chemical space (i.e. the universe of possible compounds). For example, there are more than 166 billion organic molecules up to 17 atoms of C, N, O, S, and halogens, sizes compatible with many drugs [1]. And if we consider larger molecules (e.g. MW up to 500), the best guess for the number of plausible compounds is around 1060. There is therefore an effectively endless frontier.On the other hand, whilst Machine Learning (ML) methods are known to show a higher extrapolation/interpolation power, the accuracy of the prediction will nevertheless be compromised to some extent by the similarity of the chemical environment of the atom to be predicted as compared to the data set used to train the ML model.In summary, the accuracy of these fast NMR predictors depends, to a greater or lesser extent, on the contents of the assigned database. And whilst there are very large databases such as those provided by Modgraph 13C NMRPredict, it will be impossible to cover the full chemical universe.But what if different prediction algorithms, trained with different data sets, are used together? In principle, one might expect that any deficiency of one of the available predictors can be compensated by any of the other predictors. This could help not only to get more accurate predictions but also to reduce the number of prediction outliers where the predicted values were exceptionally poor for a particular individual predictor.Actually, in the field of Machine Learning, the concept of combining multiple learning algorithms &nbsp;(e.g. classifiers) is quite popular and it is known as Ensemble Learning.<h4>Ensemble NMR Prediction</h4>Suppose that a chemical shift has been estimated by several ‘predictors’ and that each estimator can characterize in some way the ‘reliability’ of its own estimate (i.e. prediction confidence interval).How should one combine the various partial predictions and derive the statistical characteristics of the final outcome? This is a wide area of study, as there are many different special situations (contexts). A very naïve approach would consist in simply averaging the values. A weighted average using the confidence interval of each predictor would be probably a better approach. We have implemented a Bayesian mechanism that uses the information of the individual predicted chemical shifts (such as their distances) as well as their corresponding confidence intervals. A detailed description of the method is beyond the scope of this entry, but will be published elsewhere later.The overall idea is illustrated in the figure below. Mestrelab and Modgraph predictions are executed in the background. Each predictor gives both chemical shifts (δ) and an estimate of its reliability (σ). Next, those individual values are combined together to yield a final chemical shift and reliability.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_1_Combination_of_several_individual_predictors_to_yield_a_final_prediction_7cd81fd8ca.jpg" alt="Mestrelab Research: Figure 1 - Combination of several individual predictors to yield a final prediction" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_1_Combination_of_several_individual_predictors_to_yield_a_final_prediction_7cd81fd8ca.jpg 245w," sizes="100vw" width="895" height="380">In practice, this is how it works in Mnova. First of all, it is necessary to check the 1H or 13C Prediction options to ensure that different predictions are available. This might depend upon the licenses installed, but assuming that they are available, the options should look like this:<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_2_13_C_Prediction_options_in_Mnova_14_f3196b0f82.jpg" alt="Mestrelab Research: Figure 2 - 13C Prediction options in Mnova 14" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_2_13_C_Prediction_options_in_Mnova_14_f3196b0f82.jpg 182w," sizes="100vw" width="354" height="304">For the moment, Modgraph NMRPredict Server can be ignored. If the other two options are checked, when a prediction is issued, Mnova runs several predictors in the background. First, Mestrelab Predictor, which in fact, is formed by two different Machine Learning predictors (and both trained with different assigned data). Next, Modgraph Predictor is also executed (which in turn, uses two different predictors, vide supra). Finally, Mnova uses the Bayesian algorithm to combine all the individual chemical shifts and confidence intervals to yield the final predicted chemical shifts and confidence intervals.As a result, Mnova will show the unified, final results, but it is possible to visualize the individual values for each predictor by inspecting the Mnova log file, although this will not be covered in this post.<h4>Some results</h4>To illustrate the benefits offered by the combination of several predictors a simple exercise has been conducted. A small set of 13 assigned molecules abstracted from the literature has been used. This was done by an internship student who had no knowledge about the contents of the Mnova prediction assigned DBs. In principle, the ideal set of molecules should be those that were not contained within the internal prediction DBs or used in the Machine Learning training sets. However, it was found that these molecules are actually pretty well represented in the internal DBs, as shown in Table 1. It can be seen that most of the Carbon atoms can be described by 4 or 5 shells. This is not a coincidence: it confirms the high quality (and huge size) of the Modgraph’s 13C HOSE DB!<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Table_1_Distribution_of_number_of_shells_for_the_molecules_used_in_this_test_9ac66b645e.jpg" alt="Mestrelab Research: Table 1 - Distribution of number of shells for the molecules used in this test." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Table_1_Distribution_of_number_of_shells_for_the_molecules_used_in_this_test_9ac66b645e.jpg 245w," sizes="100vw" width="252" height="125"> Nevertheless, bearing this point in mind, this small set of molecules should be good enough for this simple exercise. The overall results are summarized in Table 2.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Table_2_Mean_absolute_errors_for_the_individual_and_ensemble_predictor_f01a1974d9.jpg" alt="Mestrelab Research: Table 2 - Mean absolute errors for the individual and ensemble predictor" width="194" height="100"> First, it can be observed that the Mean Absolute Error (MAE) of Modgraph is remarkably low (1.63 ppm), reflecting the previous point that showed that most of the atoms in the test set are represented by 4 or 5 shells. The other two predictors (Machine Learning 1 and Machine Learning 2) also yield very good results, although slightly worse than Modgraph.The question is: can those 3 predictors be combined in such a way that the result is better than all the individual predictors? That is the goal of the ensemble predictor and, as it is shown in Table 1, this is indeed the case: the ensemble predictor has a MAE = 1.3, a value which, in the context of 13C NMR prediction can be considered as exceptionally good.Somehow, the ensemble predictor attempts to compensate any deficiencies of any of the individual predictors. For this to work, it is crucial that each predictor provides a reliable confidence interval and a very significant effort has been put into that. Additional insights can be extracted by analyzing the histograms of the prediction errors for the individual and final ensemble predictors. They are shown in Table 3 and Figure 3.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Table_3_Distribution_of_prediction_errors_for_the_different_individual_predictors_as_well_as_for_the_final_ensemble_result_4107e5f4d6.jpg" alt="Mestrelab Research: Table 3 - Distribution of prediction errors for the different individual predictors as well as for the final, ensemble result." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Table_3_Distribution_of_prediction_errors_for_the_different_individual_predictors_as_well_as_for_the_final_ensemble_result_4107e5f4d6.jpg 239w," sizes="100vw" width="1321" height="862"><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_3_Histrogram_plot_corresponding_to_the_data_shown_in_Table_3_b7511c7058.jpg" alt="Mestrelab Research: Figure 3 - Histrogram plot corresponding to the data shown in Table 3" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_3_Histrogram_plot_corresponding_to_the_data_shown_in_Table_3_b7511c7058.jpg 245w," sizes="100vw" width="342" height="207"> The most obvious conclusion that can be drawn from these histograms is that the ensemble method helps to group the prediction error around the low MAE zone and hence, reduce the number of large prediction errors. In other words, this method assigns a higher weight in the final prediction result to that prediction with a smaller confidence interval. However, it is important to bear in mind that this is an oversimplification as the Bayesian algorithm not only uses the confidence interval but also the distance in the predicted chemical shifts amongst the different predictors.To complement this article, &nbsp;you can see some some results for 1H NMR data in the this blog post where a few assigned molecules were taken from the literature.

Ensemble NMR Prediction

<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/article/5170">Get ready for Mbook 4.0!</a> This release introduces a modern, redesigned interface with intuitive navigation and an advanced search filtering system, making ELN management more efficient and user-friendly than ever. With a refined role architecture, enhanced experiment setup, and upgraded inventory management, Mbook 4.0 is built to optimize your laboratory workflow.With Mbook 4.0, effortlessly track projects, manage inventory, generate reports, and collaborate seamlessly - all designed to enhance efficiency and optimize every stage of your workflow. Discover the latest features and improvements below and take your chemistry lab management to the next level.<h4>1. Modern Interface, Multi-Session, and Cross-Device Compatibility</h4>Mbook 4.0 presents a refreshed interface with an intuitive design that simplifies navigation and enhances usability. The improved system integrates advanced filters, enabling you to efficiently search for projects, experiments, groups, users, compounds, or bottles in just a few clicks.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_1a_Redesigned_Mbook_interface_3d3f9b7156.webp" alt="Mestrelab Research: Figure 1a. Redesigned Mbook interface" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_1a_Redesigned_Mbook_interface_3d3f9b7156.webp 245w," sizes="100vw" width="1920" height="1080">A major productivity boost comes with multi-session support, allowing users to open and manage multiple sessions simultaneously in separate browser tabs or windows. Effortlessly switch between projects without losing progress, compare data across different experiments, or explore your inventory while reviewing a reaction-all without losing progress.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_1b_Multitasking_with_multiple_Mbook_sessions_8e72fa13db.webp" alt="Mestrelab Research: Figure 1b. Multitasking with multiple Mbook sessions" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_1b_Multitasking_with_multiple_Mbook_sessions_8e72fa13db.webp 245w," sizes="100vw" width="1920" height="1080">Additionally, Mbook is now fully compatible with web browsers on tablets and phones, providing cross-device accessibility, ensuring you can access your ELN anytime, anywhere.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_1c_Mbook_accessible_across_different_devices_a5b4acfe7b.webp" alt="Mestrelab Research: Figure 1c. Mbook accessible across different devices." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_1c_Mbook_accessible_across_different_devices_a5b4acfe7b.webp 245w," sizes="100vw" width="1920" height="1080"><h4>2. Advanced Roles and Customizable Permissions</h4>Mbook 4.0 introduces an enhanced additive role and permissions system, enabling organizations to tailor user access according to specific needs. Assign different roles to users and customize specific permissions for each role or combination of roles, ensuring improved security, seamless collaboration, and optimized workflow efficiency.To further enhance security, new users are required to verify their email before gaining access, ensuring that only legitimate accounts are activated.The new “Inventory manager” role has been introduced to streamline stock management, making it ideal for users dedicated to handling inventory tasks. This role can also be assigned alongside other responsibilities, offering flexibility in lab operations and ensuring more efficient resource management.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_2_New_roles_and_permissions_workflow_including_the_Inventory_manager_role_a7bdbc47ae.webp" alt="Mestrelab Research: Figure 2. New roles and permissions workflow, including the Inventory manager role" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_2_New_roles_and_permissions_workflow_including_the_Inventory_manager_role_a7bdbc47ae.webp 245w," sizes="100vw" width="1920" height="1080"><h4>3. Inventory Improvements</h4>With the new filter capabilities, locating any compound in the Compound DB or in the Stockroom is faster and more efficient, streamlining your Inventory inquiries.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_3a_Advanced_filters_for_quick_compound_search_in_Mbook_Inventory_98a1763802.webp" alt="Mestrelab Research: Figure 3a. Advanced filters for quick compound search in Mbook Inventory" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_3a_Advanced_filters_for_quick_compound_search_in_Mbook_Inventory_98a1763802.webp 245w," sizes="100vw" width="1920" height="1080">Another key feature is the Bottles stock report, now available as a CSV file. This report provides a detailed overview of all bottle-related data in Mbook, allowing for more efficient inventory tracking and seamless in-situ stock verification.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_3b_New_Bottles_Export_report_in_CSV_format_5cea350130.webp" alt="Mestrelab Research: Figure 3b. New Bottles Export report in CSV format" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_3b_New_Bottles_Export_report_in_CSV_format_5cea350130.webp 245w," sizes="100vw" width="1920" height="1080"><h4>4. New Experiments capabilities</h4><h5>Improved reaction scheme</h5>Easily clone compounds with a single step, eliminating the need to redraw molecules and saving valuable time.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_4a_9edb852456.webp" alt="Mestrelab Research: Figure 4a. Clone compounds within a reaction scheme" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_4a_9edb852456.webp 245w," sizes="100vw" width="1920" height="1080"><h5>Enhanced text editor</h5>The experiment description section now includes:<ul><li>New formatting options: customizable text sizes, headers, background text colors.</li><li>The ability to insert images, links, formulas, tables, and quotes.</li><li>Image upload support directly from your PC, tablet, or phone for quick documentation.</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_4b_Improved_text_editor_with_enhanced_features_d895ce7a10.webp" alt="Mestrelab Research: Figure 4b. Improved text editor with enhanced features" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_4b_Improved_text_editor_with_enhanced_features_d895ce7a10.webp 245w," sizes="100vw" width="1920" height="1080"><h4>5. Enhanced Printable Reports</h4>All reports in Mbook have been redesigned for a more professional look, ensuring they align with your organization’s branding and compliance needs.<ul><li>Customization Options: Display your organization’s logo or add compliance signing fields.</li><li>Improved Performance: A new report engine significantly reduces generation time, allowing for faster access to reports.</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_5_Updated_Mbook_report_templates_6dfdb264e4.webp" alt="Mestrelab Research: Figure 5. Updated Mbook report templates" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_5_Updated_Mbook_report_templates_6dfdb264e4.webp 245w," sizes="100vw" width="1920" height="1080"><h4>6. Compound Exportation</h4>Mbook 4.0 now supports exporting compounds from the Compound Database as CSV or SDF files. Whether you need to export a single compound or a fully configurable list, this feature provides flexibility tailored to your workflow.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_6_Export_Compound_DB_as_CSV_or_SDF_files_88cde33463.webp" alt="Mestrelab Research: Figure 6. Export Compound DB as CSV or SDF files" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_6_Export_Compound_DB_as_CSV_or_SDF_files_88cde33463.webp 245w," sizes="100vw" width="1920" height="1080"><h4>7. Mnova Console Upgrade</h4>The Mnova console in Mbook 4.0 has been updated to version 15.1, bringing enhanced data processing, visualization, and reporting capabilities.<ul><li>Background Processing: The Mnova console now runs in the background, allowing you to continue working in Mbook while receiving a notification once processing is complete.</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_7_Continue_working_in_Mbook_while_Mnova_15_1_processes_data_in_the_background_24c5a3aafd.webp" alt="Mestrelab Research: Figure 7. Continue working in Mbook while Mnova 15.1 processes data in the background" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_7_Continue_working_in_Mbook_while_Mnova_15_1_processes_data_in_the_background_24c5a3aafd.webp 245w," sizes="100vw" width="1920" height="1080">

Mbook 4.0 What’s New – Top Features

Professor Radek Cibulka's laboratory has long been trying to develop systems that would facilitate chemical reactions, and thus the ways how to easily synthesize some drugs or other useful substances. The prestigious journal Chemical Science has now published a new system for chemical reductions based on deprotonated flavins, derivatives of vitamin B2. The system was developed as part of a collaboration between teams from the University of Chemical Technology, Prague and Adam Mickiewicz University within LA project from Czech Science Foundation and National Science Centre (Poland). "We feel like builders. Removing one component (a proton) from a molecule of almost colourless 3-methyllumichrome - a substance related to flavins - causes structural changes that lead to the formation of an orange flavin anion. This flavin anion is unique because its protonated form does not exist. Moreover, we used this anion as a catalyst for reductions. All you have to do is shine cyan light on it and the reduction takes place." says Radek Cibulka.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.rsc.org/en/content/articlelanding/2025/sc/d5sc01630d">Introduction of flavin anions into photoredox catalysis: acid–base equilibria of lumichrome allow photoreductions with an anion of an elusive 10-unsubstituted isoalloxazine</a></h4>Dorota Prukała, Ekaterina Zubova, Eva Svobodová, Ludmila Šimková, Naisargi Varma, Josef Chudoba, Jiří Ludvík, Gotard Burdzinski, Iwona Gulaczyk, Marek Sikorski and &nbsp;Radek CibulkaChem. Sci., 2025,16, 11255-11263<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1039/D5SC01630D">https://doi.org/10.1039/D5SC01630D</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/3.0/">CC-BY 3.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.<h4>Abstract</h4>Flavins have been established as effective catalysts in oxidative photoredox catalysis. Conversely, their use in reductive photocatalysis remains limited, mainly due to the relatively low stability of the transient flavin radicals (semiquinones), which are used in photoreductions. The fully reduced forms of flavins are also disadvantaged in photocatalysis because they absorb light in the UV rather than in the visible region. In this work, we present a new approach for reductive flavin photocatalysis that utilises a flavin (isoalloxazine) anion derived from the elusive 10-unsubstituted 3,7,8-trimethylisoalloxazine, an unstable tautomer of 3-methyllumichrome. We found the conditions under which this isoalloxazine anion is formed by in situ deprotonation/isomerisation from the readily available 3-methyllumichrome and we subsequently used it as a photoredox catalyst in the reductive dehalogenation of activated bromoarenes and their C–P coupling reaction with trimethyl phosphite to form an arylphosphonate. Steady-state and transient absorption spectroscopy, NMR and cyclic voltammetry investigations, together with quantum chemical calculations, showed that the anion of oxidised isoalloxazine has several advantages, compared to other forms of flavins used in photoreductions, such as high stability, even in the presence of oxygen, an absorption maximum in the visible region, thereby allowing the use of excitation light between 470 and 505 nm, and a relatively long-lived singlet excited-state.<h4>Materials and methods</h4>Nuclear magnetic resonance (NMR) spectra were recorded in CD3CN, DMSOd6 , DMF-d6, TFA-d3 or in their mixtures on an Agilent 400-MR DDR2 (399.94 MHz for 1H, 100.58 MHz for 13C), JEOL-ECZL400G (400 MHz for 1H, 101 MHz for 13C), or Bruker Avance III 600 MHz (600 MHz for 1H and 151 MHz for 13C) at 298 K. Data for 1H NMR are reported as follows: chemical shift (δ ppm), multiplicity (s = singlet, d = doublet, t = triplet, q = quartet, m = multiplet, dd = doublet of doublets, dt = doublet of triplets, br = broad etc.), coupling constant (Hz), and integration. All NMR spectra were processed and assigned using MestreNova. High-resolution mass spectra were obtained on <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=5&amp;search=Q-Tof+Micro">Q-Tof Micro</a> (Waters), equipped with a quadrupole and time-of-flight (TOF) analyser and a multichannel plate (MCP) detector. GC-MS analysis were performed on gas chromatograph <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=3&amp;search=Trace+GC+Ultra">Trace Ultra</a> (Themo Scientific) with a quadrupole mass spectrometer <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=3&amp;search=DSQ+II">DSQ II</a> (Thermo Scientific) equipped with DB-5ms capillary column (30 m x 0.25mm, film 0,25 um) or on gas chromatograph <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=7890">7890</a> (Agilent) with an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/473">orbital trap mass spectrometer Exploris 240</a> (Thermo Scientific) equipped with DB-5ms capillary column (30 m x 0.25mm, film 0,25 um). <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=50&amp;search=SICRIT">SICRIT</a> (Soft Ionization by Chemical Reaction In Transfer - Plasmion GmbH, Germany) was used in positive mode. The melting points were measured on a Boetius melting point apparatus and are not corrected. Photoredox catalysis was performed using Luxeon Star LED 470 nm (65 lm @ 700mA) with dominant peak wavelengths 460–485 nm in tempered aluminium block (for arrangement, see S3) and Luxeon Star LED 505 nm (76 lm @ 350mA) with dominant peak wavelengths 490–515 nm. UV-Vis absorption spectra were recorded on a <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=1&amp;search=UV-2550">UV-2550</a> spectrophotometer (Schimadzu). Steady-state emission spectra and fluorescence excitation spectra were recorded on a Jobin Yvon-SpexFluorolog 3-22 spectrofluorometer. Note that spectra were recorded using the same excitation and emission slits for uniform spectral resolution and sensitivity. Fluorescence lifetime measurements of all compounds were performed using the time-correlated single-photon counting (TCSPC) method. Decays were measured with the TCSPC Triple Illuminator as an accessory for the Fluorologs 3-22 steady-state spectrofluorometer that adds lifetime-capability in the time domain. The excitation source were NanoLED diodes (λexc = 368 nm and 389 nm) from IBH. The instrument in this hardware configuration is capable of measuring lifetimes as short as 400 ps. Deconvolution of fluorescence decay curves was performed using IBH Consultants software. The experimental uncertainties in the lifetimes are 10 %. Setup for UV-Vis transient absorption spectra measurements: The triplet states were generated and analysed using laser flash photolysis employing a nanosecond Q-switched Nd: YAG laser in combination with an optical parametric oscillator (OPO) operating at 470 nm; the laser pulse energy was kept around 0.5 mJ/pulse. The sample's solution was deoxygenated by bubbling argon for 15 min before measurements. Spectra were determined using kinetics collected between 350–700nm with a 10 nm interval. See ref.1 for details. Electrochemical measurements(cyclic voltammetry) were managed by a computer driven potentiostat <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=PGSTAT101">PGSTAT101 </a>(Autolab-Metrohm) using NOVA 1.11 software, for details see Chapter 5.

Work of Professor Cibulka's team featured on the cover of Chemical Science journal

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