In this episode of ‪ChromatographyTalk‬, ‪Organomation‬ General Manager David Oliva sits down with Imad Haidar Ahmad, Scientific Research Director at Amgen leading separations for Discovery Chemistry and Externalization. Imad's career spans Novartis, Merck, and now Amgen, and his research on computer-assisted method development has landed multiple front covers in Analytical Chemistry.We dig into how his group builds retention models from as few as nine lab experiments to map an entire separation landscape — replacing weeks of trial and error with a resolution map you can query and rebuild from. Imad breaks down where this modeling approach struggles (large molecules, SFC, fully autonomous method development), walks through two-dimensional liquid chromatography in plain terms for anyone who's only worked in 1D, and shares the story behind his group's greenness-score mapping work — including where swapping acetonitrile for methanol worked, and where it didn't.We also talk career advice: what a typical day looks like leading a discovery-stage separations team, how undergrad and grad students can bridge the academia-to-industry gap, and what Imad sees as the field's biggest unsolved problem.<h3>Video Transcription</h3>Chromatographic method development is increasingly moving beyond trial and error toward workflows built on modeling, automation, multidimensional separations, and smarter use of data. In a recent interview, Haidar Ahmad discussed how these approaches are changing pharmaceutical analysis, why understanding the fundamentals of chromatography remains essential, and where some of the biggest opportunities in separation science still lie.Ahmad’s route into analytical chemistry was not entirely planned. As an undergraduate, he initially found inorganic and organic chemistry particularly attractive, but a final-year project focused on the analysis of additives in bread introduced him to the possibilities of analytical chemistry. During his PhD, his research moved into polymer analysis and structure–property relationships. His interest in chromatography deepened when he encountered two-dimensional liquid chromatography (2D-LC), particularly its ability to reveal forms of polymer heterogeneity that could not be adequately characterized using a single separation technique. This eventually led to postdoctoral work with Peter Carr, where Ahmad focused on chromatographic separations and the fundamentals of chromatography.His subsequent career in the pharmaceutical industry exposed him to different stages of drug development, from late-stage method development and validation to drug substance characterization, purification, and finally discovery-stage separations. That breadth of experience has shaped his view of chromatography as both a scientific discipline and a practical problem-solving tool. In late-stage development, the objective may be a highly robust method capable of successful validation and transfer between laboratories. In discovery, by contrast, speed becomes critical because teams may be working with thousands of molecules while trying to identify the best candidates to advance.&nbsp;<h4>Discovery-stage chromatography: speed, prioritization, and throughput</h4>For a separations team supporting drug discovery, the working day is shaped by a constant flow of requests from medicinal chemistry and other partner groups. The challenge is not simply to perform the analyses, but to decide which requests are most urgent, remove bottlenecks, communicate with project teams, and continuously improve existing workflows.According to Ahmad, one of the most important questions is whether a task can be done faster or in a more high-throughput way. Discovery laboratories operate under strong time pressure, and efficiency therefore becomes part of the scientific strategy rather than merely an operational concern. As he describes it, the work combines science, prioritization, leadership, and communication, all with the aim of moving the most important projects forward as quickly as possible.<h4>In silico modeling: letting the computer perform the experiments</h4>One of the central themes of Ahmad’s work is in silico chromatographic modeling. In practical terms, the approach begins with a carefully designed set of laboratory experiments—often around nine runs. The resulting data are fed into software that builds retention models describing how analyte retention changes with variables such as mobile-phase composition and temperature.The software can then generate a resolution map representing a much larger separation space than could realistically be tested experimentally. Instead of running dozens or hundreds of conditions by trial and error, the analyst can identify regions where critical peak pairs are predicted to achieve the best separation.For Ahmad, the value of these maps goes beyond simply finding the condition with the highest resolution. Each point on the map represents a chromatographic condition, while the visual output shows where poor and strong separations are expected. A broad region of good resolution is particularly useful because it points toward a more robust operating space rather than a narrowly defined optimum that might be sensitive to small variations in experimental conditions.He therefore prefers to think of a chromatographic model as a map or database, rather than simply an optimization tool. If an impurity later disappears from the process, the corresponding peak can be removed from the model and new conditions selected without repeating the complete experimental study. Conversely, if a new degradation product appears during stability studies, the peak can be added and the existing model used to search for updated separation conditions.<h4>Why trial and error still has a place</h4>Modeling does not mean that conventional trial-and-error method development has disappeared. Ahmad points out that it remains useful in early discovery, particularly for fit-for-purpose methods where speed is more important than achieving a fully optimized separation.The situation changes later in development. A method intended for validation, transfer, or long-term routine use carries much greater risk. Trial and error may identify a condition that appears satisfactory without revealing whether it represents the most robust region of the separation space. If that method later fails during transfer or routine operation, the consequences can be costly.For late-stage methods, the additional investment required to build a model can therefore reduce risk over the much longer lifetime of the analytical procedure.<h4>Models are only as good as the experiments behind them</h4>Chromatographic modeling is not automatic proof against poor method development. Ahmad compares the technology to a GPS: it is highly useful, but only if the analyst understands where they are trying to go.Careful experimental design remains essential. Incorrect dwell-volume or column-volume information, as well as insufficient column re-equilibration, can produce misleading predictions. Importantly, these problems may not be obvious when the experimental runs used to construct the model are compared with their calculated values. They may emerge only when the software begins predicting new conditions within the modeled design space.Modeling is also easier for some separations than others. Ahmad describes reversed-phase LC of small molecules as comparatively straightforward, while large biomolecules are more challenging because their retention can be highly sensitive to small changes in mobile-phase composition and temperature.Other separation modes present additional difficulties. Ahmad notes that his teams have found supercritical fluid chromatography (SFC) particularly challenging to model successfully. Fully autonomous method development remains another unresolved goal: current tools can automate parts of optimization, but analyst input is still required.<h4>Why two dimensions can reveal what one dimension cannot</h4>Ahmad’s early interest in 2D-LC came from polymer characterization. He explains the principle simply: one-dimensional chromatography may answer one question about a sample, while two complementary dimensions can answer two.Two copolymers, for example, may have similar molecular size and therefore coelute in size-exclusion chromatography. A second separation mechanism based on interaction with a stationary phase may distinguish them according to chemical composition. Combining both dimensions provides a more complete description of complex distributions involving molecular weight, composition, sequence, and other forms of heterogeneity.Historically, the complexity of both instrumentation and software limited wider adoption of 2D-LC. Ahmad recalls systems requiring several computers and multiple software packages. That situation has improved substantially, and 2D-LC is now easier to implement across research and pharmaceutical development.Nevertheless, modeling multidimensional separations still presents challenges. While models can be built for individual fractions or dimensions, current software does not easily identify a common optimum across multiple models simultaneously. Much of this comparison still requires manual work.<h4>Bringing sustainability into method development</h4>Another area where Ahmad sees room for change is green analytical chemistry.Traditional pharmaceutical method development has understandably prioritized resolution, sensitivity, robustness, and turnaround time. Sustainability has often been treated as a secondary consideration, particularly at the analytical scale, where solvent use appears small compared with preparative chromatography or manufacturing.That mindset is beginning to change. Ahmad argues that greenness metrics should increasingly be incorporated directly into method-development software. If analysts could immediately see how changes in flow rate, gradient conditions, solvent choice, or other parameters affect the environmental profile of a method, sustainability could become part of routine optimization rather than an assessment performed after the method has already been developed.The objective, however, is not to pursue the greenest possible method regardless of analytical performance. Ahmad emphasizes the need to develop methods that are as green as possible while remaining fit for purpose. Modeling can potentially help balance these competing requirements by identifying conditions that preserve separation performance while allowing greener solvents or alternative chromatographic conditions to be introduced.<h4>Advice for young chromatographers: do not treat HPLC as a black box</h4>For students learning chromatography, Ahmad’s central recommendation is straightforward: do not treat the HPLC system as a black box.Rather than collecting chromatograms by making random changes, he encourages analysts to build a mental model of the separation. When temperature, gradient, or another parameter is changed, they should ask why retention or selectivity changed and use each experiment to improve their understanding of the system.He also recommends making use of educational resources on chromatographic fundamentals and method development and, importantly, not being afraid to experiment with instrument conditions. Modern systems are robust, but the analyst still needs to understand the underlying separation science in order to use them effectively.The same proactive attitude applies to career development. Ahmad strongly encourages students to seek industry internships and make connections beyond their own institutions. Conferences and poster sessions can provide natural opportunities to meet scientists from industry, discuss research, and ask about internship opportunities. Based on his own experience working with undergraduate and graduate interns, these collaborations can benefit both students and research teams and may strongly influence later academic or career decisions.<h4>What still needs to be solved?</h4>Asked about the biggest unresolved challenges in separation science, Ahmad does not begin with technology. Instead, he points to a shortage of well-trained analytical chemists with a strong understanding of chromatographic fundamentals.Alongside education, he identifies several technical opportunities. One is truly autonomous method development, where an instrument and software could optimize a separation without continuous analyst input. Another is the ability to predict chromatographic selectivity directly from molecular structure.Such capability could be especially valuable in high-throughput discovery environments. If a laboratory could predict which stationary phase and conditions were most likely to separate a particular set of compounds or isomers before entering the laboratory, method development could become considerably faster and more efficient.Taken together, Ahmad’s perspective points toward a future in which chromatography becomes increasingly model-driven, automated, multidimensional, and sustainability-aware. But the technology does not eliminate the need for chromatographic knowledge. On the contrary, as analytical tools become more powerful, understanding the fundamentals may become even more important—the software can explore the separation space, but the scientist still needs to know what question to ask and how to interpret the answer.<blockquote>This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.</blockquote><blockquote><h4>Concentrating on Chromatography Podcast</h4>Dive into the frontiers of chromatography, mass spectrometry, and sample preparation with host David Oliva. Each episode features candid conversations with leading researchers, industry innovators, and passionate scientists who are shaping the future of analytical chemistry. From decoding PFAS detection challenges to exploring the latest in AI-assisted liquid chromatography, this show uncovers practical workflows, sustainability breakthroughs, and the real-world impact of separation science. Whether you’re a chromatographer, lab professional, or researcher you'll discover inspiring content!You can find Concentrating on Chromatography Podcast in podcast apps:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://open.spotify.com/show/1wZg67rCjoGju4yhqxI3tL">Spotify</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podcasts.apple.com/tz/podcast/concentrating-on-chromatography/id1788429185">Apple podcast</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podscan.fm/podcasts/concentrating-on-chromatography">podscan</a></li></ul>and on <a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/@ChromatographyTalk/videos">YouTube channel</a></blockquote>

 In Silico Modeling, 2D-LC & Green Chromatography: Imad Ahmad on the Future of Method Development

Researchers from CATRIN at Palacký University, together with colleagues from VSB – Technical University of Ostrava and Charles University, have developed a new graphene ink for producing inexpensive and sensitive printed sensors. Using single-atom engineering, they incorporated copper atoms into the material, enabling highly sensitive detection of dopamine – a neurotransmitter responsible for transmitting signals between nerve cells. The approach, <a target="_blank" rel="noopener noreferrer" href="https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.76796">presented in the journal Advanced Science</a>, has strong potential for use in medical diagnostics.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/CATRIN_Atom_po_atomu_Vedci_ukazali_novy_smer_v_lekarske_diagnostice_8a02ad6a66.webp" alt="CATRIN: Atom by atom. Scientists reveal a new direction in medical diagnostics" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_CATRIN_Atom_po_atomu_Vedci_ukazali_novy_smer_v_lekarske_diagnostice_8a02ad6a66.webp 117w," sizes="100vw" width="1920" height="2560">Single-atom engineering makes it possible to control the chemical reactivity of materials at the level of individual atoms. Its broader application in medical diagnostics has so far been limited by a lack of atomically engineered materials and printable inks based on them. Researchers have now developed a new class of single-atom-doped graphene materials as well as an ink for producing sensitive printed sensors. Copper proved particularly effective for dopamine detection, which is important for biomedical research and the development of advanced diagnostic technologies for diseases including Alzheimer’s disease.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/CATRIN_Atom_po_atomu_Vedci_ukazali_novy_smer_v_lekarske_diagnostice_2_97e419f039.webp" alt="CATRIN: Atom by atom. Scientists reveal a new direction in medical diagnostics" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_CATRIN_Atom_po_atomu_Vedci_ukazali_novy_smer_v_lekarske_diagnostice_2_97e419f039.webp 117w," sizes="100vw" width="1920" height="2560">“The NGA-Cu ink we developed enables the production of fully inkjet-printed, sustainable paper-based electrodes at very low cost. Each sensor requires only 1.3 micrograms of active ink, costs approximately one Czech koruna and provided a stable response for at least three months. The ink is also fully compatible with commonly available materials inkjet printers. This creates significant potential for producing affordable, digitally designed sensors whose function can be tailored atom by atom,” said&nbsp;the corresponding author of the <a target="_blank" rel="noopener noreferrer" href="https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.76796">paper</a> Michal Otyepka.The basis for this new generation of single-atom inks was nitrogen-doped graphene acid (NGA), which CATRIN researchers developed several years ago. They have now progressively enriched it with different individual metal atoms, including copper, iron and manganese.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/CATRIN_Atom_po_atomu_Vedci_ukazali_novy_smer_v_lekarske_diagnostice_3_c15d831792.webp" alt="CATRIN: Atom by atom. Scientists reveal a new direction in medical diagnostics" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_CATRIN_Atom_po_atomu_Vedci_ukazali_novy_smer_v_lekarske_diagnostice_3_c15d831792.webp 117w," sizes="100vw" width="1920" height="2560">“We found that the electrochemical response depends fundamentally on the type of atom used. While copper atoms significantly enhance the dopamine signal, other metals suppress it. What matters, therefore, is not only the presence of isolated metal atoms but also the specific metal involved. This is a very important finding for the further development of biosensors,” explained first author Martin-Alex Nalepa.The published results introduce a new class of sensing materials with considerable potential for medical diagnostics. The researchers present a broadly applicable platform for developing graphene inks containing individual metal atoms and open the way towards scalable, reproducible and material-efficient production of advanced electrochemical sensors.&nbsp;“We are only at the beginning and are fascinated by the new possibilities opening up before us. We can see that by changing the type of atom, we can control the sensitivity and selectivity of the sensor. In addition to dopamine, we can also determine immunoglobulins very precisely – antibodies whose levels and types may serve as important indicators of autoimmune or infectious diseases,” emphasised Radek Zbořil, a co-author of the paper.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/CATRIN_Atom_po_atomu_Vedci_ukazali_novy_smer_v_lekarske_diagnostice_4_273cd760a5.webp" alt="CATRIN: Atom by atom. Scientists reveal a new direction in medical diagnostics" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_CATRIN_Atom_po_atomu_Vedci_ukazali_novy_smer_v_lekarske_diagnostice_4_273cd760a5.webp 117w," sizes="100vw" width="1920" height="2560">Electrochemical biosensors can find applications not only in medicine but also in food quality control, drinking-water monitoring and environmental pollution monitoring. Their key components are functionalised electrodes, and graphene-based materials have proved particularly suitable for their production. A key factor in the success of the Olomouc researchers was the targeted preparation of graphene ink with the required properties.The research was supported by the TECHSCALE project under the Johannes Amos Comenius Programme.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/CATRIN_Atom_po_atomu_Vedci_ukazali_novy_smer_v_lekarske_diagnostice_5_b1778f77f2.jpg" alt="CATRIN: Atom by atom. Scientists reveal a new direction in medical diagnostics: Project TECHSCALE (No. CZ.02.01.01/00/22_008/0004587) is financed by the MEYS OP JAC Excellent research programme, supported by ERDF/ESF." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_CATRIN_Atom_po_atomu_Vedci_ukazali_novy_smer_v_lekarske_diagnostice_5_b1778f77f2.jpg 245w," sizes="100vw" width="620" height="87"><blockquote><h4>Article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.76796">Single-Atom-Enhanced Fully Inkjet-Printed Electrochemical Sensor for Dopamine Detection</a></h4>Martin-Alex Nalepa, David Panáček, Vítězslav Hrubý, Matěj Jendřišák, Rostislav Langer, Michal Langer, Petr Jakubec, Ivan Dědek, Vojtěch Kupka, Michal Mazur, Radek Zbořil, Michal OtyepkaAdv. Sci., 2026, e76796<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1002/advs.76796">https://doi.org/10.1002/advs.76796</a><h6>licenced under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote><h4>Abstract</h4>Single-atom (SA) engineering offers atomic-level control over interfacial reactivity, yet the lack of printable SA-based inks hinders its practical translation into electrochemical sensing. Here, a fully water-based inkjet-printable ink is introduced based on nitrogen-doped graphene acid (NGA) hosting atomically dispersed Cu centers (NGA-Cu-ink). The ink enables digitally controlled, spatially defined deposition and fabrication of low-cost ($0.04 per sensor), fully inkjet-printed sustainable electrodes on paper. A comparison of NGA functionalized with different SA dopants (Cu, Mn, Fe, Ce) identifies a strong dopant-dependent electrochemical response, with Cu uniquely enhancing the analyte signal while other dopants suppress it, demonstrating that SA identity is a decisive design parameter in printed sensing interfaces profiling sensitivity and selectivity. The functional role of the NGA support is to provide dense anchoring sites (nitrogen and carboxylate groups) that stabilize atomically dispersed metal centers and create adsorption- and electron-transfer–active microenvironments. The NGA-Cu-ink yields enhanced dopamine oxidation, enabling quantitative detection on fully printed devices (limit of detection 9.7 µM; linear range 50–400 µM) and printed-on-electrode platforms (10.6 µM), while maintaining &lt;10% signal variation over 11 weeks. The approach establishes a general route to single-atom graphene-based inks for scalable, reproducible, and low-material-consumption manufacturing of advanced electrochemical sensors.

Atom by atom. Scientists reveal a new direction in medical diagnostics

<div><div>Accurately predicting NMR chemical shifts remains difficult for protons that can exchange with their environment in solution. These protons are covalently bound to electronegative atoms in hydroxyl (-OH), amine (-NH), and thiol (-SH) group. Their NMR signals are highly sensitive to hydrogen bonding, interactions with the solvent, and the continuous motion of molecules. Conventional DFT calculations typically treat the solvent in a simplified manner, often overlooking important interactions between dissolved molecules and their surroundings. A new computational approach developed by scientists led by <a target="_blank" rel="noopener noreferrer" href="https://nmr.group.uochb.cz/en">Martin Dračínský</a> from IOCB Prague addresses this problem by combining machine-learning (ML) molecular dynamics (MD) with ShiftML3, a machine-learning model for rapid prediction of NMR shielding.</div></div><div>The method first uses ML-based molecular dynamics to generate realistic ensemble of molecular structures in solution, capturing the constantly changing hydrogen-bonding patterns. ShiftML3 then predicts the chemical shifts for each configuration within fractions of a second. Although the model was originally developed for molecular solids, it also performs remarkably well in solutions. In molecular crystals, molecules are surrounded by close neighbors and experience a wide variety of intermolecular contacts, many of which closely resemble the interactions found in liquids.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/IOCB_Prague_Accurate_NMR_chemical_shifts_through_machine_learned_dynamics_2_a5e7404cff.webp" alt="IOCB Prague: Accurate NMR chemical shifts through machine-learned dynamics: A new computational approach developed by scientists led by Martin Dračínský from IOCB Prague addresses addresses often overlooked issues with simplified conventional DFT calculations. Nat. Commun. 2026, 17, 8842." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_IOCB_Prague_Accurate_NMR_chemical_shifts_through_machine_learned_dynamics_2_a5e7404cff.webp 245w," sizes="100vw" width="1620" height="911">The researchers validated the approach on chemically diverse systems, including water in organic solvents, alcohols, hydrogen-bonded nucleobases, glucose anomers, and alkylated acetamides. In all cases, the combination of ML-based molecular dynamics and ShiftML3 reproduced experimental chemical shifts with high accuracy, especially for exchangeable protons in OH and NH groups, whose signals are particularly sensitive to their local environment.The results show that combining ML-based molecular dynamics with rapid NMR chemical-shift prediction can substantially improve the accuracy of calculations for flexible and hydrogen-bonded molecules in solutions. The main advantage of the framework is that it can evaluate hundreds or thousands of solvent configurations without requiring an expensive quantum-mechanical calculation for each one. This opens the way to faster and more reliable prediction of NMR spectra, with potential applications in signal assignment, molecular structure determination and structural validation.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.nature.com/articles/s41467-026-75743-w">Quantitative Prediction of Exchangeable Proton Chemical Shifts</a></h4>Ondřej Socha, Jana Pavlišová, Debashree Manna &amp; Martin DračínskýNat. Commun., 2026, 17, 8842<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1038/s41467-026-75743-w">https://doi.org/10.1038/s41467-026-75743-w</a>&nbsp;<h6>licenced under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote><h4>Abstract</h4>Accurately predicting NMR chemical shifts of exchangeable protons in solution remains challenging because of the combined influence of solute–solvent interactions and molecular dynamics. We introduce a framework that integrates machine-learning molecular dynamics (ML-MD) with the ShiftML3 machine-learning shielding model for rapid and accurate prediction of NMR spectra in solvated molecules. Although originally developed for solids, ShiftML3 effectively captures intermolecular contributions to shielding in solution. We validate the method across a range of chemically diverse systems, including water in organic solvents, solvated alcohols, hydrogen-bonded nucleobases, glucose anomers, and alkylated acetamides. The ML-MD + ShiftML3 framework reproduces experimentally observed chemical shifts of exchangeable protons with near-quantitative accuracy, resolving subtle hydrogen-bonding and conformational effects that implicit-solvent DFT fails to capture. These results establish ML-MD + ShiftML3 as a transferable and computationally efficient way of incorporating solvation and dynamics into NMR spectroscopy, enabling realistic chemical shift predictions for flexible, hydrogen-bonded, and complex molecular systems.</div>

Accurate NMR chemical shifts through machine-learned dynamics

Article

Scientists

Interview

Science and research

Video

The Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences (IOCB Prague) is a leading scientific institution in the Czech Republic, recognized internationally. Its primary mission is basic research in the fields of chemical biology and medicinal chemistry, organic and material oriented chemistry, chemistry of natural compounds, biochemistry and molecular biology, physical chemistry, theoretical chemistry, and analytical chemistry. 

Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences

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.

CATRIN

Organomation, founded in 1959, designs and manufactures high-quality nitrogen evaporators and extraction systems. Known for innovation and durability, their lab instruments are used globally for efficient sample preparation with strong customer support.

Organomation

Gulf Bio Analytical Group

Agilent Technologies

Bruker

Thermo Fisher Scientific

Evosep Biosystems

Waters Corporation

Is your lab ready to begin its digital transformation?
Using IoT sensor technologies, AI modeling, and advanced analytics, your digital lab can safely capture data, create business intelligence,and implement actioned programs. Connect your lab digitally by watching this episode of The Lab Central.
<blockquote>
Presenter: Kamesh Janakiraman (Product Specialist - Lab Informatics, Agilent Technologies)
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Agilent Technologies: The Digital Lab Connection

The Digital Lab Connection

Using IoT sensor technologies, AI modeling, and advanced analytics, your digital lab can safely capture data.

Therapeutic oligonucleotides (oligos) have become a focus of interest and promise in biopharmaceutical research due to their potential to treat many kinds of disorders. Whether siRNA, mRNA, or DNA, separating oligos from complex matrix is difficult.
The size, nucleotide complexity, and general variations in these oligos can vary. As a result, liquid chromatography (LC) analysis and purification of oligonucleotides is challenging.
We will address chromatographic conditions for oligonucleotide analysis. Specifically, we will discuss fundamental characteristics of the LC column stationary phase, method development guidelines, and optimizations for analytical characterization through scale up to preparative purification.
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Presenter: Mohit Patel (Application Engineer, Agilent Technologies, Inc.)
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Mohit Patel is part of the CSD Online AE Team. His primary focus is to support SMLC, Bio-Columns, and consumables.
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Before joining Agilent, Mohit was an Assistant Professor of Chemistry at Temple University. Mohit developed his passion for science through his experiences as a faculty researcher and a mentor. Mohit actively taught, developed curriculum, and served as senior personnel, with a leadership position, on funded grants, mentored research projects, and co-coordinator of STEAM related outreach services.
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Mohit has earned a Doctorate degree in Chemistry from Temple University in 2016.
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Agilent Technologies: Oligonucleotide Analysis: Practical Techniques and Method Development Optimization for HPLC

Oligonucleotide Analysis: Practical Techniques and Method Development Optimization for HPLC

Chromatographic conditions for oligonucleotide analysis.

Oligonucleotides: Techniques and Method Development

Join me as I discuss various techniques and provide tips and tricks on achieving the best sensitivity and detection limits on your ICP-MS.
We will be covering tuning techniques, nebulizers, lens and other strategies that will ensure your system is performing at its best.
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Presenter: Bert Woods (Application Scientist, Agilent Technologies, Inc.)
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Joined the Agilent ICP-MS team in 2004, with previous employment in the semiconductor industry with Dominion Semiconductor (IBM/Toshiba) and Micron. Bert is a 1997 Chemistry graduate of Radford University in Virginia and an avid Washington DC Sports fan.
</blockquote>

Agilent Technologies: ICP-MS Tips and Tricks For Tuning and achieving the best Sensitivity and Detection Limits

ICP-MS Tips and Tricks For Tuning and achieving the best Sensitivity and Detection Limits

Various techniques and provide tips and tricks on achieving the best sensitivity and detection limits on your ICP-MS.

ICP-MS Tips and Tricks For Tuning and the best Sensitivity

How about a 20 minute coffee break along with a bite of <a href="https://lcms.cz/products/627">Empower</a>? Here you go!
In this session we demonstrate how to create and use Customer Fields to generate workflows in Empower
A Custom Field is a user defined field (or column) of information in a table. Creating Custom Fields gives you the ability to create tailored sample identifiers and calculations beyond what Empower provides by default. During this series, we will explore the various types of Custom Fields that can be created and provide some practical examples.
Learn more and register!
<blockquote>
Presenter: Carsten Fischer (Informatics Product Marketing, EMEA, Waters Corporation)
</blockquote>

Waters - Informatics series #12 Adjust Empower very flexible to your specific needs

Adjust Empower very flexible to your specific needs

In this session we demonstrate how to create and use Customer Fields to generate workflows in Empower.

<h4>Key learnings:</h4>
<ul>
<li>Learn the basics of GC×GC and what it offers over and above standard GC.
</li>
<li>Hear about the latest advances in both flow and thermal modulation.
</li>
<li>Find out how automated software workflows can speed up data processing.
</li>
<li>Explore the latest GC×GC applications including the analysis of sustainable aviation fuels, pyrolysis oils, and much more.
</li>
</ul>

Gulf Bio Analytical Group: Improving GC×GC workflows for petrochemical analyses

Improving GC×GC workflows for petrochemical analyses

Explore the latest GC×GC applications including the analysis of sustainable aviation fuels, pyrolysis oils, and much more.

Learn more about how the Evosep One has been used in identifying the proteins present in tumor tissues and discovering biomarkers used to detect or monitor the presence of cancer. These findings pave the way for translating research into clinical applications, including the development of new diagnostic tests, targeted therapies, and improved treatment protocols.
<h4>Deep topographic proteomics of a human brain tumour</h4>
<ul>
<li>Presenter: Roman Fischer (associate professor and Head of discovery proteomics facility, University of Oxford )</li>
</ul>
The presentation will explore the development and application of systematic spatial proteome analysis in human tumour tissue with view of resolving the spatial context of the tumour proteome. Further, the presentation will cover proteome clustering with obvious visual queues such as tumour border or vascularisation, but also show proteome based mapping and discovery of new tumour areas, tracking extracellular matrix or immune cell markers.
<h4>Proteogenomic Characterization of Chemotherapy Response in Muscle Invasive Bladder Cancer</h4>
<ul>
<li>Presenter: Matthew Holt (Laboratory Director, Baylor College of Medicine)</li>
</ul>
Only one quarter of patients with muscle invasive urothelial bladder cancer (MIBC) gain approximately 5% improvement in 5-year overall survival from standard of care cisplatin-based neoadjuvant chemotherapy (NAC). Characterizing MIBC poses challenges due to inherent muscle tissue infiltration. Proteogenomic profiling of high tumor content samples from 60 patients recapitulated previously established TCGA genomic and transcriptomic signatures. Notably, proteomic and phosphoproteomic profiles mirror RNAseq subtypes but diverge significantly in key regulatory pathways. We leveraged our recent SEPepQuant algorithm to identify isoforms and isoform families specific to chemotherapy resistance and validated these results with targeted proteomics. Our findings reinforce established MIBC subtypes, while offering new mechanistic insights into chemotherapy resistance.

Tumour tissue proteomics

Evosep One has been used in identifying the proteins present in tumor tissues and discovering biomarkers used to detect or monitor the presence of cancer.

<blockquote>
Presenter: Louis Tisinger, PhD (Application Engineer, Molecular Spectroscopy, Agilent Technologies, Inc.)
</blockquote>
<blockquote>
Louis (Lou) Tisinger has a Ph.D. in Analytical Chemistry from Miami University in Oxford Ohio, where his graduate research involved the study of ATR imaging and microscope performance in vibrational spectroscopy. Most of his career has been related to pre- and post-sales support of FTIR spectrometers, microscopes, imaging systems, and thermal analytical instruments, focusing on analyzing customer samples, demonstrations, post-sales training, troubleshooting, and R&amp;D.
</blockquote>

IR Analysis of Microplastics

A method driven system for analysis of microplastics will be demonstrated.

In this webinar, we will discuss several solid-state NMR experiments in material research, from 1D to multidimensional homonuclear and heteronuclear correlation experiments.
<blockquote>
Presenter: ochem Struppe, PhD (Sr Applications Scientist II, Bruker BioSpin)
</blockquote>
<blockquote>
Jochem Struppe received his Dr. rer. nat. in Physics from University of Stuttgart, Germany in 1996. Following a post-doctoral position at the University of California San Diego, he joined Bruker 1999 as the Applications Engineer in California. September 2000, he joined the applications group in Billerica. Leading the solid-state NMR team in Billerica he enjoys developing new experiments and exploring CPMAS probe hardware limits nondestructively and destructively.
</blockquote>
<blockquote>
Presenter: Shane Pawsey, PhD (Senior Application Scientist I, Bruker BioSpin)
</blockquote>
<blockquote>
Shane Pawsey obtained his Ph.D. in Chemistry at McGill University and was focused on the study of structure and dynamics of self-assembled monolayers by solid state NMR. This was followed by a fellowship at the National Research Council of Canada where he developed instrumentation for hyperpolarized xenon NMR to study functionalized porous materials. Shane joined Bruker BioSpin in 2008 as an applications scientist specializing in solid state NMR. In 2010 he returned to the field of hyperpolarization and joined with the Dynamic Nuclear Polarization group in Billerica to grow new applications.
</blockquote>

Bruker: Solid State NMR Applications for Materials

Solid State NMR Applications for Materials

Regulatory agencies recommend the implementation of a risk assessment strategy for potential nitrosamines in pharmaceutical products that are at risk of their presence. However, low nitrosamine concentrations and high background interference significantly increases the complexity of nitrosamines analysis. Hence, it is pivotal to select the right column chemistry and dimensions for effective nitrosamines analysis.
Key learning objectives:
<ul>
<li>Detect 8 nitrosamines in a single method, even in the presence of 35 diverse active pharmaceutical ingredients (APIs) 
</li>
<li>Learn the strategic approach to selecting your column chemistry, focusing on the distinct polarity characteristics of the drug and nitrosamines impurities 
</li>
<li>Achieve exceptional efficiency, great peak shape and exceptional resolution with <a href="https://lcms.cz/products/423">Thermo Scientific™ Hypersil Gold™</a>, <a href="https://lcms.cz/products?search=Accucore">Thermo Scientific™ Accucore™</a> and Thermo Scientific™ Acclaim™ columns for your high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS) applications.
</li>
</ul>
Register now to secure your spot and gain valuable insights that will transform your approach to pharmaceutical product testing.

ThermoFisher Scientific: Providing the Robust Liquid Chromatography solutions for Nitrosamines and NDSRI analysis

Providing the Robust Liquid Chromatography solutions for Nitrosamines and NDSRI analysis

Liquid Chromatography for Nitrosamines and NDSRI analysis

In this session we demonstrate how to create and use Customer Fields to generate workflows in Empower

Waters Corporation: Waters Empower Custom Fields

Empower Custom Fields