Search

Many contemporary challenges in liquid chromatography - such as the need for “smarter” method development tools, and deeper understanding of chromatographic phenomena - could be addressed more efficiently and effectively with larger volumes of experimental retention data than we have been accustomed to historically. The paucity of publicly accessible, high-quality measurements has been due, at least in large part, to the high cost in time and resources associated with traditional retention measurement approaches. Recently we described an approach to improve the throughput of such measurements by using very short columns (typically 5 mm), while maintaining measurement accuracy. In this paper we present a perspective on the characteristics of results obtained using this approach using a dataset containing about 13,000 retention measurements, and describe an approach to sample introduction that improves upon the prior work. The dataset is comprised of results for 35 different small molecules, nine different stationary phases, and several mobile phase compositions for each analyte/phase combination. During the acquisition of these data, we have interspersed repeated measurements of a small number of compounds for quality control purposes. The data from these measurements not only enable detection of “out of control” measurements, but also assessment of the repeatability and reproducibility of retention measurements over time. For retention factors greater than 1, the mean relative standard deviation (RSD) of replicate (typically n=5) measurements is 0.4%, and the standard deviation of RSDs is 0.4%. Most differences between selectivity values measured six months apart for 15 non-ionogenic compounds were in the range of +/- 1%, indicating good reproducibility. A critically important observation from these analyses is that selectivity (defined here as retention of a given analyte relative to the retention of a reference compound; kx/kref) is a much more consistent measure of retention over time (months) compared to the retention factor alone. While this work and dataset also highlights the importance of stationary phase stability over time for achieving reliable retention measurements, we are nevertheless optimistic that this approach will enable the compilation of large databases (>> 10,000 measurements) of retention values over long time periods (years), which can in turn be leveraged to address some of the most important contemporary challenges in liquid chromatography. All of the data discussed in the manuscript are provided as Supplemental Information.

Fragment deconvolution is a crucial step during componentization of non-targeted analysis (NTA) high-resolution mass spectrometry (HRMS) data, aiming to filter out false positive (FP) signals that do not belong to the component. Moreover, inclusion of FP fragments could lead to, for example, wrong identification further down the workflow. Commonly used methods for deconvolution of fragment signals rely on the presence of a time domain (e.g., peak apex retention time difference and correlation analysis). However, when there is no or insufficient MS2 information in the time domain, these methods are unusable and only the mass domain remains. A probability based cumulative neutral loss (CNL) model for fragment deconvolution using the mass domain information was thus developed to allow deconvolution for such cases. The optimized model, with a mass tolerance of 0.005 Da and a CNL score threshold of -0.95, was able to achieve true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 25.6%, and a reduction rate of 39.9%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 95% with FD rates between 45% and 77% and reduction rates between 10% and 24%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 93.1%, a FDr of 57.2%, and a reduction rate of 42.6%.

Today, we are faced with immense global challenges in finding sustainable equilibria between socio-economic, political, and ecological concerns. The European Chemistry Thematic Network (ECTN), the European University Association (EUA) and the European Commission are committed to sustainable improvement of the quality of university chemistry education to cope with these challenges. In this position paper, we advocate the creation of the Eurolecturer Academy (ELA), an innovative, European state of the art higher education learning platform serving academics in their continuous professional development of teaching competences and thereby supporting academics to educate students to be successful in the changing world. Within this newly established educational entity, there will be two levels of membership, Associated membership and Full membership. The ELA will not only facilitate continuous professional development of university teaching staff but will at the same time create a structure to support recognition of teaching competences of lecturers within the European dimension in teaching and learning. The certification will profit from the new 5th European Qualification Framework for micro-credentials, providing a much needed “academic currency” for the purpose of recognition of academic credentials. The ELA micro-credentials will be issued by certifying the learning outcomes of short-term learning experiences in the field of teaching and learning in higher education. The ELA will provide a micro-credentials catalogue that will address the needs for professional development of lecturers and ensure the quality of the micro-credentials through close cooperation with the internationally operating accreditation organization ASIIN (https://www.asiin.de/en/) using quality standards and valid assessment according to international best practice. Keywords: continuous professional development, chemistry education, Eurolecturer Academy, university teaching staff

Traditional liquid-chromatographic techniques, such as size-exclusion chromatography, (critical) interaction chromatography, and hydrodynamic chromatography, can all reveal certain aspects of polymers and the underlying distributions. The distribution of incorporated acid groups present in polyacrylates can be determined by non-aqueous ion-exchange chromatography, independent of other distributions present. The microstructural details on how this number of acid groups is incorporated in the polymer remains unknown. A low-molar mass polymer molecule with high acid content and a high-molar mass polymer with a low acid content may have the exact same number of acid groups. To take a next step towards understanding the polymer microstructure of water-borne resins, the distribution of incorporated acid groups over the molar-mass distribution has been investigated. For this purpose, an on-line coupling of non-aqueous ion-exchange chromatography (NAIEX) to size-exclusion chromatography (SEC) was established. Practical considerations regarding the system setup with respect to chromatography modes and column- and valve switching dimensionality are discussed. The orthogonality of NAIEX and SEC is demonstrated. Both liquid chromatography modes may be calibrated using polymer standards, yielding a calibrated separation plane. Cross-sectional data on either the molar mass distribution or the acid group distribution at a certain point of the separation plane is obtained. The value of the designed setup was demonstrated by the detailed characterization of the combined acid-group and molar-mass distribution in polymers with a low acid content, in the order of a few mass-%. Several stages of the emulsion polymerization process could be identified using the combined power of NAIEX and SEC.

The great potential gains in separation power and analysis time that can result from rigorously optimizing LC-MS and 2D-LC-MS methods for routine measurements has prompted many scientists to develop computer-aided method-development tools. The applicability of these has been proven in numerous applications, but their proliferation is still limited. Arguably, the majority of LC methods are still developed in a conventional manner, i.e. by analysts who rely on their knowledge and experience. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC separations. A closed-loop workflow was constructed that interacted directly with the LC and ran unsupervised in an automated fashion. The algorithm was tested using two newly designed strategies. The first utilized retention modeling, whereas the second used the Bayesian-optimization machine-learning approach. In both cases, the algorithm could arrive within ten iterations at an optimum of the objective function, which included resolution and measurement time. The design of the algorithm was modular, so as to facilitate compatibility with previous works in literature and its performance thus hinged on each module (e.g., signal processing, choice of retention model, objective function). Key focus areas for further improvement were identified. Bayesian optimization did not require any peak tracking or retention modeling. Accurate prediction of elution profiles was found to be indispensable for the strategy using retention modeling. This is the first interpretive algorithm demonstrated with complex samples. Peak tracking was conducted using UV-Vis absorbance detection, but use of MS detection is expected to significantly broaden the applicability of the workflow.

This article discusses innovation around the molecular structure of polymeric materials—an indispensable part of modern society—with a specific focus on sustainability. This field of research, so-called molecular correlative material characterization (MCMC), will enhance the transition into new sustainable functional copolymers.

Efforts to model and simulate various aspects of liquid chromatography (LC) separations (e.g., retention, selectivity, peak capacity, injection breakthrough) depend on experimental retention measurements to use as the basis for the models and simulations. Often these modeling and simulation efforts are limited by datasets that are too small because of the cost (time and money) associated with making the measurements. Other groups have demonstrated improvements in throughput of LC separations by focusing on “overhead” associated with the instrument itself – for example, between-analysis software processing time, and autosampler motions. In this paper we explore the possibility of using columns with small volumes (i.e., 5 mm x 2.1 mm i.d.) compared to conventional columns (e.g., 100 mm x 2.1 mm i.d.) that are typically used for retention measurements. We find that isocratic retention factors calculated for columns with these dimensions are different by about 20%; we attribute this difference – which we interpret as an error in measurements based on data from the 5 mm column – to extra-column volume associated with inlet and outlet frits. Since retention factor is a thermodynamic property of the mobile/stationary phase system under study, it should be independent of the dimensions of the column that is used for the measurement. We propose using ratios of retention factors (i.e., selectivities) to translate retention measurements between columns of different dimensions, so that measurements made using small columns can be used to make predictions for separations that involve conventional columns. We find that this approach reduces the difference in retention factors (5 mm compared to 100 mm columns) from an average of 18% to an average absolute difference of 1.7% (all errors less than 8%). This approach will significantly increase the rate at which high quality retention data can be collected to thousands of measurements per instrument per day, which in turn will likely have a profound impact on the quality of models and simulations that can be developed for many aspects of LC separations.