65 research outputs found
Creating a common ground for professional development of university chemistry (STEM) lecturers in Europe
No full textToday, 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
New methods for the characterization of essential distributions
No full textThe detailed analysis of polymeric materials is one of the necessary steps to elucidate the relationship between the chemical distributions of a polymer and the functional properties of a material. Within the UNMATCHED project (UNderstanding MATerials by CHaracterizing Essential Distributions) many techniques have been investigated – or further developed – to aid in the analysis of such materials. The primary focus of this thesis was on using liquid chromatography (LC) in innovative ways to analyze polymer chemical distributions. Additionally, chemometric strategies that help improve the interpretability of the data obtained from these methods were investigated and documented in later chapters
Under the influence of light: New chromatographic tools for elucidating photodegradation mechanisms
No full textIn photodegradation a molecule is excited by light, which causes it to degrade. It happens everywhere around us, such as in cultural-heritage, food, and in water-treatment facilities. Studying photodegradation is complex and the current monitoring techniques focus on the degradation of the main component, instead of on the formation of degradation products. In this project, a new way of studying photodegradation was developed. A new light-exposure cell was developed, based on a liquid-core-waveguide principle. One way to use this cell is by coupling it to liquid chromatography (LC) or implement it in two-dimensional liquid chromatography (2DLC) as a type of modulator. To optimize the LC separations, retention modelling (RM) can be used, where the chromatographic behaviour is predicted from scanning experiments. In this work, RM was investigated for both LC and 2DLC, as was photodegradation with different (new) light-exposure cells. For RM, new applications in the field were described, after which guidelines for scanning-experiments were formulated. Moreover, RM was applied to stationary-phase-assisted modulation (SPAM), an active modulation technique, to facilitate its optimization in 2DLC. In the second part of the thesis, the new light cell was compared to other, more-established degradation techniques, after which it was implemented in multiple-heart-cut 2DLC. Another LC setup was designed to facilitate the elucidation of photodegradation mechanisms. The thesis ends with the development of an alternative light cell that is more compatible with comprehensive 2DLC
Under the influence of light: New chromatographic tools for elucidating photodegradation mechanisms
No full textIn photodegradation a molecule is excited by light, which causes it to degrade. It happens everywhere around us, such as in cultural-heritage, food, and in water-treatment facilities. Studying photodegradation is complex and the current monitoring techniques focus on the degradation of the main component, instead of on the formation of degradation products. In this project, a new way of studying photodegradation was developed. A new light-exposure cell was developed, based on a liquid-core-waveguide principle. One way to use this cell is by coupling it to liquid chromatography (LC) or implement it in two-dimensional liquid chromatography (2DLC) as a type of modulator. To optimize the LC separations, retention modelling (RM) can be used, where the chromatographic behaviour is predicted from scanning experiments. In this work, RM was investigated for both LC and 2DLC, as was photodegradation with different (new) light-exposure cells. For RM, new applications in the field were described, after which guidelines for scanning-experiments were formulated. Moreover, RM was applied to stationary-phase-assisted modulation (SPAM), an active modulation technique, to facilitate its optimization in 2DLC. In the second part of the thesis, the new light cell was compared to other, more-established degradation techniques, after which it was implemented in multiple-heart-cut 2DLC. Another LC setup was designed to facilitate the elucidation of photodegradation mechanisms. The thesis ends with the development of an alternative light cell that is more compatible with comprehensive 2DLC
Recent trends in two-dimensional liquid chromatography
No full textMulti-dimensional liquid chromatography (MD-LC) continues to gain in popularity for applications where conventional one-dimensional liquid chromatography is insufficient to solve the analytical problem at hand. In this review we have focused on articles published in the years 2019 to early 2023 and look for trends using our previous review published in 2018 as a baseline. We have also explored usage patterns related to involvement of industrial laboratories in the published research. The two major areas of technical development have been continued work on modulation strategies that help mitigate problems associated with mobile phase mismatch when coupling complementary separation mechanisms, and development of computer-aided method development strategies. Progress in these areas is making 2D-LC easier to use, and it appears that this is translating to a shift toward more involvement by industrial laboratories. Indeed, over 34% of the more than 200 publications on 2D-LC in the last four years have had at least one-industry affiliated author. A recent inter-laboratory comparison study focused on the performance of a sophisticated multi-stage, multi-dimensional separation for therapeutic protein characterization is an exemplary indication of the increasing investment of industrial laboratories to MD-LC, and we expect this trend to continue for the foreseeable future
Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications
No full textPacked modulation loops to reduce band broadening in two-dimensional liquid chromatography
No full textModulation interfaces employing sample loops are applied in many hyphenated separations such as two-dimensional liquid chromatography (2D-LC). When the first-dimension effluent in 2D-LC is eluted from the modulation loop, dispersion effects occur due to differences in the laminar flow velocity of the filling and emptying flow. These effects were recently studied by Moussa et al. whom recommended the use of coiled loops to promote radial diffusion and reduce this effect. In the 1980s, Coq et al. investigated the use of packed loops, which also promote radial diffusion, in large volume injection 1D-LC. Unfortunately, this concept was never investigated in the context of 2D-LC modulation. Our work evaluates use of packed loops in 2D-LC modulation and compares them to unpacked coiled and uncoiled modulation loops. The effect of the solvents, loop volume, differences in filling and emptying rates, and loop elution direction on the elution profile was investigated. Statistical moments were used as a pragmatic tool to quantify elution profile characteristics. Decreased dispersion was observed in all cases for the packed loops compared to unpacked loops and unpacked coiled loops. In particular for larger loop volumes the dispersion was reduced significantly. Furthermore, countercurrent elution resulted in narrower elution profiles in all cases compared to concurrent elution. We found that packed modulation loops are of high interested when analytes are not refocussed in the second-dimension separation (e.g. for size-exclusion chromatography). Moreover, our work suggests that the use of packed loops may aid in prevention of loop overfilling.</p
Accuracy of retention model parameters obtained from retention data in liquid chromatography
No full textIn liquid chromatography, it is often very useful to have an accurate model of the retention factor, k, over a wide range of isocratic elution conditions. In principle, the parameters of a retention model can be obtained by fitting either isocratic or gradient retention factor data. However, in spite of many of our own attempts to accurately predict isocratic k values using retention models trained with gradient retention data, this has not worked in our hands. In the present study, we have used synthetic isocratic and gradient retention data for small molecules under reversed-phase liquid chromatography conditions. This allows us to discover challenges associated with predicting isocratic k values without the confounding influences of experimental issues that are difficult to model or eliminate. The results indicate that it is not currently possible to consistently predict isocratic retention factors for small molecules with accuracies better than 10%, even when using synthetic gradient retention data. Two distinct challenges in fitting gradient retention data were identified: 1) a lack of ‘uniqueness’ in the parameters and 2) an inability to find the global optimum fit in a complex fitting landscape. Working with experimental data where measurement noise is unavoidable will only make the accuracy worse
MOREPEAKS
No full textMultivariate Optimization and Refinement Program for Efficient Analysis of Key Separations (MOREPEAKS) is an optimization program for one- and two-dimensional liquid chromatography. Otherwise visualization and analysis of gas chromatography and mass spectral data is also possible.
MOREPEAKS is released with the aim of improving the valorization of academic research towards society. MOREPEAKS is a freely available tool created by the enthusiasts of the Chemometrics and Advanced Separations Team (CAST, https://cast-amsterdam.org/), who wish to allow others to benefit from chemometric tools available in literature without requiring the computational skills. We aim to incorporate all tools developed at the University of Amsterdam, but a number of relevant tools published in literature.
When any bugs are encountered, please let us know at [email protected].
For patch notes, please refer to https://cast-amsterdam.org/patchnotes
Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization
No full textContemporary complex samples require sophisticated methods for full analysis. This work describes the development of a Bayesian optimization algorithm for automated and unsupervised development of gradient programs. The algorithm was tailored to LC using a Gaussian process model with a novel covariance kernel. To facilitate unsupervised learning, the algorithm was designed to interface directly with the chromatographic system. Single-objective and multi-objective Bayesian optimization strategies were investigated for the separation of two complex (n>18, and n>80) dye mixtures. Both approaches found satisfactory optima in under 35 measurements. The multi-objective strategy was found to be powerful and flexible in terms of exploring the Pareto front. The performance difference between the single-objective and multi-objective strategy was further investigated using a retention modeling example. One additional advantage of the multi-objective approach was that it allows for a trade-off to be made between multiple objectives without prior knowledge. In general, the Bayesian optimization strategy was found to be particularly suitable, but not limited to, cases where retention modelling is not possible, although its scalability might be limited in terms of the number of parameters that can be simultaneously optimized
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