The objective of the presented module is to train students with no background in process development and scale-up of chromatographic processes to a high level of competency within forty contact hours. The key pedagogical approach is 'progression' where students' capabilities are gradually built up with appropriate scaffolding provided at each stage of their learning. The module is broken up into three steps with each step covering a different aspect of chromatography. Knowledge gained in one step is the foundation for work in the next. In the first step students investigate several chromatographic column packing materials and perform a solvent selection process. Design of Experiment (DOE) to systematically vary process parameters for method development is introduced in the second step. In the last step, students use a preparative-LC system to perform a larger scale separation. Students explore different scale-up scenarios, including volume fraction collection and column overloading. Pedagogic outcomes of the module were determined through surveys, interviews and personal interaction during the study. Results clearly indicate that students engaged well with the module while meeting overall learning objectives. The module is equally suitable for third or fourth year university students or industry practitioners unfamiliar with chromatography as part of continuing professional development.

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Approaching the level of molecular recognition of enzymes with solid catalysts is a challenging goal, achieved in this work for the competing transalkylation and disproportionation of diethylbenzene catalyzed by acid zeolites. The key diaryl intermediates for the two competing reactions only differ in the number of ethyl substituents in the aromatic rings, and therefore finding a selective zeolite able to recognize this subtle difference requires an accurate balance of the stabilization of reaction intermediates and transition states inside the zeolite microporous voids. In this work we present a computational methodology that, by combining a fast high-throughput screeening of all zeolite structures able to stabilize the key intermediates with a more computationally demanding mechanistic study only on the most promising candidates, guides the selection of the zeolite structures to be synthesized. The methodology presented is validated experimentally and allows to go beyond the conventional criteria of zeolite shape-selectivity.

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning the relations between composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by an ML model. Data sets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and properties of interest. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs for ML models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus require further investigation. Expected final online publication date for the Annual Review of Materials Research, Volume 53 is July 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

In this study, dabco MOF-1 (DMOF-1) with four different functional groups (NH₂, NO₂, Br and azobenzene) has been successfully synthesized through systematic control of the synthesis condition of their parent framework. The functionalised DMOF-1 is characterized using various analytical techniques including PXRD, TGA and N₂ sorption. The effect of the various functional groups on the performance of the MOFs for post-combustion CO₂ capture is evaluated. DMOF-1s with polar functional groups are found to have better affinity with CO₂ compared with the parent framework as indicated by higher CO₂ heat of adsorption. However, imparting steric hindrance to the framework as in Azo-DMOF-1 enhances CO₂/N₂ selectivity, potentially as a result of lower N₂ affinity for the framework.