International Journal of Hydrogen Energy Journal
Mono-metallic Pt and Rh catalysts supported on both CeO2 and TiO2 were prepared and tested for water-gas shift activity in a Flowrence, high throughput reactor system. The feed composition mimicked a typical fuel processor, steam methane reformer outlet stream. The Pt/CeO2 catalyst showed the best metal activity of ∼3.8 E-07 moles CO converted·gPt-1 s-1, at a Pt loading of 0.5 wt %, activity decreasing with increasing metal loading. Furthermore, the Pt/CeO2 catalyst produced almost no methane while the Rh based catalysts led to substantial methanation.
Applied Catalysis B: Environmental Journal
Pd-Pt/Al2O3 and Pd-Pt/CeO2-ZrO2-Y2O3-La2O3 methane oxidation catalysts were investigated under typical lean-burn gas engine conditions with respect to sulfur poisoning and during reactivation, particularly under the most efficient rich conditions. Sulfation of the noble metal and the support material led to pronounced catalyst deactivation. A pronounced transfer of sulfates to the support, particularly alumina, only partially protects the noble metal. In situ X-ray absorption spectroscopy gave insight into the nature and evolution of Pd species during regenerative treatment. Although palladium sulfate that formed during poisoning is decomposed at approximately 200°C in oxygen-free conditions, support regeneration requires higher temperatures, which result in PdS formation above 400°C. Despite its high stability under rich conditions, PdS decomposition by oxidation in lean atmosphere leads to the recovery of the activity. In addition, water vapor that is present during the rich regeneration exhibited a beneficial effect on the regeneration with higher catalytic activity after regeneration.
Nature Catalysis Journal
Platinum single sites are highly attractive due to their high atom economy and can be generated on CeO2 by an oxidative high-temperature treatment. However, their location and activity are strongly debated. Furthermore, reaction-driven structural dynamics have not been addressed so far. In this study, we were able to evidence platinum-induced CeO2 surface restructuring, locate platinum single sites on CeO2 and track the variation of the active state under reaction conditions using a complementary approach of density functional theory calculations, in situ infrared spectroscopy, operando high-energy-resolution fluorescence detected X-ray absorption spectroscopy and catalytic CO (as well as C3H6 and CH4) oxidation. We found that the onset of CO oxidation is linked to the migration of platinum single sites from four-fold hollow sites to form small clusters containing a few platinum atoms. This demonstrates that operando studies on single sites are essential to assess their fate and the resulting catalytic properties.
Dr. Alan Aspuru pushes the boundaries of autonomous lab work by combining computation, machine learning and workflow solutions. Together with collaborators around the globe he conceives platforms with the ability to self-optimize their output and, bit by bit, decrypt the art of chemistry.
Alan provides a DARPA-funded collaboration involving the groups of Lee Cronin, Jason Hein, Bartosz Grzybowski and Marty Burke that aims to create self-driving laboratories and make better optoelectronic materials.
Dr. Jost Göttert and Dr. Christian Schmitz from the HIT Institute of Surface Technology, Hochschule Niederrhein in Krefeld, provide recent advances in digital chemistry related to high throughput experimentation and machine learning which enables acceleration and optimization of coatings formulation development.
They illustrate their “iHIT Solution Engine” process with practical examples, and discuss how to use the Digital Model Information.
Data driven high-throughput experimentation is enabling accelerated screening within pharmaceutical companies. New data science tools combined with machine learning are being implemented to efficiently tackle multivariate reaction optimization challenges.
Melodie Christensen, from Merck & Co., Inc., and UBC provide an overview of the use of automation in her Data Rich Experimentation (DRE) lab and her move towards autonomous reaction screening in conjunction with digitalization.
Melodie is an Associate Principal Scientist, Merck & Co., Inc. and a Ph.D. student at the Department of Chemistry, the University of British Columbia.
She has a proven track record in high-throughput experimentation platforms to support early and late stage pharmaceutical process development.
Autonomous process optimization involves the human intervention-free exploration of a range of predefined process parameters in order to improve responses such as reaction yield and product selectivity. Utilizing off-the-shelf components, we developed a closed-loop system capable of carrying out parallel autonomous process optimization experiments in batch with significantly reduced cycle times. Upon implementation of our system in the autonomous optimization of a palladium-catalyzed stereoselective Suzuki-Miyaura coupling, we found that the definition of a set of meaningful, broad, and unbiased process parameters was the most critical aspect of a successful optimization. In addition, we found that categorical parameters such as phosphine ligand were vital to determining the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing an element of bias into the experimental design. In seeking a systematic method for the selection of a diverse set of phosphine ligands fully representative of the chemical space, we developed a strategy that leveraged computed molecular descriptor clustering analysis. This strategy allowed for the successful autonomous optimization of a stereoselective Suzuki-Miyaura coupling between a vinyl sulfonate and an arylboronic acid to selectively generate the E-product isomer in high yield.
The IBM and Chemspeed collaboration opens doors for a new era in synthetic chemistry with wide implications for the pharmaceutical and chemical industry.
Teodoro and his team push the boundaries of AI-driven lab-automation at IBM Research Europe and have successfully implemented predictive retro-synthesis in silico with the autonomous execution by a Chemspeed automated synthesis workstation.
Teodoro provides an overview of the project and how digitalization will enable us to accelerate drug development.
Teodoro is a Distinguished Research Scientist and Manager of the Future of Computing for Accelerated Discovery in the department of Cognitive Computing and Industry Solutions at the IBM Research Laboratory in Zurich.
Amino acid functionalised perylene bisimides (PBIs) form self-assembled structures in solution, the nature of which depends on the local environment. Using a high throughput photocatalysis set-up, we have studied five PBIs for the hydrogen evolution reaction (HER) under a range of conditions (pH and hole scavenger concentration) across 350 experiments to explore the relationship between supramolecular structure and photocatalytic activity. Using small angle X-ray scattering (SAXS), we show that photocatalytic activity is determined by the nature of the self-assembled aggregate that is formed with a correlation between the presence of charged flexible cylindrical aggregates and high levels of H2. Our work highlights the complexity of designing supramolecular photocatalysts, and also the power of tuning activity by the type of aggregate that is formed.
Dynamic Flowsheet Simulation of Solids Processes Journal
This work presents the fundamentals and exemplary applications of a generalized model for precipitation, aggregation and ripening processes including the formation of solid phases with two dimensions. The particle formation is governed by a widely applicable population balance approach. Solid formation processes are described via the numerically efficient Direct Quadrature Method of Moments (DQMOM), which can calculate the evolution of multiple solid phases simultaneously. The particle size distribution (PSD) is approximated by a summation of delta functions while the moment source term is approximated by a two-point quadrature. The moments to calculate the multivariate distributions are chosen carefully to represent the second order moments. Solid formation is based on the model of Haderlein et al. (2017) and is extended by a multidimensional aggregation model. Now, the influences of mixing, complex hydrochemistry and particle formation dynamics including nucleation, growth and aggregation on multiphase precipitation processes are modelled and simulated along independent dimensions with high efficiency.