Microflow chemistry and its electrification for sustainable chemical manufacturing

Sustainability is vital in solving global societal problems. Still, it requires a holistic view by considering renewable energy and carbon sources, recycling waste streams, environmentally friendly resource extraction and handling, and green manufacturing. Flow chemistry at the microscale can enable continuous sustainable manufacturing by opening up new operating windows, precise residence time control, enhanced mixing and transport, improved yield and productivity, and inherent safety. Furthermore, integrating microfluidic systems with alternative energy sources, such as microwaves and plasmas, offers tremendous promise for electrifying and intensifying modular and distributed chemical processing. This review provides an overview of microflow chemistry, electrification, their integration toward sustainable manufacturing, and their application to biomass upgrade (a select number of other processes are also touched upon). Finally, we identify critical areas for future research, such as matching technology to the scale of the application, techno-economic analysis, and life cycle assessment.

Electrified Hydrogen Production from Methane for PEM Fuel Cells Feeding: A Review

The greatest challenge of our times is to identify low cost and environmentally friendly alternative energy sources to fossil fuels. From this point of view, the decarbonization of industrial chemical processes is fundamental and the use of hydrogen as an energy vector, usable by fuel cells, is strategic. It is possible to tackle the decarbonization of industrial chemical processes with the electrification of systems. The purpose of this review is to provide an overview of the latest research on the electrification of endothermic industrial chemical processes aimed at the production of H2 from methane and its use for energy production through proton exchange membrane fuel cells (PEMFC). In particular, two main electrification methods are examined, microwave heating (MW) and resistive heating (Joule), aimed at transferring heat directly on the surface of the catalyst. For cases, the catalyst formulation and reactor configuration were analyzed and compared. The key aspects of the use of H2 through PEM were also analyzed, highlighting the most used catalysts and their performance. With the information contained in this review, we want to give scientists and researchers the opportunity to compare, both in terms of reactor and energy efficiency, the different solutions proposed for the electrification of chemical processes available in the recent literature. In particular, through this review it is possible to identify the solutions that allow a possible scale-up of the electrified chemical process, imagining a distributed production of hydrogen and its consequent use with PEMs. As for PEMs, in the review it is possible to find interesting alternative solutions to platinum with the PGM (Platinum Group Metal) free-based catalysts, proposing the use of Fe or Co for PEM application.

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub.