Operation and Optimization of Microwave-Heated Continuous-Flow Microfluidics

A US perspective on closing the carbon cycle to defossilize difficult-to-electrify segments of our economy

Electrification to reduce or eliminate greenhouse gas emissions is essential to mitigate climate change. However, a substantial portion of our manufacturing and transportation infrastructure will be difficult to electrify and/or will continue to use carbon as a key component, including areas in aviation, heavy-duty and marine transportation, and the chemical industry. In this Roadmap, we explore how multidisciplinary approaches will enable us to close the carbon cycle and create a circular economy by defossilizing these difficult-to-electrify areas and those that will continue to need carbon. We discuss two approaches for this: developing carbon alternatives and improving our ability to reuse carbon, enabled by separations. Furthermore, we posit that co-design and use-driven fundamental science are essential to reach aggressive greenhouse gas reduction targets.

Temperature-dependent complex dielectric permittivity: a simple measurement strategy for liquid-phase samples

Microwaves (MWs) are an emerging technology for intensified and electrified chemical manufacturing. MW heating is intimately linked to a material's dielectric permittivity. These properties are highly dependent on temperature and pressure, but such datasets are not readily available due to the limited accessibility of the current methodologies to process-oriented laboratories. We introduce a simple, benchtop approach for producing these datasets near the 2.45 GHz industrial, medical, and scientific (ISM) frequency for liquid samples. By building upon a previously-demonstrated bireentrant microwave measurement cavity, we introduce larger pressure- and temperature-capable vials to deduce temperature-dependent permittivity quickly and accurately for vapor pressures up to 7 bar. Our methodology is validated using literature data, demonstrating broad applicability for materials with dielectric constant ε' ranging from 1 to 100. We provide new permittivity data for water, organic solvents, and hydrochloric acid solutions. Finally, we provide simple fits to our data for easy use.

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.

Rapid Microwave-Assisted Synthesis and Electrode Optimization of Organic Anode Materials in Sodium-Ion Batteries

Sodium-ion batteries are commanding increasing attention owing to their promising electrochemical performance and sustainability. Organic electrode materials (OEMs) complement such technologies as they can be sourced from biomass and recycling them is environmentally friendly. Organic anodes based on sodium carboxylates have exhibited immense potential, except the limitation of current synthesis methods concerning upscaling and energy costs. In this work, a rapid and energy efficient microwave-assisted synthesis for organic anodes is presented using sodium naphthalene-2,6-dicarboxylate as a model compound. Optimizing the synthesis and electrode composition enables the compound to deliver a reversible initial capacity of ≈250 mAh g^-1 at a current density of 25 mA g^-1 with a high initial Coulombic efficiency (≈78%). The capacity is stable over 400 cycles and the compound also exhibits good rate performance. The successful demonstration of this rapid synthesis may facilitate the transition to preparing organic battery materials by scalable, efficient methods.

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.

An Energy Efficient Thermally Regulated Optical Spectroscopy Cell for Lab-on-Chip Devices: Applied to Nitrate Detection

Reagent-based colorimetric analyzers often heat the fluid under analysis for improved reaction kinetics, whilst also aiming to minimize energy use per measurement. Here, a novel method of conserving heat energy on such microfluidic systems is presented. Our design reduces heat transfer to the environment by surrounding the heated optical cell on four sides with integral air pockets, thereby realizing an insulated and suspended bridge structure. Our design was simulated in COMSOL Multiphysics and verified in a polymethyl methacrylate (PMMA) device. We evaluate the effectiveness of the insulated design by comparing it to a non-insulated cell. For temperatures up to 55 °C, the average power consumption was reduced by 49.3% in the simulation and 40.2% in the experiment. The designs were then characterized with the vanadium and Griess reagent assay for nitrate at 35 °C. Nitrate concentrations from 0.25 µM to 50 µM were tested and yielded the expected linear relationship with a limit of detection of 20 nM. We show a reduction in energy consumption from 195 J to 119 J per 10 min measurement using only 4 µL of fluid. Efficient heating on-chip will have broad applicability to numerous colorimetric assays.