This tutorial review explains the integration of optical chemical sensor formats for the purpose of monitoring oxygen, pH, carbon dioxide, glucose and temperature in microbioreactors.

The dawn of the new millennium saw a trend towards the dedicated use of microfluidic devices for process intensification in biotechnology. As the last decade went by, it became evident that this pattern was not a short-lived fad, since the deliverables related to this field of research have been consistently piling-up. The application of process intensification in biotechnology is therefore seemingly catching up with the trend already observed in the chemical engineering area, where the use of microfluidic devices has already been upgraded to production scale. The goal of the present work is therefore to provide an updated overview of the developments centered on the use of microfluidic devices for process intensification in biotechnology. Within such scope, particular focus will be given to different designs, configurations and modes of operation of microreactors, but reference to similar features regarding microfluidic devices in downstream processing will not be overlooked. Engineering considerations and fluid dynamics issues, namely related to the characterization of flow in microchannels, promotion of micromixing and predictive tools, will also be addressed, as well as reflection on the analytics required to take full advantage of the possibilities provided by microfluidic devices in process intensification. Strategies developed to ease the implementation of experimental set-ups anchored in the use of microfluidic devices will be briefly tackled. Finally, realistic considerations on the current advantages and limitation on the use of microfluidic devices for process intensification, as well as prospective near future developments in the field, will be presented.

• Microfluidic devices as novel bioprocess development tools. • Processes with stem cells, microbes and enzymes are viable in microfluidic devices. • Microfluidic devices with integrated sensors provide high quality data. • Laminar flow enables spatial and temporal control over transport phenomena. • Standardization of devices required for automation and industrial uptake.

Messenger RNA (mRNA) vaccines are a new alternative to conventional vaccines with a prominent role in infectious disease control. These vaccines are produced in in vitro transcription (IVT) reactions, catalyzed by RNA polymerase in cascade reactions. To ensure an efficient and cost‐effective manufacturing process, essential for a large‐scale production and effective vaccine supply chain, the IVT reaction needs to be optimized. IVT is a complex reaction that contains a large number of variables that can affect its outcome. Traditional optimization methods rely on classic Design of Experiments methods, which are time‐consuming and can present human bias or based on simplified assumptions. In this contribution, we propose the use of Machine Learning approaches to perform a data‐driven optimization of an mRNA IVT reaction. A Bayesian optimization method and model interpretability techniques were used to automate experiment design, providing a feedback loop. IVT reaction conditions were found under 60 optimization runs that produced 12 g · L⁻¹ in solely 2 h. The results obtained outperform published industry standards and data reported in literature in terms of both achievable reaction yield and reduction of production time. Furthermore, this shows the potential of Bayesian optimization as a cost‐effective optimization tool within (bio)chemical applications.