Multi-function microfluidic platform for sensor integration

Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation

Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real-time process monitoring, control, and optimization. With a digital shadow, real-time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot-scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot-scale and lab-scale fermentations were conducted for model development and validation. With all available pilot-scale data, a data-driven soft sensor was developed to estimate the main substrate concentration (glucose) with a normalized root mean squared error (N-RMSE) of 2%. This robust data-driven soft sensor was able to estimate accurately in lab-scale (volume < 20× pilot) with a N-RMSE of 7.8%. A hybrid soft sensor was developed by combining the data-driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot-scale data with N-RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale-up and leads the way to a new generation of in silico-based process development.

3D-Printed Biosensor Arrays for Medical Diagnostics

While the technology is relatively new, low-cost 3D printing has impacted many aspects of human life. 3D printers are being used as manufacturing tools for a wide variety of devices in a spectrum of applications ranging from diagnosis to implants to external prostheses. The ease of use, availability of 3D-design software and low cost has made 3D printing an accessible manufacturing and fabrication tool in many bioanalytical research laboratories. 3D printers can print materials with varying density, optical character, strength and chemical properties that provide the user with a vast array of strategic options. In this review, we focus on applications in biomedical diagnostics and how this revolutionary technique is facilitating the development of low-cost, sensitive, and often geometrically complex tools. 3D printing in the fabrication of microfluidics, supporting equipment, and optical and electronic components of diagnostic devices is presented. Emerging diagnostics systems using 3D bioprinting as a tool to incorporate living cells or biomaterials into 3D printing is also reviewed.

Towards microbioprocess control: an inexpensive 3D printed microbioreactor with integrated online real-time glucose monitoring

A 3D printed micro-bioreactor and microfluidic chip with integrated screen printed glucose biosensor for online monitoring of glucose to aid micro-bioprocess control.