Model-assisted process development, characterization and design of continuous chromatography for antibody separation

Developing physics-informed neural networks for model predictive control of periodic counter-current chromatography

The applications of continuous manufacturing technology in biopharmaceuticals require advanced design, monitoring, and control due to its complexity. Traditional mechanistic models, which rely on numerical solutions, suffer from long computational times, making them unsuitable for the timely demands of continuous processes and digital twin applications in biomanufacturing. This issue significantly limits the capability for real-time optimization and control. To overcome this challenge, this study proposes a Physics-Informed Neural Network (PINN) based General Rate Model (GRM) approach that greatly reduces computation time while maintaining high accuracy and reliability in simulations. The developed PINN is applicable for different parameters across wide ranges and is capable of parameter estimation. It presents excellent performance in both offline simulation of single-column breakthrough curves and online optimization of load conditions for four-column periodic counter-current chromatography (4C-PCC), achieving significant reductions in fitting time from 2608.6 to 110.7 s for offline simulations, and completing online simulations within 12 to 14 s. The results demonstrate the potential of PINN for real-time model predictive control and digital twin applications, offering a promising solution to the limitations of traditional numerical methods.

Isolation of Biomolecules Using MXenes

Biomolecule isolation is a crucial process in diverse biomedical and biochemical applications, including diagnostics, therapeutics, research, and manufacturing. Recently, MXenes, a novel class of two-dimensional nanomaterials, have emerged as promising adsorbents for this purpose due to their unique physicochemical properties. These biocompatible and antibacterial nanomaterials feature a high aspect ratio, excellent conductivity, and versatile surface chemistry. This timely review explores the potential of MXenes for isolating a wide range of biomolecules, such as proteins, nucleic acids, and small molecules, while highlighting key future research trends and innovative applications poised to transform the field. This review provides an in-depth discussion of various synthesis methods and functionalization techniques that enhance the specificity and efficiency of MXenes in biomolecule isolation. In addition, the mechanisms by which MXenes interact with biomolecules are elucidated, offering insights into their selective adsorption and customized separation capabilities. This review also addresses recent advancements, identifies existing challenges, and examines emerging trends that may drive the next wave of innovation in this rapidly evolving area.

Mirrored dynamics of a wild mosquito population suppression model with Ricker-type survival probability and time delay

Here, we formulated a delayed mosquito population suppression model including two switching sub-equations, in which we assumed that the growth of the wild mosquito population obeys the Ricker-type density-dependent survival function and the release period of sterile males equals the maturation period of wild mosquitoes. For the time-switched delay model, to tackle with the difficulties brought by the non-monotonicity of its growth term to its dynamical analysis, we employed an essential transformation, derived an auxiliary function and obtained some expected analytical results. Finally, we proved that under certain conditions, the number of periodic solutions and their global attractivities for the delay model mirror that of the corresponding delay-free model. The findings can boost a better understanding of the impact of the time delay on the creation/suppression of oscillations harbored by the mosquito population dynamics and enhance the success of real-world mosquito control programs.