Abstract Microplastics (MPs) are major concern due to their potential harm to ecosystems and most research has focused on their presence and fate, with limited attention to their biodegradation in aquatic ecosystems. Nevertheless, MPs act as hotspots for the colonization by a diverse range of microorganisms that can adhere to plastic surfaces, resulting in the subsequent formation of biofilms—a potential threat especially in terms of pathogenicity. This study employed 16S rRNA and 18S rRNA sequencing metagenomic analyses to investigate microbial communities within biofilms on plastic materials exposed to long‐term marine and freshwater environments. Three Arcobacter species (Arcobacter nitrofigilis, Arcobacter acticola, and Arcobacter suis) emerged as dominant species in M_MP sample, while Flavobacterium tructae was the predominant species within the F_MP sample. The 18S rRNA sequencing revealed the presence of the fungal phylum Ascomycota and the microalgal species Pseudocharaciopsis ovalis in F_MP. Although, the primary species detected on M_MP and F_MP samples include bacteria previously implicated as pathogen, the predominant species identified in this study were unconnected to MP‐associated biofilms or MP degradation. Their presence constitutes a novel discovery, opening promising avenues for the exploration of their potential involvement in the biodegradation of MPs within aquatic environments.

Digitalization with integrated devices, digital and physical assistants, automation, and simulation is setting a new direction for laboratory work. Even with complex research workflows, high staff turnover, and a limited budget some laboratories have already shown that digitalization is indeed possible. However, academic bioprocess laboratories often struggle to follow the trend of digitalization. Due to their diverse research circumstances, high variety of team composition, goals, and limitations the concepts are substantially different. Here, we will provide an overview on different aspects of digitalization and describe how academic laboratories successfully digitalized their working environment. The key aspect is the collaboration and communication between IT‐experts and scientific staff. The developed digital infrastructure is only useful if it supports the laboratory worker and does not complicate their work. Thereby, laboratory researchers have to collaborate closely with IT‐experts in order for a well‐developed and maintainable digitalization concept that fits their individual needs and level of complexity. This review may serve as a starting point or a collection of ideas for the transformation toward a digitalized laboratory.

Continuous precipitation coupled with continuous tangential flow filtration is a cost‐effective alternative for the capture of recombinant antibodies from crude cell culture supernatant. The removal of surge tanks between unit operations, by the adoption of tubular reactors, maintains a continuous harvest and mass flow of product with the advantage of a narrow residence time distribution (RTD). We developed a continuous process implementing two orthogonal precipitation methods, CaCl2 precipitation for removal of host‐cell DNA and polyethylene glycol (PEG) for capturing the recombinant antibody, with no influence on the glycosylation profile. Our lab‐scale prototype consisting of two tubular reactors and two stages of tangential flow microfiltration was continuously operated for up to 8 days in a truly continuous fashion and without any product flow interruption, both as a stand‐alone capture and as an integrated perfusion‐capture. Furthermore, we explored the use of a negatively charged membrane adsorber for flow‐through anion exchange as first polishing step. We obtained a product recovery of approximately 80% and constant product quality, with more than two logarithmic reduction values (LRVs) for both host‐cell proteins and host‐cell DNA by the combination of the precipitation‐based capture and the first polishing step.

In this work, we applied a multi‐information source modeling technique to solve a multi‐objective Bayesian optimization problem involving the simultaneous minimization of cost and maximization of growth for serum‐free C2C12 cells using a hyper‐volume improvement acquisition function. In sequential batches of custom media experiments designed using our Bayesian criteria, collected using multiple assays targeting different cellular growth dynamics, the algorithm learned to identify the trade‐off relationship between long‐term growth and cost. We were able to identify several media with >100% more growth of C2C12 cells than the control, as well as a medium with 23% more growth at only 62.5% of the cost of the control. These algorithmically generated media also maintained growth far past the study period, indicating the modeling approach approximates the cell growth well from an extremely limited data set.