A Vertically Stacked Thin-Layer Cascade Photobioreactor for Space-Efficient Indoor Microalgae Cultivation: Design, Geometric Characterization, and Operational Baseline Across Scales
Indoor microalgae production under artificial illumination is constrained by facility footprint and energy demand, making biomass output per area the central design objective. Single-layer thin-layer cascade (TLC) systems are geometrically limited to a surface-to-footprint ratio (SF) of at most 1.0 m2 m−2. This study presents a stacked thin-layer cascade (STLC) photobioreactor validated at two scales sharing the same six-channel architecture: a laboratory-scale unit (2 L) and a pilot-scale unit (250 L). Both units achieved an SF well above the single-layer ceiling (3.65 and 4.2 m2 m−2), demonstrating that vertical stacking is an inherent structural solution to the geometric limitation of open thin-layer cascade systems, independent of scale. Both units operated reliably for 11 to 13 days across a 125-fold difference in working volume, converging to comparable transitional Reynolds numbers and confirming that the film hydrodynamics central to the STLC concept remain consistent by design across scale. Footprint-normalized productivity reached 3.41 g DW m−2 d−1 at laboratory scale and 6.11 g DW m−2 d−1 at pilot scale, with pilot-scale protein reaching 42.1% DW prior to nitrogen depletion. Growth kinetics differed between scales, reflecting light/dark cycling regime, initial biomass density, and retention tank geometry as distinct, tunable operational variables. Closing the residual productivity gap requires jointly increasing photon flux density and standing biomass density, since low-density operation causes a large fraction of incident light to pass through the culture unabsorbed regardless of irradiance.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.