Respiratory viruses spread through aerosols and contaminated surfaces, yet masks, respirators, and filters only trap pathogens without inactivating them, turning used protective equipment into a secondary transmission source. Here, we report chiral ZnO nanoneedles (L-NNs or D-NNs), stabilized by L- or D-phenylalanine-penicillamine-phenylalanine (L-FPF or D-FPF), as a capture-and-kill coating that targets the heptad repeat 2 (HR2) of class I viral fusion proteins. Through chirality-matched multivalent interactions, L-NNs bind the amphipathic HR2 stem with high affinity and block the six-helix bundle (6-HB) assembly essential for membrane fusion, showing a potency (IC50 = 0.52 ng/mL) approximately 8-fold higher than that of D-NNs (IC50 = 4.11 ng/mL) and 120-fold higher than that of achiral NNs (IC50 = 66.73 ng/mL). Chiral ligands narrow the ZnO bandgap and, through chirality-induced spin selectivity that suppresses charge recombination, enable 405 nm light-driven proteolytic activity that hydrolyzes specific amide bonds between acidic and hydrophobic residues within HR2. Because photogenerated reactive species act over only nanometer distances, affinity capture immobilizes virions at the photocatalytic needle surface and converts nonspecific oxidation to spatially confined, target-directed catalysis, enabling irreversible inactivation at low light doses. Targeting the evolutionarily conserved fusion core, L-NNs exhibit broad-spectrum activity across Ebola, coronaviruses, HIV-1, and HRSV, with IC50 values below 26.88 ng/mL, and show favorable biocompatibility. This work establishes chirality-engineered nanomaterials as a precision virucidal platform for photoresponsive protective coatings that interrupt environmental transmission of diverse and emerging viruses.
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.