WP1: Biomolecule–Virus Interaction Modeling: WP1 has progressed toward their objectives by developing and consolidating a multiscale computational framework to model biomolecule-graphene and virus-receptor interactions relevant to the FLUFET dual bioreceptor concept. During this reporting period, the models were extended to simulate antibody adsorption on graphene, multi protein systems, and surface coverage effects, introducing quantitative descriptors to evaluate binding site exposure and orientation. These developments support the KPI of modelling multivalent interactions across multiple biomolecule-virus pairs and provide predictive input for selectivity under flow conditions. In parallel, work advanced toward a robust binding affinity model based on center of mass distances and contact descriptors; however, full validation of this methodology remains ongoing due to high computational demands and limited access to HPC resources, resulting in delays in achieving complete discrimination between immobilization strategies. Nevertheless, the current framework provides a solid basis for further refinement and for supporting experimental optimization in subsequent work packages.
WP2: gFET Surface Engineering: WP2 has made substantial progress by advancing the manufacture and surface engineering of gFETs for ultra sensitive virus detection. During RP2, reproducible functionalization and bioconjugation protocols were established and validated, achieving high receptor densities consistent with the KPI target (> 5,000 receptors mm⁻²) and controlled bioreceptor layer thickness within the desired 2-5 nm range. In parallel, key limitations related to fabrication variability, graphene doping, and electrical instability were identified. Addressing these issues required additional optimization steps, including device conditioning, design iterations, and alternative surface chemistries, which introduced delays in the full validations. Nevertheless, these corrective actions enabled stable and specific detection of viral targets at very low concentrations, including levels below 10² PFU mL-1, and across different buffer conditions, providing a solid basis for further validation in multiple biologically relevant media and for the scalable development of the gFET platform.
WP3: Microfluidic Integration: WP3 has developed and validated microfluidic strategies for controlled particle delivery, shear force based removal, and sample preparation to support automated and continuous sensing. Passive and active mixing approaches were implemented, with herringbone structures achieving up to a seven fold local enhancement in particle capture, addressing objectives related to efficient analyte delivery from small sample volumes. Shear force studies enabled reliable removal of larger (500 nm) particles, whereas smaller (100 nm) particles remained strongly bound due to dominant non specific and multivalent interactions, delaying full validation of sensor reuse over 10–500 cycles. In parallel, a filtration based pre concentration module was designed and tested; however, limited particle recovery due to adsorption introduced further delays toward repeated automated sample preparation cycles (10–100 cycles). These results clarify the key limiting factors and define the focus of subsequent optimization, including improved blocking strategies, alternative surface chemistries, and evaluation of newly fabricated gFET chips under flow conditions to enable selective detection of complete viral particles across multiple pathogens
WP4: System Testing and Validation.System validation, has advanced by progressing system level integration and laboratory scale validation of the FLUFET prototype, combining bioreceptors, gFET arrays, microfluidics, and readout electronics into a functional platform. During this period, a first integrated prototype was developed and deployed across partners, enabling proof of concept testing of receptor based virus detection under flow conditions. ACE2 functionalized gFETs demonstrated sensitive and selective detection of SARS CoV 2, supporting multi pathogen detection KPIs, while antibody based sensors showed reproducibility limitations due to non specific interactions, delaying full validation across multiple viruses. Progress toward automation was achieved through pressure controlled microfluidics and integrated readout; however, completion of a fully automated workflow (<30 min per sample), long term continuous monitoring (1-5 days), and successive prototype iterations remain ongoing. These delays are mainly linked to device stability, fluidic harmonization, and reader optimization, which are being addressed through iterative integration and the development of a second generation modular system.
