Multiple synergistic SERS enhancement of 3D micro-nano Ni-foam@MoS₂ for detection of exhaled breath condensate

Multiple Synergistic SERS Enhancement of 3D micro-nano Ni-foam@MoS₂ • Overcomes EBC matrix interference for ultrasensitive 2.95 nM lactate detection • AI algorithms decode complex SERS spectra for non-invasive health monitoring. To address the critical demand for highly sensitive sensing interfaces in non-invasive exhaled breath condensate (EBC) analysis, this study develops a high-performance SERS substrate featuring interlaced, needle-like MoS₂ nanoclusters grown in situ on a 3D nickel foam scaffold (Ni-foam@MoS₂). This architecture effectively overcomes the severe signal heterogeneity and poor wearable compatibility that typically plague traditional plasmonic metal substrates. The system synergizes multiple coupled SERS enhancement mechanisms: the 3D porous scaffold and interwoven needle-like arrays form abundant optical microcavities. These microcavities effectively confine incident light and significantly enhance light-matter interactions, thereby inducing electromagnetic enhancement. Concurrently, the interlaced MoS₂ arrays not only efficiently concentrate trace analytes but also induce substantial chemical mechanism via vigorous charge transfer. Consequently, the substrate achieves an outstanding average enhancement factor of 1.88 × 10 9 , alongside superb signal reproducibility and long-term environmental stability. By deploying this Ni-foam@MoS₂ substrate for the rapid off-line analysis of simulated exhaled breath condensate (EBC), we achieved the ultrasensitive detection of lactate (LA), a critical metabolic biomarker, with a limit of detection as low as 2.95 nM. Furthermore, coupling these complex spectral datasets with advanced artificial intelligence algorithms enabled the highly accurate classification of concentration-dependent LA spectral variations, demonstrating significant proof-of-concept potential for mask-integrated biomolecular sensing and non-invasive physiological monitoring.