Aluminum (Al) can actively support plasmonic response in the ultraviolet (UV) range compared to noble metals (e.g., Au, Ag) and thus has broad applications including UV sensing, displays, and photovoltaics. High-quality Al films with no oxidation are essential and critical in these applications. However, Al is very prone to fast oxidation in air, which critically depends on the fabrication process. Here, we report that by leveraging the in situ sputter etching and sputter deposition of a 1 nm tetrahedral amorphous carbon (ta-C) film on the Al nanostructures, Al plasmonic activity can be improved. The prior sputter etching process greatly reduces the oxidized layer of the Al films, and the subsequent sputter deposition of ta-C keeps Al oxidation-free. The ta-C film outperforms the naturally passivated Al2O3 layer on the Al film because the ta-C film has a denser structure, higher permittivity, and better biocompatibility. Therefore, it can effectively improve the plasmonic response of Al and be beneficial to molecule sensing, which is proved in our experiments and is also verified in simulations. Our results can enable the various applications based on plasmon resonance in the UV range.

As the leading cause of death, heart attacks result in millions of deaths annually, with no end in sight. Early intervention is the only strategy for rescuing lives threatened by heart disease. However, the detection time of the fastest heart‐attack detection system is >15 min, which is too long considering the rapid passage of life. In this study, a machine learning (ML)‐driven system with a simple process, low‐cost, short detection time (only 10 s), and high precision is developed. By utilizing a functionalized nanofinger structure, even a trace amount of biomarker leaked before a heart attack can be captured. Additionally, enhanced Raman profiles are constructed for predictive analytics. Five ML models are developed to harness the useful characteristics of each Raman spectrum and provide early warnings of heart attacks with >98% accuracy. Through the strategic combination of nanofingers and ML algorithms, the proposed warning system accurately provides alerts on silent heart‐attack attempts seconds ahead of actual attacks.