Population-wide surveillance of COVID-19 requires tests to be quick and accurate to minimize community transmissions. The detection of breath volatile organic compounds presents a promising option for COVID-19 surveillance but is currently limited by bulky instrumentation and inflexible analysis protocol. Here, we design a hand-held surface-enhanced Raman scattering-based breathalyzer to identify COVID-19 infected individuals in under 5 min, achieving >95% sensitivity and specificity across 501 participants regardless of their displayed symptoms. Our SERS-based breathalyzer harnesses key variations in vibrational fingerprints arising from interactions between breath metabolites and multiple molecular receptors to establish a robust partial least-squares discriminant analysis model for high throughput classifications. Crucially, spectral regions influencing classification show strong corroboration with reported potential COVID-19 breath biomarkers, both through experiment and in silico. Our strategy strives to spur the development of next-generation, noninvasive human breath diagnostic toolkits tailored for mass screening purposes.

The electrochemical nitrogen reduction reaction (NRR) offers a sustainable solution towards ammonia production but suffers poor reaction performance owing to preferential catalyst–H formation and the consequential hydrogen evolution reaction (HER). Now, the Pt/Au electrocatalyst d‐band structure is electronically modified using zeolitic imidazole framework (ZIF) to achieve a Faradaic efficiency (FE) of >44 % with high ammonia yield rate of >161 μg mgcat−1 h−1 under ambient conditions. The strategy lowers electrocatalyst d‐band position to weaken H adsorption and concurrently creates electron‐deficient sites to kinetically drive NRR by promoting catalyst–N2 interaction. The ZIF coating on the electrocatalyst doubles as a hydrophobic layer to suppress HER, further improving FE by >44‐fold compared to without ZIF (ca. 1 %). The Pt/Au‐NZIF interaction is key to enable strong N2 adsorption over H atom.

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

The electrochemical nitrogen reduction reaction (NRR) offers a sustainable solution towards ammonia production but suffers poor reaction performance owing to preferential catalyst–H formation and the consequential hydrogen evolution reaction (HER). Now, the Pt/Au electrocatalyst d‐band structure is electronically modified using zeolitic imidazole framework (ZIF) to achieve a Faradaic efficiency (FE) of >44 % with high ammonia yield rate of >161 μg mgcat−1 h−1 under ambient conditions. The strategy lowers electrocatalyst d‐band position to weaken H adsorption and concurrently creates electron‐deficient sites to kinetically drive NRR by promoting catalyst–N2 interaction. The ZIF coating on the electrocatalyst doubles as a hydrophobic layer to suppress HER, further improving FE by >44‐fold compared to without ZIF (ca. 1 %). The Pt/Au‐NZIF interaction is key to enable strong N2 adsorption over H atom.

Overview of the current status on emerging, multi-faceted nanosensor platform designs and data analysis strategies for rapid, point-of-need detection and monitoring of small-molecule metabolites.

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