Colorimetric lateral flow immunoassay (LFIA) with gold nanoparticles (AuNPs) as signal reporters has been widely used in point-of-care testing. Nonetheless, the potential of traditional AuNP-based LFIA for the early diagnosis of disease is often compromised by limited sensitivity due to the insufficient colorimetric signal brightness of AuNPs. Herein, we develop a "three-in-one" multifunctional catalytic colorimetric nanohybrid (Fe 3 O 4 @MOF@Pt) composed of Fe 3 O 4 nanoparticles, MIL-100(Fe), and platinum (Pt) nanoparticles. Fe 3 O 4 @MOF@Pt displays enhanced colorimetric signal brightness, fast magnetic response, and ultrahigh peroxidasemimicking activity, which are beneficial to the enhancement of the sensitivity of LFIA by coupling with magnetic separation and catalytic amplification. When integrated with the dual-antibody sandwich LFIA platform, the developed Fe 3 O 4 @MOF@Pt can achieve an ultrasensitive immunochromatographic assay of procalcitonin with a sensitivity of 0.5 pg mL −1 , which is approximately 2280-fold higher than that of conventional AuNP-based LFIA and superior to previously published immunoassays. Therefore, this work suggests that the proposed catalytic colorimetric nanohybrid can act as promising signal reporters to enable ultrasensitive immunochromatographic disease diagnostics.

Lateral flow immunoassay (LFIA) with gold nanoparticles (AuNPs) as signal reporters is a popular point-of-care diagnostic technique. However, given the weak absorbance of traditional 20-40 nm spherical AuNPs, their sensitivity is low, which greatly limits the wide application of AuNP-based LFIA. With the rapid advances in materials science and nanotechnology, the synthesis of noble metal nanoparticles (NMNPs) has enhanced physicochemical properties such as optical, plasmonic, catalytic, and multifunctional activity by simply engineering their physical parameters, including the size, shape, composition, and external structure. Using these engineered NMNPs as an alternative to traditional AuNPs, the sensitivity of LFIA has been significantly improved, thereby greatly expanding the working range and application scenarios of LFIA, particularly in trace analysis. Therefore, in this review, we will focus on the design of engineered NMNPs and their demonstration in improving LFIA. We highlight the strategies available for tailoring NMNP designs, the effect of NMNP engineering on their performance, and the working principle of each engineering design for enhancing LFIA. Finally, current challenges and future improvements in this field are briefly discussed.

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