Engineered Collaborative Size Regulation and Shape Engineering of Tremella-Like Au-MnOx for Highly Sensitive Bimodal-Type Lateral Flow Immunoassays.

Engineered collaborative size regulation and shape engineering of multi-functional nanomaterials (NPs) offer extraordinary opportunities for improving the analysis performance. It is anticipated to address the difficulty in distinguishing color changes caused by subtle variations in target concentrations, thereby facilitating the highly sensitive analysis of lateral flow immunoassays (LFIAs). Herein, tremella-like gold-manganese oxide (Au-MnOx ) nanoparticles with precise MnCl2 regulation are synthesized as immuno signal tracers via a facile one-step redox reaction in alkaline condition at ambient temperature. Avail of the tunable elemental composition and anisotropy in morphology, black-colored tremella-like Au-MnOx exhibits superb colorimetric signal brightness, enhanced antibody coupling efficiency, marvelous photothermal performance, and unrestricted immunological recognition affinity, all of which facilitate highly sensitive multi-signal transduction patterns. In conjunction with the handheld thermal reader device, a bimodal-type LFIA that combines size-regulation- and shape-engineering-mediated colorimetric-photothermal dual-response assay (coined as the SSCPD assay) with a limit of detection of 0.012 ng mL-1 for ractopamine (RAC) monitoring is achieved by integrating Au-MnOx with the competitive-type immunoreaction. This work illustrates the effectiveness of this strategy for establishing high-performance sensing, and the SSCPD assay may be extended to a wide spectrum of future point-of-care (POC) diagnostic applications.

Engineered Core-Shell Multifunctional Nano-Tracer in Raman-Silent Region with Highly Retained Affinity to Enhance Lateral Flow Immunoassays.

Stimulated surface-enhanced Raman scattering (SERS) in combination with engineered nano-tracer offers extraordinary potential in lateral flow immunoassays (LFIAs). Nonetheless, the investigation execution of SERS-LFIA is often compromised by the intricacy and overlap of the Raman fingerprint spectrum as well as the affinity-interference of nano-tracer to antibody. To circumvent these critical issues, an engineered core-shell multifunctional nano-tracer (named APNPs) with precise control of the size of nano-core (AuNPs) and coating of the nano-shell (Prussian blue nanomaterials) is prepared for SERS-LFIA via a modified enlarging particle size and coating modification strategy. Importantly, this nano-tracer exhibits enhanced coupling efficiency, highly retained affinity, reinforced colloid stability, and unique SERS signal (2156 cm-1 ) in the silent region (1800-2800 cm-1 ) with high signal-to-background ratio simultaneously, all of which are beneficial to the enhancement of the analysis performance. With a proof-of-concept demonstration for detection of ractopamine (RAC), a dual-pattern LFIA that synergizes both the enlarged particle size and coating modification supported colorimetric/biological silence Raman dual-response (coined as the ECCRD assay) is demonstrated by integrating APNPs with the competitive-type immunoreaction. This research may contribute to the rational design of multifunctional nano-tracer, and the ECCRD assay can be expanded for a wide spectrum of applications in environmental monitoring and biomedical diagnosis.

Evaluation of the Multidimensional Enhanced Lateral Flow Immunoassay in Point-of-Care Nanosensors.

Point-of-care (POC) nanosensors with high screening efficiency show promise for user-friendly manipulation in the ever-increasing on-site analysis demand for illness diagnosis, environmental monitoring, and food safety. Currently, inspired by the merits of integrating advanced nanomaterials, molecular biology, machine learning, and artificial intelligence, lateral flow immunoassay (LFIA)-based POC nanosensors have been devoted to satisfying the commercial demands in terms of sensitivity, specificity, and practicality. Herein, we examine the use of multidimensional enhanced LFIA in various fields over the past two decades, focusing on introducing advanced nanomaterials to improve the acquisition capability of small order of magnitude targets through engineering transformations and emphasizing interdomain fusion to collaboratively address the inherent challenges in current commercial applications, such as multiplexing, development of detectors for quantitative analysis, more practical on-site monitoring, and sensitivity enhancement. Specifically, this comprehensive review encompasses the latest advances in comprehending LFIA with an alternative signal transduction pattern, aiming to achieve rapid, ultrasensitive, and "sample-to-answer" available options with progressive applications for POC nanosensors. In summary, through the cross-collaboration development of disciplines, LFIA has the potential to break the barriers toward commercialization and achieve laboratory-level POC nanosensors, thus leading to the emergence of the next generation of LFIA.

A customizable automated container-free multi-strip detection and line recognition system for colorimetric analysis with lateral flow immunoassay for lean meat powder based on machine vision and smartphone.

Ractopamine (RAC) and clenbuterol (CLE) are feed additives with adverse effects of consuming too much to food safety. It is necessary to develop an efficient and accurate colorimetric analysis method for immune-based detection of RAC and CLE. Traditional human-vision-based colorimetric analysis for lateral flow immunoassay (LFIA) is non-quantifiable and low-in-automation, while container-based and analysis-instrument-based methods are unrepeatable and high-cost. Therefore, a container-free colorimetric analysis method was developed with LFIAs image captured in dark background under smartphone flash. A multi-strip detection algorithm based on contours extraction, as well as line recognition algorithm based on grayscale projection of LFIA was developed. Finally, relative grayscale (RGS) of lines were calculated and then input into editable fitting curves to estimate concentrations. Results showed the multi-strip detection algorithm reached 98.85% and 93.70% of Recall and intersection over union (IoU), while the line recognition algorithm reached 95.07% and 97.95% of Recall and color similarity, respectively. As a result, an App was fabricated through employing LFIA of RAC and CLE, with colorimetric analysis accuracy of 98.25% and 94.50%, respectively. This study provides a container-free multi-strip colorimetric analysis method with low-cost and illumination robustness, which is a substitution for container-based and single-strip colorimetric analysis methods.