SERS biosensors based on catalytic hairpin self-assembly and hybridization chain reaction cascade signal amplification strategies for ultrasensitive microRNA-21 detection.

A highly sensitive surface-enhanced Raman scattering (SERS) biosensor has been developed for the detection of microRNA-21 (miR-21) using an isothermal enzyme-free cascade amplification method involving catalytic hairpin assembly (CHA) and hybridization chain reaction (HCR). The CHA reaction is triggered by the target miR-21, which causes hairpin DNA (C1 and C2) to self-assemble into CHA products. After AgNPs@Capture captures the resulting CHA product, the HCR reaction is started, forming long-stranded DNA on the surface of AgNPs. A strong SERS signal is generated due to the presence of a large amount of the Raman reporter methylene blue (MB) in the vicinity of the SERS "hot spot" on the surface of AgNPs. The monitoring of the SERS signal changes of MB allows for the highly sensitive and specific detection of miR-21. In optimal conditions, the biosensor exhibits a satisfactory linear range and a low detection limit for miR-21 of 42.3 fM. Additionally, this SERS biosensor shows outstanding selectivity and reproducibility. The application of this methodology to clinical blood samples allows for the differentiation of cancer patients from healthy controls. As a result, the CHA-HCR amplification strategy used in this SERS biosensor could be a useful tool for miRNA detection and early cancer screening.

Rapid intracellular pH measurement based on electroporation- surface-enhanced Raman scattering.

In this study, electroporation-surface-enhanced Raman scattering (SERS) was applied to rapidly measure intracellular pH. The generation of a sensitive SERS probe for measuring pH in the range of 6.0-8.0 was accomplished through the conjugation of the pH-sensitive molecule 4-mercaptobenzoic acid (4-MBA) to the surface of gold nanoparticles (Au NPs) through its thiol functional group. This bioprobe was then rapidly introduced into nasopharyngeal carcinoma CNE-1 cells by electroporation, followed by SERS scanning and the fitting of intensity ratios of each detection point's Raman peaks at 1423 cm-1 and 1072 cm-1, to create the pH distribution map of CNE-1 cells. The electroporation-SERS assay introduces pH bioprobes into a living cell in a very short time and disperses the nanoprobe throughout the cytoplasm, ultimately enabling rapid and comprehensive pH analysis of the entire cell. Our work demonstrates the potential of electroporation-SERS for the biochemical analysis of live cells.

In Situ Collection and SERS Detection of Nitrite in Exhalations on Facemasks Based on Wettability Differences.

As one of the common carriers of biological information, along with human urine specimens and blood, exhaled breath condensate (EBC) carries reliable and rich information about the body's metabolism to track human physiological normal/abnormal states and environmental exposures. What is more, EBC has gained extensive attention because of the convenient and nondestructive sampling. Facemasks, which act as a physical filter barrier between human exhaled breath and inhaled substances from the external environment, are safe, noninvasive, and economic devices for direct sampling of human exhaled breath and inhaled substances. Inspired by the ability of fog collection of Namib desert beetle, a strategy for in situ collecting and detecting EBC with surface-enhanced Raman scattering is illustrated. Based on the intrinsic and unique wettability differences between the squares and the surrounding area of the pattern on facemasks, the hydrophilic squares can capture exhaled droplets and spontaneously enrich the analytes and silver nanocubes (AgNCs), resulting in good repeatability in situ detection. Using R6G as the probe molecule, the minimal detectable concentration can reach as low as 10-16 M, and the relative standard deviation is less than 7%. This proves that this strategy can achieve high detection sensitivity and high detection repeatability. Meanwhile, this strategy is applicable for portable nitrite analysis in EBC and may provide an inspiration for monitoring other biomarkers in EBC.

Study of two-dimensional information writing, reading and error correction at micro/nanoscale based on gold nanosphere arrays.

Surface plasmon driven photocatalytic reactions have great potential for information encryption as well as information security. In this paper, explored the detection concentrations of dye molecule Rhodamine6G (R6G) on three substrates, where complete original Raman spectra signals were still obtained at a concentration of 10-8 M. Utilized photosensitive molecules to investigate the photocatalytic characteristics of 4-nitrobenzenethiol (4-NBT) on three substrates. Excitation light at a wavelength of 633 nm enables local photocatalytic for information signals writing, while 785 nm wavelength excitation light combined with two-dimensional Mapping technology is used for information signal reading. Read information signals are often prone to reading errors due to their own lack of resolution or strong interference from back bottom signals, so error correction processing of information signals is essential. Through comparative exploration, it is found that the ratio method can obtain high-precision and high-resolution information signals, and the interference of the background signals were well suppressed. Leveraging the advantages of Raman fingerprint spectra at the micro/nanoscale, it solves the challenge of incomplete information signals presentation at smaller scales. Additionally, through error correction processing of the information signals, high precision and high-resolution information signals are obtained.

One-step synthesized multisize AuAg alloy nanoparticles with high SERS sensitivity in directly detecting SARS-CoV-2 spike protein.

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in widespread disease transmission, challenging the stability of global healthcare systems. Surface-enhanced Raman scattering (SERS) as an easy operation, fast, and low-cost technology illustrates a good potential in detecting SARS-CoV-2. In the study, one-step fabrication of gold-silver alloy nanoparticles (AuAgNPs) with adjustable metal proportions and diameters is employed as SERS substrates. The angiotensin-converting enzyme 2 (ACE2) functionalized AuAgNPs are applied as sensor surfaces to detect SARS-CoV-2 S protein. By optimizing the SERS substrates, ACE2/Au35Ag65NPs illustrate higher performance in detecting the SARS-CoV-2 S protein with a limit of detection (LOD) of 10 fg/mL in both phosphate-buffered saline (PBS) and pharyngeal swabs solution (PSS). It also provides excellent reproducibility with a relative standard deviation (RSD) of 7.7 % and 7.9 %, respectively. This easily preparable and highly reproducible SERS substrate has good potential in the practical application of detecting SARS-CoV-2.

NIR-II Surface-Enhanced Raman Scattering Nanoprobes in Biomedicine: Current Impact and Future Directions.

The field of second near-infrared (NIR-II) surface-enhanced Raman scattering (SERS) nanoprobes has made commendable progress in biomedicine. This article reviews recent advances and future development of NIR-II SERS nanoprobes. It introduces the fundamental principles of SERS nanoprobes and highlights key advances in the NIR-II window, including reduced tissue attenuation, deep penetration, maximized allowable exposure, and improved photostability. The discussion of future directions includes the refinement of nanoprobe substrates, emphasizing the tailoring of optical properties of metallic SERS-active nanoprobes, and exploring non-metallic alternatives. The intricacies of designing Raman reporters for the NIR-II resonance and the potential of these reporters to advance the field are also discussed. The integration of artificial intelligence (AI) into nanoprobe design represents a cutting-edge approach to overcome current challenges. This article also examines the emergence of deep Raman techniques for through-tissue SERS detection, toward NIR-II SERS tomography. It acknowledges instrumental advancements like improved charge-coupled device sensitivity and accelerated imaging speeds. The article concludes by addressing the critical aspects of biosafety, ease of functionalization, compatibility, and the path to clinical translation. With a comprehensive overview of current achievements and future prospects, this review aims to illuminate the path for NIR-II SERS nanoprobes to innovate diagnostic and therapeutic approaches in biomedicine.

Automated identification of pesticide mixtures via machine learning analysis of TLC-SERS spectra.

Identification of components in pesticide mixtures has been a major challenge in spectral analysis. In this paper, we assembled monolayer Ag nanoparticles on Thin-layer chromatography (TLC) plates to prepare TLC-Ag substrates with mixture separation and surface-enhanced Raman scattering (SERS) detection. Spectral scans were performed along the longitudinal direction of the TLC-Ag substrate to generate SERS spectra of all target analytes on the TLC plate. Convolutional neural network classification and spectral angle similarity machine learning algorithms were used to identify pesticide information from the TLC-SERS spectra. It was shown that the proposed automated spectral analysis method successfully classified five categories, including four pesticides (thiram, triadimefon, benzimidazole, thiamethoxam) as well as a blank TLC-Ag data control. The location of each pesticide on the TLC plate was determined by the intersection of the information curves of the two algorithms with 100 % accuracy. Therefore, this method is expected to help regulators understand the residues of mixed pesticides in agricultural products and reduce the potential risk of agricultural products to human health and the environment.

Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms.

An integrated approach combining surface-enhanced Raman spectroscopy (SERS) with a specialized deep learning algorithm to rapidly and accurately detect and quantify SARS-CoV-2 variants is developed based on an angiotensin-converting enzyme 2 (ACE2)-functionalized AgNR@SiO2 array SERS sensor. SERS spectra with concentrations of different variants were collected using a portable Raman system. After appropriate spectral preprocessing, a deep learning algorithm, CoVari, is developed to predict both the viral variant species and concentrations. Using a 10-fold cross-validation strategy, the model achieves an average accuracy of 99.9% in discriminating between different virus variants and R2 values larger than 0.98 for quantifying viral concentrations of the three viruses, demonstrating the high quality of the detection. The limit of detection of the ACE2 SERS sensor is determined to be 10.472, 11.882, and 21.591 PFU/mL for SARS-CoV-2, SARS-CoV-2 B1, and CoV-NL63, respectively. The feature importance of virus classification and concentration regression in the CoVari algorithm are calculated based on a permutation algorithm, which showed a clear correlation to the biochemical origins of the spectra or spectral changes. In an unknown specimen test, classification accuracy can achieve >90% for concentrations larger than 781 PFU/mL, and the predicted concentrations consistently align with actual values, highlighting the robustness of the proposed algorithm. Based on the CoVari architecture and the output vector, this algorithm can be generalized to predict both viral variant species and concentrations simultaneously for a broader range of viruses. These results demonstrate that the SERS + CoVari strategy has the potential for rapid and quantitative detection of virus variants and potentially point-of-care diagnostic platforms.

Uncovering Art's Vanishing Hues with Surface-Enhanced Raman Scattering: Drawing Inspiration from the Past for the Future.

The aesthetic and historical significance of art is well recognized; art can stoke emotions, invite close inquiry, and connect us to the past. However, works of art are also complex material objects that present unique challenges and opportunities for the scientific community. Identifying "fugitive" organic pigments in traditional oil paintings, for example, presents a particularly complex analytical challenge that is critical to address for their conservation and long-term preservation. In this Perspective, we discuss the benefits and technical challenges of applying surface-enhanced Raman scattering (SERS) spectroscopy to the ultrasensitive identification of fugitive pigments in paintings as well as future developments in SERS we envision that are inspired by the past.

Precision classification and quantitative analysis of bacteria biomarkers via surface-enhanced Raman spectroscopy and machine learning.

The SERS spectra of six bacterial biomarkers, 2,3-DHBA, 2,5-DHBA, Pyocyanin, lipoteichoic acid (LTA), Enterobactin, and ß-carotene, of various concentrations, were obtained from silver nanorod array substrates, and the spectral peaks and the corresponding vibrational modes were identified to classify different spectra. The spectral variations in three different concentration regions due to various reasons have imposed a challenge to use classic calibration curve methods to quantify the concentration of biomarkers. Depending on baseline removal strategy, i.e., local or global baseline removal, the calibration curve differed significantly. With the aid of convolutional neural network (CNN), a two-step process was established to classify and quantify biomarker solutions based on SERS spectra: using a specific CNN model, a remarkable differentiation and classification accuracy of 99.99 % for all six biomarkers regardless of the concentration can be achieved. After classification, six regression CNN models were established to predict the concentration of biomarkers, with coefficient of determination R2 > 0.97 and mean absolute error (MAE) < 0.27. The feature of important calculations indicates the high classification and quantification accuracies were due to the intrinsic spectral features in SERS spectra. This study showcases the synergistic potential of SERS and advanced machine learning algorithms and holds significant promise for bacterial infection diagnostics.

Construction of PCR-SERS Method for Detection of Vibrio parahaemolyticus.

A paper-based surface enhancement of a Raman scattering substrate consisting of silver-nanowires stacked on glass-fiber filter paper was prepared. At the same time, the DNA-embedding molecule Eva Green was introduced as a signaling molecule for surface-enhanced Raman scattering (SERS) detection. Polymerase chain reaction (PCR) was used to amplify target genes and the method was developed into a rapid molecular diagnostic system. The total detection time of the developed detection method was 40 min, including 30 min of PCR amplification and 10 min of SERS measurement. After 30 PCR cycles, bacterial DNA with an initial concentration of 20 fg/µL and a bacterial suspension with an initial concentration of 7.2 × 101 CFUs/mL could be detected. When the enrichment culture time was 4 h, target bacteria with an initial contamination inoculation volume of 1.5 CFUs/mL could be detected in artificially contaminated samples. The method is fast and highly sensitive, and has not been applied to the detection of V. parahaemolyticus.

Non-invasive detection of systemic lupus erythematosus using SERS serum detection technology and deep learning algorithms.

Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple symptoms, and its rapid screening is the research focus of surface-enhanced Raman scattering (SERS) technology. In this study, gold@silver-porous silicon (Au@Ag-PSi) composite substrates were synthesized by electrochemical etching and in-situ reduction methods, which showed excellent sensitivity and accuracy in the detection of rhodamine 6G (R6G) and serum from SLE patients. SERS technology was combined with deep learning algorithms to model serum features using selected CNN, AlexNet, and RF models. 92 % accuracy was achieved in classifying SLE patients by CNN models, and the reliability of these models in accurately identifying sera was verified by ROC curve analysis. This study highlights the great potential of Au@Ag-PSi substrate in SERS detection and introduces a novel deep learning approach for SERS for accurate screening of SLE. The proposed method and composite substrate provide significant value for rapid, accurate, and noninvasive SLE screening and provide insights into SERS-based diagnostic techniques.

Design of magnetic photonic crystal microdroplet for sensitive detection of cationic organic pollutants by the coupled resonance effect.

Photonic crystals (PCs), periodically arranged nanoparticles, have emerged with extraordinary optical properties for light manipulation owing to their photonic band gaps (PBGs). Here, a novel strategy and method was developed for efficient enrichment and sensitive detection of cationic organic pollutants in water. Size-controlled Fe3O4@poly (4-styrenesulfonic acid-co-maleic acid) (Fe3O4@PSSMA) was prepared, and high surface charge were formed with the coating of PSSMA layer on the surface of Fe3O4, which could be used for adsorption and removal of cationic organic pollutants. The Fe3O4@PSSMA after adsorbing cationic organic pollutant were assembled to magnetic photonic crystal microdroplet (MPCM) structure in an external magnetic field, which was used as surface-enhanced Raman scattering (SERS) substrate. By coupling the magnetically tuned PBGs with Raman laser wavelength, the light utilization efficiency can be improved and the coupled resonance effect was greatly enhanced. The enhancement factor (EF) of MB was more than 800 attributing to the dual function of enrichment and coupled resonance effect of MPCM. The developed analytical strategy is the first time to use MPCM as a SERS substrate to realize the sensitive detection of 10 nmol L-1 MB in real water, which greatly improves the application of MPCM in the field of contaminant analysis and detection in water.

Improving the detection accuracy of the dual SERS aptasensor system with uncontrollable SERS "hot spot" using machine learning tools.

BACKGROUND: Simultaneous detection of food contaminants is crucial in addressing the collective health hazards arising from the presence of multiple contaminants. However, traditional multi-competitive surface-enhanced Raman scattering (SERS) aptasensors face difficulties in achieving simultaneous accurate detection of multiple target substances due to the uncontrollable SERS "hot spots". In this study, using chloramphenicol (CAP) and estradiol (E2) as two target substances, we introduced a novel approach that combines machine learning methods with a dual SERS aptasensor, enabling simultaneous high-sensitivity and accurate detection of both target substances. RESULTS: The strategy effectively minimizes the interference from characteristic Raman peaks commonly encountered in traditional multi-competitive SERS aptasensors. For this sensing system, the Au@4-MBA@Ag nanoparticles modified with sulfhydryl (SH)-CAP aptamer and Au@DTNB@Ag NPs modified with sulfhydryl (SH)-E2 aptamer were used as signal probes. Additionally, Fe3O4@Au nanoflowers integrated with SH-CAP aptamer complementary DNA and SH-E2 aptamer complementary DNA were used as capture probes, respectively. When compared to linear regression random forest, and support vector regression (SVR) models, the proposed artificial neural network (ANN) model exhibited superior precision, demonstrating R2 values of 0.963, 0.976, 0.991, and 0.970 for the training set, test set, validation set, and entire dataset, respectively. Validation with ten spectral groups reported an average error of 244 µg L-1. SIGNIFICANCE: The essence of our study lies in its capacity to address a persistent challenge encountered by traditional multiple competitive SERS aptasensors - the interference generated by uncontrollable SERS "hot spots" that hinders simultaneous quantification. The accuracy of the predictive model for simultaneous detection of two target substances was significantly improved using machine learning tools. This innovative technique offers promising avenues for the accurate and high-sensitive simultaneous detection of multiple food and environmental contaminants.

Fabrication of SERS composite substrates using Ag nanotriangles-modified SiO2 photonic crystal and the application of malachite green detection.

A novel surface-enhanced Raman scattering (SERS) composite substrates on the basis of Ag triangular nanoplates(Ag TNPs)-modified SiO2 photonic crystals (PC) is fabricated and applied to the SERS detection of malachite green (MG). It consists of uniformly arranged Ag TNP@SiO2, a new PC. Notably, Ag TNP are uniformly aligned on the SiO2 surface, forming a three-dimensional high-density hotspot nanostructure. With the tip "hot spots" of Ag TNPs, Bragg diffraction of SiO2 and coupling enhancement between Ag TNPs and SiO2, the SERS enhancement of this composite substrates was multiplied. The effect on the SERS of Ag TNP@SiO2 composite substrate was systematically optimized by tuning Ag TNP size, size of SiO2 microspheres, coverage of Ag TNPs on SiO2 and fabrication method of Ag TNPs and PC. Moreover, the uniform of SERS composite substrates and Raman signal was dramatically increased by the method of vertical deposition. Eventually, the SERS composite substrates were employed in MG detection. Its broad detection range of 1 pM-1 µM and low limit of detection (LOD) of 0.49 pM indicated acceptable sensitivity and repeatability. This work illustrates the promising applicability in food safety analysis based on SERS composite substrates composed by Ag TNP@SiO2 with numerous SERS enhancements and excellent stability.

Rapid qualitative and quantitative analysis of benzo(b)fluoranthene (BbF) in shrimp using SERS-based sensor coupled with chemometric models.

Benzo(b)fluoranthene (BbF), a polycyclic aromatic hydrocarbon (PAH), is a carcinogenic contaminant of concern in seafood. This study developed a simple, rapid, sensitive, and cost-effective surface-enhanced Raman scattering (SERS) sensor (AuNPs) coupled with chemometric models for detecting BbF in shrimp samples. Partial least squares (PLS) regression models were optimized using uninformative variable elimination (UVE), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS). Qualitative analysis was performed using principal component analysis (PCA), linear discriminant analysis (LDA), and k-nearest neighbors (KNN) to differentiate between BbF-contaminated and uncontaminated shrimp samples. The SERS-sensor exhibited excellent sensitivity (LOD = 0.12 ng/mL), repeatability (RSD = 6.21%), and anti-interference performance. CARS-PLS model demonstrated superior predictive ability (R2 = 0.9944), and qualitative analysis discriminated between contaminated and uncontaminated samples. The sensor's accuracy was validated using HPLC, demonstrating the ability of the SERS-sensor coupled with chemometrics to rapidly and reliably detect BbF in shrimp samples.

A MXene Hydrogel-Based Versatile Microrobot for Controllable Water Pollution Management.

The urgent demand for addressing dye contaminants in water necessitates the development of microrobots that exhibit remote navigation, rapid removal, and molecular identification capabilities. The progress of microrobot development is currently hindered by the scarcity of multifunctional materials. In this study, a plasmonic MXene hydrogel (PM-Gel) is synthesized by combining bimetallic nanocubes and Ti3C2Tx MXene through the rapid gelation of degradable alginate. The hydrogel can efficiently adsorb over 60% of dye contaminants within 2 min, ultimately achieving a removal rate of >90%. Meanwhile, the hydrogel exhibits excellent sensitivity in surface enhanced Raman scattering (SERS) detection, with a limit of detection (LOD) as low as 3.76 am. The properties of the plasmonic hydrogel can be further adjusted for various applications. As a proof-of-concept experiment, thermosensitive polymers and superparamagnetic particles are successfully integrated into this hydrogel to construct a versatile, light-responsive microrobot for dye contaminants. With magnetic and optical actuation, the robot can remotely sample, identify, and remove pollutants in maze-like channels. Moreover, light-driven hydrophilic-hydrophobic switch of the microrobots through photothermal effect can further enhance the adsorption capacity and reduced the dye residue by up to 58%. These findings indicate of a broad application potential in complex real-world environments.

Morphology and optical properties of Au-Ag hybrid nanoparticles regulation and its ultra-sensitive SERS immunoassay detection in carbohydrate antigen 19-9.

The development of an ultra-sensitive detection method for carbohydrate antigen 19-9 (CA19-9) is very important for the early diagnosis of pancreatic cancer. In this work, we developed a new strategy to achieve a variety of Au-Ag hybrid nanoparticles from janus to core-satellite which is controlled by the volume of AgNO3 and the concentration of benzimidazolecarboxylic acid (MBIA). With the volume of AgNO3 increased, Au-Ag hybrid nanoparticles changed from janus to core-satellite and the characteristic absorption peak showed two opposite trends. The size and number of Ag islands were determined by the concentration of MBIA. Au-Ag core-satellites nanoparticles with a large number of small-sized Ag have the highest SERS intensity. Then we used them as SERS nanotags and Au-Polystyrene nanospheres modified by captured anti-CA19-9 antibody as solid substrates to realize the ultra-sensitive detection of CA19-9 with a low limit of detection of 1.25 × 10-6 IU/mL and a wide linear range of 1.00 × 10-5 -1.00 × 104 IU/mL. This work not only demonstrates that MBIA and AgNO3 were the key factors in the growth of Au-Ag hybrid nanoparticles from 2D to 3D structure but also supplies an ultra-sensitive detection method for CA19-9 which has a potential practicability in the clinical early diagnoses of pancreatic cancer.

Real time imaging of cell-permeable nanoreactor with SERS for insight into cellular processes.

Intracellular glucose detection is crucial due to its pivotal role in metabolism and various physiological processes. Precise glucose monitoring holds significance in diabetes management, metabolic studies, and biotechnological applications. In this study, we developed an innovative and expedient cell-permeable nanoreactor for intracellular glucose based on surface-enhanced Raman scattering (SERS). The nanoreactor was designed with gold nanoparticles (AuNPs), which were engineered with glucose oxide (GOx) and a H2O2-responsive Raman reporter 2-mercaptohydroquinone (2-MHQ). The interaction between 2-MHQ and H2O2 generated by glucose and GOx could simultaneously induce the appearance in the peak at 985 cm-1. Our results showed excellent performance in detecting glucose within the concentration range from 0.1 µM to 10 mM, with a low detection limitation of 14.72 nM. In addition, the glucose distribution in single HeLa cells was evaluated by real time SERS mapping. By combining noble metal particles and natural oxidases, the nanoreactor possesses both Raman activity and enzymatic functionality, thus enables sensitive glucose detection and facilitates imaging at a single cell level, which offers an insightful monitoring of cellular processes.

Visual and quantitative lateral flow immunoassay based on Au@PS SERS tags for multiplex cardiac biomarkers.

Rapid and sensitive detection of multiple biomarkers by lateral flow immunoassay (LFIA) remains challenging for signal amplification for commonly used nanotags. Herein, we report a novel LFIA strip for visual and highly sensitive analysis of two cardiac biomarkers based on functionalized gold nanoparticles @ polystyrene microsphere (Au@PS）microcavity as surface-enhanced Raman scattering (SERS) tags. Antibody-modified Au@PS was designed as a SERS label. The evanescent waves propagating along the surface of the PS microcavity and the localized surface plasmons of the gold nanoparticles were coupled to enhance the light-matter interaction synergistically for Raman signal enhancement. In this strategy, the proposed Au@PS SERS tags-based LFIA was carried out to quantify the content of the heart failure and infarct biomarkers synchronously within 15 min and get the limits of detection of 1 pg/mL and 10 pg/mL for cardiac troponin I (cTnI) and N-terminal natriuretic peptide precursor (NT-proBNP), respectively. The results demonstrated 10-20 folds more sensitivity than that of the standard colloidal gold strip and fluorescent strip for the same biomarkers. This novel quantitative LFIA shows promise as a high-sensitive and visual sensing method for relevant clinical and forensic analysis.