Label-free SERS detection of prostate cancer based on multi-layer perceptron surrogate model method

Prior surface-enhanced Raman spectroscopy (SERS) research has shown that pre-processing is necessary before analysis. Pre-processing also typically serves the dual purposes of removing the auto-fluorescence background and minimizing data volatility. This method allows for a more accurate comparison of spectral traits and relative SERS peak strength. However, because there are so many different kinds of samples, it can take a long time, and there is no assurance that the approach chosen will work well with a particular kind of sample. Therefore, this study employed a deep learning technique called multi-layer perceptron (MLP) to simplify the pre-processing of blood plasma SERS samples in patients with prostate cancer (PC), as well as to enhance the sensitivity and specificity of diagnosis using SERS technology. First of all, significant variations in peak intensity can be observed in the difference spectra, facilitating differentiation between PC and normal groups. Second, the data analysis was carried out in three different stages (raw data, defluorescenced data, and normalized data) using principal component analysis and linear discriminant analysis (PCA-LDA), as well as PCA-multi-layer perceptron (PCA-MLP). Finally, when SERS data was analyzed using PCA-LDA, there were significant differences in classification accuracy across each stage (The classification accuracy of three different stages were 76.90%, 85.60%, 95.20%, respectively). However, when PCA-MLP was utilized for SERS data analysis, the classification accuracy remained consistently high and stable (The classification accuracy of three different stages were 92.00%, 92.40%, 96.70%, respectively). The experimental results of PCA-MLP for classifying specific SERS data indicate that analyzing raw data directly can simplify the experimental process and enhance the efficacy of SERS analysis.

Ag Nanoparticles@Agar Gel as a 3D Flexible and Stable SERS Substrate with Ultrahigh Sensitivity

Flexible surface-enhanced Raman scattering (SERS) substrates have become one of the research hot spots due to the facile sampling by swabbing or wrapping on rough surfaces and the sensitive and nondestructive detection of contaminants. In this work, we proposed a simple and fast in situ reduction method to prepare Ag nanoparticles (Ag NPs) composited agar hydrogel (Ag NPs@Agar) flexible SERS substrate. Owing to the three-dimensional (3D) structure, good hydrophilicity and adsorption of the agar hydrogel, Ag NPs were grown uniformly in the 3D cross-linked structure. The distribution density of Ag NPs was further increased by the volume shrinkage when the hydrogel was dried in air. This high density and uniformly distribution of Ag NPs produced a large number of highly active SERS regions. In addition, the sensitivity of Ag NPs@Agar was further improved with the assistance of hydrophilic agar gel, which can trap the probe molecules into highly active SERS areas. The SERS results showed that the substrate can be used to detect dye molecules (rhodamine 6G), the minimum detectable concentration was 10^-15 M, the relative standard deviation tested at 18 different positions was only 7.58%, and the intensity of the characteristic peak at 611 cm^-1 decreased only about 10% after 49 days of storage, demonstrating the superior stability. Moreover, the Ag NPs@Agar substrate also could successfully achieve the micro-trace detection of melamine and sodium penicillin G in Xinjiang specialty camel milk powder. The above available results show that the prepared flexible Ag NPs@Agar SERS substrates possess potentials for the illegal additives and antibiotics in food safety analysis.

Label-free detection of bladder cancer and kidney cancer plasma based on SERS and multivariate statistical algorithm

In this study, we mainly aimed to investigate the diagnostic potential of surface-enhanced Raman spectroscopy for bladder cancer and kidney cancer which are the most common cancers of the urinary system, and evaluate the classification ability of three statistical algorithms: principal component analysis-linear discriminate analysis (PCA-LDA), partial least square-random forest (PLS-RF), and partial least square-support vector machine (PLS-SVM). The plasma of 26 bladder cancer patients, 38 kidney cancer patients and 39 normal subjects was mixed with the same volume of silver nanoparticles, respectively, and then high-quality SERS signal was obtained. The SERS spectra in the range of 400-1800 cm^-1 were compared and analyzed. There were some significant differences in SERS peak intensity, which may reflect the changes in the content of some biomacromolecules in the plasma of cancer patients. Based on the three algorithms of PCA-LDA, PLS-RF and PLS-SVM, the classification accuracy of SERS spectra of plasma from cancer patients and normal subjects was 98.1%, 100% and 100%, respectively. In addition, the classification accuracy of the three diagnostic algorithms to classify the SERS spectra of bladder cancer and kidney cancer was 81.3%, 91.7%, and 98.4%, respectively. This exploratory work demonstrates that SERS combined with PLS-SVM algorithm has superior performance for clinical screening of bladder cancer and kidney cancer through peripheral plasma.

Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis

In recent years, bioanalytical surface-enhanced Raman spectroscopy (SERS) has blossomed into a fast-growing research area. Owing to its high sensitivity and outstanding multiplexing ability, SERS is an effective analytical technique that has excellent potential in bioanalysis and diagnosis, as demonstrated by its increasing applications in vivo. SERS allows the rapid detection of molecular species based on direct and indirect strategies. Because it benefits from the tunable surface properties of nanostructures, it finds a broad range of applications with clinical relevance, such as biological sensing, drug delivery and live cell imaging assays. Of particular interest are early-stage-cancer detection and the fast detection of pathogens. Here, we present a comprehensive survey of SERS-based assays, from basic considerations to bioanalytical applications. Our main focus is on SERS-based pathogen detection methods as point-of-care solutions for early bacterial infection detection and chronic disease diagnosis. Additionally, various promising in vivo applications of SERS are surveyed. Furthermore, we provide a brief outlook of recent endeavours and we discuss future prospects and limitations for SERS, as a reliable approach for rapid and sensitive bioanalysis and diagnosis.

Elevating the density and intensity of hot spots by repeated annealing for high-efficiency SERS

The simultaneous output of highly sensitive and reproducible signals for surface-enhanced Raman spectroscopy (SERS) technology remains difficult. Here, we propose a two-dimensional (2D) composite structure using the repeated annealing method with MoS₂ film as the molecular adsorbent. This method provides enlarged Au nanoparticle (NP) density with much smaller gap spacing, and thus dramatically increases the density and intensity of hot spots. The MoS₂ films distribute among the hot spots, which is beneficial for uniform molecular adsorption, and further increases the sensitivity of the SERS substrate. Three kinds of molecules were used to evaluate the SERS substrate. Ultra-sensitive, highly repetitive, and stable SERS signals were obtained, which would promote the application process of SERS technology in quantitative analysis and detection.

Gold Nanoparticle-Coated Starch Magnetic Beads for the Separation, Concentration, and SERS-Based Detection of E. coli O157:H7

Here, we report gold nanoparticle-coated starch magnetic beads (AuNP@SMBs) that were prepared by in situ synthesis of AuNPs on the surface of SMBs. Upon functionalization of the surface with a specific antibody, the immuno-AuNP@SMBs were found to be effective in separating and concentrating the target pathogenic bacteria, Escherichia coli O157:H7, from an aqueous sample as well as providing a hotspot for surface-enhanced Raman scattering (SERS)-based detection. We employed a bifunctional linker protein, 4× gold-binding peptide-tagged Streptococcal protein G (4GS), to immobilize antibodies on AuNP@SMBs and AuNPs in an oriented form. The linker protein also served as a Raman reporter, exhibiting a strong and unique fingerprint signal during the SERS measurement. The amplitude of the SERS signal was shown to have a good correlation with the concentration of target bacteria ranging from 10⁰ to 10⁵ CFU/mL. The detection limit was determined to be as low as a single cell, and the background signals derived from nontarget bacteria were negligible due to the excellent specificity and colloidal stability of the immuno-AuNP@SMBs and SERS tags. The highly sensitive nature of the SERS-based detection system will provide a promising means to detect the pathogenic microorganisms in food or clinical specimen.

Hierarchical Laser-Patterned Silver/Graphene Oxide Hybrid SERS Sensor for Explosive Detection

We demonstrate an ultrafast laser-ablated hierarchically patterned silver nanoparticle/graphene oxide (AgNP/GO) hybrid surface-enhanced Raman scattering (SERS) substrate for highly sensitive and reproducible detection of an explosive marker 2,4-dinitrotoluene (2,4-DNT). A hierarchical laser-patterned silver sheet (Ag-S) is achieved by ultrafast laser ablation in air with pulse energies of 25, 50, and 100 μJ. Multiple laser pulses at a wavelength of 800 nm and a pulse repetition rate of 50 fs at 1 kHz are directly focused on Ag-S to produce and deposit AgNPs onto Ag-S. The surface morphology of ablated Ag-S was evaluated using atomic force microscopy, optical profilometry, and field emission scanning electron microscopy (FESEM). A rapid increase in the ablation rate with increasing laser energy was observed. Selected area Raman mapping is performed to understand the intensity and size distribution of AgNPs on Ag-S. Further, GO was spin-coated onto the AgNPs produced by ultrafast ablation on Ag-S. The hierarchical laser-patterned AgNP/GO hybrid structure was characterized using FESEM, high-resolution transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and Raman spectroscopy. Further, hierarchical laser-patterned AgNP/GO hybrid structures have been utilized as SERS-active substrates for the selective detection of 2,4-DNT, an explosive marker. The developed SERS-active sensor shows good stability and high sensitivity up to picomolar (pM) concentration range with a Raman intensity enhancement of ∼10¹⁰ for 2,4-DNT. The realized enhancement of SERS intensity is due to the cumulative effect of GO coated on Ag-S as a proactive layer and AgNPs produced by ultrafast ablation.

Recent progress on graphene-based substrates for surface-enhanced Raman scattering applications

Graphene-based SERS substrates are classified and introduced, and their applications in biosensing-related fields are reviewed.

Ag gyrus-nanostructure supported on graphene/Au film with nanometer gap for ideal surface enhanced Raman scattering

The physical phenomenon, surface-enhanced Raman scattering (SERS), is mainly based on the local electromagnetic fields enhancement located at the nano-gaps between metal nanostructures attributed to localized surface plasmon resonance. Therefore, nano-gaps are very important for obtaining high-density hot spots and optimal and uniform SERS signals. However, it remains a challenge to form the three-dimensional ultra-narrow nano-gaps. Here, a gyrus-inspired Gyrus-SERS substrate was fabricated with the nanostructure of Ag gyrus/graphene/Au film using an extremely simple method. The lateral and vertical hot spots respectively were obtained from the dense nano-gaps (~3 nm) between gyrus and the coupling of Ag gyrus and Au film in bilayer graphene nano-gaps (0.68 nm), which were demonstrated in experiment and theory. The proposed Gyrus-SERS platform performs an excellent SERS activity (EF~5 × 10⁹), high sensitivity (the minimum detected concentration of R6G and CV respectively is 10^-13 and 10^-12 M), and outstanding reproducibility (RSD~7.11%). For practical application, the in situ detection of Malachite green (MG) residue on prawn skin was executed using the prepared flexible Gyrus-SERS substrate, which shows the wide potential in food safety field.

A sensitive, uniform, reproducible and stable SERS substrate has been presented based on MoS2@Ag nanoparticles@pyramidal silicon

By combine the Ag nanoparticles, pyramidal silicon and molybdenum disulfide, the MoS₂@AgNPs@PSi substrate shows high performance in terms of sensitivity, uniformity, reproducibility and stability.

High-performance SERS substrate based on hybrid structure of graphene oxide/AgNPs/Cu film@pyramid Si

We present a novel surface-enhanced Raman scattering (SERS) substrate based on graphene oxide/silver nanoparticles/copper film covered silicon pyramid arrays (GO/AgNPs/PCu@Si) by a low-cost and simple method. The GO/AgNPs/PCu@Si substrate presents high sensitivity, good homogeneity and well stability with R6G molecules as a probe. The detected concentration of Rhodamine 6 G (R6G) is as low as 10⁻¹⁵ M. These sensitive SERS behaviors are also confirmed in theory via a commercial COMSOL software, the electric field enhancement is not only formed between the AgNPs, but also formed between the AgNPs and Cu film. And the GO/AgNPs/PCu@Si substrates also present good property on practical application for the detection of methylene blue (MB) and crystal violet (CV). This work may offer a novel and practical method to facilitate the SERS applications in areas of medicine, food safety and biotechnology.

Ag and Cu Monometallic and Ag/Cu Bimetallic Nanoparticle-Graphene Composites with Enhanced Antibacterial Performance

Increased proliferation of antimicrobial resistance and new strains of bacterial pathogens severely impact current health, environmental, and technological developments, demanding design of novel, highly efficient antibacterial agents. Ag, Cu monometallic and Ag/Cu bimetallic nanoparticles (NPs) were in situ grown on the surface of graphene, which was produced by chemical vapor deposition using ferrocene as precursor and further functionalized to introduce oxygen-containing surface groups. The antibacterial performance of the resulting hybrids was evaluated against Escherichia coli cells and compared through a series of parametrization experiments of varying metal type and concentration. It was found that both Ag- and Cu-based monometallic graphene composites significantly suppress bacterial growth, yet the Ag-based ones exhibit higher activity compared to that of their Cu-based counterparts. Compared with well-dispersed colloidal Ag NPs of the same metal concentration, Ag- and Cu-based graphene hybrids display weaker antibacterial activity. However, the bimetallic Ag/CuNP-graphene hybrids exhibit superior performance compared to that of all other materials tested, i.e., both the monometallic graphene structures as well as the colloidal NPs, achieving complete bacterial growth inhibition at all metal concentrations tested. This striking performance is attributed to the synergistic action of the combination of the two different metals that coexist on the surface as well as the enhancing role of the graphene support.

Boron Nitride Nanosheet-Veiled Gold Nanoparticles for Surface-Enhanced Raman Scattering

Atomically thin boron nitride (BN) nanosheets have many properties desirable for surface-enhanced Raman spectroscopy (SERS). BN nanosheets have a strong surface adsorption capability toward airborne hydrocarbon and aromatic molecules. For maximized adsorption area and hence SERS sensitivity, atomically thin BN nanosheet-covered gold nanoparticles have been prepared for the first time. When placed on top of metal nanoparticles, atomically thin BN nanosheets closely follow their contours so that the plasmonic hot spots are retained. Electrically insulating BN nanosheets also act as a barrier layer to eliminate metal-induced disturbances in SERS. Moreover, the SERS substrates veiled by BN nanosheets show an outstanding reusability in the long term. As a result, the sensitivity, reproducibility, and reusability of SERS substrates can be greatly improved. We also demonstrate that large BN nanosheets produced by chemical vapor deposition can be used to scale up the proposed SERS substrate for practical applications.