Applications of surface-enhanced Raman spectroscopy in detection fields

Ultrasensitive Hierarchical AuNRs@SiO2@Ag SERS Probes for Enrichment and Detection of Insulin and C-Peptide in Serum

Introduction

Insulin and C-peptide played crucial roles as clinical indicators for diabetes and certain liver diseases. However, there has been limited research on the simultaneous detection of insulin and C-peptide in trace serum. It is necessary to develop a novel method with high sensitivity and specificity for detecting insulin and C-peptide simultaneously.

Methods

A core-shell-satellites hierarchical structured nanocomposite was fabricated as SERS biosensor using a simple wet-chemical method, employing 4-MBA and DTNB for recognition and antibodies for specific capture. Gold nanorods (Au NRs) were modified with Raman reporter molecules and silver nanoparticles (Ag NPs), creating SERS tags with high sensitivity for detecting insulin and C-peptide. Antibody-modified commercial carboxylated magnetic bead@antibody served as the capture probes. Target materials were captured by probes and combined with SERS tags, forming a "sandwich" composite structure for subsequent detection.

Results

Under optimized conditions, the nanocomposite fabricated could be used to detect simultaneously for insulin and C-peptide with the detection limit of 4.29 × 10-5 pM and 1.76 × 10-10 nM in serum. The insulin concentration (4.29 × 10-5-4.29 pM) showed a strong linear correlation with the SERS intensity at 1075 cm-1, with high recoveries (96.4-105.3%) and low RSD (0.8%-10.0%) in detecting human serum samples. Meanwhile, the C-peptide concentration (1.76 × 10-10-1.76 × 10-3 nM) also showed a specific linear correlation with the SERS intensity at 1333 cm-1, with recoveries 85.4%-105.0% and RSD 1.7%-10.8%.

Conclusion

This breakthrough provided a novel, sensitive, convenient and stable approach for clinical diagnosis of diabetes and certain liver diseases. Overall, our findings presented a significant contribution to the field of biomedical research, opening up new possibilities for improved diagnosis and monitoring of diabetes and liver diseases.

Analysis and remediation of phthalates in aquatic matrices: current perspectives

Phthalic acid esters (PAEs) are high production volume chemicals used extensively as plasticizers, to increase the flexibility of the main polymer. They are reported to leach into their surroundings from plastic products and are now a ubiquitous environmental contaminant. Phthalate levels have been determined in several environmental matrices, especially in water. These levels serve as an indicator of plasticizer abuse and plastic pollution, and also serve as a route of exposure to different species including humans. Reports published on effects of different PAEs on experimental models demonstrate their carcinogenic, teratogenic, reproductive, and endocrine disruptive effects. Therefore, regular monitoring and remediation of environmental water samples is essential to ascertain their hazard quotient and daily exposure levels. This review summarises the extraction and detection techniques available for phthalate analysis in water samples such as chromatography, biosensors, immunoassays, and spectroscopy. Current remediation strategies for phthalate removal such as adsorption, advanced oxidation, and microbial degradation have also been highlighted.

Construction of "Coral" SERS sensor for ultrasensitive and rapid detection of harmful component macrophage migration inhibitory factor in Platelet-rich Plasma

Macrophage migration inhibitory factor (MIF) is a pro-inflammatory factor produced by residual red blood cell lysis, which can significantly influence the curative effect of Platelet-rich plasma (PRP) therapy used for osteoarthritis (OA) treatment. In this study, we proposed a novel approach for detecting the concentration of MIF in PRP using a dopamine-coated antibody-Au (core)-Ag (shell)-SERS sensor, which enables ultrasensitive and rapid detection of MIF. The best experimental conditions have a detection limit of only 90.05 pg/mL and a good linear relationship between 1-5000 ng/mL. In 40 PRP samples collected from actual clinical patients, we detected MIF concentrations ranging from 2.0-3.6 ng/mL. This indicated that the Coral SERS sensor not only allows for results highly consistent with the traditional ELISA method, but also costs less ($0.40-$0.70), needs shorter testing time (integration time is only 10s), and consumes less PRP that can greatly improve the sample quality and maximize the curative effect in clinical applications for OA treatment with PRP.

Development and validation of enzyme-linked immunosorbent assays for the measurement of infliximab and anti-drug antibody levels

Infliximab and its anti-drug antibody (ADA) serum concentrations exhibit a strong correlation with clinical response and loss of response. The use of therapeutic drug monitoring to measure the concentration of infliximab and ADA can facilitate clinical decision-making, helping patients attain optimal therapeutic effects. However, there are still limitations to the existing infliximab and its ADA detection methods. Therefore, this study aimed to develop and validate enzyme-linked immunosorbent assay (ELISA)-based methods for measuring infliximab and its ADA levels in human plasma according to the general recommendations for immunoassays. Free infliximab is bound by recombinant TNF-α and detected using HRP-labeled anti-human antibody. The ADA is captured by on-plate-coated infliximab and recognized by biotin-labeled infliximab. Two bridging ELISA assays were developed and after assay optimization and validation, these assays have been applied in ten patients with inflammatory bowel disease (IBD). In infliximab detection assay, a standard curve ranging from 0.10 μg/mL to 8.0 μg/mL with great precision and accuracy has been established. Drug tolerance of the ADA assay was that 100 ng/mL ADA could tolerate at least 5.0 μg/mL infliximab in the plasma using a commercially available monoclonal anti-infliximab antibody as the positive control. The ADA screening and confirmatory assays achieved a sensitivity of 36.74 ng/mL and 37.15 ng/mL, respectively. All other assay characteristics met the requirements. The mean concentration of infliximab in eight patients with IBD was 7.88 (1.87-21.1) μg/mL, and the ADA levels were all negative. Moreover, the concentrations of infliximab in the remaining two patients were below the LLOQ and the ADAs were positive. Thus, accurate and sensitive ELISA methods have been developed and validated for the detection of infliximab and its ADA concentrations and have been successfully applied to clinical therapeutic drug monitoring.

Application of Biosensors in Detecting Breast Cancer Metastasis

Breast cancer has garnered global attention due to its high incidence worldwide, and even more noteworthy is that approximately 90% deaths due to breast cancer are attributed to cancer metastasis. Therefore, the early diagnosis of breast cancer metastasis holds significant importance for reducing mortality outcomes. Biosensors play a crucial role in the early detection of metastatic breast cancer due to their advantages, such as ease of use, portability, and real-time analysis capabilities. This review primarily described various types of sensors for detecting breast cancer metastasis based on biomarkers and cell characteristics, including electrochemical, optical, and microfluidic chips. We offered detailed descriptions of the performance of these various biosensors and made comparisons between them. Furthermore, we described the pathology of breast cancer and summarized commonly used biomarkers for metastatic breast cancer. Finally, we discussed the advantages of current-stage biosensors and the challenges that need to be addressed, as well as prospects for their future development.

Fabrication of Ag-CaCO3 Nanocomposites for SERS Detection of Forchlorfenuron

In this study, Ag-CaCO3 nanocomposites were synthesized using silver nitrate as the precursor solution based on calcium carbonate nanoparticles (CaCO3 NPs). The synthesis involved the reaction of calcium lignosulphonate and sodium bicarbonate. The properties of Ag-CaCO3 nanocomposites were studied by various technologies, including an ultraviolet-visible spectrophotometer, a transmission electron microscope, and a Raman spectrometer. The results showed that Ag-CaCO3 nanocomposites exhibited a maximum UV absorption peak at 430 nm, the surface-enhanced Raman spectroscopy (SERS) activity of Ag-CaCO3 nanocomposites was evaluated using mercaptobenzoic acid (MBA) as the marker molecule, resulting in an enhancement factor of 6.5 × 104. Additionally, Ag-CaCO3 nanocomposites were utilized for the detection of forchlorfenuron. The results demonstrated a linear relationship in the concentration range of 0.01 mg/mL to 2 mg/mL, described by the equation y = 290.02x + 1598.8. The correlation coefficient was calculated to be 0.9772, and the limit of detection (LOD) was determined to be 0.001 mg/mL. These findings highlight the relatively high SERS activity of Ag-CaCO3 nanocomposites, making them suitable for analyzing pesticide residues and detecting toxic and harmful molecules, thereby contributing to environmental protection.

Self-assembled PVP-gold nanostar films as plasmonic substrates for surface-enhanced spectroscopies: influence of the polymeric coating on the enhancement efficiency

Colloidal nanoparticles exhibiting anisotropic morphologies are preferred in the structural design of spectroscopically active substrates due to the remarkable optical properties of this type of nano-object. In the particular case of star-like nanoparticles, their sharp tips can act as antennae for capturing and amplifying the incident light, as well as for enhancing the light emitted by nearby fluorophores or the scattering efficiency of Raman active molecules. In the current work, we aimed to implement such star-shaped nanoparticles in the fabrication of nanoparticle films and explore their use as solid plasmonic substrates for surface-enhanced optical spectroscopies. High-density, compact and robust self-assembled gold nanostar films were prepared by directly depositing them from aqueous colloidal suspension on polystyrene plates through convective self-assembly. We investigated the role of the polymeric coating, herein polyvinylpyrrolidone (PVP), in the particle assembly process, the resulting morphology and consequently, the plasmonic response of the obtained films. The efficacy of the plasmonic films as dual-mode surface-enhanced fluorescence (SEF) and surface-enhanced Raman scattering (SERS) substrates was evidenced by testing Nile Blue A (NB) and Rhodamine 800 (Rh800) molecular chromophores under visible (633 nm) versus NIR (785 nm) laser excitation. Steady-state and time-resolved fluorescence investigations highlight the fluorescence intensity and fluorescence lifetime modification effects. The experimental results were corroborated with theoretical modelling by finite-difference time-domain (FDTD) simulations. Furthermore, to prove the extended applicability of the proposed substrates in the detection of biologically relevant molecules, we tested their SERS efficiency for sensing metanephrine, a metabolite currently used for the biochemical diagnosis of neuroendocrine tumors, at concentration levels similar to other catecholamine metabolites.

Nucleic acid aptamer-based biosensors and their application in thrombin analysis

Thrombin is a multifunctional serine protease that plays an important role in coagulation and anticoagulation processes. Aptamers have been widely applied in biosensors due to their high specificity, low cost and good biocompatibility. This review summarizes recent advances in thrombin quantification using aptamer-based biosensors. The primary focus is optical sensors and electrochemical sensors, along with their applications in thrombin analysis and disease diagnosis.

Label-free surface-enhanced Raman spectroscopy of serum with machine-learning algorithms for gallbladder cancer diagnosis

Gallbladder cancer (GBC) is a rare but frequently fatal biliary tract malignancy that is typically only discovered when it is already advanced. In the search of an efficient diagnosis method. Therefore, in this study, we investigated a novel technique for the quick and non-invasive diagnosis of GBC based on serum surface-enhanced Raman spectroscopy (SERS). SERS spectra of serum from 41 patients with GBC and 72 normal subjects were recorded. Principal component analysis-linear discriminant analysis (PCA-LDA), and PCA-support vector machine (PCA-SVM), Linear SVM and Gaussian radial basis function-SVM (RBF-SVM) algorithms were used to establish the classification models, respectively. When the Linear SVM was used, the overall diagnostic accuracy for classifying the two groups could achieve 97.1%, and when RBF-SVM was used, the diagnostic sensitivity of GBC was 100%. The results demonstrated that SERS in combination with a machine learning algorithm is a promising candidate to be one of the diagnostic tools for GBC in the future.

In Vitro Diagnosis and Visualization of Cerebral Ischemia/Reperfusion Injury in Rats and Protective Effects of Ferulic Acid by Raman Biospectroscopy and Machine Learning

Ischemic stroke is a major cause of mortality with complicated pathophysiological mechanisms, and hematoxylin and eosin (HE) staining is a histochemical diagnosis technique heavily relying on subjective observation. In this study, we developed a noninvasive assay using Raman spectroscopy for in vitro diagnosis and visualization of cerebral ischemia/reperfusion injury and protective effects of ferulic acid. By establishing a middle cerebral artery occlusion (MCAO) model in Sprague-Dawley male rats, we found effective interventions by ferulic acid using the neurological function score and HE staining. Raman spectra of neuronal and neuroglial cells exhibited significant intensity changes of protein, nucleotide, lipid, and carbohydrate at 780, 814, 1002, 1012, 1176, 1224, 1402, 1520, 1586, 1614, and 1752 cm^-1. Cluster vector analysis highlighted the alterations at 1002, 1080, 1298, 1430, 1478, 1508, 1586, and 1676 cm^-1. To evaluate the levels of neuron injury and intervention performance, a random forest model was developed on Raman spectral data and achieved satisfactory accuracy (0.9846), sensitivity (0.9679-0.9932), and specificity (0.9945-0.9989), ranking peaks around 1002 cm^-1 as key fingerprint for classification. Spectral phenylalanine-to-tryptophan ratio was the biomarker to visualize neuronal injury and intervention performance of ferulic acid with a resolution of 1 μm. Our results unravel the biochemical changes in neuronal cells with cerebral ischemia/reperfusion injury and ferulic acid treatment, and prove Raman spectroscopy coupled with machine learning as a power tool to classify neuron viability and evaluate the intervention performance in pharmacological research.

The chemical adsorption effect of surface enhanced Raman spectroscopy of nitrobenzene and aniline using the density functional theory

Nitrobenzene and Aniline are representatives of the nitro or amino compounds of benzene, mainly used in the manufacture of dyes, spices, medicines, and so on. Extensive use of Nitrobenzene and Aniline may cause pesticide residue pollution and have carcinogenic effects on organisms. In this paper, the Nitrobenzene and Aniline single molecules and their complexes with gold nanoparticles are studied theoretically by Raman spectroscopy, the surface-enhanced Raman spectroscopy (SERS) and the density functional theory (DFT) simulations. Selective binding of gold nanoparticles (AuNPs) to the analyte was used to study the molecular electrostatic potential (MEP), frontier molecular orbital (FMO) and the Raman activity spectra of Nitrobenzene and Aniline, as well as the Raman activity spectrum of the complexes. The most electronegative sites of Nitrobenzene and Aniline are found in the MEP and the hypothesis that these sites might be the adsorption sites of Nitrobenzene/Aniline molecules at the gold surface. At the same time, the MEP of the Nitrobenzene/Aniline complexes also prove the existence of the charge transfer effect between Nitrobenzene/Aniline and Au. The FMO energy gap of Nitrobenzene/Aniline is 0.18983 eV and 0.18953 eV, respectively, and which, after adding the Au₃ clusters, change to 0.03376 eV and 0.0797 eV, respectively, indicating that the Nitrobenzene/Aniline-Au₃ complexes have stronger chemical activities and are more prone to the charge transfer effects. The electrophilic indices of Nitrobenzene (0.17921 eV) and Aniline (0.05635 eV) are calculated and analyzed, as well as that of Nitrobenzene/Aniline-Au₃ complexes after adding the Au₃ atomic clusters, 0.80819 eV and 0.19819 eV, respectively. The obvious increasing trend in the electrophilic indices of the Nitrobenzene/Aniline-Au₃ complexes indicate their stronger biological activities and more prone to chemical reactions. The chemisorption of Nitrobenzene/Aniline and gold nanoparticles complexes is studied by the SERS, and the Raman formation of the complexes at different binding sites of Nitrobenzene/Aniline and Nitrobenzene/Aniline-Au₃ is well explained by the surface selection rule. The reason for the selective enhancement of the spectral peaks presented in the Raman activity spectrum is calculated, and the enhancement factor of the chemical enhancement due to the charge transfer effect is calculated as well. The reason for the peak offset in the SERS spectrum to the conventional Raman spectrum is explained.

Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma

Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.

Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) has attracted much attention because of its high sensitivity, high speed, and simple sample processing, and has great potential for application in the field of pesticide residue detection. However, SERS is susceptible to the influence of a complex detection environment in the detection of pesticide residues on the surface of fruits, facing problems such as interference from the spectral peaks of detected impurities, unclear dimension of effective correlation data, and poor linearity of sensing signals. In this work, the enhanced raw data of the pesticide thiram residues on the fruit surface using gold nanoparticle (Au-NPs) solution are formed into the raw data set of Raman signal in the IoT environment of Raman spectroscopy principal component detection. Considering the non-linear characteristics of sensing data, this work adopts kernel principal component analysis (KPCA) including radial basis function (RBF) to extract the main features for the spectra in the ranges of 653∼683 cm^-1, 705∼728 cm^-1, and 847∼872 cm^-1, and discusses the effects of different kernel function widths (σ) to construct a qualitative analysis of pesticide residues based on SERS spectral data model, so that the SERS spectral data produce more useful dimensionality reduction with minimal loss, higher mean squared error for cross-validation in non-linear scenarios, and effectively weaken the interference features of detecting impurity spectral peaks, unclear dimensionality of effective correlation data, and poor linearity of sensing signals, reflecting better extraction effects than conventional principal component analysis (PCA) models.

Nanoparticle classification, physicochemical properties, characterization, and applications: a comprehensive review for biologists

Interest in nanomaterials and especially nanoparticles has exploded in the past decades primarily due to their novel or enhanced physical and chemical properties compared to bulk material. These extraordinary properties have created a multitude of innovative applications in the fields of medicine and pharma, electronics, agriculture, chemical catalysis, food industry, and many others. More recently, nanoparticles are also being synthesized ‘biologically’ through the use of plant- or microorganism-mediated processes, as an environmentally friendly alternative to the expensive, energy-intensive, and potentially toxic physical and chemical synthesis methods. This transdisciplinary approach to nanoparticle synthesis requires that biologists and biotechnologists understand and learn to use the complex methodology needed to properly characterize these processes. This review targets a bio-oriented audience and summarizes the physico–chemical properties of nanoparticles, and methods used for their characterization. It highlights why nanomaterials are different compared to micro- or bulk materials. We try to provide a comprehensive overview of the different classes of nanoparticles and their novel or enhanced physicochemical properties including mechanical, thermal, magnetic, electronic, optical, and catalytic properties. A comprehensive list of the common methods and techniques used for the characterization and analysis of these properties is presented together with a large list of examples for biogenic nanoparticles that have been previously synthesized and characterized, including their application in the fields of medicine, electronics, agriculture, and food production. We hope that this makes the many different methods more accessible to the readers, and to help with identifying the proper methodology for any given nanoscience problem.

Surface-enhanced Raman Scattering of Au-Ag bimetallic nanopillars fabricated using surface-plasmon lithography

Arrays of gold-silver (Au-Ag) bimetallic nanopillars were fabricated by a newly developed surface-plasmon lithography (SPL) and their enhancement properties as surface-enhanced Raman scattering (SERS) substrates have been studied. We demonstrated that the SPL is a low-cost and high efficiency method for the fabrication of SERS substrates with both high sensitivity and reproducibility. The nanopillars showed a good response in the detection of methylene blue molecules at a low concentration of 1.0 × 10^-11mol· l^-1. The SERS enhancement factors (EFs) are on the orders of 10⁷and the relative standard deviation of SERS intensity is <8% over an area of 50μm × 50μm. The EFs increase fast with the height increasing from 200 to 530 nm, then increase slowly when further increase the height of the nanopillars to 1100 nm. In addition, the Au-Ag bimetallic coating has shown much higher SERS enhancement than the coatings of either the pure Au or Ag. The excellent SERS enhancement and reproducibility of the Au-Ag coated nanopillars indicated that the fabricated SERS substrates can be used for the detection of biochemical molecules at trace level and the SPL is a promising method for fabrication of SERS substrates.