Culture-free diagnostics of Pseudomonas aeruginosa infection by silver nanorod array based SERS from clinical sputum samples.

Pseudomonas aeruginosa can cause major infection in immunocompromised patients, and successful antibiotic treatment of the infection relies on accurate and rapid identification of the infectious agents. Here, we reported a culture-free diagnostic method based on the surface-enhanced Raman spectroscopy (SERS) of pyocyanin (PCN), a major biomarker of P. aeruginosa. This platform can detect PCN as low as 5 ppb or 2.38 × 10(-8) mol L(-1) in both aqueous solutions and spiked clinical sputum samples. It has also been used to dynamically monitor the excretion of PCN by P. aeruginosa during its growth. The presence of PCN has been detected by SERS in 15 clinical sputum samples, which indicates P. aeruginosa infection, with 95.6% sensitivity and 93.3% specificity. The system can advantageously process multiple specimens rapidly, overcomes the need for bacterial culture and diagnostic microbiology assays, and have widespread implications in the early detection of P. aeruginosa infection. FROM THE CLINICAL EDITOR: A surface enhanced Raman spectroscopy method optimized for the detection of P. aureginosa infections is presented in this paper. The presence of pyocyanin, a marker of this bacterium has been detected in 15 clinical sputum samples utilizing this method. A sensitivity of 95.6% and 93.3% specificity was reported, which suggests that the method may enable culture-free high throughput rapid detection of this infection.

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.

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.

Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.

A rapid and cost-effective method to detect the infection of SARS-CoV-2 is fundamental to mitigating the current COVID-19 pandemic. Herein, a surface-enhanced Raman spectroscopy (SERS) sensor with a deep learning algorithm has been developed for the rapid detection of SARS-CoV-2 RNA in human nasopharyngeal swab (HNS) specimens. The SERS sensor was prepared using a silver nanorod array (AgNR) substrate by assembling DNA probes to capture SARS-CoV-2 RNA. The SERS spectra of HNS specimens were collected after RNA hybridization, and the corresponding SERS peaks were identified. The RNA detection range was determined to be 103-109 copies/mL in saline sodium citrate buffer. A recurrent neural network (RNN)-based deep learning model was developed to classify 40 positive and 120 negative specimens with an overall accuracy of 98.9%. For the blind test of 72 specimens, the RNN model gave a 97.2% accuracy prediction for positive specimens and a 100% accuracy for negative specimens. All the detections were performed in 25 min. These results suggest that the DNA-functionalized AgNR array SERS sensor combined with a deep learning algorithm could serve as a potential rapid point-of-care COVID-19 diagnostic platform.

Graphene-embedded oblique V-shaped silver nanoarrays for hydrophobic pollutants pre-concentration and high-sensitivity SERS detection.

A surface enhanced Raman scattering (SERS) substrate of silver nanorod modified with graphene and silver nanorod (AgNR@Graphene@AgNR) has been fabricated to improve the sensitivity of SERS detection of hydrophobic pollutants, in which, graphene is an interlayer and AgNR is arranged on both sides of the graphene. The embedded graphene could help the oblique V-shaped AgNR structure to improve the sensitivity of SERS detection with a significant electric field enhancement effect. The annealing treatment of the substrate, shortening the nanometer gap between the graphene and AgNR, is benefit for producing a large number of "hot spots" at the fold, which has been verified by the finite difference time domain (FDTD) simulation. The enhancement factor (EF) of AgNR@Graphene@AgNR could reach up to 1.6 × 108 with a good reproducibility. The substrate could achieve high-sensitivity detection of 4-chlorobiphenyl (PCB-3) and 3, 3', 4, 4'-tetrachlorobiphenyl (PCB-77) with the limit of detections (LODs) of 1.72 × 10-10 M and 2.11 × 10-8 M, and the effective identification of PCBs mixture has been realized through principal component analysis (PCA), which means that the AgNR@Graphene@AgNR substrate has a potential significance for the detection and analysis of hydrophobic pollutant mixtures in the environment.

Optimization Based on the Surface Plasmon Optical Properties of Adjustable Metal Nano-Microcavity System for Biosensing.

This paper mainly studies the plasma optical properties of the silver nanorod and gold film system with gap structure. During the experiment, the finite element analysis method and COMSOL Multiphysics are used for modeling and simulation. The study changes the thickness of the PE spacer layer between the silver nanorod and the gold film, the conditions of the incident light and the surrounding environment medium. Due to the anisotropic characteristics of silver nanorod, the microcavity system is extremely sensitive to the changes of internal and external conditions, and the system exhibits strong performance along the long axis of the nanorod. By analyzing the extinction spectrum of the nanoparticle and the electric field section diagrams at resonance peak, it is found that the plasma optical properties of the system greatly depend on the gap distance, and the surrounding electric field of the silver nanorod is confined in the gap. Both ends of the nanorod and the gap are distributed with high concentrations of hot spots, which reflects the strong hybridization of multiple resonance modes. Under certain excitation conditions, the plasma hybridization behavior will produce a multi-pole mode, and the surface electric field distribution of the nanorod reflects the spatial directionality. In addition, the system is also highly sensitive to the environmental media, which will cause significant changes in its optical properties. The plasma microcavity system with silver nanorod and gold film studied in this paper can be used to develop high-sensitivity biosensors, which has great value in the field of biomedical detection.

Enhanced Plasmonic Resonance Characteristics of AgNRs-Gold Film Hybrid System.

In recent years, the plasma gap resonance maintained by metal-film-coupled nanostructures has attracted extensive attention. This mainly originates from its flexible control of the spectral response and significantly enhanced field strength at the nanoparticle-film junction. In the present study, the tunability of local surface plasmon resonances (LSPRs) of nanorods coupled to a gold film is studied theoretically. To this end, the plasmonic resonances in the nanostructure of individual silver nanorod-gold film (AgNR-film) with different parameters are investigated. Obtained results show that the refractive index sensitivity (S) of nanostructures to the environment increases as the aspect ratio (A r ) of nanostructures increase. It is found that when the aspect ratio (A r ) is set to 3.5, the figure of merit (FOM) is the highest. Moreover, the variation in the gap distances of the nanorod monomer-gold film, electric field distribution of nanorods dimer, and the corresponding impact on the gold film are studied. It is concluded that the gap size of nanostructures has an exponential correlation with the resonance wavelength. Considering the remarkable influence of the gap size and the surrounding medium environment on the spectral shift of AgNR-film nanostructures, potential applications of the structure as a refractive index sensor and biomolecule measurement are proposed.

A novel electrochemical sensor based on silver/halloysite nanotube/molybdenum disulfide nanocomposite for efficient nitrite sensing.

In the present study, the silver/halloysite nanotube/molybdenum disulfide (Ag/HNT/MoS2) nanocomposite was successfully synthesized. For this purpose, the lumen of HNTs was firstly modified by silver to generate Ag nanorods via chemical process and then the MoS2 layers deposited on the Ag/HNT nanocomposite by hydrothermal method. The characterization of Ag/HNT/MoS2 nanocomposite were investigated by field emission scanning electron microscopy (FE-SEM), Fourier transform infrared (FT-IR) spectroscopy, transmission electron microscopy (TEM), X-ray photoelectron spectra (XPS), energy dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) analyses. The nanocomposite modified carbon paste electrode (CPE) was applied for the electrocatalytic detection of nitrite in aqueous solutions. It was demonstrated that the treatment of HNTs with Ag and MoS2 materials enhanced the catalytic performance of modified CPE. At optimal experimental conditions, the designed sensor displayed remarkable sensing ability toward nitrite oxidation, offering a good linearity from 2 to 425 µM. The limit of detection (LOD) of the proposed strategy was estimated to be 0.7 µM based S/N = 3. The good reproducibility, acceptable stability, fast response time and anti-interference performance of the proposed assay suggests that the modified CPE has great potential working as a nitrite electrochemical sensor for environmental applications.

Flexible Asymmetric Supercapacitor Based on Functionalized Reduced Graphene Oxide Aerogels with Wide Working Potential Window.

Flexible energy storage devices are in great demand since the advent of flexible electronics. Until now, flexible supercapacitors based on graphene analogues usually have had low operating potential windows. To this end, two dissimilar electrode materials with complementary potential ranges are employed to obtain an optimum cell voltage of 1.8 V. A low-temperature organic sol-gel method is used to prepare two different types of functionalized reduced graphene oxide aerogels (rGOA) where Ag nanorod functionalized rGOA acts as a negative electrode while polyaniline nanotube functionalized rGOA acts as a positive electrode. Both materials comprehensively exploit their unique properties to produce a device that has high energy and power densities. An assembled all-solid-state asymmetric supercapacitor gives a high energy density of 52.85 W h kg-1 and power density of 31.5 kW kg-1 with excellent cycling and temperature stability. The device also performs extraordinarily well under different bending conditions, suggesting its potential to meet the requirements for flexible electronics.

Highly sensitive determination of antimony in food by resonance Rayleigh scattering-energy transfer between grapheme oxide and I3(.).

Sb(III) was reduced to SbH3 gas and introduced to the I3(-)-grapheme oxide (GO) or I3(-)-silver nanorod (AgNR)-Victoria blue B (VBB) solutions. Resonance Rayleigh scattering energy transfer (RRS-ET) occurred between the donor GO and the acceptor I3(-) due to the overlap between the absorption peak of I3(-) and RRS peak of GO. When I3(-) was reduced by SbH3, RRS-ET weakened and the RRS intensity enhanced. The increased RRS intensity was linear to Sb concentration in the range of 2.1-376.6µg/L. In the I3(-)-AgNR-VBB solution, I3(-) combined with VBB to form VBB-I3 and there was a weak surface-enhanced Raman scattering (SERS) effect. When SbH3 reduced I3(-), the SERS intensity increased due to the release of SERS active VBB. The enhanced SERS intensity was linear for Sb concentration in the range of 8.4-292.9µg/L. The RRS-ET method was applied for determination of Sb in food with satisfactory results.

Polyvinylpyrrolidone-Poly(ethylene glycol) Modified Silver Nanorods Can Be a Safe, Noncarrier Adjuvant for HIV Vaccine.

One of the biggest obstacles for the development of HIV vaccines is how to sufficiently trigger crucial anti-HIV immunities via a safe manner. We herein integrated surface modification-dependent immunostimulation against HIV vaccine and shape-dependent biosafety and designed a safe noncarrier adjuvant based on silver nanorods coated by both polyvinylpyrrolidone (PVP) and polyethylene glycol (PEG). Such silver nanorods can significantly elevate crucial immunities of HIV vaccine and overcome the toxicity, which is a big problem for other existing adjuvants. This study thus provided a principle for designing a safe and high-efficacy material for an adjuvant and allow researchers to really have a safe and effective prophylaxis against HIV. We expect this material approach to be applicable to other types of vaccines, whether they are preventative or therapeutic.

Electromagnetic enhancement of ordered silver nanorod arrays evaluated by discrete dipole approximation.

The enhancement factor (EF) of surface-enhanced Raman scattering (SERS) from two-dimensional (2D) hexagonal silver nanorod (AgNR) arrays were investigated in terms of electromagnetic (EM) mechanism by using the discrete dipole approximation (DDA) method. The dependence of EF on several parameters, i.e., structure, length, excitation wavelength, incident angle and polarization, and gap size has been investigated. "Hotspots" were found distributed in the gaps between adjacent nanorods. Simulations of AgNR arrays of different lengths revealed that increasing the rod length from 374 to 937 nm (aspect ratio from 2.0 to 5.0) generated more "hotspots" but not necessarily increased EF under both 514 and 532 nm excitation. A narrow lateral gap (in the incident plane) was found to result in strong EF, while the dependence of EF on the diagonal gap (out of the incident plane) showed an oscillating behavior. The EF of the array was highly dependent on the angle and polarization of the incident light. The structure of AgNR and the excitation wavelength were also found to affect the EF. The EF of random arrays was stronger than that of an ordered one with the same average gap of 21 nm, which could be explained by the exponential dependence of EF on the lateral gap size. Our results also suggested that absorption rather than extinction or scattering could be a good indicator of EM enhancement. It is expected that the understanding of the dependence of local field enhancement on the structure of the nanoarrays and incident excitations will shine light on the optimal design of efficient SERS substrates and improved performance.