Nanoporous silver nanorods as surface-enhanced Raman scattering substrates

Acoustofluidic one-step production of plasmonic Ag nanoparticles for portable paper-based ultrasensitive SERS detection of bactericides

SERS measurements for monitoring bactericides in dairy products are highly desired for food safety problems. However, the complicated preparation process of SERS substrates greatly impedes the promotion of SERS. Here, we propose acoustofluidic one-step synthesis of Ag nanoparticles on paper substrates for SERS detection. Our method is economical, fast, simple, and eco-friendly. We adopted laser cutting to cut out appropriate paper shapes, and aldehydes were simultaneously produced at the cutting edge in the pyrolysis of cellulose by laser which were leveraged as the reducing reagent. In the synthesis, only 5 μL of Ag precursor was added to complete the reaction, and no reducing agent was used. Our recently developed acoustofluidic device was employed to intensely mix Ag+ ions and aldehydes and spread the reduced Ag nanoparticles over the substrate. The SERS substrate was fabricated in 1 step and 3 min. The standard R6G solution measurement demonstrated the excellent signal and prominent uniformity of the fabricated SERS substrates. SERS detection of the safe concentration of three bactericides, including tetracycline hydrochloride, thiabendazole, and malachite green, from food samples can be achieved using fabricated substrates. We take the least cost, time, reagents, and steps to fabricate the SERS substrate with satisfying performance. Our work has an extraodinary meaning for the green preparation and large-scale application of SERS.

Surface segregation of rigid polyarylene ether amidoxime on polyurethane nanofiber into hierarchical membranes as substrate of flexible SERS nanosensor for sulfamethoxazole detection

Electrospun polymeric nanofibrous membranes are emerging as the promising substrates for preparation of flexible SERS nanosensors due to their intrinsic nanoscale surface roughness, easy scalability as well as rich surface reactivity. Although the nanofiber membranes prepared from high performance thermoplastics exhibit good mechanical stability, the SERS nanosensors based on these substrates normally have lower signal-to-noise ratio because of the interference from background Raman signals of aromatic moieties. Herein, we synthesized an optically transparent polyurethane (PU) and rigid polyarylene ether amidoxime (PEA), which were electrospun into core-shell nanofibers membranes with a "beads-on-web" morphology. Furthermore, the PU-PEA membranes were coated with ultra-thin silver layer and thermally annealed to prepare the flexible SERS nanosensor without any background noises. In addition, the Raman enhancement of SERS nanosensor can be readily improved by tuning of PU-PEA composition, silver thickness as well as thermal annealing temperature. Finally, the optimized SERS nanosensor enables label-free detection of sulfamethoxazole as low as 0.1 nM with a good reproducibility and detection performance in real water sample. Meanwhile, the optimized SERS nanosensor shows long term anti-biofouling capacity. Thanks to its facile fabrication, competitive analytical performance and resistance to biofouling, the current work basically open new way for design of flexible SERS nanosensors for biomedical applications.

Designing Fast-Moving Antibacterial Microtorpedoes to Treat Lethal Bacterial Biofilm Infections

Engineering fast-moving microrobot swarms that can physically disassemble bacterial biofilms and kill the bacteria released from the biofilms is a promising way to combat bacterial biofilm infections. Here, we report electrochemical design of Ag7O8NO3 microtorpedoes with outstanding antibacterial performance and meanwhile capable of moving at speeds of hundreds of body lengths per second in clinically used H2O2 aqueous solutions. These fast-moving antibacterial Ag7O8NO3 microtorpedoes could penetrate into and disintegrate the bacterial biofilms and, in turn, kill the bacteria released from the biofilms. Based on the understanding of the growth behavior of the microtorpedoes, we could fine-tune the morphology of the microtorpedoes to accelerate the moving speed and increase their penetration depth into the biofilms simply via controlling the potential waveforms. We further developed an automatic shaking method to selectively peel off the uniformly structured microtorpedoes from the electrode surface, realizing continuous electrochemical production of the microtorpedoes. Animal experiments proved that the microtorpedo swarms greatly increased the survival rate of the mice infected by lethal biofilms to >90%. We used the electrochemical method to design and massively produce uniformly structured fast-moving antibacterial microtorpedo swarms with application potentials in treatment of lethal bacterial biofilm infections.

Improved selectivity of electrochemical aniline sensing using one-dimensional silver nanorods with high aspect ratio synthesized by ascorbic acid assisted method

BACKGROUND

Aniline serves as a pivotal precursor in many industries such as pesticides, pharmaceuticals, and chemicals. However, its ingestion can lead to severe health consequences, including the potential to induce cancer, respiratory tract irritation, and adverse effects on the nervous and digestive systems in the human body. The widespread use of aniline in industrial processes, coupled with inadequate wastewater management that allows for the direct release of aniline into the environment, leads to surface and groundwater contamination. Therefore, it becomes crucial to devise a reliable electrochemical sensor capable of detecting even trace amounts of aniline.

RESULTS

This study presents a modified polyol synthesis method for producing silver nanorods (AgNRs, length: 861-1345 nm, diameter: 66-107 nm) with preferential growth along the (111) crystal plane. Immobilizing AgNRs on a glassy carbon (GC) electrode with Nafion as a binder decreases its charge transfer resistance from 3040 to 129 kΩ and increases its electroactive area from 0.034 to 0.101 cm2. AgNRs/GC electrode exhibited an aniline detection limit of 0.032 μM and sensitivity of 1.4841 μA.M-1cm-2 within a linear range of 0-10 μM using square wave voltammetry (SWV). The reaction rate constant of aniline sensing was determined to be 0.08697 s-1. Chlorobenzene, acephate, and chlorpyrifos could not interfere aniline detection, and 26 % decrease in peak response was observed after the 10th cycle of aniline sensing. The sensor demonstrated ∼100 % recovery for aniline, comparable to the performance of high-performance liquid chromatography when applied to real-world samples like tap and river water.

SIGNIFICANCE

The electrochemical sensing of aniline is notably efficient in tap and river water within the acceptable limit, by utilizing one dimensional AgNRs functionalized GC electrode. Importantly, the presence of interferents does not compromise the sensitivity of the sensor. Therefore, one dimensional AgNRs synthesized via a modified polyol route emerge as a promising electrocatalyst for the in-situ detection and determination of aniline.

SERS-based AI diagnosis of lung and gastric cancer via exhaled breath

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

Thin layer chromatography coupled with surface enhanced Raman scattering for rapid separation and on-site detection of multi-components

The separation and detection of multi-component mixtures has always been a challenging task. Traditional detection methods often suffer from complex operation, high cost, and low sensitivity. Surface enhanced Raman scattering (SERS) technique is a high sensitivity, powerful and rapid detection tool, which can realize the specific detection of single substance components, but it must solve the problem that multi-component mixtures cannot be accurately determined. Thin layer chromatography (TLC) technology, as a high-throughput separation technology, uses chromatographic plate as the stationary phase, and could select different developing phases for separation experiments. The advantages of TLC technology in short distance and rapid separation are widely used in protein, dye and biomedical fields. However, TLC technology has limitations in detection ability and difficulty in obtaining ideal signal intensity. The combination of TLC technology and SERS technology made the operation procedure simple and the sample size small, which can achieve rapid on-site separation and quantitative detection of mixtures. Due to the rapid development of TLC-SERS technology, it has been widely used in the investigation of various complex systems. This paper reviews the application of TLC-SERS technology in food science, environmental pollution and biomedicine.

Early-stage oral cancer diagnosis by artificial intelligence-based SERS using Ag NWs@ZIF core-shell nanochains

Surface-enhanced Raman spectroscopy (SERS) has great potential in the early diagnosis of diseases by detecting the changes of volatile biomarkers in exhaled breath, because of its high sensitivity, rich chemical molecular fingerprint information, and immunity to humidity. Here, an accurate diagnosis of oral cancer (OC) is demonstrated using artificial intelligence (AI)-based SERS of exhaled breath in plasmonic-metal organic framework (MOF) nanoparticles. These plasmonic-MOF nanoparticles were prepared using a zeolitic imidazolate framework coated on Ag nanowires (Ag NWs@ZIF), which offers Raman enhancement from the plasmonic nanowires and gas enrichment from the ZIF shells. Then, the core-shell nanochains of Ag NWs@ZIF prepared with 0.5 mL Ag NWs were selected to capture gaseous methanethiol, which is a tumor biomarker, from the exhalation of OC patients. The substrate was used to collect a total of 400 SERS spectra of exhaled breath of simulated healthy people and simulated OC patients. The artificial neural network (ANN) model in the AI algorithm was trained with these SERS spectra and could classify them with an accuracy of 99%. Notably, the model predicted OC with an area under the curve (AUC) of 0.996 for the simulated OC breath samples. This work suggests the great potential of the combination of breath analysis and AI as a method for the early-stage diagnosis of oral cancer.

Spontaneous Redox-Reaction-Driven Growth of Ag Nanoparticles on Co(OH)2 Nanoflower Arrays for Surface-Enhanced Raman Scattering

A simple and reliable method is developed to fabricate Ag-nanoparticle-decorated Co(OH)₂ nanoflowers grafted on polyacrylonitrile (PAN) nanopillar arrays as uniform and sensitive surface-enhanced Raman scattering (SERS) substrates. First, Co(OH)₂-nanosheet-assembled nanoflowers are achieved on the highly uniform PAN nanopillar arrays via electrochemical deposition. Then, Ag nanoparticles (Ag-NPs) are decorated onto the Au-nanoparticle-precoated Co(OH)₂ nanoflowers based on a spontaneous redox reaction (SRR) between the silver ions and Co(OH)₂ nanosheets at room temperature. Ag-NPs can be successfully in situ synthesized on the Co(OH)₂ nanoflowers, and Au nanoparticles precoated on the surface of the Co(OH)₂ nanosheets can ensure that the Co(OH)₂ nanoflower structure does not collapse. Because of the highly uniform PAN nanopillar arrays and the high-density sub-10 nm gaps between the neighboring Ag-NPs on the surface of the Co(OH)₂ nanoflowers, the hierarchical three-dimensional Ag@Co(OH)_x grown on PAN nanopillar arrays can produce a reproducible and sensitive SERS effect. To verify the SERS performance of the substrate, 4-aminothiophenol (4-ATP) is used as the probe molecule, and the Ag@Co(OH)_x grown on PAN nanopillar arrays is employed as the SERS substrate. As a result, 4-ATP concentrations as low as 10^-10 M can still be identified, exhibiting high SERS activity. Additionally, the relative standard deviation value of the main characteristic peak of 10^-5 M 4-ATP is 9.43%, indicating good uniformity of the SERS signal of the substrate. The SRR between silver ions and Co(OH)₂ can provide a simple route to prepare heterostructures as SERS substrates, which has great potential for application in the field of analysis.

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings.

Advances in surface-enhanced Raman spectroscopy technology for detection of foodborne pathogens

Rapid control and prevention of diseases caused by foodborne pathogens is one of the existing food safety regulatory issues faced by various countries and has received wide attention from all sectors of society. The development of rapid and reliable detection methods for foodborne pathogens remains a hot research area for food safety and public health because of the limitations of complex steps, time-consuming, low sensitivity, or poor selectivity of commonly used methods. Surface-enhanced Raman spectroscopy (SERS), as a novel spectroscopic technique, has the advantages of high sensitivity, selectivity, rapid and nondestructive detection and has exhibited broad application prospects in the determination of pathogenic bacteria. In this study, the enhancement mechanisms of SERS are briefly introduced, then the characteristics and properties of liquid-phase, rigid solid-phase, and flexible solid-phase are categorized. Furthermore, a comprehensive review of the advances in label-free or label-based SERS strategies and SERS-compatible techniques for the detection of foodborne pathogens is provided, and the advantages and disadvantages of these methods are reviewed. Finally, the current challenges of SERS technology applied in practical applications are listed, and the possible development trends of SERS in the field of foodborne pathogens detection in the future are discussed.

Plasmon-driven catalytic reactions in optoplasmonic sandwich hybrid structure

As an interesting phenomenon in the field of surface enhanced Raman spectroscopy (SERS), the plasmon-driven catalytic reaction (PDSC) induced by plasmonic hot electrons has great value in the research of novel properties of surface plasmons and accuracy of SERS applications. In this work, an optoplasmonic sandwich hybrid structure is proposed for studying PDSC of p-aminothiophenol (PATP) molecules, which is composed of Au film, metal organic frameworks (MOFs) nanoparticles, zeolithic imidazolate (ZIF-8), and single S i O ₂ microsphere (Au f i l m@M O F s@S i O ₂). In order to analyze the novel, to the best of our knowledge, phenomenon of the PDSC in this micro-nano structure, the hot electron generation in the MOF without the plasmonic core is carried out by combining the plasmonic enhancement of gold film with the light concentration of microspheres. Experimental data show that the PDSC reactions is dependent on the size of the MOFs nanoparticle and the size of the S i O ₂ microsphere, which is confirmed by the electromagnetic field simulation of the finite-difference time-domain method (FDTD). Our work not only strengthens the understanding of surface plasmon in optoplasmonic hybrid structures but also has broad application prospects in the SERS and plasmon-driven catalytic fields.

Surface Defects Improved SERS Activity of Nanoporous Gold Prepared by Electrochemical Dealloying

Nanoporous metals possess excellent catalytic and optical properties that are related with surface morphology. Here, we modulated the ligament surface of nanoporous gold (NPG) by controlling electrochemical dealloying and obtained NPG with an improved enhancement of its surface-enhanced Raman scattering (SERS) property. We found that both high-density atomic steps and kinks on the curved surfaces and high-content silver atoms close to the ligament surface contributed to the high SERS ability. The presented strategy will be useful for the fabrication of nanoporous metal with an excellent surface that is needed for sensing, conversion, and catalytic.

Engineered Au@CuO Nanoparticles for Wide-Range Quantitation of Sulfur Ions by Surface-Enhanced Raman Spectroscopy

Efficient detection of sulfide ions (S^2-), especially in a wide quantitative range, is of significance but faces challenges. This work strategizes and fabricates Au@CuO nanoparticles for quantitative surface-enhanced Raman spectroscopy (SERS) detection of the S^2- ions based on the S^2- concentration-dependent ion-solid interactions. We have achieved fast and quantitative S^2- detection in a wide range from 5 ppb to 64,000 ppm (saturation concentration of the S^2- source). We also demonstrated that the optimal CuO shell thickness for the detection is about 7 nm and that the detection can be further improved by prolonging the soaking duration. Moreover, this detection method has also shown the merits of reusable substrates (especially for low S^2- concentrations) and good anti-interference ability to many common anions (Cl^-, NO₃^-, OH^-, HCOO^-, CO₃^2-, and SO₄^2-). Finally, the high feasibility of this detection in actual water (tap water and pond water) has also been demonstrated. This work provides efficient S^2- detection with great potential in practical use and also inspires the design of quantifiable SERS substrates for detecting more small inorganic molecules and ions.

Paving the way for precise diagnostics of antimicrobial resistant bacteria

The antimicrobial resistance (AMR) crisis from bacterial pathogens is frequently emerging and rapidly disseminated during the sustained antimicrobial exposure in human-dominated communities, posing a compelling threat as one of the biggest challenges in humans. The frequent incidences of some common but untreatable infections unfold the public health catastrophe that antimicrobial-resistant pathogens have outpaced the available countermeasures, now explicitly amplified during the COVID-19 pandemic. Nowadays, biotechnology and machine learning advancements help create more fundamental knowledge of distinct spatiotemporal dynamics in AMR bacterial adaptation and evolutionary processes. Integrated with reliable diagnostic tools and powerful analytic approaches, a collaborative and systematic surveillance platform with high accuracy and predictability should be established and implemented, which is not just for an effective controlling strategy on AMR but also for protecting the longevity of valuable antimicrobials currently and in the future.