Plasmonic and Electrostatic Interactions Enable Uniformly Enhanced Liquid Bacterial Surface-Enhanced Raman Scattering (SERS)

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism Mycobacterium tuberculosis (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths annually and 10 million new cases reported each year1. The causative organism, Mycobacterium tuberculosis (Mtb) can take nearly 40 days to culture2,3, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification of Mtb and rapid antibiotic susceptibility testing (AST) are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the MtB complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and in patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all 5 BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5000. We show how this instrument and our machine learning model enables combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra

Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne bacteria using a 3D nanostructure swab and deep learning-based Raman signal classification. The nanostructure swab efficiently captures foodborne pathogens, and the portable Raman instrument directly collects the Raman signals of captured bacteria. a deep learning algorithm has been demonstrated, 1D convolutional neural network with binary labeling, achieves superior performance in classifying individual bacterial species. This methodology has been extended to mixed bacterial populations, maintaining accuracy close to 100%. In addition, the gradient-weighted class activation mapping method is used to provide an investigation of the Raman bands for foodborne pathogens. For practical application, blind tests are conducted on contaminated kitchen utensils and foods. The proposed technique is validated by the successful detection of bacterial species from the contaminated surfaces. The use of a 3D nanostructure swab, portable Raman device, and deep learning-based classification provides a powerful tool for rapid identification (≈5 min) of foodborne bacterial species. The detection strategy shows significant potential for reliable food safety monitoring, making a meaningful contribution to public health and the food industry.

The Compact Integration of Multiple Exonuclease III-Assisted Cyclic Amplification Units for High-Efficiency Ratiometric Electrochemiluminescence Detection of MRSA

Methicillin-resistant Staphylococcus aureus (MRSA) exhibits multiresistance to a plethora of antibiotics, therefore, accurate detection methods must be employed for timely identification to facilitate effective infection control measures. Herein, we construct a high-efficiency ratiometric electrochemiluminescent (ECL) biosensor that integrates multiple exonuclease (Exo) III-assisted cyclic amplification units for rapid detection of trace amounts of MRSA. The target bacteria selectively bind to the aptamer, triggering the release of two single-stranded DNAs. One released DNA strand initiates the opening of a hairpin probe, inducing exonuclease cleavage to generate a single strand that can form a T-shaped structure with the double strand connecting the oxidation-reduction (O-R) emitter of N-(4-aminobutyl)-N-ethylisoluminol gold (ABEI-Au). Consequently, ABEI-Au is released upon Exo III cleavage. The other strand unwinds the hairpin DNA structure on the surface of the reduction-oxidation (R-O) emitter ZIF-8@CdS, facilitating the subsequent release of a specific single strand through Exo III cleavage. This process effectively anchors the cathode-emitting material to the electrode. The Fe(III) metal-organogel (Fe-MOG) is selected as a substrate, in which the catalytic reduction of hydrogen peroxide by Fe(III) active centers accelerates the generation of reactive oxygen species and enhances signals from both ABEI-Au and ZIF-8@CdS. In this way, the two emitters cooperate to achieve bacterial detection at the single-cell level, and a good linear range is obtained in the range of 100-107 CFU/mL. Moreover, the sensor exhibited excellent performance in detecting MRSA across various authentic samples and accurately quantifying MRSA levels in serum samples, demonstrating its immense potential in addressing clinical bacterial detection challenges.

Assessing fresh water acute toxicity with Surface-Enhanced Raman Scattering (SERS)

It's well known that the toxicity of chemicals in the environment depends not only their concentrations, but more importantly, their bio-availability. Thus, the acute toxicity test of environmental water samples is of great importance in water quality evaluation. In this work, water acute toxicity was determined via SERS approach for the first time based on the reaction between Escherichia coli (E. coli) and p-benzoquinone (BQ). The E. coli was used as the subject of toxicity assay. Under normal conditions, the BQ molecules can be transformed into Hydroquinone (HQ) by the E. coli bacteria; subsequently, the BQ will continue to react with the resulting HQ to form Quinone hydroquinone (QHQ). This process could be impaired in the presence of many toxic chemicals. Bromide modified Ag NPs was then introduced for the highly sensitive SERS detection of the product (HQ and QHQ). Several key factors that may affect water acute toxicity evaluation have been explored, which include the initial BQ and E. coli concentration, the incubation time with BQ, and the sodium chloride concentration. Later, the established system was applied for the toxicity evaluation of Cu2+. It was found that the IC50 value of Cu2+ was 0.94 mg/L, which is superior compared with literature report. This study provides a promising SERS method for assessing acute toxicity in water bodies with high sensitivity and short detection time.

Fundamental properties of smart hydrogels for tissue engineering applications: A review

Tissue engineering is an advanced and potential biomedical approach to treat patients suffering from lost or failed an organ or tissue to repair and regenerate damaged tissues that increase life expectancy. The biopolymers have been used to fabricate smart hydrogels to repair damaged tissue as they imitate the extracellular matrix (ECM) with intricate structural and functional characteristics. These hydrogels offer desired and controllable qualities, such as tunable mechanical stiffness and strength, inherent adaptability and biocompatibility, swellability, and biodegradability, all crucial for tissue engineering. Smart hydrogels provide a superior cellular environment for tissue engineering, enabling the generation of cutting-edge synthetic tissues due to their special qualities, such as stimuli sensitivity and reactivity. Numerous review articles have presented the exceptional potential of hydrogels for various biomedical applications, including drug delivery, regenerative medicine, and tissue engineering. Still, it is essential to write a comprehensive review article on smart hydrogels that successfully addresses the essential challenging issues in tissue engineering. Hence, the recent development on smart hydrogel for state-of-the-art tissue engineering conferred progress, highlighting significant challenges and future perspectives. This review discusses recent advances in smart hydrogels fabricated from biological macromolecules and their use for advanced tissue engineering. It also provides critical insight, emphasizing future research directions and progress in tissue engineering.

Solar-driven strongly coupled plasmonic Au nanoarrays on mesoporous silica nanodisks enable selective fungal and bacterial inactivation in well water

Well water is an important water source in isolated rural areas but easily suffers from microbial contamination. Herein, we anchored periodic Au nanoarrays on mesoporous silica nanodisks (Au-MSN) to fabricate a solar-driven nano-stove for well water disinfection. The solar/Au-MSN process completely inactivated 3.98, 6.55, 7.11 log10 cfu/mL, and 3.37 log10 pfu/mL of Aspergillus niger spores, Escherichia coli, chlorine-resistant Spingopyxis sp. BM1-1, and bacteriophage MS2 within 5 min, respectively. Moreover, the complete inactivation of various microorganisms (even at a viable but nonculturable state) was achieved in the flow-through reactor under natural solar light in real well water matrixes. Thorough characterizations and theoretical simulations verified that the densely anchoring strategy of Au-MSN's nanoarray worked on broadband absorption via the photon confinement effect, and trace amounts of Au can induce strong electromagnetic fields and collective localized heating. The resulting surge of 1O2 and heat synergically destroyed membranes, dysfunction cellular self-defense and metabolic system, induced intracellular oxidative stress, and ultimately inactivated microorganisms. Additionally, the 1O2-dominated oxidation and cell adhesion facilitated the selective disinfection in real well water matrixes. This study provides a cost-effective and practical solution for efficient well water disinfection, which assists isolated rural areas in getting safe drinking water.

"Dramatic Growth" of Microbial Aerosols for Visualization and Accurate Counting of Bioaerosols

While the global COVID-19 pandemic has subsided, microbial aerosol detection has become of high concern. Timely, accurate, and highly sensitive monitoring of microbial aerosols in indoor air is the basis for effective prevention and control of infectious diseases. At present, no commercial equipment or reliable technology can simultaneously control the detection time and limit at 6 h and 102 CFU/mL, respectively. Based on the "safety size range" of particulate matter in the air, we propose a new method of microbial dilation detection, which enables the pathogen to grow rapidly and dramatically into a polymeric microsphere, larger in size than the coexisting aerosol particles. "Like a crane standing among chickens", the microorganism can be easily visualized and counted. Different from routine chemical and biological sensing technologies, this method can achieve absolute counting of microbial particles, and the simple principles can be developed into devices for different life scenarios.

Asymmetric split-ring plasmonic nanostructures for the optical sensing of Escherichia coli

Strategies for in-liquid micro-organism detection are crucial for the clinical and pharmaceutical industries. While Raman spectroscopy is a promising label-free technique for micro-organism detection, it remains challenging due to the weak bacterial Raman signals. In this work, we exploit the unique electromagnetic properties of metamaterials to identify bacterial components in liquid using an array of Fano-resonant metamolecules. This Fano-enhanced Raman scattering (FERS) platform is designed to exhibit a Fano resonance close to the protein amide group fingerprint around 6030 nm. Raman signatures of Escherichia coli were recorded at several locations on the metamaterial under off-resonance laser excitation at 530 nm, where the photodamage effect is minimized. As the sizes of the Escherichia coli are comparable to the micro-gaps i.e, 0.41 µm, of the metamaterials, its local immobilisation leads to an increase in the Raman sensitivity. We also observed that the time-dependent FERS signal related to bacterial amide peaks increased during the bacteria's mid-exponential phase while it decreased during the stationary phase. This work provides a new set of opportunities for developing ultrasensitive FERS platforms suitable for large-scale applications and could be particularly useful for diagnostics and environmental studies at off-resonance excitation.

Current strategies of plasmonic nanoparticles assisted surface-enhanced Raman scattering toward biosensor studies

With the progressive nanofabrication technology, plasmonic nanoparticles (PNPs) have been increasingly deployed in the field of biosensing. PNPs have favorable biocompatibility, conductivity, and tunable optical properties. In addition, the localized surface plasmon resonance (LSPR) of PNPs plays a vital role in surface-enhanced Raman scattering (SERS). PNPs-based SERS biosensing enables wide-ranging applications for sensitive detection and high spatial and temporal resolution imaging. Numerous reviews of PNPs in the field of SERS biosensing highlight the fabrication or applications in one or more fields. However, the specific strategies for the SERS biosensor construction had not been summarized systematically. Thus, this work offers a comprehensive overview of SERS enhancement strategies based on PNPs, with a focus on SERS label-free detection along with label detection sensing construction, as well as its challenges and future trends.

Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood

Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when they are mixed with gold nanorods (GNRs), SERS enhancements of up to 1500× are achieved.We then train a ML model and achieve ≥99% classification accuracy from cellularly pure samples and ≥87% accuracy from cellularly mixed samples. We also obtain ≥90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.

Recent Progress of Surface-Enhanced Raman Spectroscopy for Bacteria Detection

There are various pathogenic bacteria in the surrounding living environment, which not only pose a great threat to human health but also bring huge losses to economic development. Conventional methods for bacteria detection are usually time-consuming, complicated and labor-intensive, and cannot meet the growing demands for on-site and rapid analyses. Sensitive, rapid and effective methods for pathogenic bacteria detection are necessary for environmental monitoring, food safety and infectious bacteria diagnosis. Recently, benefiting from its advantages of rapidity and high sensitivity, surface-enhanced Raman spectroscopy (SERS) has attracted significant attention in the field of bacteria detection and identification as well as drug susceptibility testing. Here, we comprehensively reviewed the latest advances in SERS technology in the field of bacteria analysis. Firstly, the mechanism of SERS detection and the fabrication of the SERS substrate were briefly introduced. Secondly, the label-free SERS applied for the identification of bacteria species was summarized in detail. Thirdly, various SERS tags for the high-sensitivity detection of bacteria were also discussed. Moreover, we emphasized the application prospects of microfluidic SERS chips in antimicrobial susceptibility testing (AST). In the end, we gave an outlook on the future development and trends of SERS in point-of-care diagnoses of bacterial infections.

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.

Au@SiO2 SERS nanotags based lateral flow immunoassay for simultaneous detection of aflatoxin B1 and ochratoxin A

Agricultural products are frequently contaminated by mycotoxins. Multiplex, ultrasensitive, and rapid determination of mycotoxins is still a challenging problem, which is of great significance to food safety and public health. Herein, a surface-enhanced Raman scattering (SERS) based lateral flow immunoassay (LFA) for the simultaneous on-site determination of aflatoxin B₁ (AFB₁) and ochratoxin A (OTA) on the same test line (T line) was developed, in this study. In practice, two kinds of Raman reporters 4-mercaptobenzoic acid (4-MBA), and 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB) encoded silica-encapsulated gold nanotags (Au^4-MBA@SiO₂ and Au^DNTB@SiO₂) were used as detection markers to identify the two different mycotoxins. Through systematic optimization of the experimental conditions, this biosensor has high sensitivity and multiplexing with the limits of detection (LODs) at 0.24 pg mL^-1 for AFB₁ and 0.37 pg mL^-1 for OTA. These are far below the regulatory limits set by the European Commission, in which the minimum LODs for AFB₁ and OTA are 2.0 and 3.0 μg kg^-1. In the spiked experiment, the food matrix are corn, rice, and wheat, and the mean recoveries of the two mycotoxins ranged from 91.0% ± 6.3%-104.8% ± 5.6% for AFB₁ and 87.0% ± 4.2%-112.0% ± 3.3% for OTA. These results demonstrate that the developed immunoassay has good stability, selectivity, and reliability, which can be used for routine monitoring of mycotoxin contamination.

Investigation of the Influence of Stress on Label-Free Bacterial Surface-Enhanced Raman Spectra

Label-free surface-enhanced Raman spectroscopy (SERS) has been proposed as a promising bacterial detection technique. However, the quality of the collected bacterial spectra can be affected by the time between sample acquisition and the SERS measurement. This study evaluated how storage stress stimuli influence the label-free SERS spectra of Pseudomonas syringae samples stored in phosphate buffered saline. The results indicate that when faced with nutrient limitations and changes in osmatic pressure, samples at room temperature (25 °C) exhibit more significant spectral changes than those stored at cold temperature (4 °C). At higher temperatures, bacterial communities secrete extracellular biomolecules that induce programmed cell death and result in increases in the supernatant SERS signals. Surviving cells consume cellular components to support their metabolism, thus leading to measurable declines in cell SERS intensity. Two-dimensional correlation spectroscopy analysis suggests that cellular component signatures decline sequentially in the following order: proteins, nucleic acids, and lipids. Extracellular nucleic acids, proteins, and carbohydrates are secreted in turn. After subtracting the SERS changes resulting from storage, we evaluated bacterial response to viral infection. P. syringae SERS profile changes enable accurate bacteriophage Phi6 quantification over the range of 10⁴-10¹⁰ PFU/mL. The results indicate that storage conditions impact bacterial label-free SERS signals and that such influences need to be accounted for and if possible avoided when detecting bacteria or evaluating bacterial response to stress stimuli.

Tunable Preparation of SERS-Active Au-Ag Janus@Au NPs for Label-Free Staphylococcal Enterotoxin C Detection

Trace staphylococcal enterotoxin C (SEC) in food poses a serious risk to human health, and it is vital to develop a sensitive and accurate approach for SEC monitoring. Herein, a surface-enhanced Raman scattering (SERS) aptasensor was developed for the quantitative detection of SEC. SERS-active gold-silver Janus@gold nanoparticles (Au-Ag Janus@Au NPs) were prepared and showed tunable solid and hollow nanostructures by simply controlling the pH values of the reaction system. Solid Au-Ag Janus@Au NPs exhibited intrinsic and enhanced SERS activity due to the intense plasmonic coupling effect between Au dots and Au-Ag Janus NPs, which was 2.27-fold and 17.46-fold higher than that of Au-Ag Janus NPs and hollow Au-Ag Janus@Au NPs, respectively. The attachment of multiple Au dots also protected Ag islands from oxidization, which increased the stability of Au-Ag Janus@Au NPs. Solid Au-Ag Janus@Au NPs served as a label-free, strong, and stable SERS detection probe and achieved sensitive and reliable detection of SEC. The limit of detection was as low as 0.55 pg/mL. This study will expand the application prospects of label-free SERS detection probes in complex systems for food safety monitoring.

Ag NP-filter paper based SERS sensor coupled with multivariate analysis for rapid identification of bacteria

Rapid and accurate identification of bacteria is essential to ensure food safety and prevent pathogenic bacterial infection. In this study, a highly efficient method was established for accurately identifying bacterial species by applying Ag NP-filter paper based Surface enhanced Raman spectroscopy (SERS) analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) statistical methods. The flexible Ag NP filter paper substrate with high sensitivity and uniformity was prepared by a facile and low-cost silver mirror reaction at room temperature, which exhibited desirable SERS activity in bacteria detection. Furthermore, PLS-DA was successfully employed to distinguish SERS spectra from S. aureus CMCC 26003, E. faecalis ATCC29212 and L. monocytogenes ATCC 19115 with a sensitivity of 93.3-100%, specificity of 96.7-97%, and overall predicting accuracy of 95.8%. This exploratory study demonstrates that a Ag NP-filter paper based SERS sensor coupled with PLS-DA has great potential for rapid and effective detection and identification of bacteria.

Optical Methods for Label-Free Detection of Bacteria

Pathogenic bacteria are the leading causes of food-borne and water-borne infections, and one of the most serious public threats. Traditional bacterial detection techniques, including plate culture, polymerase chain reaction, and enzyme-linked immunosorbent assay are time-consuming, while hindering precise therapy initiation. Thus, rapid detection of bacteria is of vital clinical importance in reducing the misuse of antibiotics. Among the most recently developed methods, the label-free optical approach is one of the most promising methods that is able to address this challenge due to its rapidity, simplicity, and relatively low-cost. This paper reviews optical methods such as surface-enhanced Raman scattering spectroscopy, surface plasmon resonance, and dark-field microscopic imaging techniques for the rapid detection of pathogenic bacteria in a label-free manner. The advantages and disadvantages of these label-free technologies for bacterial detection are summarized in order to promote their application for rapid bacterial detection in source-limited environments and for drug resistance assessments.

Inducing Ring Complexation for Efficient Capture and Detection of Small Gaseous Molecules Using SERS for Environmental Surveillance

Gas-phase surface-enhanced Raman scattering (SERS) remains challenging due to poor analyte affinity to SERS substrates. The reported use of capturing probes suffers from concurrent inconsistent signals and long response time due to the formation of multiple potential probe-analyte interaction orientations. Here, we demonstrate the use of multiple non-covalent interactions for ring complexation to boost the affinity of small gas molecules, SO₂ and NO₂ , to our SERS platform, achieving rapid capture and multiplex detection down to 100 ppm. Experimental and in-silico studies affirm stable ring complex formation, and kinetic investigations reveal a 4-fold faster response time compared to probes without stable ring complexation capability. By synergizing spectral concatenation and support vector machine regression, we achieve 91.7 % accuracy for multiplex quantification of SO₂ and NO₂ in excess CO₂ , mimicking real-life exhausts. Our platform shows immense potential for on-site exhaust and air quality surveillance.

Electrostatically Directed Long-Range Self-Assembly of Nucleotides with Cationic Nanoparticles To Form Multifunctional Bioplasmonic Networks

Precise control over interparticle interactions is essential to retain the functions of individual components in a self-assembled superstructure. Here, we report the design of a multifunctional bioplasmonic network via an electrostatically directed self-assembly process involving adenosine 5'-triphosphate (ATP). The present study unveils the ability of ATP to undergo a long-range self-assembly in the presence of cations and gold nanoparticles (AuNP). Modelling and NMR studies gave a qualitative insight into the major interactions driving the bioplasmonic network formation. ATP-Ca²⁺ coordination helps in regulating the electrostatic interaction, which is crucial in transforming an uncontrolled precipitation into a kinetically controlled aggregation process. Remarkably, ATP and AuNP retained their inherent properties in the multifunctional bioplasmonic network. The generality of electrostatically directed self-assembly process was extended to different nucleotide-nanoparticle systems.

Hybridizing Silver Nanoparticles in Hydrogel for High-Performance Flexible SERS Chips

An ideal surface-enhanced Raman scattering (SERS) substrate should have high sensitivity, long-term stability, excellent repeatability, and strong anti-interference. In the present work, single-layer carbon-based dot (CD)-capped Ag nanoparticle aggregates (a-AgNPs/CDs) with high SERS activity are synthesized and hybridized with a hydrogel to prepare novel hydrogel SERS chips. Benefiting from the unique properties of a-AgNPs/CDs and the hydrogel, the constructed hydrogel SERS chips show excellent performances. Taking crystal violet detection as an example, the hydrogel SERS chips show a detection limit of around 1 × 10^-16 mol/L (high sensitivity), maintain above 96.40% of SERS activity even after 14 weeks of storage (long-term stability), and display point-to-point relative standard deviation (RSD) in one chip as low as 1.43% (outstanding repeatability) and RSD in different chips as low as 2.75% (excellent reproducibility). Furthermore, the self-extraction effect of the hydrogel enables the flexible hydrogel SERS chips to be used for analyzing various real samples including soybean milk, juices, and fruits without any complex pretreatment. For instance, the hydrogel SERS chips are able to detect trace thiram and 2-(4-thiazolyl)benzimidazole with the detection limits of 1 and 5 ppb in liquid samples, respectively, and of 1 and 2.5 ng/cm² on the peel of fruits, respectively. The self-extraction functional flexible SERS chips offer a reliable and convenient platform for the quick detection and on-site monitoring of chemical contaminants.

Applications of surface-enhanced Raman spectroscopy in environmental detection

As the human population grows, the anthropogenic impacts from various agricultural and industrial processes produce unwanted contaminants in the environment. The accurate, sensitive and rapid detection of such contaminants is vital for human health and safety. Surface-enhanced Raman spectroscopy (SERS) is a valuable analytical tool with wide applications in environmental contaminant monitoring. The aim of this review is to summarize recent advancements within SERS research as it applies to environmental detection, with a focus on research published or accessible from January 2021 through December 2021 including early-access publications. Our goal is to provide a wide breadth of information that can be used to provide background knowledge of the field, as well as inform and encourage further development of SERS techniques in protecting environmental quality and safety. Specifically, we highlight the characteristics of effective SERS nanosubstrates, and explore methods for the SERS detection of inorganic, organic, and biological contaminants including heavy metals, pharmaceuticals, plastic particles, synthetic dyes, pesticides, viruses, bacteria and mycotoxins. We also discuss the current limitations of SERS technologies in environmental detection and propose several avenues for future investigation. We encourage researchers to fill in the identified gaps so that SERS can be implemented in a real-world environment more effectively and efficiently, ultimately providing reliable and timely data to help and make science-based strategies and policies to protect environmental safety and public health.

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

Self-assembled plasmonic nanoarrays for enhanced bacterial identification and discrimination

The rapid and accurate bacterial testing is a critical step for the management of infectious diseases, but challenges remain largely due to a lack of advanced sensing tools. Here we report the development of highly plasmon-active, biofunctional nanoparticle arrays for simultaneous capture, identification, and differentiation of bacteria by surface-enhanced Raman scattering (SERS). The nanoarrays were facilely prepared through an electrostatic mechanism-controlled self-assembly of metallic nanoparticles at liquid-liquid interfaces, and exhibited high SERS sensitivity beyond femtomole, good reproducibility (relative standard deviation of 2.7%) and stability. Modification of the nanoarrays with concanavalin A allowed to effective capture of both Gram-positive and Gram-negative bacteria (bacterial-capture efficiency maintained beyond 50%) at bacterial concentrations ranging from 50 to 2000 CFU mL^-1, as determined by the plate-counting method. Moreover, single-cell Raman fingerprinting and discrimination of eight different bacteria species with high signal-to-noise ratio, excellent spectral reproducibility, and a total assay time of 1.5 h was achieved under fairly mild conditions (24 μW, acquisition time: 1 s). Collectively, we believe that our biofunctionalized, SERS-based self-assembled nanoarrays have great potential to help in rapid and label-free bacterial diagnosis and phenotyping study.

Rapid, Label-Free Prediction of Antibiotic Resistance in Salmonella typhimurium by Surface-Enhanced Raman Spectroscopy

The rapid identification of bacterial antibiotic susceptibility is pivotal to the rational administration of antibacterial drugs. In this study, cefotaxime (CTX)-derived resistance in Salmonella typhimurium (abbr. CTX^r-S. typhimurium) during 3 months of exposure was rapidly recorded using a portable Raman spectrometer. The molecular changes that occurred in the drug-resistant strains were sensitively monitored in whole cells by label-free surface-enhanced Raman scattering (SERS). Various degrees of resistant strains could be accurately discriminated by applying multivariate statistical analyses to bacterial SERS profiles. Minimum inhibitory concentration (MIC) values showed a positive linear correlation with the relative Raman intensities of I₉₉₀/I₁₃₄₈, and the R² reached 0.9962. The SERS results were consistent with the data obtained by MIC assays, mutant prevention concentration (MPC) determinations, and Kirby-Bauer antibiotic susceptibility tests (K-B tests). This preliminary proof-of-concept study indicates the high potential of the SERS method to supplement the time-consuming conventional method and help alleviate the challenges of antibiotic resistance in clinical therapy.

Ultrasensitive and facile detection of multiple trace antibiotics with magnetic nanoparticles and core-shell nanostar SERS nanotags

Ultrasensitive, multiplex, rapid, and accurate quantitative determination of trace antibiotics remains a challenging issue, which is of importance to public health and safety. Herein, we presented a multiplex strategy based on magnetic nanoparticles and surface-enhanced Raman scattering (SERS) nanotags for simultaneous detection of chloramphenicol (CAP) and tetracycline (TTC). In practice, SERS nanotags based on Raman reporter probes (RRPs) encoded gold-silver core-shell nanostars were used as detection labels for identifying different types of antibiotics, and the magnetic nanoparticles could be separated simply by magnetic force, which significantly improves the detection efficiency, reduces the analysis cost, and simplifies the operation. Our results demonstrate that the as-proposed assay possesses the capacities of high sensitivity and multiplexing with the limits of detection (LODs) for CAP and TTC of 159.49 and 294.12 fg mL^-1, respectively, as well as good stability and reproducibility, and high selectivity and reliability. We believe that this strategy holds a great promising perspective for the detection of trace amounts of antibiotics in microsystems, which is crucial to our life. Additionally, the assay can also be used to detect other illegal additives by altering the appropriate antibodies or aptamers.

Optical Pulling Forces Enabled by Hyperbolic Metamaterials

We propose a novel approach to generating optical pulling forces on a gold nanowire, which are placed inside or above a hyperbolic metamaterial and subjected to plane wave illumination. Two mechanisms are found to induce the optical pulling force, including the concave isofrequency contour of the hyperbolic metamaterial and the excitation of directional surface plasmon polaritons. We systematically study the optical forces under various conditions, including the wavelength, the angle of incidence of light, and the nanowire radius. It is shown that the optical pulling force enabled by hyperbolic metamaterials is broadband and insensitive to the angle of incidence. The mechanisms and results reported here open a new avenue to manipulating nanoscale objects.

Prospects and applications of synergistic noble metal nanoparticle-bacterial hybrid systems

Hybrid systems composed of living cells and nanomaterials have been attracting great interest in various fields of research ranging from materials science to biomedicine. In particular, the interfacing of noble metal nanoparticles and bacterial cells in a single architecture aims to generate hybrid systems that combine the unique physicochemical properties of the metals and biological attributes of the microbial cells. While the bacterial cells provide effector and scaffolding functions, the metallic component endows the hybrid system with multifunctional capabilities. This synergistic effort seeks to fabricate living materials with improved functions and new properties that surpass their individual components. Herein, we provide an overview of this research field and the strategies for obtaining hybrid systems, and we summarize recent biological applications, challenges and current prospects in this exciting new arena.

Microstructured Surfaces for Reducing Chances of Fomite Transmission via Virus-Containing Respiratory Droplets

Evaporation-induced particle aggregation in drying droplets is of significant importance in the prevention of pathogen transfer due to the possibility of indirect fomite transmission of the infectious virus particles. In this study, particle aggregation was directionally controlled using contact line dynamics (pinned or slipping) and geometrical gradients on microstructured surfaces by the systematic investigation of the evaporation process on sessile droplets and sprayed microdroplets laden with virus-simulant nanoparticles. Using this mechanism, we designed robust particle capture surfaces by significantly inhibiting the contact transfer of particles from fomite surfaces. For the proof-of-concept, interconnected hexagonal and inverted pyramidal microwall were fabricated using ultraviolet-based nanoimprint lithography, which is considered to be a promising scalable manufacturing process. We demonstrated the potentials of an engineered microcavity surface to limit the contact transfer of particle aggregates deposited with the evaporation of microdroplets by 93% for hexagonal microwall and by 96% for inverted pyramidal microwall. The particle capture potential of the interconnected microstructures was also investigated using biological particles, including adenoviruses and lung-derived extracellular vesicles. The findings indicate that the proposed microstructured surfaces can reduce the indirect fomite transmission of highly infectious agents, including norovirus, rotavirus, or SARS-CoV-2, via respiratory droplets.

Fabrication of Ag@Au (core@shell) nanorods as a SERS substrate by the oblique angle deposition process and sputtering technology

Gold (Au) and silver (Ag) are the main materials exhibiting strong Surface-Enhanced Raman Scattering (SERS) effects. The Ag nano-rods (AgNRs) and Au nano-rods (AuNRs) SERS substrates prepared using the technology of the oblique angle deposition (OAD) process have received considerable attention in recent years because of their rapid preparation process and good repeatability. However, AgNR substrates are unstable due to the low chemical stability of Ag. To overcome these limitations, an Ag@Au core-shell nano-rod (NR) array SERS substrate was fabricated using the OAD process and sputtering technology. Moreover, simulation analysis was performed using finite-difference time-domain calculations to evaluate the enhancement mechanism of the Ag@Au NR array substrate. Based on the simulation results and actual process conditions, the Ag@Au core-shell NR array substrate with the Au shell thickness of 20 nm was studied. To characterize the substrate's SERS performance, 1,2-bis(4-pyridyl)ethylene (BPE) was used as the Raman probe. The limit of detection of BPE could reach 10-12 M. The Ag@Au NR array substrate demonstrated uniformity with an acceptable relative standard deviation. Despite the strong oxidation of the hydrogen peroxide (H2O2) solution, the Ag@Au NR array substrate maintains good chemical stability and SERS performance. And long-term stability of the Ag@Au NR substrate was observed over 8 months of storage time. Our results show the successful preparation of a highly sensitive, repeatable and stable substrate. Furthermore, this substrate proves great potential in the field of biochemical sensing.

Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens

The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.

Nanobiotechnology enabled approaches for wastewater based epidemiology

The impacts of the ongoing coronavirus pandemic highlight the importance of environmental monitoring to inform public health safety. Wastewater based epidemiology (WBE) has drawn interest as a tool for analysis of biomarkers in wastewater networks. Wide scale implementation of WBE requires a variety of field deployable analytical tools for real-time monitoring. Nanobiotechnology enabled sensing platforms offer potential as biosensors capable of highly efficient and sensitive detection of target analytes. This review provides an overview of the design and working principles of nanobiotechnology enabled biosensors and recent progress on the use of biosensors in detection of biomarkers. In addition, applications of biosensors for analysis of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus are highlighted as they relate to the potential expanded use of biosensors for WBE-based monitoring. Finally, we discuss the opportunities and challenges in future applications of biosensors in WBE for effective monitoring and investigation of public health threats.

Origin of layer-dependent SERS tunability in 2D transition metal dichalcogenides

Two-dimensional (2D) semiconductors are expected to replace noble metals to become the matrix materials of the next generation of commercial surface-enhanced Raman scattering (SERS) chips. Herein, we systematically studied the influence of the interlayer interaction on the SERS activity of 2D semiconductors from a brand-new perspective and comprehensively analyzed the physicochemical process of 2D semiconductor interlayer modulated SERS. Taking transition metal dichalcogenides as examples, we chose PtSe₂ with strong interlayer interactions and ReS₂ with weak interlayer interactions to analyze the physicochemical process of 2D semiconductor interlayer modulated SERS by first-principles calculations. PtSe₂ and ReS₂ samples with various thicknesses were prepared respectively, and the results of comparative experiments proved that the layer-dependent SERS tunability of 2D semiconductors is directly related to the interlayer interaction. This work provided a novel method for further improving the SERS detection limit of 2D semiconductors and a possible strategy for the industrial upgrading of commercial SERS chips.