Noninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 min

Single-molecule detection of SARS-CoV-2 N protein on multilayered plasmonic nanotraps with surface-enhanced Raman spectroscopy

The spread of the SARS-CoV-2 virus has had an unprecedented impact, both by posing a serious risk to human health and by amplifying the burden on the global economy. The rapid identification of the SARS-CoV-2 virus has been crucial to preventing and controlling the spread of SARS-CoV-2 infections. In this study, we propose a multilayered plasmonic nanotrap (MPNT) device for the rapid identification of single particles of SARS-CoV-2 virus in ultra-high sensitivity by surface-enhanced Raman scattering (SERS). The MPNT device is composed of arrays of concentric cylindrical cavities with Ag/SiO2/Ag multilayers deposited on the top and at the bottom. By varying the diameter of the cylinders and the thickness of the multilayers, the resonant optical absorption and local electric field were optimized. The SERS enhancement factors of the proposed device are of the order of 108, which enable the rapid identification of SARS-CoV-2 N protein in concentrations as low as 1.25 × 10-15－12.5 × 10-15 g mL-1 within 1 min. The developed MPNT SERS device provides a label-free and rapid detection platform for SARS-CoV-2 virus. The general nature of the device makes it equally suitable to detect other infectious viruses.

Plasmonic nanoparticle sensors: current progress, challenges, and future prospects

Plasmonic nanoparticles (NPs) have played a significant role in the evolution of modern nanoscience and nanotechnology in terms of colloidal synthesis, general understanding of nanocrystal growth mechanisms, and their impact in a wide range of applications. They exhibit strong visible colors due to localized surface plasmon resonance (LSPR) that depends on their size, shape, composition, and the surrounding dielectric environment. Under resonant excitation, the LSPR of plasmonic NPs leads to a strong field enhancement near their surfaces and thus enhances various light-matter interactions. These unique optical properties of plasmonic NPs have been used to design chemical and biological sensors. Over the last few decades, colloidal plasmonic NPs have been greatly exploited in sensing applications through LSPR shifts (colorimetry), surface-enhanced Raman scattering, surface-enhanced fluorescence, and chiroptical activity. Although colloidal plasmonic NPs have emerged at the forefront of nanobiosensors, there are still several important challenges to be addressed for the realization of plasmonic NP-based sensor kits for routine use in daily life. In this comprehensive review, researchers of different disciplines (colloidal and analytical chemistry, biology, physics, and medicine) have joined together to summarize the past, present, and future of plasmonic NP-based sensors in terms of different sensing platforms, understanding of the sensing mechanisms, different chemical and biological analytes, and the expected future technologies. This review is expected to guide the researchers currently working in this field and inspire future generations of scientists to join this compelling research field and its branches.

Yolk-Shell Hierarchical Pore Au@MOF Nanostructures: Efficient Gas Capture and Enrichment for Advanced Breath Analysis

Surface-enhanced Raman scattering (SERS) offers a promising, cost-effective alternative for the rapid, sensitive, and quantitative analysis of potential biomarkers in exhaled gases, which is crucial for early disease diagnosis. However, a major challenge in SERS is the effective detection of gaseous analytes, primarily due to difficulties in enriching and capturing them within the substrate's "hotspot" regions. This study introduces an advanced gas sensor combining mesoporous gold (MesoAu) and metal-organic frameworks (MOFs), exhibiting high sensitivity and rapid detection capabilities. The MesoAu provides abundant active sites and interconnected mesopores, facilitating the diffusion of analytes for detection. A ZIF-8 shell enveloping MesoAu further enriches target molecules, significantly enhancing sensitivity. A proof-of-concept experiment demonstrated a detection limit of 0.32 ppb for gaseous benzaldehyde, indicating promising prospects for the rapid diagnosis of early stage lung cancer. This research also pioneers a novel approach for constructing hierarchical plasmonic nanostructures with immense potential in gas sensing.

Plasmonic trimers designed as SERS-active chemical traps for subtyping of lung tumors

Plasmonic materials can generate strong electromagnetic fields to boost the Raman scattering of surrounding molecules, known as surface-enhanced Raman scattering. However, these electromagnetic fields are heterogeneous, with only molecules located at the 'hotspots', which account for ≈ 1% of the surface area, experiencing efficient enhancement. Herein, we propose patterned plasmonic trimers, consisting of a pair of plasmonic dimers at the bilateral sides and a trap particle positioned in between, to address this challenge. The trimer configuration selectively directs probe molecules to the central traps where 'hotspots' are located through chemical affinity, ensuring a precise spatial overlap between the probes and the location of maximum field enhancement. We investigate the Raman enhancement of the Au@Al2O3-Au-Au@Al2O3 trimers, achieving a detection limit of 10-14 M of 4-methylbenzenethiol, 4-mercaptopyridine, and 4-aminothiophenol. Moreover, single-molecule SERS sensitivity is demonstrated by a bi-analyte method. Benefiting from this sensitivity, our approach is employed for the early detection of lung tumors using fresh tissues. Our findings suggest that this approach is sensitive to adenocarcinoma but not to squamous carcinoma or benign cases, offering insights into the differentiation between lung tumor subtypes.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Machine Learning-Driven SERS Nanoendoscopy and Optophysiology

A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning.

A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds

BACKGROUND

The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection.

METHODS

This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted.

RESULTS

The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS.

CONCLUSION

Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.

Harnessing Cooperative Multivalency in Thioguanine for Surface-Enhanced Raman Scattering (SERS)-Based Differentiation of Polyfunctional Analytes Differing by a Single Functional Group

Small-molecule receptors are increasingly employed to probe various functional groups for (bio)chemical analysis. However, differentiation of polyfunctional analogs sharing multiple functional groups remains challenging for conventional mono- and bidentate receptors because their insufficient number of binding sites limits interactions with the least reactive yet property-determining functional group. Herein, we introduce 6-thioguanine (TG) as a supramolecular receptor for unique tridentate receptor-analyte complexation, achieving ≥97 % identification accuracy among 16 polyfunctional analogs across three classes: glycerol derivatives, disubstituted propane, and vicinal diols. Crucially, we demonstrate distinct spectral changes induced by the tridentate interaction between TG's three anchoring points and all the analyte's functional groups, even the least reactive ones. Notably, hydrogen bond (H-bond) networks formed in the TG-analyte complexes demonstrate additive effects in binding strength originating from good bond linearity, cooperativity, and resonance, thus strengthening complexation events and amplifying the differences in spectral changes induced among analytes. It also enhances spectral consistency by selectively forming a sole configuration that is stronger than the respective analyte-analyte interaction. Finally, we achieve 95.4 % accuracy for multiplex identification of a mixture consisting of multiple polyfunctional analogs. We envisage that extension to other multidentate non-covalent interactions enables the development of interference-free small molecule-based sensors for various (bio)chemical analysis applications.

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.

Surface-Enhanced Raman Spectroscopy: Current Understanding, Challenges, and Opportunities

While surface-enhanced Raman spectroscopy (SERS) has experienced substantial advancements since its discovery in the 1970s, it is an opportunity to celebrate achievements, consider ongoing endeavors, and anticipate the future trajectory of SERS. In this perspective, we encapsulate the latest breakthroughs in comprehending the electromagnetic enhancement mechanisms of SERS, and revisit CT mechanisms of semiconductors. We then summarize the strategies to improve sensitivity, selectivity, and reliability. After addressing experimental advancements, we comprehensively survey the progress on spectrum-structure correlation of SERS showcasing their important role in promoting SERS development. Finally, we anticipate forthcoming directions and opportunities, especially in deepening our insights into chemical or biological processes and establishing a clear spectrum-structure correlation.

Binding-Free Taste Visualization with Plasmonic Metasurfaces

Taste sensors using photonics, termed artificial photonic tongues, have emerged as a promising platform for intuitive taste discrimination. However, the need for complex binding protocols for each taste profile limits their applicability to a narrow range of taste molecules. Here, we introduce an intriguing "binding-free" approach to molecular taste sensing using plasmonics, eliminating the requirement for physical or chemical binding protocols. We develop a wafer-scale plasmonic metasurface constructed by coating metallic nanoparticles in a scalable manner onto a metallic mirror. This metasurface functions to detect molecular refractive indices and surface tensions via 2D projection optical images of an array of liquid droplets containing the taste molecules on top, which can immediately visualize and distinguish between the five basic tastes of molecules (including their mixtures) as well as other additional spicy and alcoholic tastes. We anticipate that this intuitive and rapid taste-sensing approach has the potential to establish a user-friendly and portable taste-sensing platform.

Machine learning-based exosome profiling of multi-receptor SERS sensors for differentiating adenocarcinoma in situ from early-stage invasive adenocarcinoma

Exosomes, extracellular vesicles released by cells, hold potential as diagnostic markers for the early detection of lung cancer. Despite their clinical promise, current technologies lack rapid and effective means to discriminate between exosomes derived from adenocarcinoma in situ (AIS) and early-stage invasive adenocarcinoma (IAC). This challenge arises from the intrinsic structural heterogeneity of exosomes, necessitating the development of advanced methodologies for precise differentiation. Here, we demonstrate a novel approach for plasma exosome detection utilizing multi-receptor surface-enhanced Raman spectroscopy (SERS) technology to differentiate between AIS and early-stage IAC. To accomplish this, we synthesized a stable and uniform two-dimensional SERS substrate (BC/Au NPs film) by fabricating gold nanoparticles onto bacterial cellulose. We then enhanced its capabilities by introducing multi-receptor SERS functionality via modifying the substrate with both low-specificity and physicochemical-selective molecules. Furthermore, by strategically combining all capturer-exosome SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of the exosome. Combining these features with partial least squares regression-discriminant analysis (PLS-DA) modeling significantly improved discriminatory accuracy, achieving 90% sensitivity and 95% specificity in distinguishing AIS from early-stage IAC. Our developed SERS sensor provides an effective method for early detection of lung cancer, thereby paving a new way for innovative advancements in diagnosing lung cancer.

Adaptive Gap-Tunable Surface-Enhanced Raman Spectroscopy

Gap plasmon (GP) resonance in static surface-enhanced Raman spectroscopy (SERS) structures is generally too narrow and not tunable. Here, we present an adaptive gap-tunable SERS device to selectively enhance and modulate different vibrational modes via active flexible Au nanogaps, with adaptive optical control. The tunability of GP resonance is up to ∼1200 cm-1 by engineering gap width, facilitated by mechanical bending of a polyethylene terephthalate substrate. We confirm that the tuned GP resonance selectively enhances different Raman spectral regions of the molecules. Additionally, we dynamically control the SERS intensity through the wavefront shaping of excitation beams. Furthermore, we demonstrate simulation results, exhibiting the mechanical and optical properties of a one-dimensional flexible nanogap and their advantage in high-speed biomedical sensing. Our work provides a unique approach for observing and controlling the enhanced chemical responses with dynamic tunability.

Bioaerosols in the atmosphere: A comprehensive review on detection methods, concentration and influencing factors

In the past few decades, especially since the outbreak of the coronavirus disease (COVID-19), the effects of atmospheric bioaerosols on human health, the environment, and climate have received great attention. To evaluate the impacts of bioaerosols quantitatively, it is crucial to determine the types of bioaerosols in the atmosphere and their spatial-temporal distribution. We provide a concise summary of the online and offline observation strategies employed by the global research community to sample and analyze atmospheric bioaerosols. In addition, the quantitative distribution of bioaerosols is described by considering the atmospheric bioaerosols concentrations at various time scales (daily and seasonal changes, for example), under various weather, and different underlying surfaces. Finally, a comprehensive summary of the reasons for the spatiotemporal distribution of bioaerosols is discussed, including differences in emission sources, the impact process of meteorological factors and environmental factors. This review of information on the latest research progress contributes to the emergence of further observation strategies that determine the quantitative dynamics of public health and ecological effects of bioaerosols.

Two-Dimensional SERS Sensor Array for Identifying and Visualizing the Gas Spatial Distributions of Two Distinct Odor Sources

The spatial distribution of gas emitted from an odor source provides valuable information regarding the composition, size, and localization of the odor source. Surface-enhanced Raman scattering (SERS) gas sensors exhibit ultra-high sensitivity, molecular specificity, rapid response, and large-area detection. In this paper, a SERS gas sensor array was developed for visualizing the spatial distribution of gas evaporated from benzaldehyde and 4-ethylbenzaldehyde odor sources. The SERS spectra of the gas were collected by scanning the sensor array using an automatic detection system. The non-negative matrix factorization algorithm was employed to extract feature and concentration information at each spot on the sensor array. A heatmap image was generated for visualizing the gas spatial distribution using concentration information. Gaussian fitting was applied to process the image for localizing the odor source. The size of the odor source was estimated using the processed image. Moreover, the spectra of benzaldehyde, 4-ethylbenzaldehyde, and their gas mixture were simultaneously detected using one SERS sensor array. The feature information was recognized using a convolutional neural network with an accuracy of 98.21%. As a result, the benzaldehyde and 4-ethylbenzaldehyde odor sources were identified and visualized. Our research findings have various potential applications, including odor source localization, environmental monitoring, and healthcare.

Recent advances in point-of-care testing of COVID-19

Advances in microfluidic device miniaturization and system integration contribute to the development of portable, handheld, and smartphone-compatible devices. These advancements in diagnostics have the potential to revolutionize the approach to detect and respond to future pandemics. Accordingly, herein, recent advances in point-of-care testing (POCT) of coronavirus disease 2019 (COVID-19) using various microdevices, including lateral flow assay strips, vertical flow assay strips, microfluidic channels, and paper-based microfluidic devices, are reviewed. However, visual determination of the diagnostic results using only microdevices leads to many false-negative results due to the limited detection sensitivities of these devices. Several POCT systems comprising microdevices integrated with portable optical readers have been developed to address this issue. Since the outbreak of COVID-19, effective POCT strategies for COVID-19 based on optical detection methods have been established. They can be categorized into fluorescence, surface-enhanced Raman scattering, surface plasmon resonance spectroscopy, and wearable sensing. We introduced next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future. Additionally, we have discussed appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era. We believe that this review will be helpful for preparing for future infectious disease outbreaks.

Achieving Molecular Recognition of Structural Analogues in Surface-Enhanced Raman Spectroscopy: Inducing Charge and Geometry Complementarity to Mimic Molecular Docking

Molecular recognition of complex isomeric biomolecules remains challenging in surface-enhanced Raman scattering (SERS) spectroscopy due to their small Raman cross-sections and/or poor surface affinities. To date, the use of molecular probes has achieved excellent molecular sensitivities but still suffers from poor spectral specificity. Here, we induce "charge and geometry complementarity" between probe and analyte as a key strategy to achieve high spectral specificity for effective SERS molecular recognition of structural analogues. We employ 4-mercaptopyridine (MPY) as the probe, and chondroitin sulfate (CS) disaccharides with isomeric sulfation patterns as our proof-of-concept study. Our experimental and in silico studies reveal that "charge and geometry complementarity" between MPY's binding pocket and the CS sulfation patterns drives the formation of site-specific, multidentate interactions at the respective CS isomerism sites, which "locks" each CS in its analogue-specific complex geometry, akin to molecular docking events. Leveraging the resultant spectral fingerprints, we achieve > 97 % classification accuracy for 4 CSs and 5 potential structural interferences, as well as attain multiplex CS quantification with < 3 % prediction error. These insights could enable practical SERS differentiation of biologically important isomers to meet the burgeoning demand for fast-responding applications across various fields such as biodiagnostics, food and environmental surveillance.

Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19

Background

Although rapid screening for and diagnosis of coronavirus disease 2019 (COVID-19) are still urgently needed, most current testing methods are long, costly or poorly specific. The objective of the present study was to determine whether or not artificial-intelligence-enhanced real-time mass spectrometry breath analysis is a reliable, safe, rapid means of screening ambulatory patients for COVID-19.

Methods

In two prospective, open, interventional studies in a single university hospital, we used real-time, proton transfer reaction time-of-flight mass spectrometry to perform a metabolomic analysis of exhaled breath from adults requiring screening for COVID-19. Artificial intelligence and machine learning techniques were used to build mathematical models based on breath analysis data either alone or combined with patient metadata.

Results

We obtained breath samples from 173 participants, of whom 67 had proven COVID-19. After using machine learning algorithms to process breath analysis data and further enhancing the model using patient metadata, our method was able to differentiate between COVID-19-positive and -negative participants with a sensitivity of 98%, a specificity of 74%, a negative predictive value of 98%, a positive predictive value of 72% and an area under the receiver operating characteristic curve of 0.961. The predictive performance was similar for asymptomatic, weakly symptomatic and symptomatic participants and was not biased by COVID-19 vaccination status.

Conclusions

Real-time, noninvasive, artificial-intelligence-enhanced mass spectrometry breath analysis might be a reliable, safe, rapid, cost-effective, high-throughput method for COVID-19 screening.

Analysis of SARS-CoV-2 spike RBD binding to ACE2 and its inhibition by fungal cohaerin C using surface enhanced Raman spectroscopy

The structure of the SARS-CoV-2 spike RBD and human ACE2 as well as changes in the structure due to binding activities were analysed using surface enhanced Raman spectroscopy. The inhibitor cohaerin C was applied to inhibit the binding between spike RBD and ACE2. Differences and changes in the Raman spectra were determined using deconvolution of the amide bands and principal component analysis. We thus demonstrate a fast and label-free analysis of the protein structures and the differentiation between bound and unbound states. The approach is suitable for sensing and screening and might be relevant to investigate other protein systems as well.

Dielectric metasurfaces for next-generation optical biosensing: a comparison with plasmonic sensing

In the past decades, nanophotonic biosensors have been extended from the extensively studied plasmonic platforms to dielectric metasurfaces. Instead of plasmonic resonance, dielectric metasurfaces are based on Mie resonance, and provide comparable sensitivity with superior resonance bandwidth, Q factor, and figure-of-merit. Although the plasmonic photothermal effect is beneficial in many biomedical applications, it is a fundamental limitation for biosensing. Dielectric metasurfaces solve the ohmic loss and heating problems, providing better repeatability, stability, and biocompatibility. We review the high-Q resonances based on various physical phenomena tailored by meta-atom geometric designs, and compare dielectric and plasmonic metasurfaces in refractometric, surface-enhanced, and chiral sensing for various biomedical and diagnostic applications. Departing from conventional spectral shift measurement using spectrometers, imaging-based and spectrometer-less biosensing are highlighted, including single-wavelength refractometric barcoding, surface-enhanced molecular fingerprinting, and integrated visual reporting. These unique modalities enabled by dielectric metasurfaces point to two important research directions. On the one hand, hyperspectral imaging provides massive information for smart data processing, which not only achieve better biomolecular sensing performance than conventional ensemble averaging, but also enable real-time monitoring of cellular or microbial behaviour in physiological conditions. On the other hand, a single metasurface can integrate both functions of sensing and optical output engineering, using single-wavelength or broadband light sources, which provides simple, fast, compact, and cost-effective solutions. Finally, we provide perspectives in future development on metasurface nanofabrication, functionalization, material, configuration, and integration, towards next-generation optical biosensing for ultra-sensitive, portable/wearable, lab-on-a-chip, point-of-care, multiplexed, and scalable applications.

Nano-biosensor for SARS-CoV-2/COVID-19 detection: methods, mechanism and interface design

The epidemic of coronavirus disease 2019 (COVID-19) was a huge disaster to human society. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to COVID-19, has resulted in a large number of deaths. Even though the reverse transcription-polymerase chain reaction (RT-PCR) is the most efficient method for the detection of SARS-CoV-2, the disadvantages (such as long detection time, professional operators, expensive instruments, and laboratory equipment) limit its application. In this review, the different kinds of nano-biosensors based on surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), field-effect transistor (FET), fluorescence methods, and electrochemical methods are summarized, starting with a concise description of their sensing mechanism. The different bioprobes (such as ACE2, S protein-antibody, IgG antibody, IgM antibody, and SARS-CoV-2 DNA probes) with different bio-principles are introduced. The key structural components of the biosensors are briefly introduced to give readers an understanding of the principles behind the testing methods. In particular, SARS-CoV-2-related RNA mutation detection and its challenges are also briefly described. We hope that this review will encourage readers with different research backgrounds to design SARS-CoV-2 nano-biosensors with high selectivity and sensitivity.

Semi-wrapped gold nanoparticles for surface-enhanced Raman scattering detection

Researchers have struggled to develop highly reliable and sensitive surface-enhanced Raman scattering (SERS) substrates for detecting compounds in complicated systems. In this work, a strategy by constructing Au cores with incompletely wrapped Prussian blue (PB) for highly reliable and sensitive SERS substrate is proposed. The wrapped PB layers can provide the internal standard (IS) to calibrate the SERS signal floatation, whereas the exposed surface of Au cores offers the enhancement effect. The balance between the signal self-calibration and enhancement (hence the trade-off between SERS reliability and sensitivity) is obtained by the approximate semi-wrapping configuration of PB layers on Au cores (i.e., SW-Au@PB). The proposed SW-Au@PB nanoparticles (NPs) exhibit the similar enhancement factor as the pristine Au NPs and contribute to the ultralow RSD (8.55%) of calibrated SERS signals using R6G as probe molecules. The simultaneously realized reliability and sensitivity of SW-Au@PB NPs also enables the detection of hazardous pesticide residues such as paraquat and thiram in herbal plants, with the average detection accuracy up to 92%. Overall, this work mainly provides a controllable synthetic strategy for incompletely wrapped NPs, and most importantly, explores the potential with a proof-of-concept practical application in accurate and sensitive Raman detection of hazardous substances with varying solubility.

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.

Detecting nanoparticles by "listening"

In the macroscopic world, we can obtain some important information through the vibration of objects, that is, listening to the sound. Likewise, we can also get some information of the nanoparticles that we want to know by the means of "listening" in the microscopic world. In this review, we will introduce two sensing methods (cavity optomechanical sensing and surface-enhanced Raman scattering sensing) which can be used to detect the nanoparticles. The cavity optomechanical systems are mainly used to detect sub-gigahertz nanoparticle or cavity vibrations, while surface-enhanced Raman scattering is a well-known technique to detect molecular vibrations whose frequency generally exceeds terahertz. Therefore, the vibrational information of nanoparticles from low-frequency to high-frequency could be obtained by these two methods. The size of the viruses is at the nanoscale and we can regard it as a kind of nanoparticles. Rapid and ultrasensitive detection of the viruses is the key strategies to break the spread of the viruses in the community. Cavity optomechanical sensing enables rapid, ultrasensitive detection of nanoparticles through the interaction of light and mechanical oscillators and surface-enhanced Raman scattering is an attractive qualitatively analytical technique for chemical sensing and biomedical applications, which has been used to detect the SARS-CoV-2 infected. Hence, investigation in these two fields is of vital importance in preventing the spread of the virus from affecting human's life and health.

Recent development of surface-enhanced Raman scattering for biosensing

Surface-Enhanced Raman Scattering (SERS) technology, as a powerful tool to identify molecular species by collecting molecular spectral signals at the single-molecule level, has achieved substantial progresses in the fields of environmental science, medical diagnosis, food safety, and biological analysis. As deepening research is delved into SERS sensing, more and more high-performance or multifunctional SERS substrate materials emerge, which are expected to push Raman sensing into more application fields. Especially in the field of biological analysis, intrinsic and extrinsic SERS sensing schemes have been widely used and explored due to their fast, sensitive and reliable advantages. Herein, recent developments of SERS substrates and their applications in biomolecular detection (SARS-CoV-2 virus, tumor etc.), biological imaging and pesticide detection are summarized. The SERS concepts (including its basic theory and sensing mechanism) and the important strategies (extending from nanomaterials with tunable shapes and nanostructures to surface bio-functionalization by modifying affinity groups or specific biomolecules) for improving SERS biosensing performance are comprehensively discussed. For data analysis and identification, the applications of machine learning methods and software acquisition sources in SERS biosensing and diagnosing are discussed in detail. In conclusion, the challenges and perspectives of SERS biosensing in the future are presented.

Raman spectroscopy for viral diagnostics

Raman spectroscopy offers the potential for fingerprinting biological molecules at ultra-low concentration and therefore has potential for the detection of viruses. Here we review various Raman techniques employed for the investigation of viruses. Different Raman techniques are discussed including conventional Raman spectroscopy, surface-enhanced Raman spectroscopy, Raman tweezer, tip-enhanced Raman Spectroscopy, and coherent anti-Stokes Raman scattering. Surface-enhanced Raman scattering can play an essential role in viral detection by multiplexing nanotechnology, microfluidics, and machine learning for ensuring spectral reproducibility and efficient workflow in sample processing and detection. The application of these techniques to diagnose the SARS-CoV-2 virus is also reviewed.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-023-01059-4.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Label-Free Saliva Test for Rapid Detection of Coronavirus Using Nanosensor-Enabled SERS

The recent COVID-19 pandemic has highlighted the inadequacies of existing diagnostic techniques and the need for rapid and accurate diagnostic systems. Although molecular tests such as RT-PCR are the gold standard, they cannot be employed as point-of-care testing systems. Hence, a rapid, noninvasive diagnostic technique such as Surface-enhanced Raman scattering (SERS) is a promising analytical technique for rapid molecular or viral diagnosis. Here, we have designed a SERS- based test to rapidly diagnose SARS-CoV-2 from saliva. Physical methods synthesized the nanostructured sensor. It significantly increased the detection specificity and sensitivity by ~ten copies/mL of viral RNA (~femtomolar concentration of nucleic acids). Our technique combines the multiplexing capability of SERS with the sensitivity of novel nanostructures to detect whole virus particles and infection-associated antibodies. We have demonstrated the feasibility of the test with saliva samples from individuals who tested positive for SARS-CoV-2 with a specificity of 95%. The SERS-based test provides a promising breakthrough in detecting potential mutations that may come up with time while also preparing the world to deal with other pandemics in the future with rapid response and very accurate results.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Asymmetric parameter enhancement in the split-ring cavity array for virus-like particle sensing

Quantitative detection of virus-like particles under a low concentration is of vital importance for early infection diagnosis and water pollution analysis. In this paper, a novel virus detection method is proposed using indirect polarization parametric imaging method combined with a plasmonic split-ring nanocavity array coated with an Au film and a quantitative algorithm is implemented based on the extended Laplace operator. The attachment of viruses to the split-ring cavity breaks the structural symmetry, and such asymmetry can be enhanced by depositing a thin gold film on the sample, which allows an asymmetrical plasmon mode with a large shift of resonance peak generated under transverse polarization. Correspondingly, the far-field scattering state distribution encoded by the attached virus exhibits a specific asymmetric pattern that is highly correlated to the structural feature of the virus. By utilizing the parametric image sinδ to collect information on the spatial photon state distribution and far-field asymmetry with a sub-wavelength resolution, the appearance of viruses can be detected. To further reduce the background noise and enhance the asymmetric signals, an extended Laplace operator method which divides the detection area into topological units and then calculates the asymmetric parameter is applied, enabling easier determination of virus appearance. Experimental results show that the developed method can provide a detection limit as low as 56 vp/150µL on a large scale, which has great potential in early virus screening and other applications.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Application of SERS in In-Vitro Biomedical Detection

Surface-enhanced Raman scattering (SERS), as a rapid and nondestructive biological detection method, holds great promise for clinical on spot and early diagnosis. In order to address the challenging demands of on spot detection of biomedical samples, a variety of strategies has been developed. These strategies include substrate structural and component engineering, data processing techniques, as well as combination with other analytical methods. This report summarizes the recent SERS developments for biomedical detection, and their promising applications in cancer detection, virus or bacterial infection detection, miscarriage spotting, neurological disease screening et al. The first part discusses the frequently used SERS substrate component and structures, the second part reports on the detection strategies for nucleic acids, proteins, bacteria, and virus, the third part summarizes their promising applications in clinical detection in a variety of illnesses, and the forth part reports on recent development of SERS in combination with other analytical techniques. The special merits, challenges, and perspectives are discussed in both introduction and conclusion sections.

Recent advances of Au@Ag core-shell SERS-based biosensors

The methodological advancements in surface-enhanced Raman scattering (SERS) technique with nanoscale materials based on noble metals, Au, Ag, and their bimetallic alloy Au-Ag, has enabled the highly efficient sensing of chemical and biological molecules at very low concentration values. By employing the innovative various type of Au, Ag nanoparticles and especially, high efficiency Au@Ag alloy nanomaterials as substrate in SERS based biosensors have revolutionized the detection of biological components including; proteins, antigens antibodies complex, circulating tumor cells, DNA, and RNA (miRNA), etc. This review is about SERS-based Au/Ag bimetallic biosensors and their Raman enhanced activity by focusing on different factors related to them. The emphasis of this research is to describe the recent developments in this field and conceptual advancements behind them. Furthermore, in this article we apex the understanding of impact by variation in basic features like effects of size, shape varying lengths, thickness of core-shell and their influence of large-scale magnitude and morphology. Moreover, the detailed information about recent biological applications based on these core-shell noble metals, importantly detection of receptor binding domain (RBD) protein of COVID-19 is provided.

Real-time measurement of the trans-epithelial electrical resistance in an organ-on-a-chip during cell proliferation

The trans-epithelial electrical resistance (TEER) is widely used to quantitatively evaluate cellular barrier function at the organ level in vitro. The measurement of the TEER in organ-on-chips (organ chips) plays a significant role in medical and pharmacological research. However, due to the limitation of the electrical equivalent model for organ chips, the existing TEER measurements usually neglect the changes of the TEER during cell proliferation, resulting in the low accuracy of the measurements. Here, we proposed a new whole-region model of the TEER and developed a real-time TEER measurement system that contains an organ chip with a plate electrode. A whole region circuit model considering the impedance of the non-cell covered region was also established, which enables TEER measurements to be independent of the changes in the cell covered region. The impedance of the non-cell covered region is here attributed to the resistance of the porous membrane. By combining the real-time measurement system and the whole region model, subtle changes in cellular activity during the proliferation stage were measured continuously every 6 minutes and a more sensitive TEER response was obtained. Furthermore, the TEER measurement accuracy was also verified by the real-time measurement of the TEER with stimulation using the permeability enhancer ethylene glycol-bis(2-aminoethylether)-N,N,N',N'-tetraacetic acid (EGTA). The obtained results indicated that the new proposed whole region model and the real-time measurement system have higher accuracy and greater sensitivity than the traditional model.

SERS detection of thiram using polyacrylamide hydrogel-enclosed gold nanoparticle aggregates

The development of sensitive and long-term signal-stable plasmonic substrates is vital to the in-field application of the surface-enhanced Raman spectroscopy (SERS) technique. The colloidal gold nanoparticles (AuNPs) system is commonly used in SERS detection, but it shows less signal stability and reproducibility due to the uncontrollable aggregation of nanoparticles by adding aggregating agents in SERS detection. In this study, we developed a new SERS detection platform based on polyacrylamide hydrogel-enclosed plasmonic gold nanoparticle aggregates (PAH-AuANs). In the system, the formation of PAH can rapidly stabilize the gold nanoparticle aggregates, avoiding the over-aggregation or precipitation of AuNPs. With the PAH concentration in the range of 6-10 % and AuNPs at the concentration of 0.2 nM, the resulting PAH-AuNAs platform exhibited both sensitive SERS activity and excellent SERS signal stability. The relative standard deviation of the 4-MBA probe SERS signal collected from the PAH-AuNAs platform was lower than 3 %. The limit of detection for the pesticide thiram was down to 0.38 μg/L with a handheld Raman spectrometer. Moreover, the procedure for preparing the PAH-AuNAs platform was easy to handle, offering a new strategy for in-field detection of environmental contaminants with a handheld Raman spectrometer in the future.

Recent advances in immunoassay technologies for the detection of human coronavirus infections

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the seventh coronavirus (CoV) that has spread in humans and has become a global pandemic since late 2019. Efficient and accurate laboratory diagnostic methods are one of the crucial means to control the development of the current pandemic and to prevent potential future outbreaks. Although real-time reverse transcription-polymerase chain reaction (rRT-PCR) is the preferred laboratory method recommended by the World Health Organization (WHO) for diagnosing and screening SARS-CoV-2 infection, the versatile immunoassays still play an important role for pandemic control. They can be used not only as supplemental tools to identify cases missed by rRT-PCR, but also for first-line screening tests in areas with limited medical resources. Moreover, they are also indispensable tools for retrospective epidemiological surveys and the evaluation of the effectiveness of vaccination. In this review, we summarize the mainstream immunoassay methods for human coronaviruses (HCoVs) and address their benefits, limitations, and applications. Then, technical strategies based on bioinformatics and advanced biosensors were proposed to improve the performance of these methods. Finally, future suggestions and possibilities that can lead to higher sensitivity and specificity are provided for further research.

Plasmonic Biosensors with Nanostructure for Healthcare Monitoring and Diseases Diagnosis

Nanophotonics has been widely utilized in enhanced molecularspectroscopy or mediated chemical reaction, which has major applications in the field of enhancing sensing and enables opportunities in developing healthcare monitoring. This review presents an updated overview of the recent exciting advances of plasmonic biosensors in the healthcare area. Manufacturing, enhancements and applications of plasmonic biosensors are discussed, with particular focus on nanolisted main preparation methods of various nanostructures, such as chemical synthesis, lithography, nanosphere lithography, nanoimprint lithography, etc., and describing their respective advances and challenges from practical applications of plasmon biosensors. Based on these sensing structures, different types of plasmonic biosensors are summarized regarding detecting cancer biomarkers, body fluid, temperature, gas and COVID-19. Last, the existing challenges and prospects of plasmonic biosensors combined with machine learning, mega data analysis and prediction are surveyed.

Emerging biotransduction strategies on soft interfaces for biosensing

As a lab-on-soft biochip providing accurate and timely biomarker information, wearable biosensors can satisfy the increasing demand for intelligent e-health services, active disease diagnosis/therapy, and huge bioinformation data. As biomolecules generally could not directly produce detectable signals, biotransducers that specifically convert biomolecules to electrical or optical signals are involved, which determines the pivotal sensing performance including 3S (sensitivity, selectivity, and stability), reversibility, etc. The soft interface poses new requirements for biotransducers, especially equipment-free, facile operation, mechanical tolerance, and high sensing performance. In this review, we discussed the emerging electrochemical and optical biotransduction strategies on wearables from the aspects of the transduction mechanism, amplification strategies, biomaterial selection, and device fabrication procedures. Challenges and perspectives regarding future biotransducers for monitoring trace amounts of biomolecules with high fidelity, sensitivity, and multifunctionality are also discussed. It is expected that through fusion with functional electronics, wearable biosensors can provide possibilities to further decentralize the healthcare system and even build biomolecule-based intelligent cyber-physical systems and new modalities of cyborgs.

Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review

Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nanostructures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates.

Pathogen Identification: Ultrasensitive Nucleic Acid Detection via a Dynamic DNA Nanosystem-Integrated Ratiometric Electrochemical Sensing Strategy

Sensitively determining trace nucleic acid is of great significance for pathogen identification. Herein, a dynamic DNA nanosystem-integrated ratiometric electrochemical biosensor was proposed to determine human immunodeficiency virus-associated DNA fragment (HIV-DNA) with high sensitivity and selectivity. The dynamic DNA nanosystem was composed of a target recycling unit and a multipedal DNA walker unit. Both of them could be driven by a toehold-mediated strand displacement reaction, enabling an enzyme-free and isothermal amplification strategy for nucleic acid determination. The target recycling unit could selectively recognize HIV-DNA and activate the multipedal DNA walker unit to roll on the electrode surface, which would lead to bidirectional signal variation for ratiometric readout with cascade signal amplification. Benefiting from the synergistic effect of the dynamic DNA nanosystem and the ratiometric output mode, the ultrasensitive detection of HIV-DNA was achieved in a wide linear range of 6 orders of magnitude with a limit of detection of 36.71 aM. The actual usability of the proposed sensor was also verified in complex biological samples with acceptable performance. This dynamic DNA nanosystem-integrated ratiometric sensing strategy might be promising in the development of reliable point-of-care diagnostic devices for highly sensitive and selective pathogen identification.

Hydrophobic expanded graphite-covered support to construct flexible and stable SERS substrate for sensitive determination by paste-sampling from irregular surfaces

Surface enhanced Raman spectroscopy (SERS) is a promising technique for trace determination. More and more attention is focused on hybrid SERS substrates, which coupled with noble metal nanoparticles and carbon-based materials. Herein, expanded graphite (EG) is used to prepare EG-covered support by ultrasonic washing and filtration. Such support is flexible and can be cut into any shape. And the contact angle (θe) for Au nanorods (Au NRs) sol on the EG-covered support was 108.2° and the hydrophobic surface is helpful for Au NRs to construct 'hot spots' during evaporation. The limits of detection (LOD) for crystal violet (CV), thiram, malachite green (MG) and methylene blue (MB) were as low as 1 ppb, 50 ppb, 1 ppb and 1 ppb, respectively. Moreover, a fast and convenient 'paste-sampling' method could be employed for trace contaminants on real samples, because EG-based Au NRs substrate is of flexibility and porosity. Thus, CV residue on shrimp could be determined lower than 1 ppb and thiram residue on grapes could be identified lower than 50 ppb. In addition to high sensitivity, the stability of EG-based Au NRs substrate is also very good. Even after acid/alkali pretreatment (pH = 4∼10) or 30 min of thermal treatment (T = 20∼100 °C), the enhancement of the substrate remained stable. What's more, the substrate could be stored as long as 30 days. The highly stable, sensitive, cost-effective and easy-to-produce EG-based Au NRs substrates exhibit a great potential to promote application of SERS for routine analysis.

Ultrasensitive NO Sensor Based on a Nickel Single-Atom Electrocatalyst for Preliminary Screening of COVID-19

A new coronavirus, SARS-CoV-2, has caused the coronavirus disease-2019 (COVID-19) epidemic. A rapid and economical method for preliminary screening of COVID-19 may help to control the COVID-19 pandemic. Here, we report a nickel single-atom electrocatalyst that can be printed on a paper-printing sensor for preliminary screening of COVID-19 suspects by efficient detection of fractional exhaled nitric oxide (FeNO). The FeNO value is confirmed to be related to COVID-19 in our exploratory clinical study, and a machine learning model that can accurately classify healthy subjects and COVID-19 patients is established based on FeNO and other features. The nickel single-atom electrocatalyst consists of a single nickel atom with N₂O₂ coordination embedded in porous acetylene black (named Ni-N₂O₂/AB). A paper-printed sensor was fabricated with the material and showed ultrasensitive response to NO in the range of 0.3-180 ppb. This ultrasensitive sensor could be applied to preliminary screening of COVID-19 in everyday life.

Label-Free Surface-Enhanced Raman Spectroscopic Analysis of Proteins: Advances and Applications

Surface-enhanced Raman spectroscopy (SERS) is powerful for structural characterization of biomolecules under physiological condition. Owing to its high sensitivity and selectivity, SERS is useful for probing intrinsic structural information of proteins and is attracting increasing attention in biophysics, bioanalytical chemistry, and biomedicine. This review starts with a brief introduction of SERS theories and SERS methodology of protein structural characterization. SERS-active materials, related synthetic approaches, and strategies for protein-material assemblies are outlined and discussed, followed by detailed discussion of SERS spectroscopy of proteins with and without cofactors. Recent applications and advances of protein SERS in biomarker detection, cell analysis, and pathogen discrimination are then highlighted, and the spectral reproducibility and limitations are critically discussed. The review ends with a conclusion and a discussion of current challenges and perspectives of promising directions.

Surface-enhanced Raman scattering as a potential strategy for wearable flexible sensing and point-of-care testing non-invasive medical diagnosis

As a powerful and effective analytical tool, surface-enhanced Raman scattering (SERS) has attracted considerable research interest in the fields of wearable flexible sensing and non-invasive point-of-care testing (POCT) medical diagnosis. In this mini-review, we briefly summarize the design strategy, the development progress of wearable SERS sensors and its applications in this field. We present SERS substrate analysis of material design requirements for wearable sensors and highlight the benefits of novel plasmonic particle-in-cavity (PIC)-based nanostructures for flexible SERS sensors, as well as the unique interfacial adhesion effect and excellent mechanical properties of natural silk fibroin (SF) derived from natural cocoons, indicating promising futures for applications in the field of flexible electronic, optical, and electrical sensors. Additionally, SERS wearable sensors have shown great potential in the fields of different disease markers as well as in the diagnosis testing for COVID-19. Finally, the current challenges in this field are pointed out, as well as the promising prospects of combining SERS wearable sensors with other portable health monitoring systems for POCT medical diagnosis in the future.

Introduction of graphene oxide-supported multilayer-quantum dots nanofilm into multiplex lateral flow immunoassay: A rapid and ultrasensitive point-of-care testing technique for multiple respiratory viruses

A lateral flow immunoassay (LFA) biosensor that allows the sensitive and accurate identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other common respiratory viruses remains highly desired in the face of the coronavirus disease 2019 pandemic. Here, we propose a multiplex LFA method for the on-site, rapid, and highly sensitive screening of multiple respiratory viruses, using a multilayered film-like fluorescent tag as the performance enhancement and signal amplification tool. This film-like three-dimensional (3D) tag was prepared through the layer-by-layer assembly of highly photostable CdSe@ZnS-COOH quantum dots (QDs) onto the surfaces of monolayer graphene oxide nanosheets, which can provide larger reaction interfaces and specific active surface areas, higher QD loads, and better luminescence and dispersibility than traditional spherical fluorescent microspheres for LFA applications. The constructed fluorescent LFA biosensor can simultaneously and sensitively quantify SARS-CoV-2, influenza A virus, and human adenovirus with low detection limits (8 pg/mL, 488 copies/mL, and 471 copies/mL), short assay time (15 min), good reproducibility, and high accuracy. Moreover, our proposed assay has great potential for the early diagnosis of respiratory virus infections given its robustness when validated in real saliva samples.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material (Section S1 Experimental section, Section S2 Calculation of the maximum number of QDs on the GO@TQD nanofilm, Section S3 Optimization of the LFA method, and Figs. S1-S17 mentioned in the main text) is available in the online version of this article at 10.1007/s12274-022-5043-6.

Recent Advances of Representative Optical Biosensors for Rapid and Sensitive Diagnostics of SARS-CoV-2

The outbreak of Corona Virus Disease 2019 (COVID-19) has again emphasized the significance of developing rapid and highly sensitive testing tools for quickly identifying infected patients. Although the current reverse transcription polymerase chain reaction (RT-PCR) diagnostic techniques can satisfy the required sensitivity and specificity, the inherent disadvantages with time-consuming, sophisticated equipment and professional operators limit its application scopes. Compared with traditional detection techniques, optical biosensors based on nanomaterials/nanostructures have received much interest in the detection of SARS-CoV-2 due to the high sensitivity, high accuracy, and fast response. In this review, the research progress on optical biosensors in SARS-CoV-2 diagnosis, including fluorescence biosensors, colorimetric biosensors, Surface Enhancement Raman Scattering (SERS) biosensors, and Surface Plasmon Resonance (SPR) biosensors, was comprehensively summarized. Further, promising strategies to improve optical biosensors are also explained. Optical biosensors can not only realize the rapid detection of SARS-CoV-2 but also be applied to judge the infectiousness of the virus and guide the choice of SARS-CoV-2 vaccines, showing enormous potential to become point-of-care detection tools for the timely control of the pandemic.

Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring

Speedy, point-of-need detection and monitoring of small-molecule metabolites are vital across diverse applications ranging from biomedicine to agri-food and environmental surveillance. Nanomaterial-based sensor (nanosensor) platforms are rapidly emerging as excellent candidates for versatile and ultrasensitive detection owing to their highly configurable optical, electrical and electrochemical properties, fast readout, as well as portability and ease of use. To translate nanosensor technologies for real-world applications, key challenges to overcome include ultralow analyte concentration down to ppb or nM levels, complex sample matrices with numerous interfering species, difficulty in differentiating isomers and structural analogues, as well as complex, multidimensional datasets of high sample variability. In this Perspective, we focus on contemporary and emerging strategies to address the aforementioned challenges and enhance nanosensor detection performance in terms of sensitivity, selectivity and multiplexing capability. We outline 3 main concepts: (1) customization of designer nanosensor platform configurations via chemical- and physical-based modification strategies, (2) development of hybrid techniques including multimodal and hyphenated techniques, and (3) synergistic use of machine learning such as clustering, classification and regression algorithms for data exploration and predictions. These concepts can be further integrated as multifaceted strategies to further boost nanosensor performances. Finally, we present a critical outlook that explores future opportunities toward the design of next-generation nanosensor platforms for rapid, point-of-need detection of various small-molecule metabolites.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.