Plasmonics has drawn significant attention in the area of biosensors for decades due to the unique optical properties of plasmonic resonant nanostructures. While the sensitivity and specificity of molecular detection relies significantly on the resonance conditions, significant attention has been dedicated to the design, fabrication, and optimization of plasmonic substrates. The adequate choice of materials, structures, and functionality goes hand in hand with a fundamental understanding of plasmonics to enable the development of practical biosensors that can be deployed in real life situations. Here we provide a brief review of plasmonic biosensors detailing most recent developments and applications. Besides metals, novel plasmonic materials such as graphene are highlighted. Sensors based on Surface Plasmon Resonance (SPR), Localized Surface Plasmon Resonance (LSPR), and Surface Enhanced Raman Spectroscopy (SERS) are presented and classified based on their materials and structure. In addition, most recent applications to environment monitoring, health diagnosis, and food safety are presented. Potential problems related to the implementation in such applications are discussed and an outlook is presented.

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Are plasmonic optical biosensors ready for use in point-of-need applications?

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.

Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing.

Gas sensors lie at the heart of various fields ranging from medical to environmental sciences, and the demand of gas sensors is instantly expanding. However, in the face of complex gas samples, how to maintain high sensitivity while performing multiplex detection still puzzles the researchers. Here, by introducing Ti3C2Tx MXene into a microfluidic gas sensor with a three-dimensional (3D) transferable SERS substrate, a powerful gas sensor having both multiplex detecting ability and high sensitivity is demonstrated. The employ of MXene endows the sensor with a universal high adsorption efficiency for various gases while the generation of in situ gas vortices in the sophisticated nanomicro structure extends the molecule residence time in SERS-active area, both leading to the increased sensitivity. In the proof-of-concept experiment, a limit of detection (LOD) of 10-50 ppb was achieved for three typical volatile organic compounds (VOCs) according to the intrinsic SERS signals of gas molecules. Besides, the well-designed periodic 3D structure solves the general repeatability problem of SERS substrates. In addition, the detailed composition of gas mixture was revealed using classic least-square analysis (CLS) with an average accuracy of 90.6%. Further, a chromatic barcode was developed based on the results of CLS to read out the complex composition of samples visually.

Ti3C2Tx MXene-Loaded 3D Substrate toward On-Chip Multi-Gas Sensing with Surface-Enhanced Raman Spectroscopy (SERS) Barcode Readout.

High Throughput Blood Analysis Based on Deep Learning Algorithm and Self-Positioning Super-Hydrophobic SERS Platform for Non-Invasive Multi-Disease Screening

A study demonstrated the reducing of protein corona (PC) formation and enhancing colloidal stability of gold nanoparticles (Au NP) by capping with silica monolayers. Au NP surface was needed to achieve silica monolayer formation. In a first attempt to have a high density ligand coverage around NS-1, an excess of 3-mercaptopropyltrimethoxysilane (MPTMS) was directly added to a Au NP hydrosol, but this resulted in immediate aggregation. The resulting trimethoxysilane moiety covering the surface of NS-3 was subsequently hydrolyzed by addition of 5 mM NaOH in a 2:1 methanol/water mixture, yielding a polymeric monolayer.

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Reducing protein corona formation and enhancing colloidal stability of gold nanoparticles by capping with silica monolayers

J.P. acknowledges an FPU fellowship from the Spanish Ministry of Science, Innovation and Universities. L.M.L.‐M. acknowledges funding from the European Research Council (ERC AdG 787510, 4DbioSERS) and the Maria de Maeztu Units of Excellence Program from the Spanish State Research Agency (Grant No. MDM‐2017‐0720). C.G.‐A. acknowledges a Juan de la Cierva Fellowship from the Spanish Ministry of Science, Innovation and Universities (FJCI‐2016‐28887). The authors thank Dr. J. Calvo and Dr. D. Otaegui at CIC biomaGUNE for support with LC/ESI‐HRMS measurements. The work of A.C. was supported by the Basque Department of Industry, Tourism and Trade (Elkartek), and the department of education (IKERTALDE IT1106‐16, also participated by A. Gomez‐Munoz), the BBVA foundation, the MINECO (SAF2016‐79381‐R (FEDER/EU); Severo Ochoa Excellence Program SEV‐2016‐0644‐18‐1; Excellence Networks SAF2016‐81975‐REDT), European Training Networks Project (H2020‐MSCA‐ITN‐308 2016 721532), the AECC (IDEAS175CARR, GCTRA18006CARR), La Caixa Foundation (HR17‐00094), and the European Research Council (starting Grant 336343, PoC 754627). CIBERONC was co‐funded with FEDER funds and funded by ISCIII. A.M. acknowledges funding from the European Research Council (Consolidator Grant 819242) and the Spanish Ministry of Science, Innovation and Universities for the excellence program SEV‐2015‐0496.J.P. acknowledges an FPU fellowship from the Spanish Ministry of Science, Innovation and Universities. L.M.L.‐M. acknowledges funding from the European Research Council (ERC AdG 787510, 4DbioSERS) and the Maria de Maeztu Units of Excellence Program from the Spanish State Research Agency (Grant No. MDM‐2017‐0720). C.G.‐A. acknowledges a Juan de la Cierva Fellowship from the Spanish Ministry of Science, Innovation and Universities (FJCI‐2016‐28887). The authors thank Dr. J. Calvo and Dr. D. Otaegui at CIC biomaGUNE for support with LC/ESI‐HRMS measurements. The work of A.C. was supported by the Basque Department of Industry, Tourism and Trade (Elkartek), and the department of education (IKERTALDE IT1106‐16, also participated by A. Gomez‐Munoz), the BBVA foundation, the MINECO (SAF2016‐79381‐R (FEDER/EU); Severo Ochoa Excellence Program SEV‐2016‐0644‐18‐1; Excellence Networks SAF2016‐81975‐REDT), European Training Networks Project (H2020‐MSCA‐ITN‐308 2016 721532), the AECC (IDEAS175CARR, GCTRA18006CARR), La Caixa Foundation (HR17‐00094), and the European Research Council (starting Grant 336343, PoC 754627). CIBERONC was co‐funded with FEDER funds and funded by ISCIII. A.M. acknowledges funding from the European Research Council (Consolidator Grant 819242) and the Spanish Ministry of Science, Innovation and Universities for the excellence program SEV‐2015‐0496.

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Multiplex SERS Detection of Metabolic Alterations in Tumor Extracellular Media

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.

https://zenodo.org/record/6651711/files/2022%20Plou%20acsphotonics.pdf

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

The development of continuous monitoring systems requires in situ sensors that are capable of screening multiple chemical species and providing real-time information. Such in situ measurements, in which the sample is analyzed at the point of interest, are hindered by underlying problems derived from the recording of successive measurements within complex environments. In this context, surface-enhanced Raman scattering (SERS) spectroscopy appears as a noninvasive technology with the ability of identifying low concentrations of chemical species as well as resolving dynamic processes under different conditions. To this aim, the technique requires the use of a plasmonic substrate, typically made of nanostructured metals such as gold or silver, to enhance the Raman signal of adsorbed molecules (the analyte). However, a common source of uncertainty in real-time SERS measurements originates from the irreversible adsorption of (analyte) molecules onto the plasmonic substrate, which may interfere in subsequent measurements. This so-called "SERS memory effect" leads to measurements that do not accurately reflect varying conditions of the sample over time. We introduce herein the design of plasmonic substrates involving a nonpermeable poly(lactic-co-glycolic acid) (PLGA) thin layer on top of the plasmonic nanostructure, toward controlling the adsorption of molecules at different times. The polymeric layer can be locally degraded by irradiation with the same laser used for SERS measurements (albeit at a higher fluence), thereby creating a micrometer-sized window on the plasmonic substrate available to molecules present in solution at a selected measurement time. Using SERS substrates coated with such thermolabile polymer layers, we demonstrate the possibility of performing over 10,000 consecutive measurements per substrate as well as accurate continuous monitoring of analytes in microfluidic channels and biological systems.

Preventing Memory Effects in Surface-Enhanced Raman Scattering Substrates by Polymer Coating and Laser-Activated Deprotection

Plasmonic Detection of Carbohydrate-Mediated Biological Events

Monitoring dynamic processes in complex cellular environments requires the integration of uniformly distributed detectors within such three-dimensional (3D) networks, to an extent that the sensor could provide real-time information on nearby perturbations in a non-invasive manner. In this context, the development of 3D-printed structures that can function as both sensors and cell culture platforms emerges as a promising strategy, not only for mimicking a specific cell niche but also toward identifying its characteristic physicochemical conditions, such as concentration gradients. We present herein a 3D cancer model that incorporates a hydrogel-based scaffold containing gold nanorods. In addition to sustaining cell growth, the printed nanocomposite inks display the ability to uncover drug diffusion profiles by surface-enhanced Raman scattering, with high spatiotemporal resolution. We additionally demonstrate that the acquired information could pave the way to designing novel strategies for drug discovery in cancer therapy, through correlation of drug diffusion with cell death.

Javier Plou

Papers

Multiplex SERS Detection of Metabolic Alterations in Tumor Extracellular Media

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

Preventing Memory Effects in Surface-Enhanced Raman Scattering Substrates by Polymer Coating and Laser-Activated Deprotection

Plasmonic Detection of Carbohydrate-Mediated Biological Events

Nanocomposite Scaffolds for Monitoring of Drug Diffusion in Three-Dimensional Cell Environments by Surface-Enhanced Raman Spectroscopy.