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.

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.

Protocol for designing AuNP-capped Ag dendrites as surface-enhanced Raman scattering sensors for trace molecular detection.

Surface-enhanced Raman spectroscopy (SERS) is a label-free, non-destructive technique for rapid identification of molecules with the interest of public safety and forensics. In the current work, we present a detailed protocol for designing a SERS-active substrate comprising Au-nanoparticles-decorated Ag nano-dendrites for the trace detection of explosives, biomolecules, dye, and pesticides. We elaborate the procedure for studying near-field enhancements in plasmonic structures. This protocol also addresses some of the challenges faced in SERS experiments and the potential solutions to overcome them. For complete details on the use and execution of this protocol, please refer to Vendamani et al. (2022).1.

Deep learning approach to overcome signal fluctuations in SERS for efficient On-Site trace explosives detection.

Surface-enhanced Raman spectroscopy (SERS) is an improved Raman spectroscopy technique to identify the analyte under study uniquely. At the laboratory scale, SERS has realised a huge potential to detect trace analytes with promising applications across multiple disciplines. However, onsite detection with SERS is still limited, given the unwanted glitches of signal reliability and blinking. SERS has inherent signal fluctuations due to multiple factors such as analyte adsorption, inhomogeneous distribution of hotspots, molecule orientation etc. making it a stochastic process. Given these signal fluctuations, validating a signal as a representation of the analyte often relies on an expert's knowledge. Here we present a neural network-aided SERS model (NNAS) without expert interference to efficiently identify reliable SERS spectra of trace explosives (tetryl and picric acid) and a dye molecule (crystal violet). The model uses the signal-to-noise ratio approach to label the spectra as representative (RS) and non-representative (NRS), eliminating the reliability of the expert. Further, experimental conditions were systematically varied to simulate general variations in SERS instrumentation, and a deep-learning model was trained. The model has been validated with a validation set followed by out-of-sample testing with an accuracy of 98% for all the analytes. We believe this model can efficiently bridge the gap between laboratory and on-site detection using SERS.

Wafer-scale silver nanodendrites with homogeneous distribution of gold nanoparticles for biomolecules detection.

We report the fabrication and demonstrate the superior performance of robust, cost-effective, and biocompatible hierarchical Au nanoparticles (AuNPs) decorated Ag nanodendrites (AgNDs) on a Silicon platform for the trace-level detection of antibiotics (penicillin, kanamycin, and ampicillin) and DNA bases (adenine, cytosine). The hot-spot density dependence studies were explored by varying the AuNPs deposition time. These substrates' potential and versatility were explored further through the detection of crystal violet, ammonium nitrate, and thiram. The calculated limits of detection for CV, adenine, cytosine, penicillin G, kanamycin, ampicillin, AN, and thiram were 348 pM, 2, 28, 2, 56, 4, 5, and 2 nM, respectively. The analytical enhancement factors were estimated to be ∼107 for CV, ∼106 for the biomolecules, ∼106 for the explosive molecule, and ∼106 for thiram. Furthermore, the stability of these substrates at different time intervals is being reported here with surface-enhanced Raman spectroscopy/scattering (SERS) data obtained over 120 days.

Machine learning for rapid quantification of trace analyte molecules using SERS and flexible plasmonic paper substrates.

Given the intrinsic nature of low reproducibility and signal blinking in the surface enhanced Raman scattering (SERS) technique, especially while detecting trace/ultra-trace amounts, it remains a major challenge to quantify the analyte under study. Here we present a simple and economically viable, flexible hydrophobic plasmonic filter paper-based SERS substrate for the quantification of two trace analytes [crystal violet (CV) and picric acid (PA)] using machine learning techniques and SERS data. The wettability of the substrate was modified with an easy and low-cost technique of coating it with silicone oil. Gold nanoparticles were synthesized using a femtosecond laser ablation in water technique. The prepared nanoparticles were characterized using UV, TEM, and SEM techniques and subsequently loaded onto filter papers before using them for SERS studies. We have considered the SERS intensities of the analytes at different concentrations with over 900 spectra to train the model. Principal component analysis (PCA) was used to reduce the dimensionality and, hence, the complexity of the model. Furthermore, support vector regression was used to quantify the analyte molecules and we achieved an R2 error of 0.9629 for CV and 0.9472 for PA. In conjunction with a portable Raman spectrometer and a computation time of less than <10 s, we believe that this is an affordable and rapid method for quantification of analytes using the SERS technique.

Trace level detection of explosives and pesticides using robust, low-cost, free-standing silver nanoparticles decorated porous silicon.

We report results from our extensive studies on the fabrication of ultra-thin, flexible, and cost-effective Ag nanoparticle (NP) coated free-standing porous silicon (FS-pSi) for superior molecular sensing. The FS-pSi has been prepared by adopting a simple wet-etching method. The deposition time of AgNO3 has been increased to improve the number of hot-spot regions, thereby the sensing abilities are improved efficiently. FESEM images illustrated the morphology of uniformly distributed AgNPs on the pSi surface. Initially, a dye molecule [methylene blue (MB)] was used as a probe to evaluate the sensing capabilities of the substrate using the surface-enhanced Raman scattering (SERS) technique. The detection was later extended towards the sensing of two important explosive molecules [ammonium nitrate (AN), picric acid (PA)], and a pesticide molecule (thiram) clearly demonstrating the versatility of the investigated substrates. The sensitivity was confirmed by estimating the analytical enhancement factor (AEF), which was ∼107 for MB and ∼104 for explosives and pesticides. We have also evaluated the limit of detection (LOD) values in each case, which were found to be 50 nM, 1 µM, 2 µM, and 1 µM, respectively, for MB, PA, AN, and thiram. Undeniably, our detailed SERS results established excellent reproducibility with a low RSD (relative standard deviation). Furthermore, we also demonstrate the reasonable stability of AgNPs decorated pSi by inspecting and studying their SERS performance over a period of 90 days. The overall cost of these substrates is attractive for practical applications on account of the above-mentioned superior qualities.