Highly Adsorptive Au-TiO2 Nanocomposites for the SERS Face Mask Allow the Machine-Learning-Based Quantitative Assay of SARS-CoV-2 in Artificial Breath Aerosols

Recent Plasmonic Gold- and Silver-Assisted Raman Spectra for Advanced SARS-CoV-2 Detection

COVID-19 has become one of the deadliest epidemics in the past years. In efforts to combat the deadly disease besides vaccines, drug therapies, and facemasks, significant focus has been on designing specific methods for the sensitive and accurate detection of SARS-CoV-2. Of these, surface-enhanced Raman scattering (SERS) is an attractive analytical tool for the identification of SARS-CoV-2. SERS is the phenomenon of enhancement of Raman intensity signals from molecular analytes anchored onto the surfaces of roughened plasmonic nanomaterials. This work gives an updated summary of plasmonic gold nanomaterials (AuNMs) and silver nanomaterials (AgNMs)-based SERS technologies to identify SARS-CoV-2. Due to extreme "hot spots" promoting higher electromagnetic fields on their surfaces, different shapes of AuNMs and AgNMs combined with Raman probes have been reviewed for enhancing Raman signals of probe molecules for quantifying the virus. It also reviews progress made recently in the design of certain specific Raman probe molecules capable of imparting characteristic SERS response/tags for SARS-CoV-2 detection.

Cascade internal electric field dominated carbon nitride decorated with gold nanoparticles as SERS substrate for thiram assay

The development of valid chemical enhancement strategy with charge transfer (CT) for semiconductors has great scientific significance in surface-enhanced Raman scattering (SERS) technology. Herein, a phosphorus doped crystalline/amorphous polymeric carbon nitride (PCPCN) is fabricated by a facile molten salt method, and is employed as a SERS substrate for the first time. Upon the synergies of phosphatization and molten salt etching, PCPCN owns a cascaded internal electric field (IEF) due to the formation of p-n homojunction (interface-IEF) and crystalline/amorphous homojunction (bulk-IEF). The interface-IEF and bulk-IEF could effectively suppress the recombination of charge carriers and promote electron transfer between PCPCN and target methylene blue (MB), respectively. The strong CT interaction endows PCPCN substrate with superior SERS activity with an enhancement factor (EF) of 5.53 × 105. Au nanoparticles (Au NPs) are subsequently decorated on PCPCN to introduce electromagnetic enhancement for a better SERS response. The Au/PCPCN substrate allows to reliably detect trace crystal violet, as well as the thiram residue on cherry tomato. This work offers an integrated solution to enhance CT efficiency based on collaborative homojunction and internal electric field, and may inspire the design of novel semiconductor-based SERS substrates.

Simultaneous Recognition and Detection of Adenosine Phosphates by Machine Learning Analysis for Surface-Enhanced Raman Scattering Spectral Data

Adenosine phosphates (adenosine 5'-monophosphate (AMP), adenosine 5'-diphosphate (ADP), and adenosine 5'-triphosphate (ATP)) play important roles in energy storage and signal transduction in the human body. Thus, a measurement method that simultaneously recognizes and detects adenosine phosphates is necessary to gain insight into complex energy-relevant biological processes. Surface-enhanced Raman scattering (SERS) is a powerful technique for this purpose. However, the similarities in size, charge, and structure of adenosine phosphates (APs) make their simultaneous recognition and detection difficult. Although approaches that combine SERS and machine learning have been studied, they require massive quantities of training data. In this study, limited AP spectral data were obtained using fabricated gold nanostructures for SERS measurements. The training data were created by feature selection and data augmentation after preprocessing the small amount of acquired spectral data. The performances of several machine learning models trained on these generated training data were compared. Multilayer perceptron model successfully detected the presence of AMP, ADP, and ATP with an accuracy of 0.914. Consequently, this study establishes a new measurement system that enables the highly accurate recognition and detection of adenosine phosphates from limited SERS spectral data.

Functionalized Face Masks as Smart Wearable Sensors for Multiple Sensing

Wearable sensors provide continuous physiological information and measure deviations from healthy baselines, resulting in the potential to personalize health management and diagnosis of diseases. With the emergence of the COVID-19 pandemic, functionalized face masks as smart wearable sensors for multimodal and/or multiplexed measurement of physical parameters and biochemical markers have become the general population for physiological health management and environmental pollution monitoring. This Review examines recent advances in applications of smart face masks based on implantation of digital technologies and electronics and focuses on respiratory monitoring applications with the advantages of autonomous flow driving, enrichment enhancement, real-time monitoring, diversified sensing, and easily accessible. In particular, the detailed introduction of diverse respiratory signals including physical, inhalational, and exhalant signals and corresponding associations of health management and environmental pollution is presented. In the end, we also provide a personal perspective on future research directions and the remaining challenges in the commercialization of smart functionalized face masks for multiple sensing.

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.

Rapid classification of SARS-CoV-2 variant strains using machine learning-based label-free SERS strategy

The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have directly impacted its biological functions, leading to enhanced transmission and triggering an immune escape effect. Therefore, prompt identification of these mutations is crucial for formulating targeted treatment plans and implementing precise prevention and control measures. In this study, the label-free surface-enhanced Raman scattering (SERS) technology combined with machine learning (ML) algorithms provide a potential solution for accurate identification of SARS-CoV-2 variants. We establish a SERS spectral database of SARS-CoV-2 variants and demonstrate that a diagnostic classifier using a logistic regression (LR) algorithm can provide accurate results within 10 min. Our classifier achieves 100% accuracy for Beta (B.1.351/501Y.V2), Delta (B.1.617), Wuhan (COVID-19) and Omicron (BA.1) variants. In addition, our method achieves 100% accuracy in blind tests of positive and negative human nasal swabs based on the LR model. This method enables detection and classification of variants in complex biological samples. Therefore, ML-based SERS technology is expected to accurately discriminate various SARS-CoV-2 variants and may be used for rapid diagnosis and therapeutic decision-making.

Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

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.