Fabrication of optoplasmonic particles through electroless deposition and the application in SERS-based screening of nodule-involved lung cancer

Wang, Zehua; Yan, Hong; Yan, Huan; Luo, Huaichao; Zhang, Yating; Li, Lintao; Lü, Shun; Chen, Yuanming; Wang, Dongsheng; Su, Yuanzhang; Guo, Yin

doi:10.1016/j.saa.2022.121483

Cited by 10 publications

(6 citation statements)

References 47 publications

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“…Serum samples of normal and lung-cancer patients were analyzed using PCA and PLS analysis to discriminate and identify the cancer samples with SERS spectra and achieved an accuracy of 92% [ 119 ]. Similarly, using a core-satellite type of plasmonic materials and SERS, serum samples of healthy, benign, and malignant cases of lung cancers were classified with a combination of principal component analysis (PCA) and support vector machines (SVMs) [ 120 ]. CtDNA-based identification of lung cancer using a DNA-rN1-DNA-mediated SERS frequency shift method was developed to achieve sub-femtomolar sensitivity [ 121 ].…”

Section: Sers For Disease Diagnosismentioning

confidence: 99%

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

2023

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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.

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Section: Sers For Disease Diagnosismentioning

confidence: 99%

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

2023

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“…Supervised learning algorithm is to obtain an optimal model which can realize prediction and classification of unknown data according to training samples (training datasets) with known categories. Common supervised learning algorithms include support vector method (SVM), [188] logistic regression, [189] classical least squares (CLS), [190] partial least square discriminate analysis (PLS-DA), [43,[191][192][193] neural network [23,47,194] and so on. [195] The combination of SERS sensor and one or more supervised learning algorithms can avoid "human error" and simultaneously achieve high throughput analysis during disease diagnosis.…”

Section: Supervised Learningmentioning

confidence: 99%

Recent Research Progress of Surface‐Enhanced Raman Scattering Dominated Analysis Strategies in Early Diagnosis of Diseases

Chen

Zheng

Tang

et al. 2023

Chemistry An Asian Journal

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Timely and powerful diagnostic means can achieve better therapeutic effects, reduce disease torment, and improve survival rate. As a powerful non‐invasive spectroscopy technology, surface‐enhanced Raman scattering (SERS) have testified to be a great potential candidate for extensive early clinical disease diagnosis. In recent years, the introduction of SERS label, combined with other analysis modes or artificial intelligence, and the emergence of miniaturized devices have made SERS technology more advantageous in early diagnosis of diseases. This review focuses on the research progress of SERS dominated analytical strategies in the field of early disease diagnosis in the past five years. The main content includes the application of label‐free SERS detection; the construction of label SERS methodologies for various disease markers; SERS dominated multimode early disease diagnosis strategies; integration of SERS and artificial intelligence; portable Raman equipment and SERS imaging; and opportunities and trends for SERS diagnostic technology in the future.

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“…Electric field enhancements of the hybrid silica microsphere covered gold/silver nanoparticles and the pure metal nanoparticles (gold/silver) were investigated in the article by Wang et al [ 277 ]. The interface of the plasmonic metal and dielectric spheres produces extra enhancement of neighboring electromagnetic field therefore both SiO 2 @Au and SiO 2 @Ag particles demonstrate substantial E-field enhancement in contrast to pure metal nanoparticles (AgNP and AuNP).…”

Section: Sers Clinical Applicationsmentioning

confidence: 99%

“…The interface of the plasmonic metal and dielectric spheres produces extra enhancement of neighboring electromagnetic field therefore both SiO 2 @Au and SiO 2 @Ag particles demonstrate substantial E-field enhancement in contrast to pure metal nanoparticles (AgNP and AuNP). It was also revealed that the hybrid silica sphere gold nanoparticles generate more enhancement of the signal than the silica-covered silver nanoparticles at 785 nm, while the silica-covered gold NPs produce higher signal at 532 nm This is because of the matched overlapping between AuNP LSPR in the hybrid particle and the excitation wavelength at 785 nm, whereas the electric field enhancement of SiO 2 @Ag particle is escalated around the LSPR of AgNP [ 277 ].…”

Section: Sers Clinical Applicationsmentioning

confidence: 99%

Trends in Application of SERS Substrates beyond Ag and Au, and Their Role in Bioanalysis

et al. 2022

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This article compares the applications of traditional gold and silver-based SERS substrates and less conventional (Pd/Pt, Cu, Al, Si-based) SERS substrates, focusing on sensing, biosensing, and clinical analysis. In recent decades plethora of new biosensing and clinical SERS applications have fueled the search for more cost-effective, scalable, and stable substrates since traditional gold and silver-based substrates are quite expensive, prone to corrosion, contamination and non-specific binding, particularly by S-containing compounds. Following that, we briefly described our experimental experience with Si and Al-based SERS substrates and systematically analyzed the literature on SERS on substrate materials such as Pd/Pt, Cu, Al, and Si. We tabulated and discussed figures of merit such as enhancement factor (EF) and limit of detection (LOD) from analytical applications of these substrates. The results of the comparison showed that Pd/Pt substrates are not practical due to their high cost; Cu-based substrates are less stable and produce lower signal enhancement. Si and Al-based substrates showed promising results, particularly in combination with gold and silver nanostructures since they could produce comparable EFs and LODs as conventional substrates. In addition, their stability and relatively low cost make them viable alternatives for gold and silver-based substrates. Finally, this review highlighted and compared the clinical performance of non-traditional SERS substrates and traditional gold and silver SERS substrates. We discovered that if we take the average sensitivity, specificity, and accuracy of clinical SERS assays reported in the literature, those parameters, particularly accuracy (93–94%), are similar for SERS bioassays on AgNP@Al, Si-based, Au-based, and Ag-based substrates. We hope that this review will encourage research into SERS biosensing on aluminum, silicon, and some other substrates. These Al and Si based substrates may respond efficiently to the major challenges to the SERS practical application. For instance, they may be not only less expensive, e.g., Al foil, but also in some cases more selective and sometimes more reproducible, when compared to gold-only or silver-only based SERS substrates. Overall, it may result in a greater diversity of applicable SERS substrates, allowing for better optimization and selection of the SERS substrate for a specific sensing/biosensing or clinical application.

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Fabrication of optoplasmonic particles through electroless deposition and the application in SERS-based screening of nodule-involved lung cancer

Cited by 10 publications

References 47 publications

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

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

Recent Research Progress of Surface‐Enhanced Raman Scattering Dominated Analysis Strategies in Early Diagnosis of Diseases

Trends in Application of SERS Substrates beyond Ag and Au, and Their Role in Bioanalysis

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