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Abstract During the operation of a localized surface plasmon resonance (LSPR) sensor made in the form of a core–shell nanoparticle with the shell acting as a sensing layer, the target molecules penetrate into the shell due to intrinsic diffusion or reaction mechanisms. As a result, these molecules or various reactants are nonuniformly distributed in the shell layer. Such sensing particles are termed composition graded plasmonic particles, and their LSPR characteristics may be quite different from those of the uniform core–shell particles. Here, under the quasi-static assumption, a perturbation theory is developed to predict the LSPR properties of composition graded plasmonic particles. The effects of the composition gradient on the LSPR properties due to a metal hydride, a dielectric, and an effective medium are either numerically calculated or analytically derived. Our results show that various configurations of the composition gradient can tune the location and the amplitude of the LSPR peak. The results are important for understanding the sensing performance of composition graded plasmonic particles, and the perturbative treatment presented here can also be used for other composition graded structures. 

Herein, deep learning (DL) is used to predict the structural parameters of Ag nanohole arrays (NAs) for spectrum‐driving and color‐driving plasmonic applications. A dataset of transmission spectra and structural parameters of NAs is generated using finite‐difference time‐domain (FDTD) calculations and is converted to vivid structural colors using the corresponding transmission spectrum. A bidirectional neural network is used to train the transmission spectrum and structural color together. The accuracy of predicting the structural parameters using a desired spectrum is tested and found to be up to 0.99, with a determination coefficient of reproducing the desired spectrum and color to be 0.97 and 0.96, respectively. These values are higher compared to those when only training for spectrum, but requiring less training time. This strategy is able to inverse design the NAs in less than 1 s to maximize surface‐enhanced Raman scattering (SERS) enhancement by matching transmission resonance and laser excitation wavelength, and accurately regenerate colored images in 7.5 s, allowing for nanoscale printing at a resolution of approximately 100 000 dots in⁻¹. This work has important implications for the efficient design of nanostructures for various plasmonic applications, such as plasmonic sensors, optical filters, metal‐enhanced fluorescence, SERS, and super‐resolution displays.

 

To sensitively detect multiple and cross-species disease-related targets from a single biological sample in a quick and reliable manner is of high importance in accurately diagnosing and monitoring diseases. Herein, a surface-enhanced Raman scattering (SERS) sensor based on a functionalized multiple-armed tetrahedral DNA nanostructure (FMTDN) immobilized silver nanorod (AgNR) array substrate and Au nanoparticle (AuNP) SERS tags is constructed to achieve both multiplex detection and enhanced sensitivity using a sandwich strategy. The sensor can achieve single, dual, and triple biomarker detections of three lung cancer-related nucleic acid and protein biomarkers, i.e. , miRNA-21, miRNA-486 and carcinoembryonic antigen (CEA) in human serum. The enhanced SERS signals in multiplex detections are due to the DNA self-assembled AuNP clusters on the silver nanorod array during the assay, and the experimentally obtained relative enhancement factor ratios, 150 for AuNP dimers and 840 for AuNP trimers, qualitatively agree with the numerically calculated local electric field enhancements. The proposed FMTDN-functionalized AgNR SERS sensor is capable of multiplex and cross-species detection of nucleic acid and protein biomarkers with improved sensitivity, which has great potential for the screening and clinical diagnosis of cancer in the early stage.