Enhanced efficient and sensitive SERS sensing via controlled Ag-nanoparticle-decorated 3D flower-like ZnO hierarchical microstructure

Micro & nano letters

article

Jun Wang¹  Aifeng Ning¹ 

[1] Donghai Institute of Ningbo University, Ningbo University

关键词: nanofabrication;    nanoparticles;    wide band gap semiconductors;    II-VI semiconductors;    silver;    surface enhanced Raman scattering;    zinc compounds;    nanocomposites;    semiconductor growth;    photocatalysis;    crystal microstructure;    particle reinforced composites;    nanosensors;    optical sensors;    substrates;    stable SERS sensor;    controlled Ag-nanoparticle-decorated 3D flower-like ZnO hierarchical microstructure;    morphology;    surface-enhanced Raman scattering;    organic pollution;    photocatalytic method;    detection sensitivity;    Rhodamine 6G;    sensitive SERS sensing;    Raman intensity;    ZnO-Ag;   

DOI  :  10.1049/mnl.2020.0416

学科分类：计算机科学（综合）

来源: Wiley

【 摘 要 】

Authorship Analysis (AA) is a process aim to extract information about an author from his/her writings. To analyze whether two anonymous short texts were written by the same author, we propose a combination of stylometry features from different categories in different progress. The majority of the previous AA studies use many stylometry features from different categories together at the beginning of a solution as a pre-processing step. During the learning process, no category-specific operations are performed; all categories used are evaluated equally. However, the proposed approach has a separate learning process for each feature category and combines these processes at the decision phase by using a Combination of Deep Neural Networks (C-DNN). To evaluate the Authorship Verification (AV) performance of the proposed approach, we designed and implemented a problem-specific Deep Neural Network (DNN) for each stylometry category we used. Experiments were conducted on two English public datasets. The results show that the proposed approach significantly improves the generalization ability and robustness of the solutions, and also have better accuracy than the single DNNs.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND