用于生物分子检测的具有均匀分布金纳米颗粒的晶圆级银纳米枝晶。

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

作者信息

Vendamani V S, Beeram Reshma, Neethish M M, Rao S V S Nageswara, Rao S Venugopal

机构信息

Advanced Centre for Research in High Energy Materials (ACRHEM), University of Hyderabad, Hyderabad 500046, India.

Department of Physics, Pondicherry University, Puducherry 605014, Puducherry, India.

出版信息

iScience. 2022 Aug 3;25(8):104849. doi: 10.1016/j.isci.2022.104849. eCollection 2022 Aug 19.

DOI:10.1016/j.isci.2022.104849

PMID:35996576

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9391580/

Abstract

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 ∼10 for CV, ∼10 for the biomolecules, ∼10 for the explosive molecule, and ∼10 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.

摘要

我们报告了在硅平台上制备并展示了用于痕量抗生素（青霉素、卡那霉素和氨苄青霉素）和DNA碱基（腺嘌呤、胞嘧啶）检测的坚固、经济高效且生物相容的分级金纳米颗粒（AuNPs）修饰银纳米枝晶（AgNDs）的卓越性能。通过改变AuNPs沉积时间探索了热点密度依赖性研究。通过检测结晶紫、硝酸铵和福美双进一步探索了这些基底的潜力和多功能性。计算得出结晶紫、腺嘌呤、胞嘧啶、青霉素G、卡那霉素、氨苄青霉素、硝酸铵和福美双的检测限分别为348 pM、2、28、2、56、4、5和2 nM。估计分析增强因子对于结晶紫约为10，对于生物分子约为10，对于爆炸分子约为10，对于福美双约为10。此外，这里报告了这些基底在不同时间间隔的稳定性以及120天内获得的表面增强拉曼光谱/散射（SERS）数据。