Gold Nanorod Synthesis with Small Thiolated Molecules

Abstract

Size and shape tunability have been widely demonstrated for gold nanorods (AuNRs), but reproducible and reliable protocols for the synthesis of small nanocrystals with high yield are still needed for potential biomedical applications. Here, we present novel seed-mediated and seedless protocols for gold nanorods by incorporating bioadditives or small thiolated molecules during the growth stage. The bioadditives glutathione (GSH), oxidized glutathione (GSSG), l-cysteine (l-cys), and l-methionine (l-met) are utilized in nanomolar and micromolar concentrations to modify the aspect ratio of AuNRs in a reproducible form. Overall, smaller aspect ratios are achieved for both synthetic approaches due to reduction in length or increment in length and width depending on the method, type of bioadditive and the strength of its interaction with the nanorod surface. For the seeded synthesis, only GSSG produces large nanorods in high yield, whereas for the seedless method GSH and GSSG form small nanorods with higher quality when compared to controls.

AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles

“…Due to the relatively low concentrations of these nanorod samples, it is unlikely that these spheroidal particles are Au nanorods standing on their tips. We note that the presence of spheroidal impurities during the synthesis of Au nanorods has been previously observed, 11,17,32 and the amount of impurities are typically estimated by optical spectroscopy or visual inspection of a few electron microscopic images, 12,16,17,[30][31][32] which is both time consuming and prone to human biases. AutoDetect-mNP determines the impurities of the Au nanorods to be 15% in long rods and 5% in short rods.…”

“…9,10 Significant research effort has focused on developing methods for the shape-controlled synthesis of mNPs, and an accurate and efficient method for their characterization has been indispensable for the success achieved by these studies. [11][12][13][14][15][16][17][18] To date, transmission electron microscopy (TEM) remains the most reliable and widely used method for characterizing the morphology of NPs, for which rapid advancement in automated highthroughput electron microscopy has drastically increased both the acquisition rate and the quality of TEM data. [19][20][21][22][23] Increased efficiency in TEM data acquisition now enables the scale of NP shape characterization to increase from tens or hundreds of particles to orders of magnitude more, extracting information at the level of more informative statistical distributions.…”

“…different shapes comprised of short and long rods and triangular prisms. AuNPs have been studied extensively by TEM, and protocols for their synthesis with controlled sizes and shapes are well established, 12,[15][16][17][30][31][32][33][34] making them an ideal candidate for demonstration of the strengths of the unsupervised algorithm. Furthermore, while shape-controlled synthesis of AuNPs have been extensively reported in literature, a generalizable quantitative method for the characterization of the shape distribution of these particles on a statistical scale beyond manual analysis of TEM images has yet to be established.…”

“…Furthermore, while shape-controlled synthesis of AuNPs have been extensively reported in literature, a generalizable quantitative method for the characterization of the shape distribution of these particles on a statistical scale beyond manual analysis of TEM images has yet to be established. 12,[15][16][17][30][31][32][33] AutoDetect-mNP is capable of processing thousands of particles from over a hundred TEM images in a matter of minutes, extracting morphological descriptors for convex Au nanoparticles and classifying the particles without the need of human intervention, thus greatly increasing the generalizability and efficiency of the process. In the future, AutoDetect-mNP can serve as a general and unbiased metric for reporting shape and shape attribute distributions of mNPs using TEM or other image acquisition strategies, and will play an important role in the future development of automated platforms for high-throughput mNP synthesis and time-resolved TEM characterization of mNP reactivity.…”

AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles

“…9,10 Significant research effort has focused on developing methods for the shape-controlled synthesis of mNPs, and an accurate and efficient method for their characterization has been indispensable for the success achieved by these studies. [11][12][13][14][15][16][17][18] To date, transmission electron microscopy (TEM) remains the most reliable and widely used method for characterizing the morphology of NPs, for which rapid advancement in automated highthroughput electron microscopy has drastically increased both the acquisition rate and the quality of TEM data. [19][20][21][22] Increased efficiency in TEM data acquisition now enables the scale of NP shape characterization to increase from tens or hundreds of particles to orders of magnitude more, extracting information at the level of more informative statistical distributions.…”