Nanoparticle Growth
Nanoparticle Growth - Related Videos
03:06
Encapsulating Nerve Growth Factor in High-Density Lipoprotein-Mimicking Nanoparticles
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2025
Source: Zhu, J., et. al., Preparation and Characterization of Novel HDL-mimicking Nanoparticles for Nerve Growth Factor Encapsulation. J. Vis. Exp. (2017)This video demonstrates the stepwise assembly of NGF-loaded HDL-mimicking nanoparticles using protamine-assisted encapsulation and lipid-based self-assembly for potential neurotrophic delivery applications.
08:24
Preparation and Characterization of Novel HDL-mimicking Nanoparticles for Nerve Growth Factor Encapsulation
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Cited by 4 •
2017
Simple homogenization was used to prepare novel, high-density, lipoprotein-mimicking nanoparticles to encapsulate nerve growth factor. Challenges, detailed protocols for nanoparticle preparation, in vitro characterization, and in vivo studies are described in this article.
11:54
Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles
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Cited by 27 •
2018
The main goal of this work is to elucidate the role of capping agents in regulating the size of palladium nanoparticles by combining in situ small angle x-ray scattering (SAXS) and ligand-based kinetic modeling.
05:41
Photochemical Oxidative Growth of Iridium Oxide Nanoparticles on CdSe@CdS Nanorods
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Cited by 4 •
2016
A protocol for the photochemical oxidative growth of small crystalline iridium oxide nanoparticles on the surface of CdSe@CdS seeded rod nanoparticles is presented.
10:45
In Situ Visualization of Axon Growth and Growth Cone Dynamics in Acute Ex Vivo Embryonic Brain Slice Cultures
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Cited by 4 •
2021
This protocol demonstrates a straightforward and robust method to study in situ axon growth and growth cone dynamics. It describes how to prepare ex vivo physiologically relevant acute brain slices and provides a user-friendly analysis pipeline.
FAQs
Nucleation creates the initial particle structures that later develop through surface deposition, diffusion, coalescence, or aggregation. The relative contribution of these processes determines how atoms, molecules, or smaller clusters become organized within the growing material. Understanding this sequence helps engineers connect early formation behavior with final particle size, shape, composition, and surface properties.
Temperature, precursor concentration, reaction time, and stabilizing agents are central control variables. Adjusting these conditions changes how material is supplied, organized, and retained during formation, thereby influencing particle dimensions and characteristics. Engineers tune the variables together rather than treating them independently when targeting specific size, shape, composition, or surface properties.
Surface deposition adds material to an existing particle, while diffusion describes movement that supports material organization. Coalescence and aggregation instead involve particles or smaller structures coming together. Distinguishing these pathways matters because each contributes differently to how particles increase in size and develop properties relevant to engineered catalysts, sensors, electronics, and energy-storage materials.
An engineering workflow begins by identifying the desired particle size, shape, composition, or surface characteristics. Temperature, precursor concentration, reaction time, and stabilizing agents are then selected and controlled to guide formation toward those targets. Examining how these conditions affect the resulting material supports improved reproducibility and helps translate laboratory control into scalable manufacturing.
Controlled particle characteristics support several engineering applications, including catalysts, sensors, electronic materials, energy-storage components, and biomedical technologies. In each case, size, shape, composition, and surface properties can be tailored to suit the intended material function. This design flexibility makes growth control important when engineers seek improved performance from nanoscale components.
Reproducibility ensures that changes in temperature, precursor concentration, reaction time, or stabilizing agents produce predictable particle characteristics across repeated preparations. Scalability extends that control beyond individual experiments toward manufacturing processes. Together, they help engineers maintain consistent size, shape, composition, and surface properties while developing materials with reliable performance in practical technologies.