ML Writes the Perfect Recipe for Nanoplates with Optical Properties: MachineLearning nanooptics nanophotonics nanomaterials ML ElsevierChemEng
By Hussain AhmedAug 16 2022Reviewed by Megan Craig, M.Sc. Silver nanoplates are used across different applications, specifically for biomedical sensing, food production, health care, and industrial purposes. A pre-proof paper from the journal Chemical Engineering Science focuses on the high-throughput production of silver nanoplates and the optimization of their optical properties using a novel machine learning algorithm.
It is therefore critical to find suitable methods for producing silver nanoplates with the appropriate characteristics. However, producing these metallic silver nanoplates has a variety of reaction factors such as metal source selection, reductants, surfactants, solvents, temperature, and response time.
Theoretical simulations demonstrate that expanding the side length aspect ratio to the silver nanoplates' thickness causes a notable red shift in the near-infrared region. Research teams have built continuous-flow systems with microreactors and online observation in recent years. These technologies can provide massive datasets required for machine learning algorithms in materials bioinformatics and unique strategies for fast and systematic identification of critical parameters using machine learning
The citrate mediator is important as a shape-directing ingredient in nanoplate production because it preferentially binds to the silver surfaces, allowing anisotropic crystal development along the lateral channel. The machine learning evaluation aided in the optimization of the synthesis conditions, resulting in high and compact absorption peaks at the appropriate wavelengths.
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