ML Writes the Perfect Recipe for Nanoplates with Optical Properties

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.

ML Writes the Perfect Recipe for Nanoplates with Optical Properties​​​​​​​

​​​​​​​Study: High-throughput synthesis of silver nanoplates and optimization of optical properties by machine learning. Image Credit: Dana.S/

Silver Nanoplates: Overview and Challenges

Metallic nanoparticles, such as silver nanoplates, have attracted considerable attention as functional materials because of their distinctive photonic, electronic, magnetic, and electrochemical characteristics. The qualities of silver nanoplates are greatly influenced by their size, structure, composition, and interparticulate distance.

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.

These variables influence the characteristics of nanoparticles through their structure, frequently displaying nonlinear reactions to the material properties. As a result,  optimizing the reaction rate by traditional approaches is extremely time-consuming and expensive.

Important Applications of Silver Nanoplates

Because of the extensively tuneable plasmonic resonating wavelength, silver nanoplates with small triangular forms are of tremendous practical value in sensing, scanning, and biomedical applications.

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.

The production of silver nanoplates with tailored plasmonic wavelengths is critical in surface-enhanced Raman scattering (SERS) detection systems. The wavelength shift towards the 'therapeutic zone', where light has deep penetration capability, is critical for the photothermal effect-based antimicrobial activity.

Novelty of High-throughput Synthesis and Characterization

High-throughput production and analysis using machine learning are extremely effective methods for optimizing multivariate networks. Combinatorial screenings that utilize an automatic liquid manipulator and a microplate scanner to run many reactions simultaneously have already been widely used in industrial settings.

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

Machine Learning-based Synthesis of Silver Nanoplates

The goal of this study was to quantify how the optical absorption wavelength of silver nanoplates can be influenced by their reaction variables using machine learning. A two-step chemical treatment using spherical seed particles produced the required silver nanoplates.

Silver nitrate was reduced with sodium borohydride in the vicinity of citrate salt to form seed particles, which were then developed into silver nanoplates using a mild reductant such as ascorbic acid.

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 amounts of citrate salt, silver resource, and the seed particle distribution supplied to the growing fluid are important factors in determining the absorption wavelength of the silver nanoplates.

Important Findings

To generate a machine learning database, the researchers investigated the absorption spectrum at 486 reaction settings by altering the silver nitrate and citrate contents as well as the seed quantities. A nonlinear machine learning algorithm was used to estimate the optical characteristics at every condition in the three-dimensional variable domain.

The machine learning evaluation aided in the optimization of the synthesis conditions, resulting in high and compact absorption peaks at the appropriate wavelengths.

Using an electronic liquid handling and a microplate reader, the assays at 486 conditions were performed in two days. A nonlinear machine learning regression was used to represent the nanoplates' peak wavelength, breadth, and height as continuous functions of the three parameters.

The high-throughput method and machine learning evaluation yielded the best formulations for producing silver nanoplates with the appropriate optical characteristics. Based on these findings, it is safe to suggest that the current machine learning-based synthesis method can provide silver nanoplates with optimized optical characteristics and be used for synthesizing other metallic nanoparticles for important industrial applications.


Kashiwagi, T. et al. (2022). High-throughput synthesis of silver nanoplates and optimization of optical properties by machine learning. Chemical Engineering Science. Available at:

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Hussain Ahmed

Written by

Hussain Ahmed

Hussain graduated from Institute of Space Technology, Islamabad with Bachelors in Aerospace Engineering. During his studies, he worked on several research projects related to Aerospace Materials & Structures, Computational Fluid Dynamics, Nano-technology & Robotics. After graduating, he has been working as a freelance Aerospace Engineering consultant. He developed an interest in technical writing during sophomore year of his B.S degree and has wrote several research articles in different publications. During his free time, he enjoys writing poetry, watching movies and playing Football.


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