Machine Learning Analysis of Reaction Parameters in UV-Mediated Synthesis of Gold Nanoparticles

Abstract

Gold nanoparticles represent an important class of functional nanomaterials for optoelectronics, biomedical applications, and catalysis. Therefore, controllable synthesis of nanoparticles with specified size and shape is important. Though reduction of gold ions is quite a simple process and may be performed with many different protocols, the reproducibility of the results and transfer of protocols between independent research groups remains a challenging task. Machine learning analysis based on statistical approaches is hardly applicable to the published data, since most of the researchers report only successful syntheses. In this work, we apply uniform sampling of the reaction parameter space. The concentrations of gold precursor, reducing agent, and surfactant were varied via an improved Latin hypercube sampling, and each run was performed under in situ UV−vis control. Based on the resulting set of optical spectra, we address the relevant chemical questions about nanoparticle formation, their shape, and period of growth. Our work demonstrates a data driven approach applied to the space of reaction parameters in a limited available set of experiments.

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

“…XGBoost [97] IR and Raman spectra Predict surface-adsorbate interaction properties ET, [98] SISSO [98] Synthesis Selected synthesis descriptors Classify selected features of spectra ET [99] The citrate to gold (III) ratio, scanning velocity and radiation intensity Predict nanoparticle size ANN [ 100] Spectroscopic characteristics based on UV-vis/DLS Optimize experimental/reaction conditions GA, [ 101] BO+DNN [ 102] Reaction conditions Predict selected spectroscopic characteristics of nanoparticles SVR [103] Target molecules Propose a sequence of chemically viable reaction steps ANN [ 104] Instrumentations and spectral preprocessing Complex 2D images Optimize illumination light source parameters CNN [ 105] Patterns of weakly scattering perturbations Design transmission matrices VAE [ 106] Scattering spectra of nanostructures Encode up to 9 bits of information for high-density optical information storage CNN [ 107] Noisy Raman spectra Remove the baseline, cosmic rays, and noise simultaneously or separately CNN, [108][109][110] ResNet, [ 111] U-net, [ 111] ANN+U-net, [ 112] PCA, [113] GAN [ 114] Spectral analysis SERS spectrum Identify the existence of the molecular fingerprints…”

“…Specifically, a lot of faults are absent from the reported results and some details might even be consciously or unconsciously left out. [99,241] However, database consisting of comprehensive facts from systematic experiments is critical for the AI-based optimization. Therefore, microfluidic systems have been regarded as an excellent alternative to produce training data because the parameters are easy to control and it can do experiments with minimal volumes in a highly efficient manner [242] (Figure 4a).…”

“…[102,103] Inspired by natural evolution, GA selected the optimal experimental conditions for the current state which was fed into the subsequent loop until converged to the target properties [101] (Figure 4d). Apart from the reinforcing models, unsupervised algorithms (e.g., PCA) can be used to profile the property clusters in response to different reaction conditions [243] and supervised algorithms (e.g., tree-based models and ANNs) together with SHAP values [102] and other statistical measures (e.g., Pearson's correlation coefficient, mean squared error) help to identify the crucial factors contributing to the growing mechanisms [99,244,246] (Figure 4e).…”

“…Reproduced with permission. [ 99 ] Copyright 2023, American Chemical Society. Reproduced with permission.…”

Optimal synthesis conditions for NBF-modified 8,13-dihydroberberine derivatives

“…60 Guda et al demonstrated 3D printed module devices for gold nanoparticle synthesis. 61 Microfluidic technologies have gained significant popularity in the field of organic synthesis and catalysis. 62 The production of various o-disubstituted benzenes in a continuous flow regime, 62 screening applications of diazomethane, 63 and anaerobic catalytic oxidation of stilbenes 64 are examples.…”