Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Abstract

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously...

Ultrafast Determination of Antimicrobial Resistant Staphylococcus aureus Specifically Captured by Functionalized Magnetic Nanoclusters

“…TEM analysis was carried out using a JEOL transmission electron microscope equipped with an in-column Omega-type energy filter (JEM-2200FS, Joel, Japan). − MNCs at 9 g L –1 were diluted 10 times, and 5 μL of the solution was added onto a TEM grid (carbon film-supported copper grid, 200 meshes, Electron Microscopy Sciences, USA) until the solvent had completely evaporated. − …”

Specific capture of Pseudomonas aeruginosa for rapid detection of antimicrobial resistance in urinary tract infections

“…TEM analysis was executed through the utilization of a JEOL TEM equipped with an in-column Omega-type energy filter (JEM-2200FS, Joel, Japan) (Wu et al, 2021(Wu et al, , 2022a(Wu et al, , 2022bPan et al, 2016Pan et al, , 2017Pan et al, , 2022aGuo et al, 2022). 5 μL of the sample after a 100-fold dilution was added to a TEM grid (Kung et al, 2018(Kung et al, , 2020Tseng et al, 2020) (Carbon Film Supported Copper Grid, 200 Meshes, Electron Microscopy Sciences, USA) till the solvent was completely evaporated (Yang et al, 2022;Pan et al, 2022b;Yu et al, 2021;Kung et al, 2021).…”

Hybrid-biotaxonomy-like machine learning enables an anticipated surface plasmon resonance of Au/Ag nanoparticles assembled on ZnO nanorods

“…The predictive model was established by hybridization of GANN by using Super PCNeuron 5.0 as previously reported. 39,40,73 The variables of heterostructures by tuning photoreduction time (X 1 ), amount of HAuCl 4 (X 2 ), EPD time X 3 , EPD time X 4 , the amount of 10 −3 M AgNO 3 (X 5 ), and the concentration of AgNO 3 (X 6 ) were input to neural network soware for training and testing steps, which further inuenced the weight x i for every datapoint to learn the interaction between variables and plasmonic resonance (the 6 outputs) by ANN. Furthermore, a GA was employed to optimize the computational model by the evolutionary process, i.e., repeatedly selective, mutation, and crossover, which provided the selection of optimal parameters from the whole population.…”

“…Such challenges can be eradicated by applying a machine learning (ML) model based on genetic algorithm neural networks (GANN), which is a powerful tool to optimize the fabrication process, predict outcomes, and, meanwhile, avoid unnecessary costly experiments [37][38][39][40] . Nowadays, many studies based on GANN have been conducted to study fluid dynamics and plasmon resonances of NPs [40][41][42][43][44][45] . To employ GANN for process optimization and outcome prediction, a careful parameter setting is necessary to construct the model accurately.…”

“…[37][38][39][40] Nowadays, many studies based on GANN have been conducted to study the uid dynamics and plasmon resonances of NPs. [40][41][42][43][44][45] To employ GANN for process optimization and outcome prediction, a careful parameter setting is necessary to construct the model accurately. In this regard, we propose a unique concept, hybrid biotaxonomy inspired by recombinant DNA technology, to select parameters for the multilayer coating of NMNPs in order to train the GANN model.…”