Neural network modelling of the inductively coupled RF plasma synthesis of silicon nanoparticles

The synthesis of silicon nanopowders by an inductively coupled plasma (ICP) process is investigated. The specific surface area (SSA) of the elaborated particles is determined by nitrogen absorption (BET) as a function of the quenching gas flow rate and the precursor feeding rate. Nanopowders with specific surface areas varying from 69 to 194 m2 g− 1, corresponding to equivalent particle sizes of 37 and 13 nm respectively, could be produced. The correlation between these two input parameters and the output SSA has been numerically modelled with linear regression and artificial neural networks approaches. It has been demonstrated that with the available data set, a regression model with quadratic regressors and a neural network modelling give a similar response.

The synthesis of silicon nanopowders by an inductively coupled plasma process is investigated. The specific surface area of the elaborated particles varied from 69 to 194 m2 g− 1 as a function of the quenching gas flow rate and the precursor feeding rate. Quadratic regression and neural network modelling give a similar response.Figure optionsDownload as PowerPoint slide