Identification of Model Particle Mixtures Using Machine-Learning-Assisted Laser Diffraction

Abstract

We put forward and demonstrate with model particles a smart laser-diffraction analysis technique aimed at particle mixture identification. We retrieve information about the size, shape, and ratio concentration of two-component heterogeneous model particle mixtures with an accuracy above 92%. We verify the method by detecting arrays of randomly located model particles with different shapes generated with a Digital Micromirror Device (DMD). In contrast to commonly-used laser diffraction schemes—In which a large number of detectors are needed—Our machine-learning-assisted protocol makes use of a single far-field diffraction pattern contained within a small angle (∼0.26°) around the light propagation axis. Therefore, it does not need to analyze particles of the array individually to obtain relevant information about the ensemble, it retrieves all information from the diffraction pattern generated by the whole array of particles, which simplifies considerably its implementation in comparison with alternative schemes. The method does not make use of any physical model of scattering to help in the particle characterization, which usually adds computational complexity to the identification process. Because of its reliability and ease of implementation, this work paves the way towards the development of novel smart identification technologies for sample classification and particle contamination monitoring in industrial manufacturing processes.

Measurement of Submicron Particle Size Using Scattering Angle-Corrected Polarization Difference with High Angular Resolution

“…The iteration format of the algorithm is shown as Equation (17). Firstly, an initial value W (1) is set for the particle size distribution W, and iteration is performed using Equations ( 17) and (18). During the iteration process, the convergence condition is judged for each iteration, and when the convergence condition is reached, the iteration ends, and the corresponding W (k) is the optimal solution.…”

“…During the iteration process, the convergence condition is judged for each iteration, and when the convergence condition is reached, the iteration ends, and the corresponding W (k) is the optimal solution. (k) ./sum(T) (18) where k is the number of iterations, E is the scattered light column vector, T is the coefficient matrices of the scattered light, W is the particle size distribution of m representative particle sizes selected artificially, • * denotes the multiplication of the corresponding elements of the vectors, •/ denotes the division of the corresponding elements of the vectors, and sum(T) denotes the summation of the elements in each column vector of the matrix T.…”

“…For the particle size measurement of an ensemble of particles in suspension, the light scattering method is widely used because of its advantages of being fast, accurate, and field-monitorable [18][19][20][21][22]. Submicron particles exhibit different scattering characteristics for incident light in different polarization states.…”

Confined hydrogen atom: endohedrals H@C36 and H@C60

“…Particularly, neural networks are artificial intelligence algorithms inspired by the workings of neurons in the human brain. These algorithms have demonstrated the capacity of pinpointing relevant specific pieces of information 'buried' in huge data sets and unveiling complex non-linear relationships between the inputs and target, which would be all but impossible to accomplish through a standard visual inspection [59][60][61].…”