Artificial neural network assisted the design of subwavelength-grating waveguides for nanoparticles optical trapping.

In this work, we propose artificial neural networks (ANNs) to predict the optical forces on particles with a radius of 50 nm and inverse-design the subwavelength-grating (SWG) waveguides structure for trapping. The SWG waveguides are applied to particle trapping due to their superior bulk sensitivity and surface sensitivity, as well as longer working distance than conventional nanophotonic waveguides. To reduce the time consumption of the design, we train ANNs to predict the trapping forces and to inverse-design the geometric structure of SWG waveguides, and the low mean square errors (MSE) of the networks achieve 2.

Adsorption, desorption and coadsorption behaviors of sulfamerazine, Pb(II) and benzoic acid on carbon nanotubes and nano-silica.

In this study, nano-silica (Nano-SiO), oxidized (O-CNTs) and graphitized multi-walled carbon nanotubes (G-CNTs) were applied as model adsorbents to study the adsorption, desorption and coadsorption behaviors of sulfamerazine (SMR), Pb(II) and benzoic acid (BA). The results showed that charge assisted H-bond (CAHB) formation played an important role in adsorption of SMR and BA on O-riched nanomaterials. The adsorption capacities of Pb(II) on CNTs were 21.