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 2.8 × 10-4. Based on the well-trained forward prediction and inverse-design network, an SWG waveguide with significant trapping performance is designed. The trapping forces in the y-direction achieve-40.39 pN when the center of the particle is placed 100 nm away from the side wall of the silicon segment, and the negative sign of the optical forces indicates the direction of the forces. The maximum trapping potential achieved to 838.16 kBT in the y-direction. The trapping performance in the x and z directions is also quite superior, and the neural network model has been further applied to design SWGs with a high trapping performance. The present work is of significance for further research on the application of artificial neural networks in other optical devices designed for particle trapping.

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

In this study, nano-silica (Nano-SiO2), 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 21.46- 26.77 times higher than that on Nano-SiO2, which was mainly attributed to surface complexation and cation-π interaction. The fraction of Pb2+ adsorbed in the inside channel of CNTs should not be ignored. In coexisting systems, the absolute sorption inhibition of the SMR (ΔQeSMR) was compared with the amount of competitor adsorbed. Competitive sorption was observed as indicated by adding Pb(II) decreased adsorption of SMR on Nano-SiO2 (ΔQeSMR > 0), but hardly affected SMR adsorption on CNTs (ΔQeSMR ≈ 0) which was attributed to cation-π interaction. In addition, CAHB formed between SMR and Nano-SiO2 (ΔpKa ≈ 4.34) was weaker than that formed between SMR and O-CNTs (ΔpKa ≈ 3.15), which also consequently resulted in stronger competition of Pb(II) to SMR on Nano-SiO2 than that on O-CNTs. Moreover, coexisting BA increased adsorption of SMR on Nano-SiO2 and G-CNTs (ΔQeSMR < 0), but did not result in an apparent competition on SMR adsorption by O-CNTs (ΔQeSMR ≈ 0). These results emphasize that the environmental behaviors of a certain pollutant should be assessed carefully by considering the presence of other pollutants.