Machine-learning reinforcement for optimizing multilayered thin films: applications in designing broadband antireflection coatings.

The design and fabrication of nanoscale multilayered thin films play an essential role in regulating the operation efficiency of sensitive optical sensors and filters. In this paper, we introduce a packaged tool that employs flexible electromagnetic calculation software with machine learning in order to find the optimized double-band antireflection coatings in intervals of wavelength from 3 to 5 µm and 8 to 12 µm. Instead of computing or modeling an extremely enormous set of thin film structures, this tool enhanced with machine learning can swiftly predict the optical properties of a given structure with >99.7% accuracy and a substantial reduction in computation costs. Furthermore, the tool includes two learning methods that can infer a global optimal structure or suitable local optimal ones. Specifically, these well-trained models provide the highest accurate double-band average transmission coefficient combined with the lowest number of layers or the thinnest total thickness starting from a reference multilayered structure. Finally, the more sophisticated enhancement method, called the double deep Q-learning network, exhibited the best performance in finding optimal antireflective multilayered structures with the highest double-band average transmission coefficient of about 98.95%.

Dioxin contamination in soils of Southern Vietnam.

Dioxin is the common name for polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs), and is abbreviated as PCDD/Fs. In the case of Southern Vietnam, is of high concern dioxin contamination in the areas affected by past-use chemical defoliants. Our study related to the zone considered as a "hot spot"--Bien Hoa Airbase and surrounding areas (Bien Hung Lake). Although the war ended over 30 years ago, the adverse effects on this territory still continue. Soil and sediment were selected for our research as they act as a sink for PCDD/Fs. The samples were taken and analyzed in CECOTOX laboratory following certified procedures. The total amounts of PCDD/Fs (2,3,7,8 related congeners) in the samples were converted into WHO-TEQ and compared with standard values proposed by Canadian environmental quality guidelines. The obtained data shows a relatively high risk (up to 20.4 times higher than (probable effect level) PEL value for sediment and 46 times higher than standard value for soil). The research is continuing on the assessment of dioxin transport in food chain. Moreover, considering the obtained data a complete solution should be found urgently to solve the problem of dioxin contamination in the studied areas.