Experimental analysis and prediction of radionuclide solubility using machine learning models: Effects of organic complexing agents.

Radioactive wastes contain organic complexing agents that can form complexes with radionuclides and enhance the solubility of these radionuclides, increasing the mobility of radionuclides over great distances from a radioactive waste repository. In this study, four radionuclides (cobalt, strontium, iodine, and uranium) and three organic complexing agents (ethylenediaminetetraacetic acid, nitrilotriacetic acid, and iso-saccharic acid) were selected, and the solubility of these radionuclides was assessed under realistic environmental conditions such as different pHs (7, 9, 11, and 13), temperatures (10 °C, 20 °C, and 40 °C), and organic complexing agent concentrations (10-5-10-2 M). A total of 720 datasets were generated from solubility batch experiments. Four supervised machine learning models such as the Gaussian process regression (GPR), ensemble-boosted trees, artificial neural networks, and support vector machine were developed for predicting the radionuclide solubility. Each ML model was optimized using Bayesian optimization algorithm. The GPR evolved as a robust model that provided accurate predictions within the underlying solubility patterns by capturing the intricate relationships of the independent parameters of the dataset. At an uncertainty level of 95%, both the experimental results and GPR simulated estimations were closely correlated, confirming the suitability of the GPR model for future explorations.

A Waveguide Inline Binary Metasurface for Wavelength-Selective Transmission and Standing Wave Focusing.

This study presents an innovative inline metasurface design for selective wavelength transmission and focusing. When integrated into optical fibers, it improves the stability and compatibility with techniques like wavelength division multiplexing and phase modulation. Precise parameters, determined through analytical calculations and simulations, allow for the design of multifunctional lenses within the optical fiber platform. The numerical results demonstrate unmodulated transmission for specific wavelengths, while others exhibit standing wave focusing with a 0.67 µm beam radius and a 0.31 µm depth of focus. This technology holds promise for applications in quantum experiments, sensing, and optical communication.

Design Parameter Optimization of a Silicon-Based Grating Waveguide for Performance Improvement in Biochemical Sensor Application.

We performed numerical analysis and design parameter optimization of a silicon-based grating waveguide refractive index (RI) sensor. The performance of the grating waveguide RI sensor was determined by the full-width at half-maximum (FWHM) and the shift in the resonance wavelength in the transmission spectrum. The transmission extinction, a major figure-of-merit of an RI sensor that reflects both FWHM and resonance shift performance, could be significantly improved by the proper determination of three major grating waveguide parameters: duty ratio, grating period, and etching depth. We analyzed the transmission characteristics of the grating waveguide under various design parameter conditions using a finite-difference time domain method. We achieved a transmission extinction improvement of >26 dB under a given bioenvironmental target change by the proper choice of the design procedure and parameters. This design procedure and choice of appropriate parameters would enable the widespread application of silicon-based grating waveguide in high-performance RI biochemical sensor.