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Rare earth elements (REEs) make up a group of unique elements with diverse applications in energy, medicine, and technology. Increasing global demand and limited supplies have led to exploring the economic viability of domestic feedstock extraction from sources such as coal. Little is known about the release of REEs from coal due to the environmentally driven processes of photodissolution. In this study, the photodissolution of water-soluble REEs and dissolved organic carbon (DOC) from subbituminous coal was investigated using laboratory-simulated sunlight exposures. The effects of the solar intensity, temperature, and exposure time on photodissolution were also examined. Following irradiation, water-soluble REE and DOC concentrations increased significantly above nonirradiated controls, indicating photodissolution is a significant process. Both solar intensity and exposure time influenced photodissolution rates, while temperature did not. Results from this study provide motivation to further investigate the photodissolution pathways of REEs from subbituminous coal and interaction with DOC ligands, given that photosolubilized REEs may be organic associated. These findings may have implications, both positive and negative, for the environmental impact of REEs. 

Abstract We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.