Oxygen-enriched carbon quantum dots from coffee waste: Extremely active organic photocatalyst for sustainable solar-to-H2O2 conversion.

Solar-driven artificial photosynthesis offers a promising avenue for hydrogen peroxide (H2O2) generation, an efficient and economical replacement for current methods. The efficiency and selectivity hurdles of the two-electron oxygen reduction reaction (ORR) in solar-to- H2O2 conversion are substantial barriers to large scale production. In this manuscript, a simple biomass-assisted synthesis was performed to produce oxygen-enriched carbon quantum dots (OE-CQDs) from spent coffee waste, acting as an efficient photocatalyst for solar-powered H2O2 production. OE-CQDs can stabilize and store light-generated electrons effectively, boosting charge separation and enhancing photocatalytic performance with longevity. The maximal photocatalytic H2O2 production was achieved viz the utilization of OE-CQDs with generation rate of 356.86 µmol g-1 h-1 by retaining 80% activity without any external sacrificial donors. The outstanding performance of synthesized OE-CQDs under light exposure at wavelength (λ) of 280 nm has been ensured by the quantum yield value of 9.4% upon H2O2 generation. The combinatorial benefits of OE-CQDs with their authentic crystalline structure and oxygen enrichment, is expected to be enhancing the ORR activity through accelerating charge transfer, and optimizing oxygen diffusion. Consequently, our eco-friendly method holds considerable promise for creating highly efficient, metal-free photocatalysts for artificial H2O2 production.

Automatic control of simulated moving bed process with deep Q-network.

Optimal control of a simulated moving bed (SMB) process is challenging because the system dynamics is represented as nonlinear partial differential-algebraic equations combined with discrete events. In addition, product purity constraints are active at the optimal operating condition, which implies that these constraints can be easily violated by disturbance. Recently, artificial intelligence techniques have received significant attention for their ability to address complex problems, involving a large number of state variables. In this study, a data-based deep Q-network, which is a model-free reinforcement learning method, is applied to the SMB process to train a near-optimal control policy. Using a deep Q-network, the control policy of a complex dynamic system can be trained off-line as long as a sufficient number of data is provided. These data can be efficiently generated by performing numerical simulations in parallel on multiple machines. The on-line computation of the control input using a trained Q-network is fast enough to satisfy the computational time limit for the SMB process. However, because the Q-network does not predict the future state, it is not possible to explicitly impose state constraints. Instead, the state constraints are indirectly imposed by providing a relatively large penalty (negative reward) when the constraints are violate. Furthermore, logic-based switching control is utilized to limit the ranges of the extract and raffinate purities, which helps to satisfy the state constraints and reduce the regions in the state space for reinforcement learning to explore. The simulation results demonstrate the advantages of applying deep reinforcement learning to control the SMB process.