Perforated nitrogen-rich graphene-like carbon nanolayers supported Cu-In catalyst for boosting CO_(2) electroreduction to CO

The combination of a powerful CO_(2)-enriching carrier and robust active component provides a new idea for the construction of efficient catalysts for electrocatalytic CO_(2)reduction.Herein,novel perforated nitrogen-rich graphene-like carbon nanolayers(PNGC)are prepared from biomass derivatives,which promotes the oriented deposition of In-doped Cu_(2)(OH)_(3)(NO_(3))nanosheet patches.A robust Cu-In/PNGC composite catalyst is then obtained via simple in-situ electrochemical reduction.Unsurprisingly,CuIn/PNGC exhibits a CO Faradaic efficiency(FECO)of 91.3%and a remarkable CO partial current density(jCO)of 136.4 m A cm^(-2)at a moderate overpotential of 0.59 V for electrocatalytic CO_(2)reduction reaction(CO_(2)RR).DFT calculations and experimental studies indicate that the strong carrier effect of PNGC makes PNGC carried Cu-In nanosheets improved the adsorption capacity of CO_(2)gas,reconfigured electronic structure,and reduced free energy of key intermediate formation,thereby the CO_(2)activation and conversion are promoted.

Segmented thermoelectric generator modelling and optimization using artificial neural networks by iterative training

Renewable energy technologies are central to emissions reduction and essential to achieve net-zero emission.Segmented thermoelectric generators(STEG)facilitate more efficient thermal energy recovery over a large temperature gradient.However,the additional design complexity has introduced challenges in the modelling and optimization of its performance.In this work,an artificial neural network(ANN)has been applied to build accurate and fast forward modelling of the STEG.More importantly,we adopt an iterative method in the ANN training process to improve accuracy without increasing the dataset size.This approach strengthens the proportion of the high-power performance in the STEG training dataset.Without increasing the size of the training dataset,the relative prediction error over high-power STEG designs decreases from 0.06 to 0.02,representing a threefold improvement.Coupling with a genetic algorithm,the trained artificial neural networks can perform design optimization within 10 s for each operating condition.It is over 5,000 times faster than the optimization performed by the conventional finite element method.Such an accurate and fast modeller also allows mapping of the STEG power against different parameters.The modelling approach demonstrated in this work indicates its future application in designing and optimizing complex energy harvesting technologies.