Facile synthesis of hierarchical Na2Fe(SO4)2@rGO/C as high-voltage cathode for energy density-enhanced sodium-ion batteries

Fe-based sulfates are ideal cathode candidates for sodium-ion batteries(SIBs) owing to their high operating voltage and low cost but suffer from the nature of poor power performance. Herein, a hierarchical porous Na2Fe(SO4)2@reduced graphene oxide/carbon dot(Na2Fe(SO4)2@rGO/C) with low carbon content(4.12 wt%) was synthesized via a facile homogeneous strategy benefiting for engineering application,which delivers excellent sodium storage performance(high voltage plateau of 3.75 V, 85 m Ah g-1 and330 Wh kg-1 at 0.05 C;5805 W kg-1 at 10 C) and high Na+diffusion coefficient(1.19 × 10-12 cm2 s-1).Moreover, the midpoint voltage of assembled full cell could reach 3.0 V. The electron transfer and reaction kinetics are effectively boosted since the nanoscale Na2Fe(SO4)2 is supported by a robust crosslinked carbon matrix with rGO sheets and carbon dots. The slight rGO sheets sufficiently enhance the electron transfer like a current collecter and restrain the aggregation, as well as ensure smooth ion channels. Meanwhile, the carbon dots in the whole space connect with Na2Fe(SO4)2 and help rGO to promote the conductivity of the electrode. Ex-situ X-ray powder diffraction and X-ray photoelectron spectrometry analysis confirm the high reversibility of this sodiation/desodiation process.

Prediction of sorption enhanced steam methane reforming products from machine learning based soft-sensor models

Carbon dioxide-abated hydrogen can be synthesised via various processes,one of which is sorption enhanced steam methane reforming(SE-SMR),which produces separated streams of high purity H_(2) and CO_(2).Properties of hydrogen and the sorbent material hinder the ability to rapidly upscale SE-SMR,therefore the use of artificial intelligence models is useful in order to assist scale up.Advantages of a data driven soft-sensor model over ther-modynamic simulations,is the ability to obtain real time information dependent on actual process conditions.In this study,two soft sensor models have been developed and used to predict and estimate variables that would otherwise be difficult direct measured.Both artificial neural networks and the random forest models were devel-oped as soft sensor prediction models.They were shown to provide good predictions for gas concentrations in the reformer and regenerator reactors of the SE-SMR process using temperature,pressure,steam to carbon ratio and sorbent to carbon ratio as input process features.Both models were very accurate with high R^(2) values,all above 98%.However,the random forest model was more precise in the predictions,with consistently higher R^(2) values and lower mean absolute error(0.002-0.014)compared to the neural network model(0.005-0.024).