A series of hard-soft carbon composite materials is produced from biomass and oil waste and applied as low-cost anodes for sodium-ion batteries to study the fundamentals behind the dependence of Na storage on their structural features. A good reversible capacity of 282 mAh g −1 is obtained at a current density of 30 mA g −1 with a high initial Coulombic efficiency of 80% at a carbonization temperature of only 1000 °C by adjusting the ratio of hard to soft carbon. The performance is superior to the pure hard or soft carbon anodes produced at the same temperatures. This synergy between hard and soft carbon resulting in an excellent performance is due to the blockage of some open pores in hard carbon by the soft carbon, which suppresses the solid electro lyte interface formation and increases the reversible sodium storage capacity.

We report the synthesis of sustainable ordered mesoporous carbons (OMCs) produced from lignin, using the evaporation induced self-assembly (EISA) method.

In this study atom probe tomography was used to investigate the microstructure of AA2198 (Al-1.35Cu-3.55Li-0.29Mg-0.08Ag) over a range of ageing conditions to examine the evolution of phases in the alloy, in particular aiming to reveal the nucleation mechanism of the strengthening T1 phase, which has been under debate for decades. T 1 precursor phases were observed from early ageing, a significant number of which were connected to solute-enriched dislocations. Ag and Mg segregation to T 1 interfaces was convincingly observed when the plates were oriented perpendicular to the probing direction, which is the condition under which the spatial resolution of the atom probe data is highest.

Progress in the reconstruction for atom probe tomography has been limited since the first implementation of the protocol proposed by Bas et al. in 1995. This approach and those subsequently developed assume that the geometric parameters used to build the three-dimensional atom map are constant over the course of an analysis. Here, we test this assumption within the analyses of low-alloyed materials. By building upon methods recently proposed to measure the tomographic reconstruction parameters, we demonstrate that this assumption can introduce significant limitations in the accuracy of the analysis. Moreover, we propose a strategy to alleviate this problem through the implementation of a new reconstruction algorithm that dynamically accommodates variations in the tomographic reconstruction parameters.

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