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Lithium-ion battery performance and longevity depend critically on the conducting salt utilized in the electrolyte. With new avenues for multifunctional integration and optimization of functional properties, conducting salts beyond lithium hexafluorophosphate (LiPF$_6$) need to be studied. Herein we elucidate on viscosity, ionicity, anion self-diffusion and ionic conductivity through variation of the length of the perfluoroalkyl side chain present in the anions of the used lithium imidazole salts. Specifically, we study LiPF$_6$ in comparison with lithium 4,5-dicyano-2-(trifluoromethyl)imidazolide (LiTDI), lithium 4,5-dicyano-2-(pentafluoroethyl)imidazolide (LiPDI), and lithium 4,5-dicyano-2-(n-heptafluoropropyl)imidazolide (LiHDI). We find that the ion mobility of LiPF$_6$ depends the least on viscosity and its ionicity is the highest among the electrolytes investigated here. LiTDI shows the strongest correlation between ion mobility and viscosity and the lowest ionicity. LiPDI and LiHDI range between these two regarding their ionicity and the correlation of mobility with viscosity. The previously rarely studied anion self-diffusion coefficients exhibit a strong correlation with viscosity as it was to be expected. Differences between the LiTDI, LiPDI and LiHDI salts are minute.

Several LiFePO4/C composites were prepared and characterized electrochemically in lithium half-cells. Pressed pellet conductivities correlated well with the electrochemical performance in lithium half-cells. It was found that carbon structural factors such as sp2/sp3, D/G, and H/C ratios, as determined by Raman spectroscopy and elemental analysis, influenced the conductivity and rate behavior strongly. The structure of the residual carbon could be manipulated through the use of additives during LiFePO4 synthesis. Increasing the pyromellitic acid (PA) content in the precursor mix prior to calcination resulted in a significant lowering of the D/G ratio and a concomitant rise in the sp2/sp3 ratio of the carbon. Addition of both iron nitrate and PA resulted in higher sp2/sp3 ratios without further lowering the D/G ratios, or increasing carbon contents. The best electrochemical results were obtained for LiFePO4 processed with both ferrocene and PA. The improvement is attributed to better decomposition of the carbon sources, as evidenced by lower H/C ratios, a slight increase of the carbon content (still below 2 wt. percent), and more homogeneous coverage. A discussion of the influence of carbon content vs. structural factors on the composite conductivities and, by inference, the electrochemical performance, is included.

Battery research initiatives and giga-scale production generate an abundance of diverse data spanning myriad fields of science and engineering. Modern battery development is driven by the confluence of traditional domains of natural science with emerging fields like artificial intelligence and the vast engineering and logistical knowledge needed to sustain the global reach of battery Gigafactories. Despite the unprecedented volume of dedicated research targeting affordable, high-performance, and sustainable battery designs, these endeavours are held back by the lack of common battery data and vocabulary standards, as well as, machine readable tools to support interoperability. An ontology is a data model that represents domain knowledge as a map of concepts and the relations between them. A battery ontology offers an effective means to unify battery-related activities across different fields, accelerate the flow of knowledge in both human- and machine-readable formats, and support the integration of artificial intelligence in battery development. Furthermore, a logically consistent and expansive ontology is essential to support battery digitalization and standardization efforts, such as, the battery passport. This review summarizes the current state of ontology development, the needs for an ontology in the battery field, and current activities to meet this need., The authors acknowledge BATTERY 2030+ funded by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 957213 and the project BIG-MAP with funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 957189., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000917-S).