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In statistical landslide susceptibility evaluation, the quality of the model and its prediction image heavily depends on the quality of the landslide inventories used for calibration. However, regional-scale inventories made available by public territorial administrations are typically affected by an unknown grade of incompleteness and mapping inaccuracy. In this research, a procedure is proposed for verifying and solving such limits by applying a two-step susceptibility modeling procedure. In the Torto River basin (central-northern Sicily, Italy), using an available regional landslide inventory (267 slide and 78 flow cases), two SUFRA_1 models were first prepared and used to assign a landslide susceptibility level to each slope unit (SLU) in which the study area was partitioned. For each of the four susceptibility classes that were obtained, 30% of the mapping units were randomly selected and their stable/unstable status was checked by remote analysis. The new, increased inventories were finally used to recalibrate two SUFRA_2 models. The prediction skills of the SUFRA_1 and SUFRA_2 models were then compared by testing their accuracy in matching landslide distribution in a test sub-basin where a high-resolution systematic inventory had been prepared. According to the results, the strong limits of the SUFRA_1 models (sensitivity: 0.67 and 0.57 for slide and flow, respectively) were largely solved by the SUFRA_2 model (sensitivity: 1 for both slide and flow), suggesting the proposed procedure as a possibly suitable modeling strategy for regional susceptibility studies.

Low first-cycle Coulombic efficiency is especially poor for silicon (Si)-based anodes due to the high surface area of the Si-active material and extensive electrolyte decomposition during the initial cycles forming the solid electrolyte interphase (SEI). Therefore, developing successful prelithiation methods will greatly benefit the development of lithium-ion batteries (LiBs) utilizing Si anodes. In pursuit of this goal, in this study, lithium oxide (Li2O) was added to a LiNi0.6Mn0.2Co0.2O2 (NMC622) cathode using a scalable ball-milling approach to compensate for the initial Li loss at the anode. Different milling conditions were tested to evaluate the impact of particle morphology on the additive performance. In addition, Co3O4, a well-known oxygen evolution reaction catalyst, was introduced to facilitate the activation of Li2O. The Li2O + Co3O4 additives successfully delivered an additional capacity of 1116 mAh/gLi2O when charged up to 4.3 V in half cells and 1035 mAh/gLi2O when charged up to 4.1 V in full cells using Si anodes.