We report a facile and versatile strategy to prepare multi-dimensional nanocarbons hybridized with mesoporous SiO2. Carbon nanoplatelets (CNPs, two-dimensional structure of nanocarbons) were combined with carbon nanotubes (CNTs, onedimensional nanocarbons) to form multi-dimensional carbons (2D–1D, CNP–CNTs). The CNP–CNTs were synthesized by directly growing CNTs on CNPs. A simple solution-based process using TEOS (tetraethyl orthosilicate) resulted in coating or hybridizing CNP–CNTs with mesoporous silica to produce CNP–CNTs@SiO2. The nanocarbons’ surface area significantly increased as the amount of TEOS increased. Electrochemical characterizations of CNP–CNTs@SiO2 as supercapactior electrodes including cyclic voltammetry and galvanostatic charge–discharge in 3 M KOH (aq) reveal excellent-specific capacitance of 23.84 mF cm−2 at 20 mV s−1, stable charge–discharge operation, and low internal resistance. Our work demonstrates mesoporous SiO2 on nanocarbons have great potential in electrochemical energy storage.

Recommender systems based on Collaborative Filtering (CF) algorithms have established an extensive means for retailers to suggest personalized item lists that will maximize each consumer’s utility. Nevertheless, in the mobile game industry, which characterized by the intense competition from the avalanche of other game options and fast-changing demands of game users, there has been no marked success with recommender systems. Instead, app stores merely show summaries of general market trends without any individual-level information, fail to suggest personalized lists based on preferences of the future. For modeling dynamics of game usage, we assume that an individual’s preferences on games can be represented as the proportion of each game’s running time, which can be calculated in daily basis by the individual’s usage time for each game apps divided by the individual’s total capacity. Then, we construct a tensor filled with the induced preferences. For the next step, we apply Bayesian probabilistic tensor factorization (BPTF), an extension of Singular Value Decomposition (SVD) to consider dynamic pattern, to restore all the empty entries of the tensor. Each restored component becomes an estimate of each user’s preference on each game at certain period. For empirical analysis, we use mobile log data in app-level for total 1,000 panels over 2 years. Top 100 mobile games in cumulative usage time are treated as focal apps in this study, making the dimension of the tensor by 1000 (users) * 100 (focal games) * 730 (2 years). We compare the model performance by the root-mean-squared error (RMSE) with that of baseline model, the static counterpart in collaborative filtering algorithm (Salakhutdinov, and Mnih, 2008). The results showed that our model (BPTF) defeats the baseline throughout overall user-game pairs, especially outperforming under the conditions that there are severe fluctuations in daily usage pattern and when the life span of newly adopted apps are relatively short. Furthermore, we compose personalized suggestions, which consists of the top-10 highly likable lists according to the predicted usage patterns for each individual, and compare the performance with that of the established general recommender system in app stores. For that matter, our suggestion also outweighed the existing recommender system by the typical performance metrics that commonly used in the mobile game industry.