To improve the performance of carbon paper used for applications such as electrodes for electrochemical devices and air filters, two types of long carbon nanofibers (CNFs) with average diameters of 20 and 49 nm were prepared by the liquid pulse injection (LPI) technique by adjusting reaction conditions. Carbon paper was made from the CNFs through a simple filtration process. The paper prepared from the CNFs with an average diameter of 20 nm (LPI-CNF(20) paper) was firm and flexible even though it was prepared without using any binders. LPI-CNF(20) paper also had a high surface area and showed a high electrical conductivity and a moderate gas permeability according to its void size. These properties are required for cathodes in the latest battery systems such as lithium–air batteries. In electrochemical experiments conducted to evaluate the performance of LPI-CNF(20) paper as a cathode, the paper showed a larger discharge capacity on the basis of the cathode weight than a conventional cathode (a commercially available carbon paper combined with a porous carbon), which indicated that it has a high potential to be used as a cathode in lithium–air batteries.

Artificial intelligence is a promising concept in modern and future societies. Presently, software programs are used but with a bulky computer size and large power consumption. Conversely, hardware systems named neuromorphic systems are suggested, with a compact computer size and low power consumption. An important factor is the number of processing elements that can be integrated. In the present study, three decisive technologies are proposed: (1) amorphous metal oxide semiconductor thin films, one of which, Ga–Sn–O (GTO) thin film, is used. GTO thin film does not contain rare metals and can be deposited by a simple process at room temperature. Here, oxygen-poor and oxygen-rich layers are stacked. GTO memristors are formed at cross points in a crossbar array; (2) analog memristor, in which, continuous and infinite information can be memorized in a single device. Here, the electrical conductance gradually changes when a voltage is applied to the GTO memristor. This is the effect of the drift and diffusion of the oxygen vacancies (Vo); and (3) autonomous local learning, i.e., extra control circuits are not required since a single device autonomously modifies its own electrical characteristic. Finally, a neuromorphic system is assembled using the abovementioned three technologies. The function of the letter recognition is confirmed, which can be regarded as an associative memory, a typical artificial intelligence application.