In this paper, nitrogen (N)-doped ultra-porous carbon derived from lignin is synthesized through hydrothermal carbonization, KOH activation, and post-doping process for CO2 adsorption. The specific surface areas of obtained N-doped porous carbons range from 247 to 3064 m2/g due to a successful KOH activation. N-containing groups of 0.62–1.17 wt% including pyridinic N, pyridone N, pyridine-N-oxide are found on the surface of porous carbon. N-doped porous carbon achieves the maximum CO2 adsorption capacity of 13.6 mmol/g at 25 °C up to 10 atm and high stability over 10 adsorption/desorption cycles. As confirmed by enthalpy calculation with the Clausius–Clapeyron equation, an adsorption heat of N-doped porous carbon is higher than non-doped porous carbon, indicating a role of N functionalities for enhanced CO2 adsorption capability. The overall results suggest that this carbon has high CO2 capture capacity and can be easily regenerated and reused without any clear loss of CO2 adsorption capacity.

This research is about visualizing data for efficient use in agriculture, by measuring the understanding of graphs according to their features. A questionnaire consisting of one graph, either a bar graph or line graph, and a question about data, was made. According to the results, the time spent answering the question did not differ between the line graph and the bar graph. However, the rate of correct answers differed: that of the bar graph was 50% and the line graph was 0%. This implies that participants answered using the bar graph more accurately regarding whether the data involved understanding the absolute value. From this result, it is clear that using proper features of graphs according to the characteristics of data improves the understanding of information. In addition, a strategy to utilize information and communication technology in agriculture effectively was discussed.