Gold, one of the most commonly studied plasmonic nanomaterials, has a high thermal conversion efficiency. However, its high cost, low thermal durability, and complicated synthesis procedures prevent its application on a large scale. To address these issues, cost-effective, inert metal nitrides such as titanium nitride (TiN) can be considered as alternative materials. TiN has high thermal stability and exhibits metallic properties at visible wavelengths. In this study, TiN nanoparticles (NPs) were prepared using a facile ball milling method suitable for large-scale production. The time and rotational speed of the ball mill were varied to evaluate the effect of particle size and the amorphous content of TiN NPs on the photothermal properties under white light. Moreover, the effects of particle size and the amorphous content on photoheating were evaluated by measuring the increase in temperature of a dispersive TiN NP solution exposed to a white light-emitting diode (LED) source. We found that TiN NPs with a diameter of approximately 100 nm exhibited a maximum temperature increase of 52.5 °C, which was in agreement with the theoretical calculations. The theoretical calculations were conducted on the basis of Mie theory under conditions identical to those used in the experiment. The best photothermal conversion efficiency was obtained using 100 nm TiN NPs prepared by milling at a high rotational speed (600 rpm) for a short time (0.5 h). In addition, experimental and theoretical analyses confirmed that an increasingly amorphous TiN structure impeded the photoheating of the dispersive TiN NP solution. The results of this research have potential implications for indoor environmental heating systems, including heating a room with TiN-embedded walls or floors or distilling water using only visible-light energy.

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With recent globalization in industries, the number of failures and troubles of products caused by using them in unexpected ways has increased. In order to avoid such troubles, it is necessary not only to assume various ways of use thoroughly, but also to verify whether the design plan can fulfill required functions when the product is utilized in those ways. From this point of view, the authors proposed a functional verification method considering ways of use based on qualitative modeling of behavior of entities and cause-and-effect relationships among physical phenomena using Petri nets. It is, however, impossible to detect failures concerning to specification which requires dealing with quantitative information. This paper provides a method for quantitative modeling of behavior of entities and cause-and-effect relationships among physical phenomena. Two types of tokens were defined for dealing with positive and negative values and four types of arcs were for controlling changes of those values. These new elements of Petri net made it possible to represent behavior of entities and cause-and-effect relationships quantitatively. Application of this new modeling method to the functional detection method enables automatic detection of failures concerning to both functions and specification. The detection method using this modeling method was applied to an example, and its effectiveness was proven.

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