Daido University

About: Daido University is a education organization based out in Nagoya, Japan. It is known for research contribution in the topics: Ultimate tensile strength & Proton exchange membrane fuel cell. The organization has 209 authors who have published 423 publications receiving 3223 citations.

[Fe4] and [Fe6] Hydride Clusters Supported by Phosphines: Synthesis, Characterization, and Application in N2 Reduction

Abstract: Multiple iron atoms bridged by hydrides is a common structural feature of the active species that have been postulated in the biological and industrial reduction of N2. In this study, the reactions of an Fe(II) amide complex with pinacolborane in the presence/absence of phosphines afforded a series of hydride-supported [Fe4] and [Fe6] clusters Fe4(μ-H)4(μ3-H)2{N(SiMe3)2}2(PR3)4 (PR3 = PMe3 (2a), PMe2Ph (2b), PEt3 (2c)), Fe6(μ-H)10(μ3-H)2(PMe3)10 (3), and (η6-C7H8)Fe4(μ-H)2{μ-N(SiMe3)2}2{N(SiMe3)2}2 (4), which were characterized crystallographically and spectroscopically. Under ambient conditions, these clusters catalyzed the silylation of N2 to furnish up to 160 ± 13 equiv of N(SiMe3)3 per 2c (40 equiv per Fe atom) and 183 ± 18 equiv per 3 (31 equiv per Fe atom). With regard to the generation of the reactive species, dissociation of phosphine and hydride ligands from the [Fe4] and [Fe6] clusters was indicated, based on the results of the mass spectrometric analysis on the [Fe6] cluster, as well as the for...

Impact of Driver Behavior on Fuel Consumption: Classification, Evaluation and Prediction Using Machine Learning

Abstract: Driving behavior has a large impact on vehicle fuel consumption. Dedicated study on the relationship between the driving behavior and fuel consumption can contribute to decreasing the energy cost of transportation and the development of the behavior assessment technology for the ADAS system. Therefore, it is vital to evaluate this relationship in order to develop more ecological driving assistance systems and improve the vehicle fuel economy. However, modeling driving behavior under the dynamic driving conditions is complex, making a quantitative analysis of the relationship between the driving behavior and the fuel consumption difficult. In this paper, we introduce two kinds of machine learning methods for evaluating the fuel efficiency of driving behavior using the naturalistic driving data. In the first stage, we use an unsupervised spectral clustering algorithm to study the macroscopic relationship between driving behavior and fuel consumption, using the data collected during the natural driving process. In the second stage, the dynamic information from the driving environment and natural driving data is integrated to generate a model of the relationship between various driving behaviors and the corresponding fuel consumption features. The dynamic environment factors are coded into a processable, digital form using a deep learning-based object detection method so that the environmental data can be linked with the vehicle's operating signal data to provide the training data for the deep learning network. The training data are labeled according to its fuel consumption feature distribution, which is obtained from the road segment data and historical driving data. This deep learning-based model can then be used as a predictor of the fuel consumption associated with different driving behaviors. Our results show that the proposed method can effectively identify the relationship between the driving behavior and the fuel consumption on both macro and micro levels, allowing for end-to-end fuel consumption feature prediction, which can then be applied in the advanced driving assistance systems.

Molybdenum-Catalyzed Ammonia Formation Using Simple Monodentate and Bidentate Phosphines as Auxiliary Ligands.

Abstract: We have found molybdenum-catalyzed ammonia formation using simple and commercially available monodentate and bidentate phosphines as auxiliary ligands with a simple and convenient procedure. Molybdenum complexes generated in situ from [MoI3(THF)3] and the corresponding phosphines such as PMePh2 and 1,5-bis(diphenylphosphino)pentane worked effectively toward ammonia formation.

Tribological characteristics of polyethylene glycol (PEG) as a lubricant for wear resistance of ultra-high-molecular-weight polyethylene (UHMWPE ) in artificial knee join

Abstract: For the longevity of total knee joint prostheses, we have developed an artificial lubricant using polyethylene glycol (PEG) for the prevention of wear of ultra-high-molecular-weight polyethylene (UHMWPE). In the present study, the lubricative function of this PEG lubricant was evaluated by a wear test using Co–Cr alloy and UHMWPE counter surface samples. As a result, human synovial fluid including the PEG lubricant showed good result regarding the wear volume and a worn surface of UHMWPE. Considering its lubrication mechanism, it is suspected that interaction between the PEG molecules and the proteins in synovial fluid was involved. Since PE molecules are also organic compounds having a hydroxyl group at one or both ends, the albumin and PEG molecule complex would have bound more strongly to the metal oxide surface and UHMWPE surfaces might enhance and stabilize the lubricating film between the contact surfaces under the boundary lubrication. This study suggests that PEG lubricant as an intra-articular viscous supplement has the potential to prevent wear of UHMWPE by mixing with synovial fluid and to contribute to the longevity of knee joint prostheses.