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Institution

Toyota

CompanySafenwil, Switzerland
About: Toyota is a company organization based out in Safenwil, Switzerland. It is known for research contribution in the topics: Internal combustion engine & Battery (electricity). The organization has 40032 authors who have published 55003 publications receiving 735317 citations. The organization is also known as: Toyota Motor Corporation & Toyota Jidosha KK.


Papers
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Patent
04 Apr 1985
TL;DR: In this paper, a process for joining a first kind of synthetic resin (1, 101) to a second kind of synthetically enriched resins (2, 2) includes several steps.
Abstract: A process for joining a first kind of synthetic resin (1, 101) to a second kind of synthetic resin (2) includes several steps. A first step is providing the first kind of synthetic resin (1, 101), which may include fillers (102), e. g., glass fibers. A second step is setting a second contact surface (21) of the second kind of synthetic resin (2) on a first contact surface (11) of the first kind of synthetic resin (1, 101). A third step is exposing an outer surface of the second kind of synthetic resin (2) to a laser beam. The second kind of synthetic resin (2) transmits the laser beam to the first kind of synthetic resin (1, 101 which stores up heat of the laser beam. The second contact surface (21) of the second kind of synthetic resin (2) and the first contact surface (11) of the first kind of synthetic resin (1, 101) are melted by the heat of the laser beam. A plurality of projections and a plurality of recesses are formed on both the first contact surface (11) of the first kind of synthetic resin (1, 101) and the second contact surface (21) of the second kind of synthetic resin (2). A plurality of the projections of the first kind of synthetic resin (1, 101) engage with a plurality of the recesses of the second kind of synthetic resin (2). If fillers (102) are provided, at least some of them (102) will extend between the first and second synthetic resins (1, 101, 2). Accordingly, the second kind of synthetic resin (2) is securely joined to the first kind of synthetic resin (1, 101).

132 citations

Patent
Hiroshi Tsujii1, Hatsuo Nakao1, Shuji Nagano1, Kojiro Kuramochi1, Yasuki Nakagawa1 
10 Aug 1998
TL;DR: In this article, the auxiliary machines are operated by a motor generator in a state where the engine is stopped so as to reduce electric power consumption and a battery is connected to the motor generator through an inverter.
Abstract: In an economy running mode, auxiliary machines are operated by a motor generator in a state where the engine is stopped so as to reduce electric power consumption. The motor generator, the engine and the auxiliary machines are connected through pulleys and belts. Moreover, a clutch is provided for switching enabling/disabling of power transmission between the motor generator and the engine. A battery is connected to the motor generator through an inverter. When the engine is stopped, the clutch is disengaged and the auxiliary machines are operated by the motor generator.

132 citations

Journal ArticleDOI
10 Apr 2019
TL;DR: The machine learning accelerated approach yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm, which makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible.
Abstract: Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets. Where datasets are lacking, unbiased data generation can be achieved with genetic algorithms. Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. The approach is used to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery, e.g., nanoalloy catalysts. The machine learning accelerated approach, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations.

131 citations

Journal ArticleDOI
TL;DR: A strategy for carrier optimization is demonstrated in a hybrid inorganic–organic superlattice of TiS2[tetrabutylammonium]x[hexylam monium]y, achieving an ultrahigh power factor of 904 μW m−1 K−2 at 300‬K for flexible thermoelectrics, approaching the values achieved in conventional inorganic semiconductors.
Abstract: Hybrid inorganic-organic superlattice with an electron-transmitting but phonon-blocking structure has emerged as a promising flexible thin film thermoelectric material. However, the substantial challenge in optimizing carrier concentration without disrupting the superlattice structure prevents further improvement of the thermoelectric performance. Here we demonstrate a strategy for carrier optimization in a hybrid inorganic-organic superlattice of TiS2[tetrabutylammonium] x [hexylammonium] y , where the organic layers are composed of a random mixture of tetrabutylammonium and hexylammonium molecules. By vacuum heating the hybrid materials at an intermediate temperature, the hexylammonium molecules with a lower boiling point are selectively de-intercalated, which reduces the electron density due to the requirement of electroneutrality. The tetrabutylammonium molecules with a higher boiling point remain to support and stabilize the superlattice structure. The carrier concentration can thus be effectively reduced, resulting in a remarkably high power factor of 904 µW m-1 K-2 at 300 K for flexible thermoelectrics, approaching the values achieved in conventional inorganic semiconductors.

131 citations


Authors

Showing all 40045 results

NameH-indexPapersCitations
Derek R. Lovley16858295315
Edward H. Sargent14084480586
Shanhui Fan139129282487
Susumu Kitagawa12580969594
John B. Buse117521101807
Meilin Liu11782752603
Zhongfan Liu11574349364
Wolfram Burgard11172864856
Douglas R. MacFarlane11086454236
John J. Leonard10967646651
Ryoji Noyori10562747578
Stephen J. Pearton104191358669
Lajos Hanzo101204054380
Masashi Kawasaki9885647863
Andrzej Cichocki9795241471
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
202232
2021942
20201,846
20192,981
20182,541