Zobrazeno 1 - 10
of 54 015
pro vyhledávání: '"A. Wurtz"'
Publikováno v:
In Journal of Energy Storage 1 December 2022 56 Part A
Publikováno v:
In Materials Chemistry and Physics 1 January 2022 275
Autor:
Beraich, M., Shaili, H., Hafidi, Z., Benhsina, E., Majdoubi, H., Taibi, M., Guenbour, A., Bellaouchou, A., Mzerd, A., Bentiss, F., Zarrouk, A., Fahoume, M.
Publikováno v:
In Journal of Alloys and Compounds 10 December 2020 845
Autor:
Crosland, Maurice
Publikováno v:
The British Journal for the History of Science, 2003 Sep 01. 36(3), 333-361.
Externí odkaz:
https://www.jstor.org/stable/4028158
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Inorganic Chemistry; 9/5/2022, Vol. 61 Issue 35, p13700-13707, 8p
Autor:
Aleksander Sztejnberg
Publikováno v:
Revista CENIC Ciencias Químicas, Vol 52, Iss 2 (2021)
Charles-Adolphe Wurtz (1817-1884) was an outstanding French chemist of the second half of the the XIX century. He was one of the founders of of modern organic chemistry. He discovered many organic compounds. The purpose of this paper is to familiariz
Externí odkaz:
https://doaj.org/article/0bc6b255cacd47158e7cd42ce26a2c05
Autor:
ESSnuSB, Aguilar, J., Anastasopoulos, M., Baussan, E., Bhattacharyya, A. K., Bignami, A., Blennow, M., Bogomilov, M., Bolling, B., Bouquerel, E., Bramati, F., Branca, A., Brunetti, G., Bustinduy, I., Carlile, C. J., Cederkall, J., Choi, T. W., Choubey, S., Christiansen, P., Collins, M., Morales, E. Cristaldo, Cupiał, P., Danared, H., de André, J. P. A. M., Dracos, M., Efthymiopoulos, I., Ekelöf, T., Eshraqi, M., Fanourakis, G., Farricker, A., Fasoula, E., Fukuda, T., Gazis, N., Geralis, Th., Ghosh, M., Giarnetti, A., Gokbulut, G., Hagner, C., Halić, L., Hooft, M., Iversen, K. E., Jachowicz, N., Jenssen, M., Johansson, R., Kasimi, E., Topaksu, A. Kayis, Kildetof, B., Kordas, K., Leisos, A., Lindroos, M., Longhin, A., Maiano, C., Marangoni, S., Marrelli, C., Meloni, D., Mezzetto, M., Milas, N., Muñoz, J., Niewczas, K., Oglakci, M., Ohlsson, T., Olvegård, M., Pari, M., Patrzalek, D., Petkov, G., Petridou, Ch., Poussot, P., Psallidas, A., Pupilli, F., Saiang, D., Sampsonidis, D., Schwab, C., Sordo, F., Sosa, A., Stavropoulos, G., Tarkeshian, R., Terranova, F., Tolba, T., Trachanas, E., Tsenov, R., Tsirigotis, A., Tzamarias, S. E., Vankova-Kirilova, G., Vassilopoulos, N., Vihonen, S., Wurtz, J., Zeter, V., Zormpa, O.
This study provides an analysis of atmospheric neutrino oscillations at the ESSnuSB far detector facility. The prospects of the two cylindrical Water Cherenkov detectors with a total fiducial mass of 540 kt are investigated over 10 years of data taki
Externí odkaz:
http://arxiv.org/abs/2407.21663
Autor:
Wilson, Blake A., Wurtz, Jonathan, Mkhitaryan, Vahagn, Bezick, Michael, Wang, Sheng-Tao, Kais, Sabre, Shalaev, Vladimir M., Boltasseva, Alexandra
Large-scale optimization problems are prevalent in several fields, including engineering, finance, and logistics. However, most optimization problems cannot be efficiently encoded onto a physical system because the existing quantum samplers have too
Externí odkaz:
http://arxiv.org/abs/2407.13830
Autor:
Kornjača, Milan, Hu, Hong-Ye, Zhao, Chen, Wurtz, Jonathan, Weinberg, Phillip, Hamdan, Majd, Zhdanov, Andrii, Cantu, Sergio H., Zhou, Hengyun, Bravo, Rodrigo Araiza, Bagnall, Kevin, Basham, James I., Campo, Joseph, Choukri, Adam, DeAngelo, Robert, Frederick, Paige, Haines, David, Hammett, Julian, Hsu, Ning, Hu, Ming-Guang, Huber, Florian, Jepsen, Paul Niklas, Jia, Ningyuan, Karolyshyn, Thomas, Kwon, Minho, Long, John, Lopatin, Jonathan, Lukin, Alexander, Macrì, Tommaso, Marković, Ognjen, Martínez-Martínez, Luis A., Meng, Xianmei, Ostroumov, Evgeny, Paquette, David, Robinson, John, Rodriguez, Pedro Sales, Singh, Anshuman, Sinha, Nandan, Thoreen, Henry, Wan, Noel, Waxman-Lenz, Daniel, Wong, Tak, Wu, Kai-Hsin, Lopes, Pedro L. S., Boger, Yuval, Gemelke, Nathan, Kitagawa, Takuya, Keesling, Alexander, Gao, Xun, Bylinskii, Alexei, Yelin, Susanne F., Liu, Fangli, Wang, Sheng-Tao
Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant res
Externí odkaz:
http://arxiv.org/abs/2407.02553