Microstructure and magnetocaloric properties of partially crystallized Gd60Co30Fe10 amorphous alloy prepared by different solidification cooling rates

Rare Metals ◽  
2021 ◽  
Author(s):  
Hui-Yan Zhang ◽  
Zi-Yang Zhang ◽  
Ya-Fang Xu ◽  
Ai-Lin Xia ◽  
Wei-Huo Li ◽  
...  
2021 ◽  
Vol 127 (7) ◽  
Author(s):  
Long Hou ◽  
Xiaoyu Xiang ◽  
Ying Huang ◽  
Biao Zhang ◽  
Chao Jiang ◽  
...  

2017 ◽  
Vol 728 ◽  
pp. 747-751 ◽  
Author(s):  
B.Z. Tang ◽  
D.Q. Guo ◽  
L. Xia ◽  
D. Ding ◽  
K.C. Chan

2013 ◽  
Vol 745-746 ◽  
pp. 799-808
Author(s):  
Kai Xu ◽  
Yan Wang ◽  
Qiang Li

In this work, the critical cooling rate Rc for glass formation of a series of Fe80-xCoxP13C7 (x = 0, 5, 10, 15, 20 at.%) alloys was determined by means of constructing CCT curves using Uhlmanns method. The calculated critical cooling rates for x = 0, 5, 10, 15, 20 at.% are 621, 441, 548, 894, 922 K/s, respectively. These results well coincide with the maximum diameters of Fe80-xCoxP13C7 amorphous alloys determined by experiments varying with the content of Co. The calculated Rc was also on the reasonable order of magnitudes. In addition, the values of three common GFA criterions of Trg, ΔTx and γ were calculated according to the thermodynamic data determined from DSC and DTA curves of Fe80-xCoxP13C7 (x = 0, 5, 10, 15, 20 at.%) bulk amorphous alloy. The validity of these GFA criterions in the series of Fe80-xCoxP13C7 (x = 0, 5, 10, 15, 20 at.%) alloys were investigated and it was pointed out that these three GFA criterions were not able to explain the experimental results of the maximum diameters of Fe80-xCoxP13C7 amorphous alloys varying with the content x of Co.


2013 ◽  
Vol 33 (8) ◽  
pp. 1374-1382
Author(s):  
Shaowei YAN ◽  
Hui FAN ◽  
Chuan LIANG ◽  
Zhong LI ◽  
Zhihui YU

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 545a-545
Author(s):  
M.D. Boyette

Prompt cooling to remove field heat is an essential part of proper postharvest handling for many types of fresh fruits and vegetables. Growers, consultants, and horticultural agents are often encouraged to collect cooling data (time vs. temperature) in order to compare cooling rates for different systems, containers, etc. These data can be misleading and confusing and seldom yield much useful information. With proper analysis, cooling data can yield a large amount of information. The problem is not the fault of the data, as much as the lack of simple methods to analyze these data. This presentation will demonstrate several simple methods to extract useful information from cooling data.


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