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| 1 | Effects of Y content and temperature on the damping capacity of extruded Mg-Y sheets显示文摘The damping behavior of extruded Mg-xY(x=0.5,1.0,3.0 wt.%)sheets were investigated in detail concerning the effects of Y addition and temperature,and the relationship between damping capacity and yield strength was discussed.At room temperature(RT),with Y content increasing from 0.5%to 3.0%,the damping capacity(Q-1)significantly decreased from 0.037 to 0.015.For all the studied sheets,the relationship between strain amplitude and Q-1 fitted well with the Granato and Liicke(G-L)dislocation damping model.With temperature increased,the G-L plots deviated from linearity indicating that the dislocation damping was not the only dominate mechanism,and the grain boundary sliding(GBS)could contribute to damping capacity.Consequently,the Q-1 increased remarkably above the critical temperature,and the critical temperature increased significantly from 50℃ to 290℃ with increasing Y contents from 0 to 3.0wt.%.This result implied that the segregation of Y solutes at grain boundary could depress the GBS,which was consistent with the recent finding of segregation tendency for rare-earth solutes.The extruded Mg-IY sheet exhibited slightly higher yield strength(Rp0.2)and Q-1 comparing with high-damping Mg-0.6Zr at RT.At an elevated temperature of 325℃,the Mg-IY sheet had similar Q-1 but over 3 times larger Rp0.2 than that of the pure Mg.The present study indicated that the extruded Mg-Y based alloys exhibited promising potential for developing high-performance damping alloys,especially for the elevated-temperature application. | Y.T.Tang C.Zhang L.B.Ren W.Yang D.D.Yin G.H.Huang H.Zhou Y.B.Zhang | 2019 | Journal of Magnesium and Alloys2019，7，3： | 5 |
| 2 | Machine learning enables polymer cloud-point engineering via inverse design显示文摘Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers,particularly when designing polymers with desired phase behavior.Here we demonstrate high-accuracy tuning of poly(2-oxazoline)cloud point via machine learning.With a design space of four repeating units and a range of molecular masses,we achieve an accuracy of 4℃ root mean squared error(RMSE)in a temperature range of 24–90℃,employing gradient boosting with decision trees.The RMSE is>3x better than linear and polynomial regression.We perform inverse design via particle-swarm optimization,predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80℃.Our approach challenges the status quo in polymer design with a machine learning algorithm,that is capable of fast and systematic discovery of new polymers. | Jatin N.Kumar Qianxiao Li Karen Y.T.Tang Tonio Buonassisi Anibal L.Gonzalez-Oyarce Jun Ye | 2019 | npj Computational Materials2019，，1： | 4 |
| 3 | γ'variant-sensitive deformation behaviour of Inconel 718 superalloy显示文摘Strengthening in Inconel 718 superalloy is derived from dislocation interaction withγ'precipitates,which exist in disk-shaped three possible orientation variants with their{100}habit plane normal to each other.The interactions between dislocations andγ'precipitates vary according to theγ'orienta-tion variants,which makes the deformation behaviour complicated and difficult to reveal experimentally.In this work,γ'variant distributions of Inconel 718 samples were tailored by ageing heat treatment under either tensile or compressive stress.Theγ'variant-sensitive deformation behaviours were then studied by in situ tensile tests via neutron diffraction at room temperature.It is demonstrated that yield-ing first takes place in grains oriented with<110>parallel to the loading direction.An identical lattice strain response to applied stress of both theγmatrix and theγ'precipitates was observed during yield-ing,suggesting that dislocations shearing through theγ'precipitates is predominant at this stage.Vari-ations in yield strength for samples with differentγ'variant distributions were observed,which can be attributed to different strengthening that arises from interactions between dislocation and differentγ'variants. | R.Y.Zhang H.L.Qin Z.N.Bi Y.T.Tang J.Araújo de Oliveira T.L.Lee C.Panwisawas S.Y.Zhang J.Zhang J.Li H.B.Dong | 2022 | Journal of Materials Science & Technology2022，，31： | 1 |
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