为了提高Sn基材料表面CO_(2)电化学还原为甲酸盐的法拉第效率,利用一步溶剂热法合成了具有丰富硫空位的Cu掺杂SnS_(2)纳米花(Cu-SnS_(2-x))催化剂,在较宽的电位窗口实现了CO_(2)电化学还原制备甲酸盐。结果表明,通过调控催化剂制备过程...为了提高Sn基材料表面CO_(2)电化学还原为甲酸盐的法拉第效率,利用一步溶剂热法合成了具有丰富硫空位的Cu掺杂SnS_(2)纳米花(Cu-SnS_(2-x))催化剂,在较宽的电位窗口实现了CO_(2)电化学还原制备甲酸盐。结果表明,通过调控催化剂制备过程中Cu和Sn的摩尔比,在-1.1 V vs.RHE电位条件下得到了72.64%的FE_(formate),电流密度J_(formate)达到-14.38 mA/cm^(2)。二维纳米片阵列增加了催化活性位点,Cu掺杂所产生的硫空位能够协同提高催化活性、促进电子转移,从而提高甲酸盐的选择性。展开更多
The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformati...The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.展开更多
文摘为了提高Sn基材料表面CO_(2)电化学还原为甲酸盐的法拉第效率,利用一步溶剂热法合成了具有丰富硫空位的Cu掺杂SnS_(2)纳米花(Cu-SnS_(2-x))催化剂,在较宽的电位窗口实现了CO_(2)电化学还原制备甲酸盐。结果表明,通过调控催化剂制备过程中Cu和Sn的摩尔比,在-1.1 V vs.RHE电位条件下得到了72.64%的FE_(formate),电流密度J_(formate)达到-14.38 mA/cm^(2)。二维纳米片阵列增加了催化活性位点,Cu掺杂所产生的硫空位能够协同提高催化活性、促进电子转移,从而提高甲酸盐的选择性。
文摘The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.
