A practical catalytic method to oxidize α-ionone with molecular oxygen using N-hydroxyphthalimide(NHPI)combined with acetylacetone cobatt(Ⅱ)(Co(acac)2)was developed,and the probable catalytic mechanism was proposed....A practical catalytic method to oxidize α-ionone with molecular oxygen using N-hydroxyphthalimide(NHPI)combined with acetylacetone cobatt(Ⅱ)(Co(acac)2)was developed,and the probable catalytic mechanism was proposed.The influences of the reaction conditions on conversion of α-ionone and the selectivity of the major product(5-keto-α-ionone)were investigated,and the technical parameters for 5-keto-α-ionone were optimized.The results show that the primary product is 5-keto-α-ionone,and by-products include epoxy-α-ionone,as well as rearrangement products 4-keto-β-ionone and epoxy-β-ionone,which are characterized by infrared spectra,proton nuclear magnetic resonance spectra,mass spectra and elemental analysis.The selectivity of 5-keto-α-ionone and the conversion of α-ionone are 55.0% and 97.0%,respectively,when 30%(molar fraction)NHPI,1.0%(molar fraction)Co(acac)2 and no solvent are employed under O2 pressure of 1.0 MPa and the reaction temperature of 65℃for 11 h.The procedure shows good reproducibility in the parallel experiments.展开更多
Based on a method combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm, the thermo-mechanical fatigue reliability of plastic ball grid array (PBGA) solder joints was studied. The s...Based on a method combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm, the thermo-mechanical fatigue reliability of plastic ball grid array (PBGA) solder joints was studied. The simulation experiments of accelerated thermal cycling test were performed by ANSYS software. Based on orthogonal array experiments, a back-propagation artificial neural network (BPNN) was used to establish the nonlinear multivariate relationship between thermo-mechanical fatigue reliability and control factors. Then, PSO was applied to obtaining the optimal levels of control factors by using the output of BPNN as the affinity measure. The results show that the control factors, such as print circuit board (PCB) size, PCB thickness, substrate size, substrate thickness, PCB coefficient of thermal expansion (CTE), substrate CTE, silicon die CTE, and solder joint CTE, have a great influence on thermo-mechanical fatigue reliability of PBGA solder joints. The ratio of signal to noise of ANN-PSO method is 51.77 dB and its error is 33.3% less than that of Taguchi method. Moreover, the running time of ANN-PSO method is only 2% of that of the BPNN. These conclusions are verified by the confirmative experiments.展开更多
基金Project(50573019)supported by the National Natural Science Foundation of China
文摘A practical catalytic method to oxidize α-ionone with molecular oxygen using N-hydroxyphthalimide(NHPI)combined with acetylacetone cobatt(Ⅱ)(Co(acac)2)was developed,and the probable catalytic mechanism was proposed.The influences of the reaction conditions on conversion of α-ionone and the selectivity of the major product(5-keto-α-ionone)were investigated,and the technical parameters for 5-keto-α-ionone were optimized.The results show that the primary product is 5-keto-α-ionone,and by-products include epoxy-α-ionone,as well as rearrangement products 4-keto-β-ionone and epoxy-β-ionone,which are characterized by infrared spectra,proton nuclear magnetic resonance spectra,mass spectra and elemental analysis.The selectivity of 5-keto-α-ionone and the conversion of α-ionone are 55.0% and 97.0%,respectively,when 30%(molar fraction)NHPI,1.0%(molar fraction)Co(acac)2 and no solvent are employed under O2 pressure of 1.0 MPa and the reaction temperature of 65℃for 11 h.The procedure shows good reproducibility in the parallel experiments.
基金Project(60371046) supported by the National Natural Science Foundation of ChinaProject(9140C0301060C03001) supported by the National Defense Science and Technology Foundation of Key Laboratory, China
文摘Based on a method combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm, the thermo-mechanical fatigue reliability of plastic ball grid array (PBGA) solder joints was studied. The simulation experiments of accelerated thermal cycling test were performed by ANSYS software. Based on orthogonal array experiments, a back-propagation artificial neural network (BPNN) was used to establish the nonlinear multivariate relationship between thermo-mechanical fatigue reliability and control factors. Then, PSO was applied to obtaining the optimal levels of control factors by using the output of BPNN as the affinity measure. The results show that the control factors, such as print circuit board (PCB) size, PCB thickness, substrate size, substrate thickness, PCB coefficient of thermal expansion (CTE), substrate CTE, silicon die CTE, and solder joint CTE, have a great influence on thermo-mechanical fatigue reliability of PBGA solder joints. The ratio of signal to noise of ANN-PSO method is 51.77 dB and its error is 33.3% less than that of Taguchi method. Moreover, the running time of ANN-PSO method is only 2% of that of the BPNN. These conclusions are verified by the confirmative experiments.
