A Pd-Cu catalyst, with primary B2-type phase, supported by VulcanXC-7R carbon was synthesized via a solvothermal method. The catalysts were physically and electrochemically characterized by X-ray diffraction （XRD）, ...A Pd-Cu catalyst, with primary B2-type phase, supported by VulcanXC-7R carbon was synthesized via a solvothermal method. The catalysts were physically and electrochemically characterized by X-ray diffraction （XRD）, X-ray photoelectron spectroscopy （XPS）, trans- mission electron microscopy （TEM） and both cyclic and linear sweep voltammetry using a rotating disk electrode （RDE）. During the RDE testing, the half-wave potential of the Pd-Cu/Vulcan catalyst was 50 mV higher compared to that of commercial Pt/C catalyst for the oxygen reduction reaction （ORR） in alkaline media. The Pd-Cu/Vulcan exhibited a specific activity of 1.27 mA/cm2 and a mass activity of 0.59 A/mgpd at 0.9 V, which were 4 and 3 times greater than that of the commercial Pt/C catalyst, respectively. The Pd-Cu/Vulcan catalyst also showed higher in-situ alkaline exchange membrane fuel cell （AEMFC） performance, with operating power densities of 1100 MW/cm2 operating on H2/O2 and 700 MW/cm2 operating on H2/Air （CO2-free）, which were markedly higher than those of the commercial Pt/C. The Pd-Cu/ Vulcan catalyst also exhibited high stability during a short-term, in-situ AEMFC durability test, with only around 11% performance loss after 30 hours of operation, an improve- ment over most AEMFCs reported in the literature to date.展开更多
The so called "alterable-element method" (AEM) was introduced to deal with the coupling interac-tion of vehicle and sub-structure considering the actual transient jump of wheel, while the classical "con...The so called "alterable-element method" (AEM) was introduced to deal with the coupling interac-tion of vehicle and sub-structure considering the actual transient jump of wheel, while the classical "contact all along" assumption based on which wheels and lower structure are always contact was abandoned. The alterable element used in this method is a conceptional element, which is used to calculate the coupling interaction of upper and lower structures and has some typical characteristics: firstly it flows along with the moving of contact point; secondly whether it is used for calculation depends on the contact state; thirdly its sizes could change according to specific problems and so on. VISUAL FORTRAN program was coded, and different moving vehicle models were presented taking into consideration the effects of random corrugation in the numerical study. The numerical solutions are favored comparing with the results obtained by alternative methods when there is no jump phenomenon existed. With abrupt irregularity, the transient jump of wheel was studied using the present method.展开更多
Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising met...Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising methods primarily deal with data directly,without analyzing the data in detail;thus,the results are not always satisfactory.In this paper,we propose a method based on dictionary learning for EM data denoising.This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal.In the process of dictionary learning,the random noise is fi ltered out as residuals.To verify the eff ectiveness of this dictionary learning approach for denoising,we use a fi xed overcomplete discrete cosine transform(ODCT)dictionary algorithm,the method-of-optimal-directions(MOD)dictionary learning algorithm,and the K-singular value decomposition(K-SVD)dictionary learning algorithm to denoise decay curves at single points and to denoise profi le data for diff erent time channels in time-domain AEM.The results show obvious diff erences among the three dictionaries for denoising AEM data,with the K-SVD dictionary achieving the best performance.展开更多
文摘A Pd-Cu catalyst, with primary B2-type phase, supported by VulcanXC-7R carbon was synthesized via a solvothermal method. The catalysts were physically and electrochemically characterized by X-ray diffraction （XRD）, X-ray photoelectron spectroscopy （XPS）, trans- mission electron microscopy （TEM） and both cyclic and linear sweep voltammetry using a rotating disk electrode （RDE）. During the RDE testing, the half-wave potential of the Pd-Cu/Vulcan catalyst was 50 mV higher compared to that of commercial Pt/C catalyst for the oxygen reduction reaction （ORR） in alkaline media. The Pd-Cu/Vulcan exhibited a specific activity of 1.27 mA/cm2 and a mass activity of 0.59 A/mgpd at 0.9 V, which were 4 and 3 times greater than that of the commercial Pt/C catalyst, respectively. The Pd-Cu/Vulcan catalyst also showed higher in-situ alkaline exchange membrane fuel cell （AEMFC） performance, with operating power densities of 1100 MW/cm2 operating on H2/O2 and 700 MW/cm2 operating on H2/Air （CO2-free）, which were markedly higher than those of the commercial Pt/C. The Pd-Cu/ Vulcan catalyst also exhibited high stability during a short-term, in-situ AEMFC durability test, with only around 11% performance loss after 30 hours of operation, an improve- ment over most AEMFCs reported in the literature to date.
基金the Science and Technology Commissionof Shanghai Municipality (No. 03DZ12017)the Shang-hai Municipal Informatization Commission
文摘The so called "alterable-element method" (AEM) was introduced to deal with the coupling interac-tion of vehicle and sub-structure considering the actual transient jump of wheel, while the classical "contact all along" assumption based on which wheels and lower structure are always contact was abandoned. The alterable element used in this method is a conceptional element, which is used to calculate the coupling interaction of upper and lower structures and has some typical characteristics: firstly it flows along with the moving of contact point; secondly whether it is used for calculation depends on the contact state; thirdly its sizes could change according to specific problems and so on. VISUAL FORTRAN program was coded, and different moving vehicle models were presented taking into consideration the effects of random corrugation in the numerical study. The numerical solutions are favored comparing with the results obtained by alternative methods when there is no jump phenomenon existed. With abrupt irregularity, the transient jump of wheel was studied using the present method.
基金financially supported the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA14020102)the National Natural Science Foundation of China (Nos. 41774125,41530320 and 41804098)the Key National Research Project of China (Nos. 2016YFC0303100,2017YFC0601900)。
文摘Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising methods primarily deal with data directly,without analyzing the data in detail;thus,the results are not always satisfactory.In this paper,we propose a method based on dictionary learning for EM data denoising.This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal.In the process of dictionary learning,the random noise is fi ltered out as residuals.To verify the eff ectiveness of this dictionary learning approach for denoising,we use a fi xed overcomplete discrete cosine transform(ODCT)dictionary algorithm,the method-of-optimal-directions(MOD)dictionary learning algorithm,and the K-singular value decomposition(K-SVD)dictionary learning algorithm to denoise decay curves at single points and to denoise profi le data for diff erent time channels in time-domain AEM.The results show obvious diff erences among the three dictionaries for denoising AEM data,with the K-SVD dictionary achieving the best performance.
