The structured low-rank model for parallel magnetic resonance(MR)imaging can efficiently reconstruct MR images with limited auto-calibration signals.To improve the reconstruction quality of MR images,we integrate the ...The structured low-rank model for parallel magnetic resonance(MR)imaging can efficiently reconstruct MR images with limited auto-calibration signals.To improve the reconstruction quality of MR images,we integrate the joint sparsity and sparsifying transform learning(JTL)into the simultaneous auto-calibrating and k-space estimation(SAKE)structured low-rank model,named JTLSAKE.The alternate direction method of multipliers is exploited to solve the resulting optimization problem,and the optimized gradient method is used to improve the convergence speed.In addition,a graphics processing unit is used to accelerate the proposed algorithm.The experimental results on four in vivo human datasets demonstrate that the reconstruction quality of the proposed algorithm is comparable to that of JTL-based low-rank modeling of local k-space neighborhoods with parallel imaging(JTL-PLORAKS),and the proposed algorithm is 46 times faster than the JTL-PLORAKS,requiring only 4 s to reconstruct a 200×200 pixels MR image with 8 channels.展开更多
Single-atom catalysts(SACs)have attracted considerable interest in the fields of energy and environmental science due to their adjustable catalytic activity.In this study,we investigated the matching of valence electr...Single-atom catalysts(SACs)have attracted considerable interest in the fields of energy and environmental science due to their adjustable catalytic activity.In this study,we investigated the matching of valence electron numbers between single atoms and adsorbed intermediates(O,N,C,and H)in MXene-anchored SACs(M-Ti_(2)C/M-Ti_(2)CO_(2)).The density functional theory results demonstrated that the sum of the valence electron number(VM)of the interface-doped metal and the valence electron number(VA)of the adsorbed intermediates in M-Ti_(2)C followed the 10-valence electron matching law.Furthermore,based on the 10-valence electron matching law,we deduced that the sum of the valence electron number(k)and VMfor the molecular adsorption intermediate interactions in M-Ti_(2)CO_(2)adhered to the 11-valence electron matching law.Electrostatic repulsion between the interface electrons in M-Ti_(2)CO_(2)and H_(2)O weakened the adsorption of intermediates,Furthermore,we applied the 11-valence electron matching law to guide the design of catalysts for nitrogen reduction reaction,specifically for N_(2)→NNH conversion,in the MTi_(2)CO_(2)structure.The sure independence screening and sparsifying operator algorithm was used to fit a simple three-dimensional descriptor of the adsorbate(R_(2)up to 0.970)for catalyst design.Our study introduced a valence electron matching principle between doped metals(single atoms)and adsorbed intermediates(atomic and molecular)for MXene-based catalysts,providing new insights into the design of high-performance SACs.展开更多
基金the Yunnan Fundamental Research Projects(No.202301AT070452)the National Natural Science Foundation of China(No.61861023)。
文摘The structured low-rank model for parallel magnetic resonance(MR)imaging can efficiently reconstruct MR images with limited auto-calibration signals.To improve the reconstruction quality of MR images,we integrate the joint sparsity and sparsifying transform learning(JTL)into the simultaneous auto-calibrating and k-space estimation(SAKE)structured low-rank model,named JTLSAKE.The alternate direction method of multipliers is exploited to solve the resulting optimization problem,and the optimized gradient method is used to improve the convergence speed.In addition,a graphics processing unit is used to accelerate the proposed algorithm.The experimental results on four in vivo human datasets demonstrate that the reconstruction quality of the proposed algorithm is comparable to that of JTL-based low-rank modeling of local k-space neighborhoods with parallel imaging(JTL-PLORAKS),and the proposed algorithm is 46 times faster than the JTL-PLORAKS,requiring only 4 s to reconstruct a 200×200 pixels MR image with 8 channels.
基金funded by the National Natural Science Foundation of China(61701288,51706128)the Natural Science Basic Research Program of Shaanxi Province(2021JM-485)+2 种基金the Key Scientific Research Project of Shaanxi Provincial Education Department(20JS019)the High-level Achievement Cultivation Project of Collaborative Innovation Center for Comprehensive Development of Qinba Biological Resources(QBXT-17-8)the Postgraduate Innovation Project of Shaanxi University of Technology(SLGYCX2410).
文摘Single-atom catalysts(SACs)have attracted considerable interest in the fields of energy and environmental science due to their adjustable catalytic activity.In this study,we investigated the matching of valence electron numbers between single atoms and adsorbed intermediates(O,N,C,and H)in MXene-anchored SACs(M-Ti_(2)C/M-Ti_(2)CO_(2)).The density functional theory results demonstrated that the sum of the valence electron number(VM)of the interface-doped metal and the valence electron number(VA)of the adsorbed intermediates in M-Ti_(2)C followed the 10-valence electron matching law.Furthermore,based on the 10-valence electron matching law,we deduced that the sum of the valence electron number(k)and VMfor the molecular adsorption intermediate interactions in M-Ti_(2)CO_(2)adhered to the 11-valence electron matching law.Electrostatic repulsion between the interface electrons in M-Ti_(2)CO_(2)and H_(2)O weakened the adsorption of intermediates,Furthermore,we applied the 11-valence electron matching law to guide the design of catalysts for nitrogen reduction reaction,specifically for N_(2)→NNH conversion,in the MTi_(2)CO_(2)structure.The sure independence screening and sparsifying operator algorithm was used to fit a simple three-dimensional descriptor of the adsorbate(R_(2)up to 0.970)for catalyst design.Our study introduced a valence electron matching principle between doped metals(single atoms)and adsorbed intermediates(atomic and molecular)for MXene-based catalysts,providing new insights into the design of high-performance SACs.
