Blood-pool agents (BPAs) are MRI contrast agents (CAs) characterized by their long circulation in the vascular system to provide an extended time window for high-resolution MR angiography (MRA). Prolonged vascular retention, however, impedes the excretion of BPAs. Therefore, chemical strategy to regulate the balance between retention and clearance is important to reach optimal pharmacokinetics. We recently developed MnP2, the first Mn(III)-porphyrin (MnP) based BPA. MnP2 shows high T1 relaxivity (r1) and high affinity to human serum albumin (HSA) that leads to up to 48-h vascular retention in rats. However, upon albumin binding, the r1 is decreased. To modulate vascular retention time and plasma r1, a regioisomer of MnP2, m-MnP2, was synthesized. The free m-MnP2 exhibits lower r1 than that of MnP2 at magnetic fields above 2 MHz, which agrees with their relative hydrodynamic sizes. The HSA binding of m-MnP2 was evaluated using UV-Vis spectroscopy and found to have tuned-down affinity in comparison with MnP2. Upon HSA binding, the protein complex of m-MnP2 exhibits an r1 of 11.8 mM−1 s−1 at 3 T, which is higher than that of MnP2 bound to HSA. Taken together, this demonstrated the role of molecular geometry in optimizing the pharmacokinetics of albumin-targeting BPAs.

The commercial mature gallium nitride high electron mobility transistors (GaN HEMT) technology has drawn much attention for its great potential in industrial power electronic applications. GaN HEMT is known for low on-state resistance, high withstand voltage, and high switching frequency. This paper presents comparative experimental evaluations of GaN HEMT and conventional Si insulated gate bipolar transistors (Si IGBTs) of similar power rating. The comparative study is carried out on both the element and converter level. Firstly, on the discrete element level, the steady and dynamic characteristics of GaN HEMT are compared with Si-IGBT, including forward and reverse conducting character, and switching time. Then, the elemental switching losses are analyzed based on measured data. Finally, on a complementary buck converter level, the overall efficiency and EMI-related common-mode currents are compared. For the tested conditions, it is found that the GaN HEMT switching loss is much less than for the same power class IGBT. However, it is worth noting that special attention should be paid to reverse conduction losses in the PWM dead time (or dead band) of complementary-modulated converter legs. When migrating from IGBT to GaN, choosing a dead-time and negative gate drive voltage in conventional IGBT manner can make GaN reverse conducting losses high. It is suggested to use 0 V turn-off gate voltage and minimize the GaN dead time in order to make full use of the GaN advantages.

To get better control performance in motor control, more and more researches tend to apply non-linear control laws in the field of motor control. However, most conventional non-linear control theory is based on explicit model of controlled object and often resulting in complexity. Besides, the control parameters tuning is mainly aiming at stability of the system. No valid direct performance-oriented nonlinear control theory has been proposed. Facing the limitations, this paper presents a direct motor position control in an implicit data-driven manner. Unlike conventional non-linear motor controls that are based on explicit models and with stability-based parameters tuning, this study gives performance-oriented non-linear control by mastering non-linear discrete optimal control law in an implicit data-learning manner. Firstly, optimal data of position tracking problem is obtained by solving optimization problem. Secondly, the implicit discrete optimal control law hidden in data is learned by a BP neural network. Finally, the learned control law is implemented in real-time control to reproduce optimal control performance. Simulation and experiment results validated the feasibility of the data-driven controller, which could be helpful for performance-oriented non-linear control designs. The merits and further improvements are also discussed.INDEX TERMS Position control, implicit discrete optimal control, artificial neural network, motor, data learning.

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