Plasmonic photothermal therapy (PPTT) using plasmonic nanoparticles as efficient photoabsorbing agents has been proposed previously. One critical step in PPTT is to effectively deliver gold nanoparticles into the cells. This study demonstrates that the delivery of gold nanorods (AuNRs) can be greatly enhanced by combining the following three mechanisms: AuNRs encapsulated in protein-shell microbubbles (AuMBs), molecular targeting, and sonoporation employing acoustic cavitation of microbubbles (MBs). Both in vitro and in vivo tests were performed. For molecular targeting, the AuMBs were modified with anti-VEGFR2. Once bound to the angiogenesis markers, the MBs were destroyed by ultrasound to release the AuNRs and the release was confirmed by photoacoustic measurements. Additionally, acoustic cavitation was induced during MB destruction for sonoporation (i.e., increase in transient cellular permeability). The measured inertial cavitation dose was positively correlated with the temperature increase at the tumor site. The quantity of AuNRs delivered into the cells was also determined by measuring the mass spectrometry and observed using third-harmonic-generation microscopy and two-photon fluorescence microscopy. A temperature increase of 20°C was achieved in vitro. The PPTT results in vivo also demonstrated that the temperature increase (>45°C) provided a sufficiently high degree of hyperthermia. Therefore, synergistic delivery of AuNRs was demonstrated.

A process was developed for fine fabrication of amorphous IGZO TFTs and integrated circuits on flexible and colorless polyimide substrates. TFTs with field‐effect mobilities of ∼10 cm2/Vs and ring oscillators with propagation delay of 0.35 μs per stage were achieved on the polyimide substrates.

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Most recent research on object tracking sensor networks has focused on collecting all data from the sensor network into the sink, which delivers the predicted locations to the corresponding nodes in order to accurately predict object movement. The communication cost of this centralized scenario is higher than that of a distributed method. Centralized data collection affects the freshness of the data and increases latency in movement trajectory prediction. In addition, due to the large amount of packets being sent and received, sensor node energy is quickly exhausted. Although this data collection method might result in higher accuracy for prediction, the sensor network lifetime is not reduced. In this paper, a distributed object tracking method is proposed using the network structure of convex polygons, called faces. The nodes in the faces cooperate to find the trajectories of an object and then these trajectories are used to predict the objects' movement. The proposed method, based on trajectory tree construction, can reduce both the storage space of collected trajectories and the time spent on trajectory prediction analysis. Simulations show that the proposed method can reduce the energy consumption of the nodes and make prediction of nodes moving direction accurately than the existing approaches.

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