Photothermal therapy (PTT) at present, following the temperature definition for conventional thermal therapy, usually keeps the temperature of lesions at 42–45 °C or even higher. Such high temperature kills cancer cells but also increases the damage of normal tissues near lesions through heat conduction and thus brings about more side effects and inhibits therapeutic accuracy. Here we use temperature-feedback upconversion nanoparticle combined with photothermal material for real-time monitoring of microscopic temperature in PTT. We observe that microscopic temperature of photothermal material upon illumination is high enough to kill cancer cells when the temperature of lesions is still low enough to prevent damage to normal tissue. On the basis of the above phenomenon, we further realize high spatial resolution photothermal ablation of labelled tumour with minimal damage to normal tissues in vivo. Our work points to a method for investigating photothermal properties at nanoscale, and for the development of new generation of PTT strategy.

In this Communication, we report fabrication of ultrabright water-dispersible silicon nanoparticles (SiNPs) with quantum yields (QYs) up to 75% through a novelly designed chemical surface modification. A simple one-pot surface modification was developed that improves the photoluminescent QYs of SiNPs from 8% to 75% and meanwhile makes SiNPs water-dispersible. Time-correlated single photon counting and femtosecond time-resolved photoluminescence techniques demonstrate the emergence of a single and uncommonly highly emissive recombination channel across the entire NP ensemble induced by surface modification. The extended relatively long fluorescence lifetime (FLT), with a monoexponential decay, makes such surface-modified SiNPs suitable for applications involving lifetime measurements. Experimental results demonstrate that the surface-modified SiNPs can be utilized as an extraordinary nanothermometer through FLT imaging.

Fluorescence intensity (upper panel) and lifetime (bottom panel) images of KB cells stained with FITC (B), Si NPs (C) and unstained control (A).

With the increasing scale of tasks in cloud computing, the problem of high energy consumption becomes increasingly serious. To deal with the problem, we propose a cloud computing energy consumption model, which takes into account the execution and transmission cost of the processor. Then, based on this model, we put forward a task scheduling optimization algorithm named modified particle swarm optimization (M-PSO) to handle the local optimum and slow convergence problem. Different from the PSO, M-PSO can dynamically adjust the inertia weight coefficient to improve the speed of convergence according to the number of iterations. Finally, the performance of the proposed algorithm is evaluated through the CloudSim toolkit, and the experimental resultsshow that the M-PSO can efficiently reduce total cost compared with other algorithms.

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