Organic small-molecule-based photothermal agents such as cyanine dyes have received increasing attention in developing novel cancer therapies with potential clinical utility but suffer from poor stability, low photothermal efficiency, and limited accumulation at tumor sites in molecular forms. Self-assembly of small-molecule dyes into supramolecular assemblies may address these concerns by controlling the molecular organization of dye monomers to form structures of a higher order. Among them, H-aggregates of dyes favor face-to-face contacts with strongly overlapping areas, which always have a negative connotation to exhibit low or no fluorescence in most cases but may emanate energy in nonradiative forms such as heat for photothermal cancer therapy applications. Here, the synergistic self-assembly of cyanine dyes into H-aggregates is developed as a new supramolecular strategy to fabricate small-molecule-based photothermal nanomaterials. Compared to the free cyanine dyes, the H-aggregates assembled from pyrene or tetraphenylethene (TPE) conjugating cyanine exhibit the expected absorption spectral blue shift and fluorescence self-quenching but unique photothermal properties. Remarkably, the obtained H-aggregates are saucer-shaped nanoparticles that exhibit passive tumor-targeting properties to induce imaging-guided photothermal tumor ablation under irradiation. This supramolecular strategy presented herein may open up new opportunities for constructing next-generation small-molecule-based self-assembly nanomaterials for PTT cancer therapy in clinics.
Organic−inorganic oligo(ethylene glycol)−polyhedral oligomeric silsesquioxanes (OEG n −POSS) hybrid materials are woven into macroscopically shaped entities by thiol−ene chemistry. The mechanical behavior and interfacial nature of the OEG n −POSS materials are easily tailored by changing the length of OEG n . The nanostructured OEG n −POSS materials exhibited excellent bioactivity to form hydroxyapatite, whose morphology was also dependent on the molecular weight of OEG n . Among them, OEG 2 −POSS materials enhanced the in vitro differentiation of adipose-derived stem cells to osteoblasts and promoted the in vivo bone formation within a femoral condyle defect site, but they could be limited by the mismatch rates between the degradation and new bone formation. Thus, OEG 2 −POSS could be practically applied for bone regeneration by optimizing the degradation rate based on its key structural features, which would be of great benefit to bone tissue engineering in the future.
The linear motor feed system has been in service in complex working conditions for a long time, thus causing the nonuniform distribution of the temperature field distribution. Thus, thermal error has become a key factor affecting system motion accuracy. To maximize the accuracy and efficiency of thermal error compensation for linear motor feed system, an improved modeling method for the thermal error of the linear motor feed system based on Bayesian neural networks is proposed in combination with the strong generalization performance and avoidance of overfitting of Bayesian neural networks. And the specific modeling ideas are as follows: Firstly, the X-Y cross-type two-axis linear motor feed system is as the test object. Aiming at the slow convergence , over fitting and under fitting problems of traditional neural network, Bayesian neural network is used to model the thermal error of linear motor feed system .Secondly, In order to avoid the influence of multicollinearity data on the final results, the grey relation analysis method is used to screen the temperature measuring points, and the data with large relation degree is selected for modeling to ensure the prediction accuracy of neural network. Thirdly, and the temperature variables of sensitive points and thermal positioning errors are taken as data input samples. Fourthly, a Bayesian neural network model is established. Fifthly, the hyperparameters of the Bayesian neural network is determined by a calculating method of Hessian matrix by Gauss Newton approximation. And finally, a thermal error prediction model is established. The comparison and analysis with the neural network constructed by ordinary Levenberg-Marquardt algorithm after a series of experimental demonstrations see that the prediction accuracy of the proposed method can be enhanced by up to 10%. It also shows that the prediction model has the advantages of high precision, strong generalization ability, strong anti-disturbance ability and strong robustness, etc. Therefore, the prediction model is expected to be widely used in the prediction and compensation of thermal error of the feed system of high-speed CNC machine tools in practical machining occasions.
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