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The purpose of this research is as follows: DPP-BOH-PVA has been synthesized from 1,1′:3′,1″-terphenyl-5'-boronic acid (DPP-OH) and polyvinyl alcohol (PVA). The afterglow lifetime of DPP-BOH-PVA was studied by changing contents of DPP-OH (1, 2 and 4 Wt.%). These films were characterized with Fourier transform infrared, X-ray diffraction as structural analysis and DSC as thermal analysis. Afterglow lifetimes were evaluated as time-resolved emission decay profile analysis. Fiber films of DPP-BOH-PVA-2-E have been prepared by electrospinning method with the diameter of 5 μm and afterglow life time of 2.1 s (@ 535 nm) under ambient conditions. Stimulus responsive properties with afterglow emission for fiber film were investigated.

During the synthesis of the polymer, modification was carried out using DPP-OH/PVA with a molar ratio of 1/4, under an alkalinity medium with ammonium hydroxide and with a temperature of 80°C.

XRD results indicate that DPP-BOH-PVA film had high crystallinity, which is crucial for preparing organic room temperature phosphorescence (RTP) materials.

The reaction mixture must be stirred continuously. Temperature should be controlled to prevent the rapid evaporation of ammonium hydroxide.

This study provides technical information for the synthesis of multidimensional stimulation response RTP micron fiber thin film. The electrospinning technology may also promote the applications of the large areas of RTP films.

This resin will be used for the multidimensional stimulation response RTP fiber thin film.

The diameter of fiber film of PP-BOH-PVA-2-E by electrospinning method was in the range of 5 μm, and its afterglow lifetime decayed to be 2.1 s.

Continuous fiber-reinforced thermoplastic composites are being widely used in industry, but the fundamental understanding of their properties is still limited. The purpose of this paper is to quantitatively study the effects of carbon fiber content on the tensile strength of continuous carbon fiber-reinforced polylactic acid (CCFRPLA) fabricated through additive manufacturing using the fused deposition modeling (FDM) process.

The strength of these materials is highly dependent on the interface that forms between the continuous fiber and the plastic. A cohesive zone model is proposed as a theoretical means to understand the effect of carbon fiber on the tensile strength properties of CCFRPLA. The interface formation mechanism is explored, and the single fiber pulling-out experiment is implemented to investigate the interface properties of CCFRPLA. The fracture mechanism is also explored by using the cohesive zone model.

The interface between carbon fiber and PLA plays the main role in transferring external load to other fibers within CCFRPLA. The proposed model established in this paper quantitatively reveals the effects of continuous carbon fiber on the mechanical properties of CCFRPLA. The experimental results using additively manufacturing CCFRPLA provide validation and explanation of the observations based on the quantitative model that is established based on the micro-interface mechanics.

The predict model is established imagining that all the fibers and PLA form a perfect interface. While in a practical situation, only the peripheral carbon fibers of the carbon fiber bundle can fully infiltrate with PLA and form a transmission interface. These internal fibers that cannot contract with PLA fully, because of the limit space of the nozzle, will not form an effective interface.

This paper theoretically reveals the fracture mechanism of CCFRPLA and provides a prediction model to estimate the tensile strength of CCFRPLA with different carbon fiber contents.

Considering the influence of deformation error, the target poses must be corrected when compensating for positioning error but the efficiency of existing positioning error compensation algorithms needs to be improved. Therefore, the purpose of this study is to propose a high-efficiency positioning error compensation method to reduce the calculation time.

The corrected target poses are calculated. An improved back propagation (BP) neural network is used to establish the mapping relationship between the original and corrected target poses. After the BP neural network is trained, the corrected target poses can be calculated with short notice on the basis of the pose correction similarity.

Under given conditions, the calculation time when the trained BP neural network is used to predict the corrected target poses is only 1.15 s. Compared with the existing algorithm, this method reduces the calculation time of the target poses from the order of minutes to the order of seconds.

The proposed algorithm is more efficient while maintaining the accuracy of the error compensation.

This method can be used to quickly position the error compensation of a large parallel mechanism.