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- 저자 : Jinpeng Wen (검색결과 2 건)
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Datao Hu,Jinpeng Wen,Xinxin Zhao,Kailai Liu,Yuchen Zhang,Yizhuo Bu,Ke Wang 한국생체재료학회 2023 생체재료학회지 Vol.27 No.00
Background Antibacterial activity and on-demand removability are key characteristics governing the effectiveness of clinic wound dressing. However, the excellent tissue adhesion of new dressings is often overemphasized without a detailed discussion of dressing replacement. Besides, the inherent antibacterial ability of dressings is beneficial for promoting the healing of infected wound. Therefore, we rationally design an injectable antibacterial wound dressing with on-demand removability to accelerate infected wound healing. Method We design this wound dressing with a simple and feasible method based on the electrostatic self-assembly of hyaluronic acid and ε-polylysine. We investigated the efficacy of this dressing in terms of its microtopography, rheology, self-healing performance, adhesive ability, antimicrobial, hemostatic, on-demand removal properties, and wound healing promotion through various tests. Results The prepared dressing possesses injectability, self-healing ability and antibacterial activity, showing NaCl-triggered on-demand dissolution due to the disruption of electrostatic interactions. When used as dressings for healing full-thickness wounds, it could effectively accelerate wound healing by killing bacteria, downregulating inflammation, promoting collagen deposition, enhancing keratinocyte migration and angiogenesis due to its excellent adhesion ability, favorable hemostatic property, and potent antibacterial performance. Conclusion All results indicate that this is a simple and practical dressing for clinical application. This strategy provides a novel idea for developing on-demand removal dressings with antibacterial and injectable properties.
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A novel method to aging state recognition of viscoelastic sandwich structures
ZhouSuo Zhang,Jinxiu Qu,Xue Luo,Bing Li,Jinpeng Wen 국제구조공학회 2016 Steel and Composite Structures, An International J Vol.21 No.6
Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, nonstationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.
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