Conformal Electrodeposition of Antimicrobial Hydrogels Formed by Self-Assembled Peptide Amphiphiles

The colonization of biomedical surfaces by bacterial biofilms is concerning because these microorganisms display higher antimicrobial resistance in biofilms than in liquid cultures. Developing antimicrobial coatings that can be easily applied to medically-relevant complex-shaped objects, such as implants and surgical instruments, is an important and challenging research direction. This work reports the preparation of antibacterial surfaces via the electrodeposition of a conformal hydrogel of self-assembling cationic peptide-amphiphiles (PAs). Hydrogels of three PAs are electrodeposited: C16K2, C16K3, and C18K2, where Cn is an alkyl chain of n methylene groups and Km is an oligopeptide of m lysines. The processing variables (electrodeposition time, potential, pH, salt concentration, agitation) enable fine control of film thickness, demonstrating the flexibility of the method and allowing to unravel the mechanisms underlying electrodeposition. The electrochemically prepared hydrogels inhibit the growth of Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa in agar plates, and prevent the formation of biofilms of Acinetobacter baumannii and P. aeruginosa and the formation of A. baumannii colonies in solid media. C16K2 and C16K3 hydrogels outperform the antimicrobial activity of those of C18K2 while maintaining good compatibility with human cells

A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop data-based fusion methods, such as neural networks fusion, while little research has focused on fusion architecture or the fusion of different method kinds. In this paper, a data hierarchical fusion using improved weighted Dempster–Shaffer evidence theory (WDS) is proposed, and the integration of data-based and model-based methods is presented for engine gas-path fault diagnosis. For the purpose of simplifying learning machine typology, a recursive reduced kernel based extreme learning machine (RR-KELM) is developed to produce the fault probability, which is considered as the data-based evidence. Meanwhile, the model-based evidence is achieved using particle filter-fuzzy logic algorithm (PF-FL) by engine health estimation and component fault location in feature level. The outputs of two evidences are integrated using WDS evidence theory in decision level to reach a final recognition decision of gas-path fault pattern. The characteristics and advantages of two evidences are analyzed and used as guidelines for data hierarchical fusion framework. Our goal is that the proposed methodology provides much better performance of gas-path fault diagnosis compared to solely relying on data-based or model-based method. The hierarchical fusion framework is evaluated in terms to fault diagnosis accuracy and robustness through a case study involving fault mode dataset of a turbofan engine that is generated by the general gas turbine simulation. These applications confirm the effectiveness and usefulness of the proposed approach

Conformal Electrodeposition of Antimicrobial Hydrogels Formed by Self‐Assembled Peptide Amphiphiles

Abstract The colonization of biomedical surfaces by bacterial biofilms is concerning because these microorganisms display higher antimicrobial resistance in biofilms than in liquid cultures. Developing antimicrobial coatings that can be easily applied to medically‐relevant complex‐shaped objects, such as implants and surgical instruments, is an important and challenging research direction. This work reports the preparation of antibacterial surfaces via the electrodeposition of a conformal hydrogel of self‐assembling cationic peptide‐amphiphiles (PAs). Hydrogels of three PAs are electrodeposited: C16K2, C16K3, and C18K2, where Cn is an alkyl chain of n methylene groups and Km is an oligopeptide of m lysines. The processing variables (electrodeposition time, potential, pH, salt concentration, agitation) enable fine control of film thickness, demonstrating the flexibility of the method and allowing to unravel the mechanisms underlying electrodeposition. The electrochemically prepared hydrogels inhibit the growth of Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa in agar plates, and prevent the formation of biofilms of Acinetobacter baumannii and P. aeruginosa and the formation of A. baumannii colonies in solid media. C16K2 and C16K3 hydrogels outperform the antimicrobial activity of those of C18K2 while maintaining good compatibility with human cells