Tooling design and microwave curing technologies for the manufacturing of fiber-reinforced polymer composites in aerospace applications

The increasing demand for high-performance and quality polymer composite materials has led to international research effort on pursuing advanced tooling design and new processing technologies to satisfy the highly specialized requirements of composite components used in the aerospace industry. This paper reports the problems in the fabrication of advanced composite materials identified through literature survey, and an investigation carried out by the authors about the composite manufacturing status in China’s aerospace industry. Current tooling design technologies use tooling materials which cannot match the thermal expansion coefficient of composite parts, and hardly consider the calibration of tooling surface. Current autoclave curing technologies cannot ensure high accuracy of large composite materials because of the wide range of temperature gradients and long curing cycles. It has been identified that microwave curing has the potential to solve those problems. The proposed technologies for the manufacturing of fiber-reinforced polymer composite materials include the design of tooling using anisotropy composite materials with characteristics for compensating part deformation during forming process, and vacuum-pressure microwave curing technology. Those technologies are mainly for ensuring the high accuracy of anisotropic composite parts in aerospace applications with large size (both in length and thickness) and complex shapes. Experiments have been carried out in this on-going research project and the results have been verified with engineering applications in one of the project collaborating companies

Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model

Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine