An engineering-statistical model for synthesis process of nanomaterials

Nanomaterials possess many desirable qualities, which make them play a central role in the field of material. However, due to the limited understanding of synthesis mechanism and large variation of the synthesis process, the synthesis process of nanomaterials is very complicated and extremely sensitive to many factors, even a minor change in any of these factors may lead to totally different growth direction. So there are not any efficient methods available for quality control of the synthesis process till now. First of all, this paper presents a short review of some efforts to address the problem that nanomaterials possessing well-defined properties cannot be produced in large quantities with a cost-effective technique. Then we divide the existing approaches into two categories, which are physical approach and statistical approach respectively. On one hand, the predictive capability of physical model is limited by its assumptions. On the other hand, statistical model has its own drawbacks, such as outlier and over-fitting. Such problems probably lead to conclusions against physical laws, especially when statistical model is used for extrapolation. So it is believed that integrating these models to establish an engineering-statistical model has the potential to overcome the drawbacks of both two models. Then the sequential model building strategy is introduced to construct engineering-statistical model mentioned above. A case study of density control of nanowires is presented to illustrate the effectiveness of our modeling strategy for controlled-growth of nanomaterials.