Within the fast-growing field of nanoscience and nanotechnology, observing and understanding the relation between physical and chemical properties of nanostructures and their potential applications plays a critical role in fueling and channeling future innovations and future perspectives of the field.
Nanowires, in particular, have been shown to be excellent object of study to explore new fundamental physical phenomena, yet their integration into working devices has not been progressing at the same rate. The potential commercialization of nanowires could hardly be reliant only on a simple count of growing scientific publication. Therefore, it is necessary to expend the view towards patent publications, with a goal to perform detailed comparative analysis of both technological and scientific knowledge. Patent documents are a superb source of technical information that is not published elsewhere, but at the same time are not peer-reviewed documents. For this reason, separate analysis of knowledge stored in scientific literature or patent databases might lead to an invalid path/picture of current nanowire technology development.
Here, we present possibilities of technologies such as the Natural Language Processing and Machine Learning used in the framework of patent and scientific publications search to support heuristic stage of design of new products and technologies. As a result, the content analysis, together with profiling results can shed a light on the overlap or possible gap between scientific discoveries and technological innovations. Finally, the implementation of heuristic methods such as TRIZ, which are proven to be effective tools for problem solving for classical physics and engineering could inspire combination of different methods, approaches and technologies in the field of nanowires, leading towards inventive solutions in the potential new devices, concepts and technologies, especially at the conceptual design stage.