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Advanced Technology Evolution Pathways of Nanogenerators: A Novel Framework Based on Multi-Source Data and Knowledge

Yufei Liu1, Guan Wang2, Yuan Zhou3

  • 1Center for Strategic Studies, Chinese Academy of Engineering, Beijing 100088, China.

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Summary
This summary is machine-generated.

This study introduces a new method to track the evolution of nanogenerator technology. It analyzes grant, paper, and patent data to reveal technology development pathways and identify emerging research areas.

Keywords:
knowledge graphmulti-source datananogeneratorrepresentation learningtechnology evolution pathway

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Area of Science:

  • Materials Science
  • Energy Technology
  • Innovation Studies

Background:

  • Nanogenerators are a rapidly advancing nano energy technology.
  • Previous studies analyzing technological evolution often overlooked semantic information and the grant-to-paper-to-patent progression.

Purpose of the Study:

  • To propose a novel framework for analyzing the evolutionary pathways of advanced technologies in the nanogenerator field.
  • To address limitations in previous studies by incorporating semantic information and the complete innovation lifecycle from grants to patents.

Main Methods:

  • Development of a separated three-layer knowledge graph incorporating grant, paper, and patent data across multiple time slices.
  • Application of representation learning and clustering algorithms to identify technology-specific clusters within each layer and time slice.
  • Calculation of inter-layer cluster similarity to map the evolutionary pathways from grants to papers to patents.

Main Results:

  • Identification of distinct technology clusters representing specific advancements in nanogenerators at different stages of the innovation lifecycle.
  • Visualization of evolutionary pathways, illustrating the progression of technologies from initial research grants through academic publications to patent filings.
  • Monitoring of established technology pathways and discovery of emerging research fronts within the nanogenerator field.

Conclusions:

  • The proposed knowledge graph framework effectively captures the semantic richness and temporal evolution of nanogenerator technology.
  • The study provides valuable insights into technology development trends and aids in identifying nascent research areas for future innovation.
  • This approach offers a robust method for analyzing technological evolution in emerging fields beyond nanogenerators.