Efficient Construction of Heterogeneous Scientific Battery Databases via a Distilled Dual-Model Framework

Battery Data Extraction from PDF Literature This Python script automates the extraction of battery experimental data from PDF files using AI models. It employs a two-stage approach: first classifying whether a document is battery-related, then extracting detailed experimental conditions. Features Automated PDF Processing: Supports both PyPDF2 and PyMuPDF for robust PDF text extraction Two-Stage AI Pipeline: Classification model to identify battery-related documents Extraction model to extract detailed experimental conditions OpenAI-Compatible API: Works with DashScope and ModelScope APIs Comprehensive Statistics: Tracks processing time, token usage, and error rates JSON Output: Structured data output for easy integration Retry Mechanism: Automatic retry for failed API calls

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	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
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