▎ 摘　　要

A graphene chemical vapor sensor with an unmodified surface has been fabricated and thoroughly characterized upon exposure to headspace vapor of a variety of solvents and related compounds. The vapor sensor exhibits excellent discrimination toward a variety of chemical compounds. Principle component analysis (PCA) was performed to explore the extent of grouping for each compound and separation between compounds and chemical classes. The prediction accuracy of the sensor is evaluated with linear discrimination analysis, k-nearest neighbor, random forest, and support vector classifiers. The combination of PCA and prediction accuracies demonstrates the discrimination capability of an unmodified graphene chemical vapor sensor. Such a vapor sensor is very attractive for application in small, low-power, robust, and adaptable cross-reactive arrays in electronic noses.