You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Covalently functionalized double-walled carbon nanotubes combine high sensitivity and selectivity in the electrical detection of small molecules. Atom-thick materials such as single-walled carbon nanotubes (SWCNTs) and graphene exhibit ultrahigh sensitivity to chemical perturbation partly because all of the constituent atoms are surface atoms. However, low selectivity due to nonspecific binding on the graphitic surface is a challenging issue to many applications including chemical sensing. Here, we demonstrated simultaneous attainment of high sensitivity and selectivity in thin-film field effect transistors (TFTs) based on outer-wall selectively functionalized double-walled carbon nanotubes (DWCNTs). With carboxylic acid functionalized DWCNT TFTs, we obtained excellent gate modulation (on/off ratio as high as 4000) with relatively high ON currents at a CNT areal density as low as 35 ng/cm(2). The devices displayed an NH(3) sensitivity of 60 nM (or ~1 ppb), which is comparable to small molecule aqueous solution detection using state-of-the-art SWCNT TFT sensors while concomitantly achieving 6000 times higher chemical selectivity toward a variety of amine-containing analyte molecules over that of other small molecules. These results highlight the potential of using covalently functionalized double-walled carbon nanotubes for simultaneous ultrahigh selective and sensitive detection of chemicals and illustrate some of the structural advantages of this double-wall materials strategy to nanoelectronics.
{"Chemical": ["carbon", "carboxylic acid", "amine", "graphene", "NH(3)"]}