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:

Organic Solvent-Based Graphene Oxide Liquid Crystals: A Facile Route toward the Next Generation of Self-Assembled Layer-by-Layer Multifunctional 3D Architectures. We introduce soft self-assembly of ultralarge liquid crystalline (LC) graphene oxide (GO) sheets in a wide range of organic solvents overcoming the practical limitations imposed on LC GO processing in water. This expands the number of known solvents which can support amphiphilic self-assembly to ethanol, acetone, tetrahydrofuran, N-dimethylformamide, N-cyclohexyl-2-pyrrolidone, and a number of other organic solvents, many of which were not known to afford solvophobic self-assembly prior to this report. The LC behavior of the as-prepared GO sheets in organic solvents has enabled us to disperse and organize substantial amounts of aggregate-free single-walled carbon nanotubes (SWNTs, up to 10 wt %) without compromise in LC properties. The as-prepared LC GO-SWNT dispersions were employed to achieve self-assembled layer-by-layer multifunctional 3D hybrid architectures comprising SWNTs and GO with unrivalled superior mechanical properties (Young's modulus in excess of 50 GPa and tensile strength of more than 500 MPa).
{"Chemical": ["Graphene Oxide", "N-cyclohexyl-2-pyrrolidone", "carbon", "ethanol", "N-dimethylformamide", "acetone", "tetrahydrofuran", "graphene oxide"]}