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:

Unprecedented transformation of tetrathienoanthracene into pentacene on Ni(111). The imaging and characterization of single-molecule reaction events is essential to both extending our basic understanding of chemistry and applying this understanding to challenges at the frontiers of technology, for example, in nanoelectronics. Specifically, understanding the behavior of individual molecules can elucidate processes critical to the controlled synthesis of materials for applications in multiple nanoscale technologies. Here, we report the synthesis of an important semiconducting organic molecule through an unprecedented reaction observed with submolecular resolution by scanning tunneling microscopy (STM) under ultrahigh vacuum (UHV) conditions. Our images reveal a sulfur abstraction and cyclization reaction that converts tetrathienoanthracene precursors into pentacene on the Ni(111) surface. The identity of the final reaction product was confirmed by time-of-flight secondary ion mass spectrometry (TOF-SIMS). This reaction has no known literature analogue, and highlights the power of local-probe techniques for exploring new chemical pathways.
{"Chemical": ["Ni(111)", "tetrathienoanthracene", "sulfur", "pentacene"]}