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:

Grazing-incidence neutron-induced fluorescence probes density profiles of labeled molecules at solid/liquid interfaces. We report on the use of characteristic prompt γ-fluorescence after neutron capture induced by an evanescent neutron wave to probe densities and depth profiles of labeled molecules at solid/liquid interfaces. In contrast to classical scattering techniques and X-ray fluorescence, this method of "grazing-incidence neutron-induced fluorescence" combines direct chemical specificity, provided by the label, with sensitivity to the interface, inherent to the evanescent wave. We demonstrate that the formation of a supported lipid membrane can be quantitatively monitored from the characteristic fluorescence of (157)Gd(3+) ions bound to the headgroup of chelator lipids. Moreover, we were able to localize the (157)Gd(3+) ions along the surface normal with nanometer precision. This first proof of principle with a well-defined model system suggests that the method has a great potential for biology and soft matter studies where spatial resolution and chemical sensitivity are required.
{"Chemical": ["(157)Gd(3+)"]}