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:

The effect of process parameters on audible acoustic emissions from high-shear granulation. Product quality in high-shear granulation is easily compromised by minor changes in raw material properties or process conditions. It is desired to develop a process analytical technology (PAT) that can monitor the process in real-time and provide feedback for quality control. In this work, the application of audible acoustic emissions (AAEs) as a PAT tool was investigated. A condenser microphone was placed at the top of the air exhaust on a PMA-10 high-shear granulator to collect AAEs for a design of experiment (DOE) varying impeller speed, total binder volume and spray rate. The results showed the 10 Hz total power spectral densities (TPSDs) between 20 and 250 Hz were significantly affected by the changes in process conditions. Impeller speed and spray rate were shown to have statistically significant effects on granulation wetting, and impeller speed and total binder volume were significant in terms of process end-point. The DOE results were confirmed by a multivariate PLS model of the TPSDs. The scores plot showed separation based on impeller speed in the first component and spray rate in the second component. The findings support the use of AAEs to monitor changes in process conditions in real-time and achieve consistent product quality.
{"Chemical": []}