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:

Controlled fabrication of hexagonally close-packed Langmuir-Blodgett silica particulate monolayers from binary surfactant and solvent systems. We describe a controllable method to fabricate hexagonally close-packed Langmuir-Blodgett (LB) monolayers with stearic acid (SA) as co-surfactant and methanol as co-solvent. The optimal SA concentrations and volume ratios of chloroform to methanol are 0.8 mg/mL and 3:1 for particles of 140 nm, 0.50 mg/mL and 4:1 for particles of 300 nm, and 0.05 mg/mL and 5:1 for particles of 550 nm, respectively. Additionally, SEM detections of the monolayers transferred at different surface pressures indicate that the monolayers deposited from the binary systems are more compressible. The experimental results indicate that the interparticle repulsions and particle-water interactions can be enhanced without decreasing the particle hydrophobicity by adding SA and methanol; thus, particulate monolayers with large hexagonally close-packed domains composed of small silica particles can be successfully fabricated using LB technique. We propose that the enhanced interparticle repulsion is attributed to the Columbic repulsion resulting from the attachment of SA molecules to the CTAB modified particles around the three phase contact line.
{"Chemical": ["chloroform", "CTAB", "methanol", "stearic acid", "silica"]}