Nanohydroxyapatite (n-HAp) as a pickering stabilizer in oil-in-water (O/W) emulsions: a stability study

Surfactant-free emulsions, such as Pickering emulsions, have been gaining an increased interest. They constitute green alternatives to the current industrial practices in emulsion technology finding diverse technological uses. In this work, nano-hydroxyapatite (n-HAp) was tested as an oil-in-water (...

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Detalhes bibliográficos
Autor principal: Ribeiro, Andreia (author)
Outros Autores: Manrique, Yaidelin A. (author), Ferreira, Isabel C.F.R. (author), Barreiro, M.F. (author), Lopes, José Carlos B. (author), Dias, Madalena M. (author)
Formato: article
Idioma:eng
Publicado em: 2018
Assuntos:
Texto completo:http://hdl.handle.net/10198/23424
País:Portugal
Oai:oai:bibliotecadigital.ipb.pt:10198/23424
Descrição
Resumo:Surfactant-free emulsions, such as Pickering emulsions, have been gaining an increased interest. They constitute green alternatives to the current industrial practices in emulsion technology finding diverse technological uses. In this work, nano-hydroxyapatite (n-HAp) was tested as an oil-in-water (O/W) Pickering stabilizer using a chemical system comprising sunflower oil at different O/W ratios (10/90 to 60/40) and n-HAp at concentrations ranging from 0.5 to 15 wt%. The produced emulsions were characterized, and stability evaluated over a two-month period. Based on the results achieved for the 20/80 series, a model to predict the emulsion stability taking into account the O/W ratio, total solids content, droplet diameter, and n-HAp dimensions, was developed. Cryo-SEM evidenced the attachment of n-HAp particles at the oil surface (oil core-n-HAp shell morphology), corroborating their role as Pickering stabilizers. The experimental results, versus the predicted results were compared using a ternary phase diagram, which evidenced the formation of three zones (unstable, stable and gel) depending on the used O/W ratio and n-HAp concentration. Moreover, the comparison of the predicted with the obtained experimental data validated the model predictability.