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Publication Detail
A novel capsule-based smell test fabricated via coaxial dripping
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Ismail AS, Goodwin GR, Castrejon-Pita JR, Noyce AJ, Azevedo HS
  • Publisher:
    Royal Society, The
  • Publication date:
    28/04/2021
  • Journal:
    Journal of the Royal Society Interface
  • Volume:
    18
  • Issue:
    177
  • Article number:
    rsif.2021.0039
  • Status:
    Published
  • Print ISSN:
    1742-5662
  • Language:
    en
Abstract
In this paper, we demonstrate that aromatic oil capsules, produced by dripping droplets, can offer a simple, yet effective, testing tool to aid in the diagnosis of various diseases, in which the loss of smell is a key symptom. These include chronic neurological conditions such as Parkinson's and Alzheimer's diseases, and acute respiratory infections such as that caused by COVID-19. The capsules were fabricated by concentrically dripping oil/alginate droplets, from a coaxial nozzle, into an oppositely charged ionic liquid. This fabrication technique enables full control over the capsule size, the shell thickness and the volume of the encapsulated oil. After formation, liquid capsules were left to dry and form a solid crust surrounding the oil. The prototype test consists of placing a standardized number of capsules between adhesive strips that users crush and pull apart to release the smell. In addition to the fabrication method, a simple mathematical model was developed to predict the volume of encapsulated oil within the capsule in terms of the flow rate ratio and the nozzle size. Tensile tests show that capsule strength is inversely proportional to its size owing to an increase in the shell thickness. By increasing the alginate concentration, the load required to rupture the capsule increases, to the point where capsules are too stiff to be broken by a fingertip grip. Results from a preliminary screening test, within a group of patients with Parkinson's disease, found that smells were detectable using a ‘forced choice’ paradigm.
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