The Synergistic Effect on the Mimetic Optical Structure of Feline Eyes toward Household Health Monitoring of Acute and Chronic Diseases

ACS Nano. 2024 Feb 13;18(6):4944-4956. doi: 10.1021/acsnano.3c10468. Epub 2024 Feb 1.

Abstract

A breakthrough in the performance of bionic optical structures will only be achieved if we can obtain an in-depth understanding of the synergy mechanisms operating in natural optical structures and find ways to imitate them. In this work, inspired by feline eyes, an optical substrate that takes advantage of a synergistic effect that occurs between resonant and reflective structures was designed. The synergistic effect between the reflective and resonant components leads to a Raman enhancement factor (EF) of 1.16 × 107, which is much greater than that achieved using the reflective/resonant cavities on their own. Finite-difference time-domain (FDTD) simulations and experimental results together confirm that the mechanism of this synergistic effect is achieved by realizing multiple reflections and repeated absorptions of light, generating a strong local electric field. Thus, a 2-3 order of magnitude increase in sensitivity could be achieved. More importantly, with the homemade centrifugal device, above optical substrates were further used to develop a rapidly highly sensitive household health monitoring system (detection time <3 min). It can thus be used to give early warning of acute diseases with high risk (e.g., acute myocardial infarction (AMI) and cerebral peduncle). Due to the good reusability and storability (9% and 8% reduction in EF after washing 30 times and 9 months of storage, respectively) of the substrates, the substrates thus reduce detection costs (to ∼$1), making them much cheaper to use than the current gold-standard methods (e.g., ∼$16 for gout detection).

Keywords: bionic optics; feline eyes; household health monitoring; surface-enhanced Raman scattering; synergistic effect.

MeSH terms

  • Animals
  • Cats
  • Chronic Disease
  • Humans
  • Spectrum Analysis, Raman* / methods