Login / Signup

Development of Gold Nanoparticle Micropatterns for the Electrical Detection of Proteins.

Geonwoo LimKibeom KimYuri ParkMyoung-Hwan Park
Published in: Nanomaterials (Basel, Switzerland) (2021)
Protein analysis can be used to efficiently detect the early stages of various diseases. However, conventional protein detection platforms require expensive or complex equipment, which has been a major obstacle to their widespread application. In addition, uncertain signals from non-specific adhesion interfere with the precise interpretation of the results. To overcome these problems, the development of a technique that can detect the proteins in a simple method is needed. In this study, a platform composed of gold nanoparticles (GNPs) was fabricated through a simple imprinting method for protein detection. The corrugated surface naturally formed by the nanoparticle assemblies simultaneously increases the efficiency of adhesion and binding with analytes and reduces undesired interactions. After forming the GNP micropatterns, post-functionalization with both cationic and neutral ligands was performed on the surface to manipulate their electrostatic interaction with proteins. Upon protein binding, the change in the electrical values of the micropatterns was recorded by using a resistance meter. The resistance of the positively charged micropatterns was found to increase due to the electrostatic interaction with proteins, while no significant change in resistance was observed for the neutral micropatterns after immersion in a protein solution. Additionally, the selective adsorption of fluorescent proteins onto the micropatterns was captured using confocal microscopy. These simply imprinted GNP micropatterns are sensitive platforms that can detect various analytes by measuring the electrical resistance with portable equipment.
Keyphrases
  • gold nanoparticles
  • binding protein
  • protein protein
  • amino acid
  • label free
  • high throughput
  • real time pcr
  • small molecule
  • high resolution
  • staphylococcus aureus
  • data analysis