Molecularly imprinted nanoparticles based plasmonic sensors for real-time Enterococcus faecalis detection


BIOSENSORS & BIOELECTRONICS, cilt.126, ss.608-614, 2019 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Cilt numarası: 126
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.bios.2018.11.030
  • Sayfa Sayıları: ss.608-614


Human fecal contamination poses a crucial environmental and health threat in recent years, resulting in the difficulties of access to clean water. According to the World Health Organization, several fecal bacteria, particularly Enterococci species, are present in human intestinal flora. Enterococcus faecalis (E. faecalis) is one of the indicator bacteria that have been utilized as a pollution indicator in water. However, existing technologies and detection strategies face multiple challenges in terms of low affinity for detection and labelling requirements that limit their access to large scale applications. Here, we present a label-free molecular fingerprinting strategy on a plasmonic sensor to detect E. fecalis from aqueous and seawater samples. The kinetic performance of platform was comprehensively evaluated and the platform provided four orders of magnitude detection range with a low limit of detection (down to similar to 100 bacteria/mL) and a high correlation coefficient value ( > 0.99) in the range of 2 x 10(4)-1 x 10(8) cfu/mL. The platform also denoted a selectivity and specificity while other bacteria (E. coil, B. subtilis, and S. aureus) samples were applied. Multiple time use and relatively long shelf-life are superior to the existing modality. The presented method is one of the fascinating surface modification technique that utilizes biotarget as a recognition element itself, providing a broad range of versatility to replica other biotargets with different molecular structure, size, and physicochemical properties. Such a reliable and versatile platform would hold potential applications from microbiome characterization to forensics by revitalizing obsolescent detection strategies.