Detail View

Rapid detection of airborne fungal contamination using a molecularly imprinted polymer approach for ergosterol

Citations

WEB OF SCIENCE

Citations

SCOPUS

Metadata Downloads

Title
Rapid detection of airborne fungal contamination using a molecularly imprinted polymer approach for ergosterol
Issued Date
2026-04
Citation
SCIENTIFIC REPORTS, v.16, no.1
Type
Article
Author Keywords
Molecularly imprinted polymerErgosterolScreen-printed electrodeFungiIndoor air
Keywords
LANGMUIR ISOTHERM
ISSN
2045-2322
Abstract

Fungi are major biological contaminants in indoor air, and their concentration is typically assessed using the culture-based CFU method, which is labor-intensive and time-consuming. Ergosterol, a major fungal cell membrane component, has emerged as a preferred target for alternative analytical approaches. However, ergosterol is highly hydrophobic, and specific affinity probes such as antibodies or aptamers have not yet been successfully developed. In this study, we fabricated ergosterol-specific probes using molecularly imprinted polymers (MIPs) immobilized on carbon nanotubes (CNTs) and integrated them into a screen-printed electrode (SPE) platform. Surface polymerization was initiated through a thiol-ene click reaction using pentaerythritol tetrakis(3-mercaptopropionate) (PETMP) and glyoxal bis(diallyl acetal) (GO), which were selected based on predicted stable conformations for MIP synthesis. The resulting MIP@CNT sensor achieved an imprinting factor (IF) of 19.26 and a limit of detection (LOD) of 0.22 pM for ergosterol. Ergosterol levels in indoor air samples collected on PVC filters were quantified using the MIP@CNT sensor and showed significant correlation with GC/MS measurement (R-2 = 0.5136, p < 0.0001), moderate but statistically significant correlation. This work provides a valuable reference for developing sensing platforms for highly hydrophobic molecules such as sterols and phytosterols, which represent important analytical targets in environmental and biological monitoring.

더보기
URI
https://scholar.dgist.ac.kr/handle/20.500.11750/60419
DOI
10.1038/s41598-026-47624-1
Publisher
NATURE PORTFOLIO
Show Full Item Record

File Downloads

공유

qrcode
공유하기

Related Researcher

김은주
Kim, Eunjoo김은주

Division of AI, Big data and Block chain

read more

Total Views & Downloads

???jsp.display-item.statistics.view???: , ???jsp.display-item.statistics.download???: