Single‐Microdroplet Imprinting Enables Specific Nanocavities for Scarce Bioanalyte Recognition and Label‐Free SERS Detection

ABSTRACT Molecularly imprinted polymers (MIPs) offer a more affordable, durable yet equally practical alternative to natural antibodies. However, current syntheses of MIPs face challenges the need for large amounts of pure target, low yields, a lengthy process, and the resulting MIPs exhibiting nonspecific recognition in complex samples. Here, we propose a single microdroplet imprinting (SMI) strategy to create high‐affinity nanocavities over surface‐enhanced Raman scattering (SERS) substrates for the selective recognition of extracellular vesicles (EVs), used here as a model class of cancer‐associated bioanalytes. Experimentally, the SMI is reproducible (coefficient of variation <4%), and high‐yielding (285‐times less reagent volume and 6‐times faster imprinting process), enabling the imprinting of diverse analytes and dummy molecules with precise control over imprinted layer characteristics. Furthermore, a label‐free SERS‐based detection mechanism is developed that scrutinizes the permeability of nanocavities post‐EVs recognition, utilizing a Raman reporter to quantify EVs, thereby achieving high specificity. This platform enables the reproducible quantitation of EVs in 35 µL untreated urine samples, with a sensitivity as low as 317 EVs particles µL −1 . The entire workflow is streamlined into three simple steps, dropping, dipping, and sensing. The SMI approach and detection mechanism are readily adaptable to a broad range of other bioanalytes and MIP‐based platforms.

Authors

Institutions

Publication Details

Journal
Advanced Functional Materials
Published
2026-07-22
DOI
https://doi.org/10.1002/adfm.77090
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Single‐Microdroplet Imprinting Enables Specific Nanocavities for Scarce Bioanalyte Recognition and Label‐Free SERS Detection

Lingxin Chen, Jaebum Choo, Maryam Arabi, Mengdan Lu et al.
Advanced Functional Materials
Extracellular vesicles in disease
article

Single‐Microdroplet Imprinting Enables Specific Nanocavities for Scarce Bioanalyte Recognition and Label‐Free SERS Detection

Lingxin Chen, Jaebum Choo, Maryam Arabi, Mengdan Lu, Jiadong Chen, Abbas Ostovan, Sung‐Gyu Park, M U Kim, Y N Wu, Zhiyang Zhang
article en

Abstract

ABSTRACT Molecularly imprinted polymers (MIPs) offer a more affordable, durable yet equally practical alternative to natural antibodies. However, current syntheses of MIPs face challenges the need for large amounts of pure target, low yields, a lengthy process, and the resulting MIPs exhibiting nonspecific recognition in complex samples. Here, we propose a single microdroplet imprinting (SMI) strategy to create high‐affinity nanocavities over surface‐enhanced Raman scattering (SERS) substrates for the selective recognition of extracellular vesicles (EVs), used here as a model class of cancer‐associated bioanalytes. Experimentally, the SMI is reproducible (coefficient of variation <4%), and high‐yielding (285‐times less reagent volume and 6‐times faster imprinting process), enabling the imprinting of diverse analytes and dummy molecules with precise control over imprinted layer characteristics. Furthermore, a label‐free SERS‐based detection mechanism is developed that scrutinizes the permeability of nanocavities post‐EVs recognition, utilizing a Raman reporter to quantify EVs, thereby achieving high specificity. This platform enables the reproducible quantitation of EVs in 35 µL untreated urine samples, with a sensitivity as low as 317 EVs particles µL −1 . The entire workflow is streamlined into three simple steps, dropping, dipping, and sensing. The SMI approach and detection mechanism are readily adaptable to a broad range of other bioanalytes and MIP‐based platforms.

Advanced Functional Materials
Yonsei University (KR), Yantai Institute of Coastal Zone Research (CN), Korea Institute of Materials Science (KR), Qingdao National Laboratory for Marine Science and Technology (CN), Chung-Ang University (KR)
Openalex Percentile: Top 14%
Extracellular vesicles in disease
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.