Sensing with Molecularly Imprinted Membranes on Two-Dimensional Solid-Supported Substrates.

Lishuang Wang, Nan Li, Xiaoyan Zhang, Ivan Bobrinetskiy, Ivana Gadjanski, Wangyang Fu

Abstract readReview

In one paragraph

Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lishuang WangSchool of Pharmaceutical Sciences, Capital Medical University, Beijing 100069, China.

Nan LiSchool of Pharmaceutical Sciences, Capital Medical University, Beijing 100069, China.ORCID 0009-0002-9003-6535

Xiaoyan ZhangSchool of Pharmaceutical Sciences, Capital Medical University, Beijing 100069, China.

Ivan BobrinetskiyBioSense Institute, University of Novi Sad, Dr Zorana Đinđića 1a, 21000 Novi Sad, Serbia.ORCID 0000-0003-2380-2594

Ivana GadjanskiBioSense Institute, University of Novi Sad, Dr Zorana Đinđića 1a, 21000 Novi Sad, Serbia.ORCID 0000-0003-0713-7897

Wangyang FuSchool of Materials Science and Engineering, Tsinghua University, No. 1 Tsinghua Yuan, Haidian District, Beijing 100084, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecularly imprinted membranes (MIMs) have been a focal research interest since 1990, representing a breakthrough in the integration of target molecules into membrane structures for cutting-edge sensing applications. This paper traces the developmental history of MIMs, elucidating the diverse methodologies employed in their preparation and characterization on two-dimensional solid-supported substrates. We then explore the principles and diverse applications of MIMs, particularly in the context of emerging technologies encompassing electrochemistry, surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), and the quartz crystal microbalance (QCM). Furthermore, we shed light on the unique features of ion-sensitive field-effect transistor (ISFET) biosensors that rely on MIMs, with the notable advancements and challenges of point-of-care biochemical sensors highlighted. By providing a comprehensive overview of the latest innovations and future trajectories, this paper aims to inspire further exploration and progress in the field of MIM-driven sensing technologies.

Indexed as

all-solid-statebiochemical sensorsion-sensitive field-effect transistormolecularly imprinted membranespoint-of-care testing

Identifiers

PMID39204816

DOI10.3390/s24165119

PMCPMC11358988

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.