Evidence map›Paper›PMID 41440289›Full record

ReviewBiosensors2025

Polymer-Mediated Signal Amplification Mechanisms for Bioelectronic Detection: Recent Advances and Future Perspectives.

Ying Sun, Dan Gao

Abstract readReview
In one paragraph

Review in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

2 authors.

Ying SunCollege of Food Science, South China Agricultural University, Guangzhou 510642, China.ORCID 0009-0002-9079-9604
Dan GaoInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.ORCID 0000-0003-1018-2656

Funding

the Open Fund of Guangdong Provincial Key Laboratory of Nanophotonic Manipulation No. 2023B1212010008
6 · The paper itself

Abstract

In recent years, polymer-mediated signal amplification has drawn wide attention in bioelectronic sensing. With the rapid progress of biosensing and flexible electronics, polymers with excellent electron-ion transport properties, tunable molecular structures, and good biocompatibility have become essential materials for enhancing detection sensitivity and interfacial stability. However, current sensing systems still face challenges such as signal attenuation, surface fouling, and multi-component interference in complex biological environments, limiting their use in medical diagnosis and environmental monitoring. This review summarizes the progress of conductive polymers, molecularly imprinted polymers, hydrogels, and composite polymers in medical diagnosis, food safety, and environmental monitoring, focusing on their signal amplification mechanisms and structural optimization strategies in electronic transport regulation, molecular recognition enhancement, and antifouling interface design. Overall, polymers improve detection performance through interfacial electronic reconstruction and multidimensional synergistic amplification, offering new ideas for developing highly sensitive, stable, and intelligent biosensors. In the future, polymer-based amplification systems are expected to expand in multi-parameter integrated detection, long-term wearable monitoring, and in situ analysis of complex samples, providing new approaches to precision medicine and sustainable environmental health monitoring.

Indexed as

Biosensing TechniquesPolymersEnvironmental MonitoringHumansPolymersbioelectronic sensingconductive polymersmolecular recognitionpolymer materialssignal amplification

Identifiers

PMID41440289
PMCPMC12731175

What OpenQuestion holds

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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.