Evidence map›Paper›PMID 40430706›Full record

ReviewPolymers2025

Application of Molecularly Imprinted Polymers in the Analysis of Explosives.

Chenjie Wei, Lin Feng, Xianhe Deng, Yajun Li, Hongcheng Mei, Hongling Guo, Jun Zhu, Can Hu

Abstract readReview
In one paragraph

Review in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Chenjie WeiSchool of Investigation, Peoples' Public Security University of China, Beijing 100038, China.
Lin FengSchool of Investigation, Peoples' Public Security University of China, Beijing 100038, China.
Xianhe DengInsititute of Forensic Science, Ministry of Public Security, Beijing 100038, China.
Yajun LiInsititute of Forensic Science, Ministry of Public Security, Beijing 100038, China.
Hongcheng MeiInsititute of Forensic Science, Ministry of Public Security, Beijing 100038, China.
Hongling GuoInsititute of Forensic Science, Ministry of Public Security, Beijing 100038, China.
Jun ZhuInsititute of Forensic Science, Ministry of Public Security, Beijing 100038, China.
Can HuInsititute of Forensic Science, Ministry of Public Security, Beijing 100038, China.

Funding

Beijing Nova Program 20240484529
6 · The paper itself

Abstract

The detection of explosives is highly important for the investigation of explosion cases and public safety management. However, the detection of trace explosive residues in complex matrices remains a major challenge. Molecularly imprinted polymers (MIPs), which mimic the antigen-antibody recognition mechanism, can selectively recognize and bind target explosive molecules. They offer advantages such as high efficiency, specificity, renewability, and ease of preparation, and they have shown significant potential for the efficient extraction and highly sensitive detection of trace explosive residues in complex matrices. This review comprehensively discusses the applications of MIPs in the analysis of explosives; systematically summarizes the preparation methods; and evaluates their performance in detecting nitroaromatic explosives, nitrate esters, nitroamine explosives, and peroxide explosives. Finally, this review explores the future potential of emerging technologies in enhancing the MIP-based analysis of explosives. The aim is to support the further application of MIPs in the investigation of explosion cases and safety management, providing more effective technical solutions for public safety.

Indexed as

explosivesmolecularly imprinted polymerssample pretreatmentsensors

Identifiers

PMID40430706
PMCPMC12115212

What Socratic 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 Socratic graph.