Evidence map›Paper›PMID 41600295›Full record

ReviewSensors (Basel, Switzerland)2026

Design and Applications of MOF-Based SERS Sensors in Agriculture and Biomedicine.

Alemayehu Kidanemariam, Sungbo Cho

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2026. 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. 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.

Alemayehu KidanemariamDepartment of Electronic Engineering, Gachon University, Seongnam-si 13120, Republic of Korea.ORCID 0000-0003-2945-2062
Sungbo ChoDepartment of Electronic Engineering, Gachon University, Seongnam-si 13120, Republic of Korea.ORCID 0000-0003-3839-6410

Funding

National Research Foundation of Korea NRF-2023R1A2C1003669
6 · The paper itself

Abstract

Metal-organic framework (MOF)-based surface-enhanced Raman scattering (SERS) sensors have emerged as a versatile platform for high-sensitivity and selective detection in agricultural, environmental, and biomedical applications. By integrating plasmonic nanostructures with tunable MOF architectures, these hybrid systems combine ultrahigh signal enhancement with molecular recognition, analyte preconcentration, and controlled hotspot distribution. This review provides a comprehensive overview of the fundamental principles underpinning MOF-SERS performance, including EM and chemical enhancement mechanisms, and highlights strategies for substrate design, such as metal-MOF composites, plasmon-free frameworks, ligand functionalization, and hierarchical or core-shell architectures. We further examine their applications in environmental monitoring, pesticide and contaminant detection, pathogen identification, biomarker analysis, and theranostics, emphasizing real-sample performance, molecular selectivity, and emerging integration with portable Raman devices and AI-assisted data analysis. Despite notable advances, challenges remain in reproducibility, quantitative reliability, matrix interference, scalability, and biocompatibility. Future developments are likely to focus on rational MOF design, sustainable fabrication, intelligent spectral interpretation, and multifunctional integration to enable robust, field-deployable sensors. Overall, MOF-based SERS platforms represent a promising next-generation analytical tool poised to bridge laboratory innovation and practical, real-world applications.

Indexed as

AgricultureBiosensing TechniquesMetal-Organic FrameworksSpectrum Analysis, RamanEnvironmental MonitoringHumansPesticidesMetal-Organic FrameworksPesticidesagricultural sensorsbiomarker monitoringbiomedical sensorsmetal–organic frameworks (MOFs)nanomaterial designpesticide detectionportable sensing devicessensor functionalizationsensor stabilitysurface-enhanced raman scattering (SERS)

Identifiers

PMID41600295
PMCPMC12845593

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.