Recent advances on <scp>MOF</scp> ‐based colorimetric sensors

Abstract Metal–organic frameworks (MOFs) have emerged as promising platforms for colorimetric sensing due to their tunable structures, high porosity, and ability to support diverse optical responses. Among various sensing technologies, colorimetric sensing stands out for its simplicity, cost‐effectiveness, and visual interpretability, making it especially suitable for point‐of‐need applications. This review highlights recent advances in MOF‐based colorimetric sensors, with a particular focus on the mechanisms by which analyte‐induced signals are transduced into visible color changes. We classify these sensing systems into three mechanistic categories: (i) direct chromogenic responses arising from interactions between analytes and intrinsic or modified MOF components; (ii) structural or refractive index changes in the MOF framework itself; and (iii) MOF‐mediated external chromogenic reactions, in which the MOF acts as a catalyst or host. Representative examples for each category are discussed, along with data acquisition methods and strategies for signal analysis. By emphasizing the mechanistic underpinnings of color generation, this review aims to provide a framework for the rational design of MOF‐based sensors. Key challenges and future opportunities are also discussed, including enhancing selectivity, environmental robustness, and single‐sensor multiplexing capabilities.

Authors

Institutions

Publication Details

Journal
Bulletin of the Korean Chemical Society
Published
2025-09-15
DOI
https://doi.org/10.1002/bkcs.70066
Citations
6
Primary Topic
Advanced Nanomaterials in Catalysis
Type
article
Field-Weighted Citation Impact
3.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Recent advances on MOF ‐based colorimetric sensors

Hyejin Yoo, Jin Yeong Kim, Solmin Lee
6 citations
Bulletin of the Korean Chemical Society
Advanced Nanomaterials in Catalysis
3.11
article

Recent advances on MOF ‐based colorimetric sensors

Hyejin Yoo, Jin Yeong Kim, Solmin Lee
article en
6 citations

Abstract

Abstract Metal–organic frameworks (MOFs) have emerged as promising platforms for colorimetric sensing due to their tunable structures, high porosity, and ability to support diverse optical responses. Among various sensing technologies, colorimetric sensing stands out for its simplicity, cost‐effectiveness, and visual interpretability, making it especially suitable for point‐of‐need applications. This review highlights recent advances in MOF‐based colorimetric sensors, with a particular focus on the mechanisms by which analyte‐induced signals are transduced into visible color changes. We classify these sensing systems into three mechanistic categories: (i) direct chromogenic responses arising from interactions between analytes and intrinsic or modified MOF components; (ii) structural or refractive index changes in the MOF framework itself; and (iii) MOF‐mediated external chromogenic reactions, in which the MOF acts as a catalyst or host. Representative examples for each category are discussed, along with data acquisition methods and strategies for signal analysis. By emphasizing the mechanistic underpinnings of color generation, this review aims to provide a framework for the rational design of MOF‐based sensors. Key challenges and future opportunities are also discussed, including enhancing selectivity, environmental robustness, and single‐sensor multiplexing capabilities.

Bulletin of the Korean Chemical SocietyVol. 46(10)
Seoul National University of Education (KR)
Openalex Percentile: Top 8%
Advanced Nanomaterials in Catalysis
3.11
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.