Evidence map›Paper›PMID 42733358›Full record

ArticleAngewandte Chemie (International ed. in English)2026

Deciphering the Role of Energy Transfer Configuration in PFAS Recognition by Metal-Organic Frameworks.

Jiatong Huo, Zongsu Han, Zhaoyi Liu, Xincheng Liu, Joshua Rushlow, Hong-Cai Zhou

Abstract read
In one paragraph

Article in Angewandte Chemie (International ed. in English), 2026. 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.

Jiatong HuoDepartment of Chemistry, Texas A&M University, College Station, Texas, USA.ORCID https://orcid.org/0009-0000-6446-1728
Zongsu HanDepartment of Chemistry, Texas A&M University, College Station, Texas, USA.
Zhaoyi LiuDepartment of Chemistry, Texas A&M University, College Station, Texas, USA.ORCID https://orcid.org/0009-0005-7942-9700
Xincheng LiuDepartment of Chemistry, The University of Sheffield, Western Bank, Sheffield, United Kingdom.
Joshua RushlowDepartment of Chemistry, Texas A&M University, College Station, Texas, USA.ORCID https://orcid.org/0009-0002-5701-1547
Hong-Cai ZhouDepartment of Chemistry, Texas A&M University, College Station, Texas, USA.ORCID https://orcid.org/0000-0002-9029-3788

Funding

National Science Foundation 2330245Robert A. Welch Foundation A-0030
6 · The paper itself

Abstract

Per- and polyfluoroalkyl substances (PFAS) have attracted widespread attention as persistent environmental contaminants. However, their rapid detection remains challenging due to their weak light absorption capacities. Luminescence sensing provides a rapid and efficient approach for PFAS monitoring, while ratiometric sensing can further amplify signal variations and improve detection sensitivity with self-referenced optical responses. Lanthanide metal-organic frameworks (Ln-MOFs), featuring tunable porous environments, accessible host-guest interactions, and well-defined multicolor emissions, have emerged as promising platforms for constructing ratiometric sensing materials. Nevertheless, current luminescence sensing has primarily focused on optimizing interactions between the framework and analytes, whereas the influence of intraframework energy transfer configuration on sensing performance remains largely unexplored. Here, Eu/Tb spatial organization is deliberately engineered in EuTb-FDA to decipher the role of energy transfer configuration in PFAS recognition. By regulating the spatial distribution of emissive centers, the Tb

Indexed as

energy transfer configurationlanthanide metal‐organic frameworkPFAS detectionratiometric sensing

Identifiers

PMID42733358
PMCPMC13676422

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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.