Evidence map›Paper›PMID 39807849›Full record

ReviewJournal of the American Chemical Society2025

Solid-State Nuclear Magnetic Resonance Spectroscopy for Surface Characterization of Metal Oxide Nanoparticles: State of the Art and Perspectives.

Pan Gao, Yi Ji, Guangjin Hou

Abstract readReview
In one paragraph

Review in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. 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

3 authors.

Pan GaoState Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China.ORCID 0000-0003-4997-6218
Yi JiState Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China.
Guangjin HouState Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China.ORCID 0000-0001-8216-863X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metal oxide materials have found wide applications across diverse fields; in most cases, their functionalities are dictated by their surface structures and properties. A comprehensive understanding of the intricate surface features is critical for their further design, optimization, and applications, necessitating multi-faceted characterizations. Recent advances in solid-state nuclear magnetic resonance (ssNMR) spectroscopy have significantly extended its applications in the detailed analysis of multiple metal oxide nanoparticles, offering unparalleled atomic-level information on the surface structures, properties, and chemistries. Herein, we present an overview of the current state of the art from an NMR perspective. We begin with a brief introduction to contemporary ssNMR methodologies. Subsequently, we introduce and provide critical reviews on the applications of different ssNMR techniques in the detailed characterizations of the surface local structures, disorders, defects, active sites, and acidity on metal oxide nanoparticles, as well as the revelation of mechanisms behind some intriguing chemistries that occur on the surfaces, referencing representative recent studies. Finally, we address the challenges beyond the current status and provide perspectives on the future development and application of advanced ssNMR methodologies in this emerging field.

Identifiers

PMID39807849
PMCPMC11783548

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Registered trials

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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.