Evidence map›Paper›PMID 37117491›Full record

ReviewNature reviews. Chemistry2022

Controlling dynamics in extended molecular frameworks.

Liang Feng, R Dean Astumian, J Fraser Stoddart

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. All-visible-light-responsive porous aromatic frameworks manipulate COProceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Functional dynamics in framework materials.Communications chemistry · 2023
    Review
  12. 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.

Liang FengDepartment of Chemistry, Northwestern University, Evanston, IL, USA. liang.feng@northwestern.edu.ORCID http://orcid.org/0000-0001-9693-1595
R Dean AstumianDepartment of Physics and Astronomy, University of Maine, Orono, ME, USA. astumian@maine.edu.ORCID http://orcid.org/0000-0001-7472-187X
J Fraser StoddartDepartment of Chemistry, Northwestern University, Evanston, IL, USA. stoddart@northwestern.edu.ORCID http://orcid.org/0000-0003-3161-3697

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular machines are essential dynamic components for fuel production, cargo delivery, information storage and processing in living systems. Scientists have demonstrated that they can design and synthesize artificial molecular machines that operate efficiently in isolation - for example, at high dilution in solution - fuelled by chemicals, electricity or light. To organize the spatial arrangement and motion of these machines within close proximity to one another in solid frameworks, such that useful macroscopic work can be performed, remains a challenge in both chemical and materials science. In this Review, we summarize the progress that has been made during the past decade in organizing dynamic molecular entities in such solid frameworks. Emerging applications of these dynamic smart materials in the contexts of molecular recognition, optoelectronics, drug delivery, photodynamic therapy and water desalination are highlighted. Finally, we review recent work on a new non-equilibrium adsorption phenomenon for which we have coined the term mechanisorption. The ability to use external energy to drive directional processes in mechanized extended frameworks augurs well for the future development of artificial molecular factories.

Identifiers

PMID37117491

What OpenQuestion holds

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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.