Evidence map›Paper›PMID 28195175›Full record

ArticleNature communications2017

Chemical and entropic control on the molecular self-assembly process.

Daniel M Packwood, Patrick Han, Taro Hitosugi

Abstract read
In one paragraph

Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Structure Prediction of Organic/Inorganic Interfaces with Genarris.Journal of chemical theory and computation · 2026
    Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Interfacial Charge Transfer Influences Thin-Film Polymorphism.The journal of physical chemistry. C, Nanomaterials and interfaces · 2022
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Role of Entropy in Colloidal Self-Assembly.Entropy (Basel, Switzerland) · 2020
    Review
  16. Detecting stable adsorbates of (1Beilstein journal of nanotechnology · 2020
    Article
  17. Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2019
    Article
  18. Article
  19. Orientation-Dependent Work-Function Modification Using Substituted Pyrene-Based Acceptors.The journal of physical chemistry. C, Nanomaterials and interfaces · 2017
    Article
  20. 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.

Daniel M PackwoodAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai 980-8577, Japan.
Patrick HanAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai 980-8577, Japan.ORCID 0000-0002-4901-9592
Taro HitosugiAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai 980-8577, Japan.ORCID 0000-0002-7795-0683

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular self-assembly refers to the spontaneous assembly of molecules into larger structures. In order to exploit molecular self-assembly for the bottom-up synthesis of nanomaterials, the effects of chemical control (strength of the directionality in the intermolecular interaction) and entropic control (temperature) on the self-assembly process should be clarified. Here we present a theoretical methodology that unambiguously distinguishes the effects of chemical and entropic control on the self-assembly of molecules adsorbed to metal surfaces. While chemical control simply increases the formation probability of ordered structures, entropic control induces a variety of effects. These effects range from fine structure modulation of ordered structures, through to degrading large, amorphous structures into short, chain-shaped structures. Counterintuitively, the latter effect shows that entropic control can improve molecular ordering. By identifying appropriate levels of chemical and entropic control, our methodology can, therefore, identify strategies for optimizing the yield of desired nanostructures from the molecular self-assembly process.

Identifiers

PMID28195175
PMCPMC5316874

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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.