Evidence map›Paper›PMID 35671460›Full record

ArticleAccounts of chemical research2022

Nucleobase-Interaction-Directed Biomimetic Supramolecular Self-Assembly.

Amrita Sikder, Cem Esen, Rachel K O'Reilly

Abstract read
In one paragraph

Article in Accounts of chemical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Direct Detection of Hydrogen Bonds in Supramolecular Systems UsingJournal of the American Chemical Society · 2022
    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.

Amrita SikderSchool of Chemistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K.
Cem EsenDepartment of Chemistry, Faculty of Arts and Sciences, Aydın Adnan Menderes University, 09010 Aydın, Turkey.ORCID 0000-0003-4125-1642
Rachel K O'ReillySchool of Chemistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K.ORCID 0000-0002-1043-7172

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The design and fabrication of synthetic self-assembled systems that can mimic some biological features require exquisitely sophisticated components that make use of supramolecular interactions to attain enhanced structural and functional complexity. In nature, nucleobase interactions play a key role in biological functions in living organisms, including transcription and translation processes. Inspired by nature, scientists are progressively exploring nucleobase synthons to create a diverse range of functional systems with a plethora of nanostructures by virtue of molecular-recognition-directed assembly and flexible programmability of the base-pairing interactions. To that end, nucleobase-functionalized molecules and macromolecules are attracting great attention because of their versatile structures with smart and adaptive material properties such as stimuli responsiveness, interaction with external agents, and ability to repair structural defects. In this regard, a range of nucleobase-interaction-mediated hierarchical self-assembled systems have been developed to obtain biomimetic materials with unique properties. For example, a new "grafting to" strategy utilizing complementary nucleobase interactions has been demonstrated to temporarily control the functional group display on micellar surfaces. In a different approach, complementary nucleobase interactions have been explored to enable morphological transitions in functionalized diblock copolymer assembly. It has been demonstrated that complementary nucleobase interactions can drive the morphological transformation to produce highly anisotropic nanoparticles by controlling the assembly processes at multiple length scales. Furthermore, nucleobase-functionalized bottle brush polymers have been employed to generate stimuli-responsive hierarchical assembly. Finally, such interactions have been exploited to induce biomimetic segregation in polymer self-assembly, which has been employed as a template to synthesize polymers with narrow polydispersity. It is evident from these examples that the optimal design of molecular building blocks and precise positioning of the nucleobase functionality are essential for fabrication of complex supramolecular assemblies. While a considerable amount of research remains to be explored, our studies have demonstrated the potential of nucleobase-interaction-mediated supramolecular assembly to be a promising field of research enabling the development of biomimetic materials.This Account summarizes recent examples that employ nucleobase interactions to generate functional biomaterials by judicious design of the building blocks. We begin by discussing the molecular recognition properties of different nucleobases, followed by different strategies to employ nucleobase interactions in polymeric systems in order to achieve self-assembled nanomaterials with versatile properties. Moreover, some of their prospective biological/material applications such as enhanced drug encapsulation, superior adhesion, and fast self-healing properties facilitated by complementary nucleobase interactions are emphasized. Finally, we identify issues and challenges that are faced by this class of materials and propose future directions for the exploration of functional materials with the aim of promoting the development of nucleobase-functionalized systems to design the next generation of biomaterials.

Indexed as

BiomimeticsPolymersBiocompatible MaterialsMacromolecular SubstancesProspective StudiesBiocompatible MaterialsMacromolecular SubstancesPolymers

Identifiers

PMID35671460
PMCPMC9219111

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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.