Evidence map›Paper›PMID 30645772›Full record

ReviewAdvanced materials (Deerfield Beach, Fla.)2019

Multiscale Design of Graphyne-Based Materials for High-Performance Separation Membranes.

Jingjie Yeo, Gang Seob Jung, Francisco J Martín-Martínez, Jennifer Beem, Zhao Qin, Markus J Buehler

Abstract readReview
In one paragraph

Review in Advanced materials (Deerfield Beach, Fla.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Helium Isotopes Quantum Sieving through Graphtriyne Membranes.Nanomaterials (Basel, Switzerland) · 2020
    Article
  5. 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

6 authors.

Jingjie YeoDepartment of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA.
Gang Seob JungLaboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Francisco J Martín-MartínezLaboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Jennifer BeemLaboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Zhao QinLaboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Markus J BuehlerLaboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID https://orcid.org/0000-0002-4173-9659

Funding

Models to Predict Protein Biomaterial PerformanceU01EB014976 · NIBIB · TUFTS UNIVERSITY MEDFORD · PI BUEHLER, MARKUS J., KAPLAN, DAVID L. · 2012 to 2020
$5.2M
National Science Foundation ACI-1053575National Science Foundation Graduate Research Fellowship 1122374NIBIB NIH HHS U01 EB014976NIH HHS FA9550-15-1-0514NIH HHS U01 EB014976
6 · The paper itself

Abstract

By varying the number of acetylenic linkages connecting aromatic rings, a new family of atomically thin graph-n-yne materials can be designed and synthesized. Generating immense scientific interest due to its structural diversity and excellent physical properties, graph-n-yne has opened new avenues toward numerous promising engineering applications, especially for separation membranes with precise pore sizes. Having these tunable pore sizes in combination with their excellent mechanical strength to withstand high pressures, free-standing graph-n-yne is theoretically posited to be an outstanding membrane material for separating or purifying mixtures of either gases or liquids, rivaling or even dramatically exceeding the capabilities of current, state-of-art separation membranes. Computational modeling and simulations play an integral role in the bottom-up design and characterization of these graph-n-yne materials. Thus, here, the state of the art in modeling α-, β-, γ-, δ-, and 6,6,12-graphyne nanosheets for synthesizing graph-2-yne materials and 3D architectures thereof is discussed. Different synthesis methods are described and a broad overview of computational characterizations of graph-n-yne's electrical, chemical, and thermal properties is provided. Furthermore, a series of in-depth computational studies that delve into the specifics of graph-n-yne's mechanical strength and porosity, which confer superior performance for separation and desalination membranes, are reviewed.

Indexed as

graphynemateriomicsmultiscale modelingseparation membranes

Identifiers

PMID30645772
PMCPMC7252433

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.