Systematic Design of Pore Size and Functionality in Isoreticular MOFs and Their Application in Methane Storage
Systematic Design of Pore Size and Functionality in Isoreticular MOFs and Their Application in Methane Storage is a research paper published in Science (2002). On theSindex it has a DataRank of 1.3. It has been cited 8,007 times.
Abstract
A strategy based on reticulating metal ions and organic carboxylate links into extended networks has been advanced to a point that allowed the design of porous structures in which pore size and functionality could be varied systematically. Metal-organic framework (MOF-5), a prototype of a new class of porous materials and one that is constructed from octahedral Zn-O-C clusters and benzene links, was used to demonstrate that its three-dimensional porous system can be functionalized with the organic groups -Br, -NH2, -OC3H7, -OC5H11, -C2H4, and -C4H4 and that its pore size can be expanded with the long molecular struts biphenyl, tetrahydropyrene, pyrene, and terphenyl. We synthesized an isoreticular series (one that has the same framework topology) of 16 highly crystalline materials whose open space represented up to 91.1% of the crystal volume, as well as homogeneous periodic pores that can be incrementally varied from 3.8 to 28.8 angstroms. One member of this series exhibited a high capacity for methane storage (240 cubic centimeters at standard temperature and pressure per gram at 36 atmospheres and ambient temperature), and others the lowest densities (0.41 to 0.21 gram per cubic centimeter) for a crystalline material at room temperature.
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FAIR Checklist
Context only (not used in score)- Has DOI
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
DataRank Breakdown
Base Score Contribution
1.3
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.