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Attention Score in Context
| Title |
The effect of spontaneous curvature on a two-phase vesicle
|
|---|---|
| Published in |
Nonlinearity, February 2015
|
| DOI | 10.1088/0951-7715/28/3/773 |
| Pubmed ID | |
| Authors |
Geoffrey Cox, John Lowengrub |
| Abstract |
Vesicles are membrane-bound structures commonly known for their roles in cellular transport and the shape of a vesicle is determined by its surrounding membrane (lipid bilayer). When the membrane is composed of different lipids, it is natural for the lipids of similar molecular structure to migrate towards one another (via spinodal decomposition), creating a multi-phase vesicle. In this article, we consider a two-phase vesicle model which is driven by nature's propensity to maintain a minimal state of elastic energy. The model assumes a continuum limit, thereby treating the membrane as a closed three-dimensional surface. The main purpose of this study is to reveal the complexity of the Helfrich two-phase vesicle model with non-zero spontaneous curvature and provide further evidence to support the relevance of spontaneous curvature as a modelling parameter. In this paper, we illustrate the complexity of the Helfrich two-phase model by providing multiple examples of undocumented solutions and energy hysteresis. We also investigate the influence of spontaneous curvature on morphological effects and membrane phenomena such as budding and fusion. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 27% |
| Student > Master | 2 | 18% |
| Professor | 1 | 9% |
| Other | 1 | 9% |
| Researcher | 1 | 9% |
| Other | 0 | 0% |
| Unknown | 3 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 3 | 27% |
| Biochemistry, Genetics and Molecular Biology | 1 | 9% |
| Physics and Astronomy | 1 | 9% |
| Agricultural and Biological Sciences | 1 | 9% |
| Materials Science | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 3 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 February 2015.
All research outputs
#18,475,157
of 22,893,031 outputs
Outputs from Nonlinearity
#430
of 879 outputs
Outputs of similar age
#261,949
of 358,848 outputs
Outputs of similar age from Nonlinearity
#4
of 18 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 879 research outputs from this source. They receive a mean Attention Score of 1.3. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.