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Mendeley readers
Attention Score in Context
| Title |
Strain analysis for the prediction of the preferential nucleation sites of stacked quantum dots by combination of FEM and APT
|
|---|---|
| Published in |
Discover Nano, December 2013
|
| DOI | 10.1186/1556-276x-8-513 |
| Pubmed ID | |
| Authors |
Jesús Hernández-Saz, Miriam Herrera, Sébastien Duguay, Sergio I Molina |
| Abstract |
The finite elements method (FEM) is a useful tool for the analysis of the strain state of semiconductor heterostructures. It has been used for the prediction of the nucleation sites of stacked quantum dots (QDs), but often using either simulated data of the atom positions or two-dimensional experimental data, in such a way that it is difficult to assess the validity of the predictions. In this work, we assess the validity of the FEM method for the prediction of stacked QD nucleation sites using three-dimensional experimental data obtained by atom probe tomography (APT). This also allows us to compare the simulation results with the one obtained experimentally. Our analysis demonstrates that FEM and APT constitute a good combination to resolve strain-stress problems of epitaxial semiconductor structures. |
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 % |
|---|---|---|
| Unknown | 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 8% |
| France | 1 | 4% |
| Unknown | 22 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 32% |
| Lecturer | 3 | 12% |
| Student > Doctoral Student | 2 | 8% |
| Student > Master | 2 | 8% |
| Professor > Associate Professor | 2 | 8% |
| Other | 4 | 16% |
| Unknown | 4 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Materials Science | 9 | 36% |
| Physics and Astronomy | 4 | 16% |
| Chemistry | 2 | 8% |
| Medicine and Dentistry | 1 | 4% |
| Computer Science | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 7 | 28% |
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 11 December 2013.
All research outputs
#17,286,379
of 25,374,917 outputs
Outputs from Discover Nano
#538
of 1,146 outputs
Outputs of similar age
#204,751
of 320,227 outputs
Outputs of similar age from Discover Nano
#15
of 22 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,146 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 320,227 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 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.