Electrical properties and mechanical stability of anchoring groups for single-molecule electronics

• Riccardo Frisenda,
• Simge Tarkuç,
• Elena Galán,
• Mickael L. Perrin,
• Rienk Eelkema,
• Ferdinand C. Grozema and
• Herre S. J. van der Zant

Beilstein J. Nanotechnol. 2015, 6, 1558–1567, doi:10.3762/bjnano.6.159

• conductance axis. The histograms show regions of high counts above 1G0, due to stable atomic configurations of the gold electrodes. In the sub-G0 region, the most probable conductance value of each molecule is extracted from the peaks in the histograms, fitted by a log-normal distribution. In this

• distribution, the logarithm of the random variable is normally distributed and the two fit parameters are μ, the location parameter, and σ, the scale parameter, respectively related to the mean and the geometric standard deviation of the normal distribution. The parameters extracted from the fit are listed in

• an asymmetric peak, with a tail at larger energies. To quantify injection barrier and coupling we fit each parameter distribution to a log-normal distribution, as shown in Section 3 of Supporting Information File 1. We calculate subsequently the geometric mean of each distribution corresponding in a

The convenient preparation of stable aryl-coated zerovalent iron nanoparticles

• Olga A. Guselnikova,
• Andrey I. Galanov,
• Anton K. Gutakovskii and
• Pavel S. Postnikov

Beilstein J. Nanotechnol. 2015, 6, 1192–1198, doi:10.3762/bjnano.6.121

• an organic layer coating of 10 ± 2 nm. The ZVI NP size distribution was calculated by visual particle counting with no less than 500 particles and fitted to a log–normal distribution with a number-based geometric standard deviation of 1.6 according to [22][31]. The mean particle core size was

Observing the morphology of single-layered embedded silicon nanocrystals by using temperature-stable TEM membranes

• Sebastian Gutsch,
• Daniel Hiller,
• Jan Laube,
• Margit Zacharias and
• Christian Kübel

Beilstein J. Nanotechnol. 2015, 6, 964–970, doi:10.3762/bjnano.6.99

• ultrathin layers suffer from significant electron beam damage that needs to be minimized in order to image the pristine sample morphology. Finally we demonstrate how the silicon nanocrystal size distribution develops from a broad to a narrow log-normal distribution, when the initial precipitation layer

• -normal distribution. The results strongly reflect the ability to control the Si NC size by geometrical one-dimensional confinement of the SRON layers. Furthermore, the influence of the SRON stoichiometry on Si nanoparticle formation is demonstrated in Figure 4c–e. Interestingly, increasing the Si excess

• nm SiO0.93), (b) S6 (4.5 nm SiO0.93), (c) S7 (3.5 nm SiO0.93), (d) S8 (3.5 nm SiO0.85), (e) S9 (3.5 nm SiO0.64). List of TEM membrane samples fabricated within this work. Extracted parameters from the EFTEM analysis, dNC indicates the maximum of the log-normal distribution fit, whereas ANC is the Si

Ni nanocrystals on HOPG(0001): A scanning tunnelling microscope study

• Michael Marz,
• Keisuke Sagisaka and
• Daisuke Fujita

Beilstein J. Nanotechnol. 2013, 4, 406–417, doi:10.3762/bjnano.4.48

• dimensions were determined by fitting these cross sections with a rectangular function. After fitting a large number (79–150) of clusters randomly selected from several scanning areas for each experiment, height and width histograms were plotted (not shown). The resulting histograms showed a monomodal normal

• distribution for the diameter and the height of the clusters. To determine the mean values for each experiment, the histograms were fitted with Gaussian curves, and details are given below. The relative coverage of the surface and the number of clusters were extracted by implementing the appropriate options in

Formation of SiC nanoparticles in an atmospheric microwave plasma

• Martin Vennekamp,
• Ingolf Bauer,
• Matthias Groh,
• Evgeni Sperling,
• Susanne Ueberlein,
• Maksym Myndyk,
• Gerrit Mäder and
• Stefan Kaskel

Beilstein J. Nanotechnol. 2011, 2, 665–673, doi:10.3762/bjnano.2.71

• equation describes the growth rate of larger particles, which is known as Ostwald ripening, resulting in the well-known log-normal distribution of the particle sizes. Taking into account, that D = ƒ(1 = ptotal, Tx), with 1 ≤ x ≤ 2, one can give the following proportionalities for the growth rate of a

Effect of large mechanical stress on the magnetic properties of embedded Fe nanoparticles

• Srinivasa Saranu,
• Sören Selve,
• Ute Kaiser,
• Luyang Han,
• Ulf Wiedwald,
• Paul Ziemann and
• Ulrich Herr

Beilstein J. Nanotechnol. 2011, 2, 268–275, doi:10.3762/bjnano.2.31

• distribution generated under these conditions was examined by scanning electron microscopy (SEM) and atomic force microscopy (AFM). Figure 1 shows a representative sample of Fe nanoparticles deposited on a silicon wafer. The particle diameters follow a log-normal distribution, typical for the gas condensation

Functional morphology, biomechanics and biomimetic potential of stem–branch connections in Dracaena reflexa and Freycinetia insignis

• Tom Masselter,
• Sandra Eckert and
• Thomas Speck

Beilstein J. Nanotechnol. 2011, 2, 173–185, doi:10.3762/bjnano.2.21

• ). Therefore parametric tests (normal distribution) or non-parametric tests (no normal distribution) were used for calculating statistical significance of parameter correlation or difference amongst groups (Table 1). - The (parametric) ‘One Way Analysis of Variance’ or the (non-parametric) ‘Kruskal–Wallis One