Controlled growth of hexagonal gold nanostructures during thermally induced self-assembling on Ge(001) surface

Jany, Benedykt R.; Gauquelin, Nicolas; Willhammar, Tom; Nikiel, Marek; Bos, K.H.W. van den; Janas, Arkadiusz; Szajna, Konrad; Verbeeck, Johan; Aert, Sandra Van; Tendeloo, Gustaaf Van

doi:10.1038/srep42420

Cited by 28 publications

(22 citation statements)

References 38 publications

(65 reference statements)

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“…During the formation of nanostructures from thin metallic films, it is necessary to take into account, in addition to dewetting, also directional solidification of eutectics [3,5,19–21]. In the gold–silicon system, an Au-rich eutectic melts at a temperature of 363 °C.…”

Section: Resultsmentioning

confidence: 99%

“…8 were annealed at a temperature much higher than the eutectic temperature, but one cannot see the Au 100− x Si x intermetallic alloy, which should be observed for the samples manufactured through directional solidification of the eutectic. However, the occurrence of Au–Si inclusions is not a rule [19]. Au–Si bonds were neither observed in the energy range corresponding to the Si region, shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

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Au–Si plasmonic platforms: synthesis, structure and FDTD simulations

Gapska¹,

Łapiński²,

Syty³

et al. 2018

Beilstein J. Nanotechnol.

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Plasmonic platforms based on Au nanostructures have been successfully synthesized by directional solidification of a eutectic from Au and the substrate. In order to determine homogeneous shape and space distribution, the influence of annealing conditions and the initial thickness of the Au film on the nanostructures was analyzed. For the surface morphology studies, SEM and AFM measurements were performed. The structure of platforms was investigated using XRD and XPS methods. Structural investigations confirmed, that nanostructures consist of metallic Au, growing along the [111] direction. The most homogeneous seems to be the platform obtained by solidification of a 2.8 nm Au film, annealed at 550 °C for 15 min. This sample was subsequently chosen for theoretical calculations. Simulations of electromagnetic field propagation through the produced samples were performed using the finite-difference time domain (FDTD) method. The calculated absorbance, as a result of the FDTD simulation shows a quite good agreement with experimental data obtained in the UV–vis range.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Au–Si plasmonic platforms: synthesis, structure and FDTD simulations

Gapska¹,

Łapiński²,

Syty³

et al. 2018

Beilstein J. Nanotechnol.

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“…For this system, when annealed to temperatures above the Au/Ge eutectic 7/18 Fig. 2: Results of the presented approach applied to atomically resolved HAADF STEM Image (2048x960 pixels) of nanoisland of Au fcc/hcp phase grown on Ge(001) a) (Jany et al, 2017a) (CC by 4.0). In b) PCA Scree Plot of first 30 principal components is shown.…”

Section: Atomically Resolved Haadf Stemmentioning

confidence: 99%

“…However, depending on the rate of the cooling process, Au can adopt typical crystalline fcc structure or unusual for it a hcp structure. Thus, the formed final nanoislands are made of two phases of Au, i.e., cubic fcc phase and rare and unusual for Au hexagonal hcp phase separated from each other by defected inter-growth domain along (111) crystallographic planes (Jany et al, 2017a).…”

Section: Atomically Resolved Haadf Stemmentioning

confidence: 99%

Automatic microscopic image analysis by moving window local Fourier Transform and Machine Learning

Jany

Janas

2020

Micron

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The microscopic image analysis is a tedious work which requires patience and time, usually done manually by microscopist after collecting the data. The results obtained in such a way might be biased by the human which performed the analysis. Here we introduce an approach of automatic image analysis, which is based on the locally applied Fourier Transform and Machine Learning methods. In this approach, the whole image is scanned by local moving window with defined size and the 2D Fourier Transform is calculated for each window. All the Local Fourier Transforms are fed into Machine Learning processing. Firstly, the number of components in the data is estimated from Principal Component Analysis (PCA) Scree Plot performed on the data. Secondly, the data are decomposed blindly by Non-Negative Matrix Factorization (NMF) into interpretable spatial maps (loadings) and corresponding Fourier Transforms (factors). Finally, the microscopic image is analyzed, the features on the image are automatically discovered, based on the local changes in Fourier Transform, without the human bias. The user selects only the size and movement of the scanning local window which defines the final analysis resolution. This automatic approach was

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“…This has the advantage that all the image scattering is associated with a particular atomic column. Intermixing between the Au and In-Sb phases is studied column-by-column, in a similar way to 49 . As a result of the noise inherent 22/31 to the experiment, we used Machine Learning algorithms i.e.…”

Section: Machine Learning Haadf Stem Image Quantificationmentioning

confidence: 99%

Towards the understanding of the gold interaction with AIII-BV semiconductors at the atomic level

Jany

Janas²,

Piskorz³

et al. 2020

Nanoscale

Self Cite

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AIII-BV semiconductors have been considered for decades to be a promising material in overcoming the limitations of silicone semiconductor devices. One of the important aspects within AIII-BV semiconductor technology are gold-semiconductor interactions on the nanoscale, since Au is widely used to catalyze the growth of AIII-BV nanostructures.We report on the chemical interactions of Au atoms with AIII-BV semiconductor crystals by an investigation of the nanostructures formation in the process of thermally-induced Au self-assembly on various AIII-BV surfaces, and this by means of atomically resolved HAADF STEM measurements. We have found that the formation of nanostructures is a consequence of the surface diffusion and nucleation of adatoms produced by Au induced chemical reactions on AIII-BV semiconductor surfaces. Only for InSb crystal we have found that there is efficient diffusion of Au atoms into the bulk, which we 2/31 experimentally studied by Machine Learning HAADF STEM image quantification. The process of Au dissolution in InSb lattice has been additionally characterized by DFT calculations with inclusion of finite temperature effects. Furthermore, based on the stoichiometry of nanostructures grown, the effective number of Au atoms needed to release one AIII metal atom has been estimated. The experimental finding reveals a difference in the Au interactions with In-and Ga-based groups of AIII-BV semiconductors. Our comprehensive and systematic studies uncover the details of the Au interactions with the AIII-BV surface at the atomic level with chemical sensitivity.AIII-BV semiconductors due to their unique properties, such as high electron mobility and direct bandgap, are being considered material for the new generation of nanoscale electronic devices 1-4 .Recently, new technologies were developed such as template-assisted selective epitaxy (TASE) by IBM Zurich group 5 and epitaxial lift off (ELO) technique 6 to integrate AIII-BV with Si at the nanoscale. This will allow one to expand the use of AIII-BV crystals by extending the conventional Si-based technology for future AIII-BV/Si nanodevice fabrication 7 . For many applications the arrays of standing, vertically aligned AIII-BV nanowires grown on semiconductor surfaces are desired [8][9][10] . A device for efficient water splitting can be built from such AIII-BV nanowires arrays 11,12 . Also the AIII-BV nanowire LED device integrated on Si has been developed, with 3 orders of magnitude higher efficiency than the conventional device 13 . To control the physico-chemical properties of the devices local information on the atomic arrangement is necessary as has recently been shown by atomic resolution spectrum imaging in STEM 14 .The growth of such monocrystalline AIII-BV nanowires is mainly catalyzed by Au seeded nanoparticles 15 . Studies on the role of Au in the process of nanowires growth, in a closed system being in equilibrium, have been performed by Dick et al. 16 . They have shown that deposited Au is not inert with respect to AIII-BV material and i...

show abstract

Controlled growth of hexagonal gold nanostructures during thermally induced self-assembling on Ge(001) surface

Cited by 28 publications

References 38 publications

Au–Si plasmonic platforms: synthesis, structure and FDTD simulations

Au–Si plasmonic platforms: synthesis, structure and FDTD simulations

Automatic microscopic image analysis by moving window local Fourier Transform and Machine Learning

Towards the understanding of the gold interaction with AIII-BV semiconductors at the atomic level

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