Metal-organic framework-based nanomaterials for CO₂ storage: A review

• Ha Huu Do,
• Iqra Rabani and
• Hai Bang Truong

Beilstein J. Nanotechnol. 2023, 14, 964–970, doi:10.3762/bjnano.14.79

• amount of absorbed CO2 was significantly improved. Notably, the optimal Mg-MOF-74/graphene-based composite yielded a high CO2 adsorption capacity of 8.4 mmol·g−1 at 298 K and 1 bar. Grand canonical Monte Carlo simulation for CO2 storage prediction Grand canonical Monte Carlo (GCMC) simulation is an

The effect of cobalt on morphology, structure, and ORR activity of electrospun carbon fibre mats in aqueous alkaline environments

• Markus Gehring,
• Tobias Kutsch,
• Osmane Camara,
• Alexandre Merlen,
• Hermann Tempel,
• Hans Kungl and
• Rüdiger-A. Eichel

Beilstein J. Nanotechnol. 2021, 12, 1173–1186, doi:10.3762/bjnano.12.87

• further improved by applying a copper tape connecting the sample and the graphite tape. To identify the particles decorating the nanofibres, EDX was performed using an Octane Super EDX detector (EDAX). The programme “monte CArlo SImulation of electroN trajectory in sOlids” (CASINO) [23], which simulates

Is the Ne operation of the helium ion microscope suitable for electron backscatter diffraction sample preparation?

• Annalena Wolff

Beilstein J. Nanotechnol. 2021, 12, 965–983, doi:10.3762/bjnano.12.73

• the steady-state condition is reached later, allowing for a higher Ga impurity implantation concentration. The experimental result is in good agreement with the theoretical prediction from the Monte Carlo simulation and subsequent calculations. The Cu3Ga phase-transformed regions are larger for the

A review of defect engineering, ion implantation, and nanofabrication using the helium ion microscope

• Frances I. Allen

Beilstein J. Nanotechnol. 2021, 12, 633–664, doi:10.3762/bjnano.12.52

Simulations of the 2D self-assembly of tripod-shaped building blocks

• Łukasz Baran,
• Wojciech Rżysko and
• Edyta Słyk

Beilstein J. Nanotechnol. 2020, 11, 884–890, doi:10.3762/bjnano.11.73

• ” active sites, respectively. In a) we can see that the arms are marked as A, B, and C. e) Parameters of the molecular dynamics model. Part of the configurations for a) NT111 at T* = 0.58, b) NL111 at T* = 0.54, and c) WT111 at T* = 0.58, all with ρ* = 0.2. d) Corresponding Monte Carlo simulation on a

Charged particle single nanometre manufacturing

• Philip D. Prewett,
• Cornelis W. Hagen,
• Claudia Lenk,
• Steve Lenk,
• Marcus Kaestner,
• Tzvetan Ivanov,
• Ahmad Ahmad,
• Ivo W. Rangelow,
• Xiaoqing Shi,
• Stuart A. Boden,
• Alex P. G. Robinson,
• Dongxu Yang,
• Sangeetha Hari,
• Marijke Scotuzzi and
• Ejaz Huq

Beilstein J. Nanotechnol. 2018, 9, 2855–2882, doi:10.3762/bjnano.9.266

• the resist through sputtering and loss of substrate crystallinity. The situation for Ga+ ions is further complicated by the property of Ga+ as a p-type dopant of silicon. Scattering and range can be calculated using Monte Carlo simulation codes with typical results as shown in Figure 3 [30]. The

SO₂ gas adsorption on carbon nanomaterials: a comparative study

• Deepu J. Babu,
• Divya Puthusseri,
• Frank G. Kühl,
• Sherif Okeil,
• Michael Bruns,
• Manfred Hampe and
• Jörg J. Schneider

Beilstein J. Nanotechnol. 2018, 9, 1782–1792, doi:10.3762/bjnano.9.169

• the heat of adsorption followed by an increase, VACNTs maintain an almost constant value of heat of adsorption. One reason for this behavior might be the presence of multiple adsorption sites in VACNTs. From the grand canonical Monte Carlo simulation studies of the SO2 adsorption on VACNTs, it is

Formation and development of nanometer-sized cybotactic clusters in bent-core nematic liquid crystalline compounds

• Yuri P. Panarin,
• Sithara P. Sreenilayam,
• Jagdish K. Vij,
• Anne Lehmann and
• Carsten Tschierske

Beilstein J. Nanotechnol. 2018, 9, 1288–1296, doi:10.3762/bjnano.9.121

• -Carlo simulation for biaxial particles via an anisotropic pair potential [3]. Such a system exhibits three phases: biaxial crystalline at absolute zero temperature, uniaxial nematic and a rotationally disordered isotropic phase. In spite of the several theoretical studies having been successfully

• with a smaller B-parameter (B→W) have a tendency to form a uniaxial nematic phase and shapes with a higher B-parameter (B→L) would have a tendency to form a uniaxial discotic phase. In general the biaxial system can be characterized by four order parameters [2]. Similar results were obtained by a Monte

Gas-assisted silver deposition with a focused electron beam

• Luisa Berger,
• Katarzyna Madajska,
• Iwona B. Szymanska,
• Katja Höflich,
• Mikhail N. Polyakov,
• Jakub Jurczyk,
• Carlos Guerra-Nuñez and
• Ivo Utke

Beilstein J. Nanotechnol. 2018, 9, 224–232, doi:10.3762/bjnano.9.24

• area. Grey arrows indicate the molecule flow. (c) Monte Carlo simulation of the radial distribution of backscattered electrons (black) from the measured primary electron beam profile (blue) at 25 keV and a FWHM of 0.4 µm. The blue dashed line indicates the full width FW (99.9%) of the primary electron

Transition from silicene monolayer to thin Si films on Ag(111): comparison between experimental data and Monte Carlo simulation

• Alberto Curcella,
• Romain Bernard,
• Yves Borensztein,
• Silvia Pandolfi and
• Geoffroy Prévot

Beilstein J. Nanotechnol. 2018, 9, 48–56, doi:10.3762/bjnano.9.7

• different growth models, and to determine if the differences observed between Si growth in the 470–540 K temperature range result from two different growth modes, we have computed the evolution of the distribution of film thickness during evaporation, in the frame of a Monte Carlo simulation, and compared

• the Auger signals, and comparison with calculated IMFP [40]. Parameters of the Monte Carlo simulation corresponding to the fit of the AES data shown in Figure 3a–e. Acknowledgements This work was supported by French state funds managed by the ANR within the Investissements d’Avenir program under

Electron beam induced deposition of silacyclohexane and dichlorosilacyclohexane: the role of dissociative ionization and dissociative electron attachment in the deposition process

• Ragesh Kumar T P,
• Sangeetha Hari,
• Krishna K Damodaran,
• Oddur Ingólfsson and
• Cornelis W. Hagen

Beilstein J. Nanotechnol. 2017, 8, 2376–2388, doi:10.3762/bjnano.8.237

• better estimate of the number of low-energy electrons in the relevant energy range, a Monte Carlo simulation of the angular distribution of electrons escaping from a Si half sphere on the top of a Si surface was conducted. The sample for the simulation was a flat Si substrate with a 1 nm diameter Si half

• ) SCH. The first (1) and last (25) pillars are indicated and the order of deposition is as shown in Figure 7b. Monte Carlo simulation of the angular distribution of electrons emitted from a flat Si substrate with a 1 nm diameter Si half sphere on top (a), upon exposure with a “zero-diameter” 20 keV

Fixation mechanisms of nanoparticles on substrates by electron beam irradiation

• Daichi Morioka,
• Tomohiro Nose,
• Taiki Chikuta,
• Kazutaka Mitsuishi and
• Masayuki Shimojo

Beilstein J. Nanotechnol. 2017, 8, 1523–1529, doi:10.3762/bjnano.8.153

• -scattering leads to an increase in line width and thus reduces the resolution of this patterning technique. Keywords: accelerating voltage; electron beam; gold; Monte Carlo simulation; nanoparticle array; Introduction Techniques to fabricate assemblies or arrays of nanostructures on a desired area have

• nanoparticles as the first step. The diameter of the silica particles was 100 nm. An electron beam (20 kV, 3 × 10−10 A) was scanned in a line of 10 μm in length at a scanning speed of 0.1 μm/s. Finally, the substrate was washed in ethanol for 5 min. Monte Carlo simulation of scattering electrons for the

• : The average energy loss at each scattering event, which is called stopping power, is where J is the mean ionization potential given by J = 9.76Z + 58.5Z−0.19, and S is the product of ρ and s. We have developed a software written in C# language with reference to the Monte Carlo simulation modeling for

Needs and challenges for assessing the environmental impacts of engineered nanomaterials (ENMs)

• Michelle Romero-Franco,
• Hilary A. Godwin,
• Muhammad Bilal and
• Yoram Cohen

Beilstein J. Nanotechnol. 2017, 8, 989–1014, doi:10.3762/bjnano.8.101

• , the authors followed the scoring with a Monte-Carlo simulation to sample from a given probability distribution for each parameter to arrive at probabilities for the ranked ENMs for each of the categories. Such analysis suggested that CdSe was of greatest concern among the analyzed ENMs, ranking in the

Tandem polymer solar cells: simulation and optimization through a multiscale scheme

• Fanan Wei,
• Ligang Yao,
• Fei Lan,
• Guangyong Li and
• Lianqing Liu

Beilstein J. Nanotechnol. 2017, 8, 123–133, doi:10.3762/bjnano.8.13

• photovoltaics to achieve high performance. Consistency between the optimization results and the reported experimental results proved the effectiveness of the proposed simulation scheme. Keywords: genetic algorithm; Monte Carlo simulation; simplex searching; tandem polymer solar cells; Introduction Polymer

Current–voltage characteristics of manganite–titanite perovskite junctions

• Benedikt Ifland,
• Patrick Peretzki,
• Birte Kressdorf,
• Philipp Saring,
• Andreas Kelling,
• Michael Seibt and
• Christian Jooss

Beilstein J. Nanotechnol. 2015, 6, 1467–1484, doi:10.3762/bjnano.6.152

• volume and the space charge region. The electron beam generation volume was calculated using the CASINO implementation of a Monte Carlo simulation developed by Drouin et al. [55]. In the simulation, we use the SCR width dPCMO = 0.2–2.5 nm (Figure 3b for dPCMO = 2.5 nm) and dSTNO = 27 nm, as suggested by

Surface excitations in the modelling of electron transport for electron-beam-induced deposition experiments

• Francesc Salvat-Pujol,
• Roser Valentí and
• Wolfgang S. Werner

Beilstein J. Nanotechnol. 2015, 6, 1260–1267, doi:10.3762/bjnano.6.129

• present a general perspective of recent works on the subject of surface excitations and on low-energy electron transport, highlighting the most relevant aspects for the modelling of electron transport in FEBID simulations. Keywords: focused-electron-beam-induced deposition (FEBID); Monte Carlo simulation

• the distribution of energy losses per unit path length, the basic quantity that is needed to describe energy losses in a detailed Monte-Carlo simulation of electron transport. In this section we briefly outline the basic steps of these calculations and highlight the underlying assumptions. Further

• -Carlo simulation of electron transport (see section “Monte-Carlo simulation of electron energy-loss spectra”), a method that has been successfully used in the last decades. Inelastic collisions in the vicinity of a planar surface The scheme outlined in the previous section to describe inelastic

Scanning reflection ion microscopy in a helium ion microscope

• Yuri V. Petrov and
• Oleg F. Vyvenko

Beilstein J. Nanotechnol. 2015, 6, 1125–1137, doi:10.3762/bjnano.6.114

• composition. A simple geometrical analysis of the reflection process was performed together with a Monte Carlo simulation of the angular dependence of the reflected ion yield. An interpretation of the RIM image formation and a quantification of the height of the surface steps were performed. The minimum

• effect of all these factors separately. Angular dependence of the ion reflection coefficient The dependence of the reflection coefficient for singly charged He ions on the grazing angle as obtained by a Monte Carlo simulation with SRIM software [39] for different materials is depicted in Figure 8. Note

• that the SRIM Monte Carlo simulation assumes that the ions are reflected from an amorphous target and all of them are collected by a detector. As it can be seen in Figure 8, the ion reflection coefficient is a monotonically decreasing function that tends to the yield of backscattered ions when the

Fabrication of high-resolution nanostructures of complex geometry by the single-spot nanolithography method

• Alexander Samardak,
• Margarita Anisimova,
• Aleksei Samardak and
• Alexey Ognev

Beilstein J. Nanotechnol. 2015, 6, 976–986, doi:10.3762/bjnano.6.101

• Si substrate at a dose of 0.25 pC (left) and 0.35 pC (right); (b) the ring patterned on a polycrystalline Au film at a dose of 0.25 pC (left) and 2.5 pC (right). (a,c) Monte Carlo simulation of 250 electron scattering trajectories at 10 keV incident energy in a 75 nm thick PMMA layer on bulk Si and

Electron-stimulated purification of platinum nanostructures grown via focused electron beam induced deposition

• Brett B. Lewis,
• Michael G. Stanford,
• Jason D. Fowlkes,
• Kevin Lester,
• Harald Plank and
• Philip D. Rack

Beilstein J. Nanotechnol. 2015, 6, 907–918, doi:10.3762/bjnano.6.94

• form that is assumed to instantaneously oxidize amorphous carbon. The resulting COx is liberated from the deposit via subsequent diffusion to the surface. The details of the simulation will be provided in a future publication and only a brief summary is provided here. The Monte Carlo simulation is

• scanning procedure) and the energy is accumulated at the center of the scanning pattern (x = 0, y = 0) in order to emulate the transiently evolving inelastic energy profile with depth (z-coordinate) during real experiments at the pad center. Thus, the critical parameter in the Monte Carlo simulation is the

• the experimentally observed deposit densification – ρ(z), Z(z) and A(z) are recalculated by volume averaging based on PtCx(z) for the next Monte Carlo simulation iteration. In the transport simulation, O2 is introduced into the deposit based on a surface impingement rate derived from the input

Synthesis of embedded Au nanostructures by ion irradiation: influence of ion induced viscous flow and sputtering

• Udai B. Singh,
• D. C. Agarwal,
• S. A. Khan,
• S. Mohapatra,
• H. Amekura,
• D. P. Datta,
• Ajay Kumar,
• R. K. Choudhury,
• T. K. Chan,
• Thomas Osipowicz and
• D. K. Avasthi

Beilstein J. Nanotechnol. 2014, 5, 105–110, doi:10.3762/bjnano.5.10

• Carlo simulation in dynamic mode, is used for a better understanding of the recoil implantation while taking into account the required dose and ion energy for a thin film target. The observed formation of embedded Au nanostructures in glass can be recognized as the effects of sputtering, of recoil

• nanostructures that are embedded near the surface. These embedded Au nanostructures have great potential for the application as substrates for surface enhanced Raman spectroscopy (SERS). Such a SERS substrate is expected to be reusable due to the embedded nanostructures. TRIDYN [20][21], a binary-collision Monte

Simulation of electron transport during electron-beam-induced deposition of nanostructures

• Francesc Salvat-Pujol,
• Harald O. Jeschke and
• Roser Valentí

Beilstein J. Nanotechnol. 2013, 4, 781–792, doi:10.3762/bjnano.4.89

• -induced growth of tungsten nanostructures on SiO2 substrates by using a Monte Carlo simulation of the electron transport. This study gives a quantitative insight into the deposition of energy and charge in the substrate and in the already existing metallic nanostructures in the presence of the electron

• during post-growth electron-beam treatments. Keywords: electron backscattering; electron transport; (F)EBID; Monte Carlo simulation; PENELOPE; Introduction Electron-beam-induced deposition (EBID) [1][2][3] is a suitable method for the template-free fabrication of nanostructures. Molecules of a

Guided immobilisation of single gold nanoparticles by chemical electron beam lithography

• Patrick A. Schaal and
• Ulrich Simon

Beilstein J. Nanotechnol. 2013, 4, 336–344, doi:10.3762/bjnano.4.39

• CASINO v2.48 (monte CArlo SImulation of electroN trajectory in sOlids) [28]. Therefore the following density values were used: ρ(Si) = 2.3290 g·cm−3 [29], ρ(SiO2) = 2.196 g·cm−3 [29], ρ(CSPETCS) = 1.35 g·cm−3 (estimation using density values of commercially available solutions). Schematic drawing of used

Size-dependent phase diagrams of metallic alloys: A Monte Carlo simulation study on order–disorder transitions in Pt–Rh nanoparticles

• Johan Pohl,
• Christian Stahl and
• Karsten Albe

Beilstein J. Nanotechnol. 2012, 3, 1–11, doi:10.3762/bjnano.3.1

• parameters. Concentration-averaged short-range order parameters were used to remove the surface segregation bias of the conventional short-range order parameters. Using this procedure, it was shown that the short-range order in the particles at high temperatures is bulk-like. Keywords: Monte Carlo

• simulation; nanoparticles; nanothermodynamics; phase diagram; Pt-Rh; thermodynamics; Introduction Pt–Rh is an important alloy due to its catalytic activity in different reactions. In the past it was assumed that Pt–Rh is immiscible at low temperatures [1][2], but theoretical studies revealed that Pt–Rh