Electrical, electrochemical and structural studies of a chlorine-derived ionic liquid-based polymer gel electrolyte

• Ashish Gupta,
• Amrita Jain,
• Manju Kumari and
• Santosh K. Tripathi

Beilstein J. Nanotechnol. 2021, 12, 1252–1261, doi:10.3762/bjnano.12.92

• %)} was chosen as the optimized blend. After the optimization of polymer and ionic liquid blends, a liquid electrolyte was introduced in the system. Accordingly, different weight percentages of the polymer/ionic liquid blend, {(PVdF-HFP)-[BDiMIM][Cl]} (4:6), in the solution of an optimized liquid

• electrolyte PC-Mg(ClO4)2 (0.3 M) system were tested and the optimization curve is shown in Figure 4. As it can be seen from Figure 4, the maximum conductivity of the liquid electrolyte PC-Mg(ClO4)2 at room temperature is found to be ≈3.36 × 10−3 S·cm−1. Upon the addition of different weight percentages of a

A review on slip boundary conditions at the nanoscale: recent development and applications

• Ruifei Wang,
• Jin Chai,
• Bobo Luo,
• Xiong Liu,
• Jianting Zhang,
• Min Wu,
• Mingdan Wei and
• Zhuanyue Ma

Beilstein J. Nanotechnol. 2021, 12, 1237–1251, doi:10.3762/bjnano.12.91

• . Investigating the effect of gas bubbles on the effective slip length is also of great value especially in the design and optimization of superhydrophobic surfaces, which have lots of applications due to their superlubricating potential [112][113][114]. The liquid flow behavior on such surfaces can be described

Two dynamic modes to streamline challenging atomic force microscopy measurements

• Alexei G. Temiryazev,
• Andrey V. Krayev and
• Marina P. Temiryazeva

Beilstein J. Nanotechnol. 2021, 12, 1226–1236, doi:10.3762/bjnano.12.90

• that errors in choosing this parameter can lead to image distortion and noise, as well as damage to the probe. It can be seen from the foregoing that the choice of scanning parameters depends on both the characteristics of the sample and the sharpness of the probe. Optimization, as a rule, requires a

• , can have fixed values. For example, we can use p1 = 70–80%, p2 = p1 + 4%, Tline = 1 s, Tdir = 0.8 s, Vup = 5 µm/s, Vdown = 2 µm/s. Such a set will provide high-quality images of various samples. Further optimization of the parameters may be needed to address specific goals, for example, to reduce the

First-principles study of the structural, optoelectronic and thermophysical properties of the π-SnSe for thermoelectric applications

• Muhammad Atif Sattar,
• Najwa Al Bouzieh,
• Maamar Benkraouda and
• Noureddine Amrane

Beilstein J. Nanotechnol. 2021, 12, 1101–1114, doi:10.3762/bjnano.12.82

• ) volumes, respectively, and Bo and represent the bulk modulus and its first-order pressure derivative, respectively [9][56][57]. The volume–energy optimization curve of the π-SnSe is shown in Figure 2. The computed ground-state structural parameters are presented in Table 1. For the π-SnSe system, our DFT

• . Each Sn atoms sits on the apex of the trigonal pyramidal surrounded by three Se atoms at the trigonal base with a Sn–Se bond distance of 2.6 Å. Volume–energy optimization curve of the π-SnSe fitted to the Murnaghan’s equation of state. The relaxed lattice constant is ab = 12.108 Å and the optimized

Assessment of the optical and electrical properties of light-emitting diodes containing carbon-based nanostructures and plasmonic nanoparticles: a review

• Keshav Nagpal,
• Erwan Rauwel,
• Frédérique Ducroquet and
• Protima Rauwel

Beilstein J. Nanotechnol. 2021, 12, 1078–1092, doi:10.3762/bjnano.12.80

• determined. Enhancing hole mobility: hole injection and transport layers The optimization of charge carrier injection also consists of reducing the driving voltage of the LED. For achieving this, holes should be readily injected from the high-work-function anode surface (e.g., ITO, SWNT), while the electrons

A new method for obtaining model-free viscoelastic material properties from atomic force microscopy experiments using discrete integral transform techniques

• Berkin Uluutku,
• Enrique A. López-Guerra and
• Santiago D. Solares

Beilstein J. Nanotechnol. 2021, 12, 1063–1077, doi:10.3762/bjnano.12.79

• in the time convolution of Equation 11 and simplifying, we obtain the equation we have previously used as the basis for extracting the viscoelastic model parameters and corresponding properties using non-linear least-squares optimization in our previous work: An alternate strategy is to turn to the

Uniform arrays of gold nanoelectrodes with tuneable recess depth

• Elena O. Gordeeva,
• Ilya V. Roslyakov,
• Alexey P. Leontiev,
• Alexey A. Klimenko and
• Kirill S. Napolskii

Beilstein J. Nanotechnol. 2021, 12, 957–964, doi:10.3762/bjnano.12.72

• segment were selectively etched away after turning the AAO template upside down (Figure 1d). Segment 1 – copper Optimization of the deposition potential (Ed) for the formation of the first Cu segment was performed in the range from −0.1 to −0.5 V. In the case of more negative potentials, an intensive

• AAO top surface. As a consequence, a narrower Ed range from −0.3 to −0.1 V was chosen for an optimization of the electrodeposition conditions for the third segment of Cu. The current density as a function of the time, recorded at different Ed, is demonstrated in Figure 2a. At stage I, a sharp drop in

• the commercial electrolyte Ecomet MB-16 [41]. Optimization of the deposition potential (Ed) in the range from −0.1 to −0.5 V was performed. The current efficiency was determined using gravimetric analysis of Cu layers on the plain Au electrodes. Au was electrodeposited from the commercial electrolyte

The role of convolutional neural networks in scanning probe microscopy: a review

• Ido Azuri,
• Irit Rosenhek-Goldian,
• Neta Regev-Rudzki,
• Georg Fantner and
• Sidney R. Cohen

Beilstein J. Nanotechnol. 2021, 12, 878–901, doi:10.3762/bjnano.12.66

• each training iteration, some of the “neurons” are ignored and their parameters are not updated during the optimization. This leads to reduction in the memorization of the training set resulting in better generalization. Dropout layers [13][18] in CNNs are usually used after the dense layers

• learning and linear programming (an optimization method) for tracking single cells in live-cell imaging of both fluorescent and bright-field images of the cell cytoplasm [112]. Newby et al. developed a CNN for fully automated submicrometer-scale localization of particles such as viruses, proteins, and drug

• . In data-driven control strategies such as the ones developed in one of our labs [128] the traditional optimization-based solvers can be replaced by machine learning, thereby ensuring faster and more accurate tracking of the sample without exciting unwanted resonances. CNNs are particularly promising

Comprehensive review on ultrasound-responsive theranostic nanomaterials: mechanisms, structures and medical applications

• Sepand Tehrani Fateh,
• Lida Moradi,
• Elmira Kohan,
• Michael R. Hamblin and
• Amin Shiralizadeh Dezfuli

Beilstein J. Nanotechnol. 2021, 12, 808–862, doi:10.3762/bjnano.12.64

• opportunity for the development of new biomaterials. The optimization of their responses to local/environmental stimuli can provide better-controlled drug delivery and superior therapeutic effects through the synergistic effect of various environmental stimuli [8][9]. These systems have been discussed in

• of PFP droplets. Therefore, a bubble-forming strategy may be useful in the clinical settings because the volume and morphology of the thermal ablation can be controlled by changes in both droplet concentration and acoustic pressure. This approach offers a new opportunity for the optimization of HIFU

Nanogenerator-based self-powered sensors for data collection

• Yicheng Shao,
• Maoliang Shen,
• Yuankai Zhou,
• Xin Cui,
• Lijie Li and
• Yan Zhang

Beilstein J. Nanotechnol. 2021, 12, 680–693, doi:10.3762/bjnano.12.54

• through material optimization and charge-accumulation strategies [4][62][64][68][69]. In addition, TENGs can be directly used as sensors. For example, TENGs can collect irregular and low-frequency wave energy to generate electricity from blue energy [70]. Self-powered sensors based on TENGs can collect

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

Stability and activity of platinum nanoparticles in the oxygen electroreduction reaction: is size or uniformity of primary importance?

• Kirill O. Paperzh,
• Anastasia A. Alekseenko,
• Vadim A. Volochaev,
• Ilya V. Pankov,
• Olga A. Safronenko and
• Vladimir E. Guterman

Beilstein J. Nanotechnol. 2021, 12, 593–606, doi:10.3762/bjnano.12.49

• urgent task and the optimization of their structure seems to be one of the possible solutions. In the present paper, Pt/C electrocatalysts containing small (2–2.6 nm) nanoparticles (NPs) of a similar size, uniformly distributed over the surface of a carbon support, were obtained by the original method of

On the stability of microwave-fabricated SERS substrates – chemical and morphological considerations

• Limin Wang,
• Aisha Adebola Womiloju,
• Christiane Höppener,
• Ulrich S. Schubert and
• Stephanie Hoeppener

Beilstein J. Nanotechnol. 2021, 12, 541–551, doi:10.3762/bjnano.12.44

• ], nanogratings [18], or the customization of readily available structures (i.e., HR-DVD templates) [19]. The aim of these developments is not only the optimization of the nanoparticle arrangement to obtain high enhancement factors, but also to guarantee uniformity, homogeneity, the ease of fabrication, low

Boosting of photocatalytic hydrogen evolution via chlorine doping of polymeric carbon nitride

• Malgorzata Aleksandrzak,
• Michalina Kijaczko,
• Wojciech Kukulka,
• Daria Baranowska,
• Martyna Baca,
• Beata Zielinska and
• Ewa Mijowska

Beilstein J. Nanotechnol. 2021, 12, 473–484, doi:10.3762/bjnano.12.38

• . Three of the most popular modifications are: (i) coupling with other semiconductors [22][23], (ii) self-optimization of the crystal structure [24][25], and (iii) doping with heteroatoms [26][27]. Therefore, PCN is called the "holy grail" because it is believed that its modifications will result in

Rapid controlled synthesis of gold–platinum nanorods with excellent photothermal properties under 808 nm excitation

• Jialin Wang,
• Qianqian Duan,
• Min Yang,
• Boye Zhang,
• Li Guo,
• Pengcui Li,
• Wendong Zhang and
• Shengbo Sang

Beilstein J. Nanotechnol. 2021, 12, 462–472, doi:10.3762/bjnano.12.37

• + and K2PtCl4 on the morphology and optical properties of the synthesized Au@Pt NRs. After continuous optimization of the synthesis process, a precise and controllable synthesis method of dumbbell-like Au@Pt NRs was proposed (Scheme 1). Au@Pt NRs with ideal size and structure formed after depositing Pt

Reconstruction of a 2D layer of KBr on Ir(111) and electromechanical alteration by graphene

• Zhao Liu,
• Antoine Hinaut,
• Stefan Peeters,
• Sebastian Scherb,
• Ernst Meyer,
• Maria Clelia Righi and
• Thilo Glatzel

Beilstein J. Nanotechnol. 2021, 12, 432–439, doi:10.3762/bjnano.12.35

• presented in Supporting Information File 1, Figure S2. Initially, a system with a periodicity of Ir(111) along the direction of 21 × aIr and along of × aIr (21 × ) was considered, resulting in a moiré pattern similar to the experimental observations. The structural optimization in a variable cell of this

• our previous investigations [56][57], because these corrections often overestimate the adsorption energies on metallic substrates [58][59]. The process of geometry optimization was stopped when the total energy and the forces converged under thresholds of 1 × 10−4 Ry and 1 × 10−3 Ry/bohr, respectively

• a triple-atom duplication along “BBC”) indicated in (a), respectively. DFT simulation of a monolayer KBr on Ir(111). (a) Optimization of a reconstructed KBr layer on Ir(111) with a cell size of 4 × 2 superimposed on the Ir(111) model and experimental data (Br− in red and K+ in blue, the size of the

The patterning toolbox FIB-o-mat: Exploiting the full potential of focused helium ions for nanofabrication

• Victor Deinhart,
• Lisa-Marie Kern,
• Jan N. Kirchhof,
• Sabrina Juergensen,
• Joris Sturm,
• Enno Krauss,
• Thorsten Feichtner,
• Sviatoslav Kovalchuk,
• Michael Schneider,
• Dieter Engel,
• Bastian Pfau,
• Bert Hecht,
• Kirill I. Bolotin,
• Stephanie Reich and
• Katja Höflich

Beilstein J. Nanotechnol. 2021, 12, 304–318, doi:10.3762/bjnano.12.25

• , is not trivial. Here, we introduce the Python toolbox FIB-o-mat for automated pattern creation and optimization, providing full flexibility to accomplish demanding patterning tasks. FIB-o-mat offers high-level pattern creation, enabling high-fidelity large-area patterning and systematic variations in

• geometry and raster settings. It also offers low-level beam path creation, providing full control over the beam movement and including sophisticated optimization tools. Three applications showcasing the potential of He ion beam nanofabrication for two-dimensional material systems and devices using FIB-o

• enables the creation of arbitrarily shaped pattern geometries in combination with geometry-adapted beam paths and optimization/automation tools. The code and Python package documentation can be found online at gitlab under the gpl3 license [18][28][29]. Pre-build packages are available on pypi [30]. The

TiO_x/Pt₃Ti(111) surface-directed formation of electronically responsive supramolecular assemblies of tungsten oxide clusters

• Marco Moors,
• Yun An,
• Agnieszka Kuc and
• Kirill Yu. Monakhov

Beilstein J. Nanotechnol. 2021, 12, 203–212, doi:10.3762/bjnano.12.16

• calculated values based on the used deposition rate. Tests with a varied deposition rate showed no significant impact on the cluster adsorption behavior. Density functional theory simulations The simulations of W3O9 and W3O8 clusters were performed using the AMS suite [31]. The geometry optimization and

Paper-based triboelectric nanogenerators and their applications: a review

• Jing Han,
• Nuo Xu,
• Yuchen Liang,
• Mei Ding,
• Junyi Zhai,
• Qijun Sun and
• Zhong Lin Wang

Beilstein J. Nanotechnol. 2021, 12, 151–171, doi:10.3762/bjnano.12.12

• the design of certain structures, such as solar sails [130]. Dudte et al. provided a scale-independent optimization algorithm to program the problem of determining generalized Miura-ori tessellations that conform to prescribed surfaces [131]. The flat and folded unit cells of a standard and of a

Fusion of purple membranes triggered by immobilization on carbon nanomembranes

• René Riedel,
• Natalie Frese,
• Fang Yang,
• Martin Wortmann,
• Raphael Dalpke,
• Daniel Rhinow,
• Norbert Hampp and
• Armin Gölzhäuser

Beilstein J. Nanotechnol. 2021, 12, 93–101, doi:10.3762/bjnano.12.8

• optimistic that complete substrate coverage can be achieved through further optimization measures. One approach would be to alternate the polarity of the electric field several times after deposition to remove unbound PM patches. WT PM deposited on NBPT CNM using drop-casting and electrophoretic

Nanocasting synthesis of BiFeO₃ nanoparticles with enhanced visible-light photocatalytic activity

• Thomas Cadenbach,
• Maria J. Benitez,
• A. Lucia Morales,
• Cesar Costa Vera,
• Luis Lascano,
• Francisco Quiroz,
• Alexis Debut and
• Karla Vizuete

Beilstein J. Nanotechnol. 2020, 11, 1822–1833, doi:10.3762/bjnano.11.164

• impregnation nanocasting technique using SBA-15 as the hard-template and in situ generated mixed metal carboxylates as precursors. Reaction optimization included the study of the influence of different organic ligands, solvents, molar ratio of the precursors, and calcination temperature. The techniques used to

Electron beam-induced deposition of platinum from Pt(CO)₂Cl₂ and Pt(CO)₂Br₂

• Aya Mahgoub,
• Hang Lu,
• Rachel M. Thorman,
• Konstantin Preradovic,
• Titel Jurca,
• Lisa McElwee-White,
• Howard Fairbrother and
• Cornelis W. Hagen

Beilstein J. Nanotechnol. 2020, 11, 1789–1800, doi:10.3762/bjnano.11.161

• containing deposits. The shapes of deposits and their growth rates were addressed and compared to deposits made from the commonly used MeCpPtMe3 precursor. Although this work did not focus on the optimization of deposition conditions, the highest growth rates we found were 0.045 nm3/electron for Pt(CO)2Cl2

Seebeck coefficient of silicon nanowire forests doped by thermal diffusion

• Shaimaa Elyamny,
• Elisabetta Dimaggio and
• Giovanni Pennelli

Beilstein J. Nanotechnol. 2020, 11, 1707–1713, doi:10.3762/bjnano.11.153

• achieve the maximum power factor. Metal-assisted chemical etching is a very advantageous technique, because it allows for the fabrication of silicon nanowires with high aspect ratio on a very large scale. However, MACE is incompatible with the high doping concentrations necessary for the optimization of

Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

• Jari Järvi,
• Patrick Rinke and
• Milica Todorović

Beilstein J. Nanotechnol. 2020, 11, 1577–1589, doi:10.3762/bjnano.11.140

• Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1S)-camphor on the Cu(111) surface. We identify the optimal structure

• properties, and ultimately allow us to tune the functionality of advanced materials. Keywords: Bayesian optimization; camphor; Cu(111); density-functional theory; electronic structure; organic surface adsorbates; physical chemistry; structure search; surface science; Introduction Current frontier

• difficult to apply and can lead to biased or incorrect results. For example, with only partial knowledge of the PES, a metastable local minimum energy structure could easily be misinterpreted as the most stable global minimum. Recently, Gaussian processes (GPs) [19] and Bayesian optimization (BO) [20] have

Electrokinetic characterization of synthetic protein nanoparticles

• Daniel F. Quevedo,
• Cody J. Lentz,
• Adriana Coll de Peña,
• Yazmin Hernandez,
• Nahal Habibi,
• Rikako Miki,
• Joerg Lahann and
• Blanca H. Lapizco-Encinas

Beilstein J. Nanotechnol. 2020, 11, 1556–1567, doi:10.3762/bjnano.11.138

• integrates the main EK phenomena acting on the particles, and it has the potential to be used for the design and optimization of these systems. The eEEEC is analogous to an EK signature, that is, this value remains constant for a particular particle type in any insulator-based EK system. Differences in the