ReactorAFM/STM – dynamic reactions on surfaces at elevated temperature and atmospheric pressure

• Tycho Roorda,
• Hamed Achour,
• Matthijs A. van Spronsen,
• Marta E. Cañas-Ventura,
• Sander B. Roobol,
• Willem Onderwaater,
• Mirthe Bergman,
• Peter van der Tuijn,
• Gertjan van Baarle,
• Johan W. Bakker,
• Joost W. M. Frenken and
• Irene M. N. Groot

Beilstein J. Nanotechnol. 2025, 16, 397–406, doi:10.3762/bjnano.16.30

• pressure controllers, is connected to the AFM/STM reactor, permitting pressures of up to 20 bar. Four gases plus a carrier gas can be mixed and transported to and from the reactor by capillaries at gas mixing ratios ranging from 1:1 up to 1:100 with a flow up to 40 mL/min controlled via a Python script. A

The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential

• Dimitra-Danai Varsou,
• Arkaprava Banerjee,
• Joyita Roy,
• Kunal Roy,
• Giannis Savvas,
• Haralambos Sarimveis,
• Ewelina Wyrzykowska,
• Mateusz Balicki,
• Tomasz Puzyn,
• Georgia Melagraki,
• Iseult Lynch and
• Antreas Afantitis

Beilstein J. Nanotechnol. 2024, 15, 1536–1553, doi:10.3762/bjnano.15.121

• benefits of this algorithm besides its robustness include resistance to overfitting and the ability to process datasets with numerous variables without the need of feature scaling [42]. This algorithm was implemented in Python, using scikit-learn package, a widely used library for ML models. Adaboost

• regression model The development of the ZP QSPR model involved the utilization of the Adaptive Boosting (AdaBoost) ML methodology, implemented through Python 3.8.8 and the scikit-learn library (version 0.24.1). AdaBoost represents an early instance of leveraging boosting algorithms to address complex problem

Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies

• Fabio Le Piane,
• Mario Vozza,
• Matteo Baldoni and
• Francesco Mercuri

Beilstein J. Nanotechnol. 2024, 15, 1498–1521, doi:10.3762/bjnano.15.119

A low-kiloelectronvolt focused ion beam strategy for processing low-thermal-conductance materials with nanoampere currents

• Annalena Wolff,
• Nico Klingner,
• William Thompson,
• Yinghong Zhou,
• Jinying Lin and
• Yin Xiao

Beilstein J. Nanotechnol. 2024, 15, 1197–1207, doi:10.3762/bjnano.15.97

• modelling approach. To simulate the heat accumulation of multiple ion impacts occurring within a time frame of several nanoseconds, Python was used to implement a forward time–centered space method as a finite-difference method for three dimensions, similar to [29][30]. According to a von Neumann stability

Atomistic insights into the morphological dynamics of gold and platinum nanoparticles: MD simulations in vacuum and aqueous media

• Evangelos Voyiatzis,
• Eugenia Valsami-Jones and
• Antreas Afantitis

Beilstein J. Nanotechnol. 2024, 15, 995–1009, doi:10.3762/bjnano.15.81

• angle. The scattering functions g are computed using the expressions proposed by Cromer and Mann [81]. A λ value of 0.15418 nm is employed, representing Cu Kα radiation. Python codes to compute the Berry parameter and the X-ray powder diffraction pattern of a NP are available at https://github.com

Directed growth of quinacridone chains on the vicinal Ag(35 1 1) surface

• Niklas Humberg,
• Lukas Grönwoldt and
• Moritz Sokolowski

Beilstein J. Nanotechnol. 2024, 15, 556–568, doi:10.3762/bjnano.15.48

• (35 1 1) surface is shown in Figure S1a of Supporting Information File 1. The STM image reveals that the Ag steps are not regularly spaced. Instead, the distribution of the terrace widths is very broad. The step distribution that was obtained by evaluating STM images with an Python script reported by

On the mechanism of piezoresistance in nanocrystalline graphite

• Sandeep Kumar,
• Simone Dehm and
• Ralph Krupke

Beilstein J. Nanotechnol. 2024, 15, 376–384, doi:10.3762/bjnano.15.34

• , which was constructed in-house and automated using Python. Then, sheet resistance measurements under externally applied strain are discussed. Raman spectroscopy of the NCG under strain is studied, which gives insights into the distribution of strain in the film. Utilizing electrical and optical

• Keithley 2636A device. The substrate holder and contacts holder were machined and attached to the stepper motor as shown in Figure 1a. A detailed description of the setup has been given by Kumar [23]. The complete setup was automated via self-programmed Python code. To completely eliminate any strain

Multiscale modelling of biomolecular corona formation on metallic surfaces

• Parinaz Mosaddeghi Amini,
• Ian Rouse,
• Julia Subbotina and
• Vladimir Lobaskin

Beilstein J. Nanotechnol. 2024, 15, 215–229, doi:10.3762/bjnano.15.21

• decomposition, generating PMFs via traditional or machine-learning approaches, and constructing a coarse-grained representation for input to UA. To simplify this procedure for more complex molecules, we have developed a Python script (MolToFragments.py) employing RDKit [46] to automate splitting larger

Dual-heterodyne Kelvin probe force microscopy

• Benjamin Grévin,
• Fatima Husainy,
• Dmitry Aldakov and
• Cyril Aumaître

Beilstein J. Nanotechnol. 2023, 14, 1068–1084, doi:10.3762/bjnano.14.88

• , a Python routine is used to toggle the output configuration of the HF2LI modulation module (see Supporting Information File 1), as a function of a trigger signal sent by the Mimea unit. The full phase spectrum (Φn) is eventually reconstructed by shifting the data set from Φ0 (n_4 sidebands) or Φ0

Two-step single-reactor synthesis of oleic acid- or undecylenic acid-stabilized magnetic nanoparticles by thermal decomposition

• Mykhailo Nahorniak,
• Pamela Pasetto,
• Jean-Marc Greneche,
• Volodymyr Samaryk,
• Sandy Auguste,
• Anthony Rousseau,
• Nataliya Nosova and
• Serhii Varvarenko

Beilstein J. Nanotechnol. 2023, 14, 11–22, doi:10.3762/bjnano.14.2

• Python 2.7/3.x package for fitting, sharing, and estimating the parameters of transients via user-contributed transient models) involving quadrupolar and magnetic components with Lorentzian lines. The isomer shift values are referred to as that of α-Fe at RT. The ATR-FTIR measurements were performed on a

A new method for obtaining the magnetic shape anisotropy directly from electron tomography images

• Cristian Radu,
• Ioana D. Vlaicu and
• Andrei C. Kuncser

Beilstein J. Nanotechnol. 2022, 13, 590–598, doi:10.3762/bjnano.13.51

• electron tomography techniques is reported in this work. The new methodology is implemented in an under-development software package called Magn3t, written in Python and C++. A novel image-filtering technique that reduces the highly undesired diffraction effects in the tomography tilt-series has been also

• : electron tomography; magnetite; Python; shape anisotropy; Introduction For any nanoparticle (NP) system, among the most important pieces of physical information for scientists is information related to the morphology (size, shape, and organization) of its constituents. In nanoscale systems, this

A chemiresistive sensor array based on polyaniline nanocomposites and machine learning classification

• Jiri Kroutil,
• Alexandr Laposa,
• Ali Ahmad,
• Jan Voves,
• Vojtech Povolny,
• Ladislav Klimsa,
• Marina Davydova and
• Miroslav Husak

Beilstein J. Nanotechnol. 2022, 13, 411–423, doi:10.3762/bjnano.13.34

• Scikit-learn, which is one of the most popular machine learning libraries of python. Anaconda installer for Python 3.8 was used to run all the libraries and Jupyter Notebooks. This work contains parts from the thesis of J. Kroutil, "Gas sensor array with nanocomposite films", Czech Technical University

Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning

• Richard Liam Marchese Robinson,
• Haralambos Sarimveis,
• Philip Doganis,
• Xiaodong Jia,
• Marianna Kotzabasaki,
• Christiana Gousiadou,
• Stacey Lynn Harper and
• Terry Wilkins

Beilstein J. Nanotechnol. 2021, 12, 1297–1325, doi:10.3762/bjnano.12.97

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

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

• pattern geometries, especially with curved edges, the available features of commercial patterning control software are not sufficient to even create the corresponding adapted beam paths. To address these issues, we developed the pattern generation toolbox FIB-o-mat with a Python interface. FIB-o-mat

• 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

Wafer-level integration of self-aligned high aspect ratio silicon 3D structures using the MACE method with Au, Pd, Pt, Cu, and Ir

• Mathias Franz,
• Romy Junghans,
• Paul Schmitt,
• Adriana Szeghalmi and
• Stefan E. Schulz

Beilstein J. Nanotechnol. 2020, 11, 1439–1449, doi:10.3762/bjnano.11.128

• Python community and the Matplotlib-Team [32].

Extracting viscoelastic material parameters using an atomic force microscope and static force spectroscopy

• Cameron H. Parvini,
• M. A. S. R. Saadi and
• Santiago D. Solares

Beilstein J. Nanotechnol. 2020, 11, 922–937, doi:10.3762/bjnano.11.77

• . While the current approach is primarily geared towards MATLAB implementation, the original process was outlined by Lopez et al. [17] in Python, and is available in a public Github repository. Conditioning raw static force spectroscopy datasets Traditionally, AFM-SFS experiments generate a variety of

Three-dimensional solvation structure of ethanol on carbonate minerals

• Hagen Söngen,
• Ygor Morais Jaques,
• Peter Spijker,
• Christoph Marutschke,
• Stefanie Klassen,
• Ilka Hermes,
• Ralf Bechstein,
• Lidija Zivanovic,
• John Tracey,
• Adam S. Foster and
• Angelika Kühnle

Beilstein J. Nanotechnol. 2020, 11, 891–898, doi:10.3762/bjnano.11.74

• conservation. The output data was collected every 1 ps during the production run, providing enough statistics for all required analysis. MD simulations were performed in Lammps code [33]. The analysis was performed using the Python library MDAnalysis [34][35]. Calcite and magnesite were described by the force

Playing with covalent triazine framework tiles for improved CO₂ adsorption properties and catalytic performance

• Giulia Tuci,
• Andree Iemhoff,
• Housseinou Ba,
• Lapo Luconi,
• Andrea Rossin,
• Vasiliki Papaefthimiou,
• Regina Palkovits,
• Jens Artz,
• Cuong Pham-Huu and
• Giuliano Giambastiani

Beilstein J. Nanotechnol. 2019, 10, 1217–1227, doi:10.3762/bjnano.10.121

• 15:85 CO2/N2 mixture at a total pressure of 1 bar was determined from Equation 2: where (χi)ads represent the adsorbed molar fractions of the two gases [72] as derived from the application of the free python software pyIAST (https://github.com/CorySimon/pyIAST) to the experimental N2 and CO2

A comparison of tarsal morphology and traction force in the two burying beetles Nicrophorus nepalensis and Nicrophorus vespilloides (Coleoptera, Silphidae)

• Liesa Schnee,
• Benjamin Sampalla,
• Josef K. Müller and
• Oliver Betz

Beilstein J. Nanotechnol. 2019, 10, 47–61, doi:10.3762/bjnano.10.5

• half cycle were extracted by automated analysis with a script written in Python 2.7 [58] and NumPy [59]. The arithmetic means of the coefficients of friction of the three middle cycles of a measurement were taken for further statistical analyses. We recorded 12 extreme values that exceeded three

Biomimetic surface structures in steel fabricated with femtosecond laser pulses: influence of laser rescanning on morphology and wettability

• Camilo Florian Baron,
• Alexandros Mimidis,
• Daniel Puerto,
• Evangelos Skoulas,
• Emmanuel Stratakis,
• Javier Solis and
• Jan Siegel

Beilstein J. Nanotechnol. 2018, 9, 2802–2812, doi:10.3762/bjnano.9.262

• well defined. In terms of biomimetics, these structures resemble the tiles found on the skin of the Python regius snake, whose microstructure makes it very resistant to damage from wear by reducing friction (c.f. Figure 2E). Laser-based surface texturing has been used to mimic this structure in steel

• when keeping the total number of pulses constant. In particular, the obtained structures resemble those found on the skin of the Texas horned lizard, the Python regius snake, the western diamondback rattlesnake, as well as on the integument of the bark bug, featuring fluid transport and friction

• License, http://creativecommons.org/licenses/by/2.0, copyright 2011 P. Comanns et al., i.e. the authors of [30]), Python regius snake (adapted from https://pixabay.com/es/snake-pit%C3%B3n-bola-python-regius-605344/), and Western Diamondback rattlesnake (image adapted from [31], an article under a Creative

Self-assembled quasi-hexagonal arrays of gold nanoparticles with small gaps for surface-enhanced Raman spectroscopy

• Emre Gürdal,
• Simon Dickreuter,
• Fatima Noureddine,
• Pascal Bieschke,
• Dieter P. Kern and
• Monika Fleischer

Beilstein J. Nanotechnol. 2018, 9, 1977–1985, doi:10.3762/bjnano.9.188

• . The SEM images for each sample were evaluated using a python script that applies a threshold in order to generate binary images. Blob detection is used to find the particles in the binary images and to evaluate the pixel count for each individual particle. From this pixel count, the area coverage and

Evaluation of replicas manufactured in a 3D-printed nanoimprint unit

• Manuel Caño-García,
• Morten A. Geday,
• Manuel Gil-Valverde,
• Xabier Quintana and
• José M. Otón

Beilstein J. Nanotechnol. 2018, 9, 1573–1581, doi:10.3762/bjnano.9.149

• or a transformation of the same that will be eventually processed by the second script. The first script was written in Python language, through a virtual Pygwy console of a Gwyddion package [10]. Gwyddion is a software environment specifically designed for data analysis of scanning probe microscopy

• output of the Python script to evaluate the second set of parameters, those derived from graphical inputs as described above: Vertical RMS deviations: Every copy is compared to a perfect saw-tooth pattern having the same pitch and blazing/back angles. For every point of the 512 × 512 2D mesh, the height

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

• Richard L. Marchese Robinson,
• Mark T. D. Cronin,
• Andrea-Nicole Richarz and
• Robert Rallo

Beilstein J. Nanotechnol. 2015, 6, 1978–1999, doi:10.3762/bjnano.6.202

• business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a “Toy Dataset” presented in the Supporting Information. The strengths and

• creating ISA-TAB-Nano files containing specific, relevant (meta)data manually harvested from the scientific literature; a corresponding set of business rules for populating these templates which build upon the generic ISA-TAB-Nano specification; a Python program for converting the resulting ISA-TAB-Nano

• business rules which were created for populating these templates. Section 5 provides an overview of the Python program written to facilitate analysis and databases submission of datasets created using these templates. Section 6 presents a “Toy Dataset” created using these templates. Section 7 presents a

The eNanoMapper database for nanomaterial safety information

• Nina Jeliazkova,
• Charalampos Chomenidis,
• Philip Doganis,
• Bengt Fadeel,
• Roland Grafström,
• Barry Hardy,
• Janna Hastings,
• Markus Hegi,
• Vedrin Jeliazkov,
• Nikolay Kochev,
• Pekka Kohonen,
• Cristian R. Munteanu,
• Haralambos Sarimveis,
• Bart Smeets,
• Pantelis Sopasakis,
• Georgia Tsiliki,
• David Vorgrimmler and
• Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165