Evaluating metal-organic precursors for focused ion beam-induced deposition through solid-layer decomposition analysis

• Benedykt R. Jany,
• Katarzyna Madajska,
• Aleksandra Butrymowicz-Kubiak,
• Franciszek Krok and
• Iwona B. Szymańska

Beilstein J. Nanotechnol. 2025, 16, 1942–1951, doi:10.3762/bjnano.16.135

• results would be replicable and relevant to a wide range of potential precursors. The collected SEM BSE and EDX data together with an exemplary Python Jupyter notebook to analyze EDX hyperspectral data are freely available from Zenodo [37]. Conclusion In this research, we studied the ion-beam-induced

Beyond the bilayer: multilayered hygroscopic actuation in pine cone scales

• Kim Ulrich,
• Max David Mylo,
• Tom Masselter,
• Fabian Scheckenbach,
• Sophia Fischerbauer,
• Martin Nopens,
• Silja Flenner,
• Imke Greving,
• Linnea Hesse and
• Thomas Speck

Beilstein J. Nanotechnol. 2025, 16, 1695–1710, doi:10.3762/bjnano.16.119

• , “Affine_parameters.txt” and “BSpline_parameters.txt” can be found in Supporting Information File 1. Based on the resulting displacement field, the Green–Lagrangian strain was calculated using the Insight Toolkit strain filter extension (ITK: ver. 5.3.0, itk-strain: ver. 0.4.0) [40] in Python (ver. 3.11.9) [41]. Finally

• analyses, and visualization were done with Python (ver. 3.12.8) using the packages pandas (ver. 2.2.3) [42], scipy (ver. 1.14.1) [43] and seaborn (ver. 0.13.2) [44]. The threshold of statistical significance was set at p < 0.05. Results Gravimetric water uptake The gravimetric water uptake measurements

Parylene-coated platinum nanowire electrodes for biomolecular sensing applications

• Chao Liu,
• Peker Milas,
• Michael G. Spencer and
• Birol Ozturk

Beilstein J. Nanotechnol. 2025, 16, 1392–1400, doi:10.3762/bjnano.16.101

• calculate the LoD, a penalized spline version of the asymmetric least squares algorithm in Python (pybaselines.spline.pspline_asls) was used to subtract the baseline of DPV curves. To get a clear result, the smoothing parameter “lam” and penalizing weighting factor “p” were adjusted for each test. This

• used for peak current calculation. The same Python algorithm was used for baseline subtraction. Optical images of (a) a DENA-grown platinum nanowire between two electrodes while still in the growth solution, (b) a platinum nanowire in air after removed from the growth solution, (c) a single platinum

Automated collection and categorisation of STM images and STS spectra with and without machine learning

• Dylan Stewart Barker and
• Adam Sweetman

Beilstein J. Nanotechnol. 2025, 16, 1367–1379, doi:10.3762/bjnano.16.99

• [19]. A custom Python script was written with a graphical interface. The script would show each spectrum individually, with a choice of four labels depending on the visibility of the surface state (SS): SS “good”, SS “step visible”, SS “peak visible”, and SS “not visible”. When classifying the data

Deep-learning recognition and tracking of individual nanotubes in low-contrast microscopy videos

• Vladimir Pimonov,
• Said Tahir and
• Vincent Jourdain

Beilstein J. Nanotechnol. 2025, 16, 1316–1324, doi:10.3762/bjnano.16.96

• localization precision of the optical setup is 0.33 µm. Frames were then aligned using a template matching algorithm from the OpenCV Python library. Shade correction was applied to compensate for uneven illumination caused by the optics. Residual noise was reduced using fast Fourier transform (FFT) band-pass

• steps: (1) object recognition, (2) tracking of recognized objects, (3) verification of tracking, and (4) kinetic curve extraction and analysis. The initial stage of the process utilizes the Mask-RCNN neural network implemented in PyTorch Python library [11]. This architecture integrates several neural

Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images

• Samuel Gelman,
• Irit Rosenhek-Goldian,
• Nir Kampf,
• Marek Patočka,
• Maricarmen Rios,
• Marcos Penedo,
• Georg Fantner,
• Amir Beker,
• Sidney R. Cohen and
• Ido Azuri

Beilstein J. Nanotechnol. 2025, 16, 1129–1140, doi:10.3762/bjnano.16.83

• formed from silicon nitride (NanoWorld). Images were subject to plane leveling and alignment using Gwyddion 2.62, an open-source software for SPM data analysis [40]. The Gwyddion files were converted to Python .npy files for input to the computational pipeline. Computational Pipeline The research aims to

• models used. Individual methods and models were compared using box plots, and full comparisons were done using adjacency matrices. Code In this study, all code was written in the Python programming language [48]. In addition to its common packages, we used, OpenCV (v. 4.7.0) [41], PyTorch (v. 2.1.2) [49

Characterization of ion track-etched conical nanopores in thermal and PECVD SiO₂ using small angle X-ray scattering

• Shankar Dutt,
• Rudradeep Chakraborty,
• Christian Notthoff,
• Pablo Mota-Santiago,
• Christina Trautmann and
• Patrick Kluth

Beilstein J. Nanotechnol. 2025, 16, 899–909, doi:10.3762/bjnano.16.68

• Struve function given by: To measure the distribution of the nanopore sizes, we implemented a narrow Schulz–Zimm distribution [21][13][47][56]. Readers are referred to these works for detailed information on the implementation of polydispersity. The fits are performed using a custom C- and Python-based

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