Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO₂ with periodic table descriptors using machine learning approaches

• Joyita Roy,
• Souvik Pore and
• Kunal Roy

Beilstein J. Nanotechnol. 2023, 14, 939–950, doi:10.3762/bjnano.14.77

• nanomaterials based on structure similarities with known substances. Materials with similar structures are likely to produce similar toxicity through comparable mechanisms. The development of machine learning (ML) approaches, such as artificial neural networks (ANNs), decision trees, logistic regression (LR

Frontiers of nanoelectronics: intrinsic Josephson effect and prospects of superconducting spintronics

• Anatolie S. Sidorenko,
• Horst Hahn and
• Vladimir Krasnov

Beilstein J. Nanotechnol. 2023, 14, 79–82, doi:10.3762/bjnano.14.9

• : artificial neural networks; functional nanostructures; intrinsic Josephson effect; nanoelectronics; spintronics; The twenty-first century is marked by an explosive growth in the flow of information, which is necessary to process, archive, and transmit data through communication systems. For that purpose

• development in superconducting spintronics, based on functional nanostructures and Josephson junctions, has taken place [13][14]. The implementation of such devices in building blocks for quantum computers and for novel computers using non-von Neumann architecture with brain-like artificial neural networks

Design of surface nanostructures for chirality sensing based on quartz crystal microbalance

• Yinglin Ma,
• Xiangyun Xiao and
• Qingmin Ji

Beilstein J. Nanotechnol. 2022, 13, 1201–1219, doi:10.3762/bjnano.13.100

• higher interaction energy for the host–guest complexes to discriminate the enantiomers in the inclusion process [66]. Fietzek et al. measured the selective adsorption of chiral limonene in three different β-cyclodextrin (β-CD) derivatives by QCM and artificial neural networks (ANN) to evaluate the chiral

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

• . Neural networks were first proposed by Warren McCulloch and Walter Pitts in 1943 [25]. This provided the groundwork for the eventual use of artificial neural networks (ANNs) in machine learning. ANNs comprise an end-to-end process, where the neural network learns, extracts, and selects those features

• could fire when a vertical edge is observed. Artificial neural networks are now applied to various tasks in image analysis such as image classification, object detection, image retrieval, and segmentation. A subset of ANNs, deep neural networks (DNNs), uses a set of algorithms in machine learning based

• on artificial neural networks, which allow for a much more autonomous method for characterizing images than traditional algorithms [26][28][29][30][31][32]. The “architecture” for these networks will be described below. In this review, we adopt the term traditional machine learning when the

Recent progress in actuation technologies of micro/nanorobots

• Ke Xu and
• Bing Liu

Beilstein J. Nanotechnol. 2021, 12, 756–765, doi:10.3762/bjnano.12.59

• promoting the research of neural networks and neural connectivity, offering reproducibility, selectivity, and precise connection. This provides a potential platform for advanced in vitro controllable models of artificial neural networks. With the research on single magnetic field-driven micro/nanorobots

Functional nanostructures for electronics, spintronics and sensors

• Anatolie S. Sidorenko

Beilstein J. Nanotechnol. 2020, 11, 1704–1706, doi:10.3762/bjnano.11.152

• theoretical and experimental investigations of S/F superlattices. Such superlattices can be used as tunable kinetic inductivity synapses in artificial neural networks of a superconducting computer with non-von Neumann architecture. A further example by Novikov et al. [14] demonstrated the concept of “read-out

Controlling the proximity effect in a Co/Nb multilayer: the properties of electronic transport

• Sergey Bakurskiy,
• Mikhail Kupriyanov,
• Nikolay V. Klenov,
• Igor Soloviev,
• Andrey Schegolev,
• Roman Morari,
• Yury Khaydukov and
• Anatoli S. Sidorenko

Beilstein J. Nanotechnol. 2020, 11, 1336–1345, doi:10.3762/bjnano.11.118

• superlattices can be used as tunable kinetic inductors designed for artificial neural networks representing the information in a “current domain”. Keywords: cryogenic computing; spin-valve; superconducting neural network; superconducting spintronics; Introduction Multilayer superconductor/ferromagnetic (S/F

• elements, including synapses. This new type of application will be discussed in more detail. The creation of artificial neural networks is one of the current trends in the development of superconductor electronics [10][11][12][13][14][15]. Such an artificial neural network contains layers of elements that

• field. This effect enables the construction of a magnetically tunable kinetic inductor for artificial neural networks. (a) Sketch of the investigated multilayer Co(1.5 nm)/Nb(6 nm)/Co(2.5 nm)/Nb(6 nm) structure. (b) The simplest splitter model based on this TKI. (a) Pair amplitude, Δ, in a S(F1sF2s)x3F1

Anomalous current–voltage characteristics of SFIFS Josephson junctions with weak ferromagnetic interlayers

• Tairzhan Karabassov,
• Anastasia V. Guravova,
• Aleksei Yu. Kuzin,
• Elena A. Kazakova,
• Shiro Kawabata,
• Boris G. Lvov and
• Andrey S. Vasenko

Beilstein J. Nanotechnol. 2020, 11, 252–262, doi:10.3762/bjnano.11.19

• , e.g., single-flux quantum circuits [46][47], spintronic devices [48], memory elements [49][50][51][52][53][54][55][56][57][58] and spin-valves [59][60][61][62][63][64][65], magnetoelectronics [66][67][68], qubits [69], artificial neural networks [70], microrefrigerators [71][72], and low-temperature

Beyond Moore’s technologies: operation principles of a superconductor alternative

• Igor I. Soloviev,
• Nikolay V. Klenov,
• Sergey V. Bakurskiy,
• Mikhail Yu. Kupriyanov,
• Alexander L. Gudkov and
• Anatoli S. Sidorenko

Beilstein J. Nanotechnol. 2017, 8, 2689–2710, doi:10.3762/bjnano.8.269

• mentioned localization of information and high non-linearity of Josephson junctions make superconductor circuits to be ideally suited for the implementation of unconventional computational paradigms like cellular automata [94][95], artificial neural networks [96][97][98] or quantum computing [99][100][101

Adiabatic superconducting cells for ultra-low-power artificial neural networks

• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Igor I. Soloviev and
• Maxim V. Tereshonok

Beilstein J. Nanotechnol. 2016, 7, 1397–1403, doi:10.3762/bjnano.7.130

• . We optimize their parameters for application in three-layer perceptron and radial basis function networks. Keywords: adiabatic superconductor cells; artificial neural networks; energy efficiency; Josephson effect; superconductivity; Findings Artificial neural networks (ANNs) are famous for their