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

• pristine and aged NPs, considering the size, coating, absolute electronegativity, and periodic table descriptors. Finally, advances of artificial intelligence (AI) have been also considered in the computational assessment of the ZP. Yan et al. [35] employed deep learning techniques and developed a

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

• , Turin, Corso Duca degli Abruzzi 24, Italy 10.3762/bjnano.15.119 Abstract This perspective article explores the convergence of advanced digital technologies, including high-performance computing (HPC), artificial intelligence, machine learning, and sophisticated data management workflows. The primary

• digital methodologies in advanced research. Keywords: artificial intelligence; high-performance computing; HPC; machine learning; materials modelling; multiscale modelling; nanomaterials; semantic data management; Introduction Digital technologies have ushered in a new era of materials science, enabling

• time and length scales, from atomic and molecular-level interactions to the macroscale, that govern the structural, mechanical, and thermal properties of materials [4][5]. More recently, data-driven approaches, such as machine learning (ML) and artificial intelligence (AI), are revolutionizing

Polymer lipid hybrid nanoparticles for phytochemical delivery: challenges, progress, and future prospects

• Iqra Rahat,
• Pooja Yadav,
• Aditi Singhal,
• Mohammad Fareed,
• Jaganathan Raja Purushothaman,
• Mohammed Aslam,
• Raju Balaji,
• Sonali Patil-Shinde and
• Md. Rizwanullah

Beilstein J. Nanotechnol. 2024, 15, 1473–1497, doi:10.3762/bjnano.15.118

Various CVD-grown ZnO nanostructures for nanodevices and interdisciplinary applications

• The-Long Phan,
• Le Viet Cuong,
• Vu Dinh Lam and
• Ngoc Toan Dang

Beilstein J. Nanotechnol. 2024, 15, 1390–1399, doi:10.3762/bjnano.15.112

• ordering can also be established in ZnO lattices upon doping with transition-metal and/or rare-earth elements (known as magnetic semiconductors, DMSs). This is expected to enable the development of next-generation spintronic devices [14] applicable to quantum and neuromorphic computing for artificial

• intelligence and internet of things [15][16][17]. Particularly during material fabrication processes, it has been discovered that ZnO exhibits many interesting structures in the nanoscale, such as rods, wires, rings, tubes, helixes, stars, bows, propellers, and cages [18][19][20][21][22][23][24]. Together with

AI-assisted models to predict chemotherapy drugs modified with C₆₀ fullerene derivatives

• Jonathan-Siu-Loong Robles-Hernández,
• Dora Iliana Medina,
• Katerin Aguirre-Hurtado,
• Marlene Bosquez,
• Roberto Salcedo and
• Alan Miralrio

Beilstein J. Nanotechnol. 2024, 15, 1170–1188, doi:10.3762/bjnano.15.95

• regression and IBM Watson artificial intelligence (AI), achieved mean absolute percentage errors below 12%, driven by AI-identified key variables. The predictive models included mainly quantitative descriptors collected from datasets as well as computed ones. In addition, a water-soluble fullerene was used

• /QSPR predictive models for the docking score were obtained from MLR and from IBM Watson artificial intelligence, yielding models with a MAPE of lower than 12% in all three cases. Although MLR exhibits the best evaluation metrics in the case of drug–C60 and drug–C60–COOH complexes, an improvement is

Recent progress on field-effect transistor-based biosensors: device perspective

• Billel Smaani,
• Fares Nafa,
• Mohamed Salah Benlatrech,
• Ismahan Mahdi,
• Hamza Akroum,
• Mohamed walid Azizi,
• Khaled Harrar and
• Sayan Kanungo

Beilstein J. Nanotechnol. 2024, 15, 977–994, doi:10.3762/bjnano.15.80

• variants for designing highly sensitive FET biosensors. However, there are still several possibilities that can be recommended for future work, such as implementation of artificial intelligence (AI) and machine learning (ML) algorithms for 3D and 2D FET-based biosensors. In this regard, the ML-based neural

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

• Shan He,
• Julen Segura Abarrategi,
• Harbil Bediaga,
• Sonia Arrasate and
• Humberto González-Díaz

Beilstein J. Nanotechnol. 2024, 15, 535–555, doi:10.3762/bjnano.15.47

• and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on

• N2D3Ss for diagnosis and treatment [12][13][14][15]. Also, over the last few years, artificial intelligence/machine learning (AI/ML) models have been applied successfully to solve problems in different disciplines, especially in the interface of chemistry and ND research [16][17][18][19]. In this regard

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

• contributing features) were further used for modeling using RF, AdaBoost, Gradient Boost, and Extreme Gradient Boost algorithms. Model development This section introduces four classification models; all of them are ensemble learning models. ML is a subset of artificial intelligence where the machine learns

Molecular nanoarchitectonics: unification of nanotechnology and molecular/materials science

• Katsuhiko Ariga

Beilstein J. Nanotechnol. 2023, 14, 434–453, doi:10.3762/bjnano.14.35

• methods such as artificial intelligence will make this possible in current science [143][144]. In fact, the fusion of nanoarchitectonics and materials informatics has been proposed [145]. Nanotechnology worked as a game changer. Nanoarchitectonics integrates nanotechnology and traditional sciences to a

A cantilever-based, ultrahigh-vacuum, low-temperature scanning probe instrument for multidimensional scanning force microscopy

• Hao Liu,
• Zuned Ahmed,
• Sasa Vranjkovic,
• Manfred Parschau,
• Andrada-Oana Mandru and
• Hans J. Hug

Beilstein J. Nanotechnol. 2022, 13, 1120–1140, doi:10.3762/bjnano.13.95

• frequency of microfabricated cantilevers combined with high-bandwidth cantilever deflection detection permits video-rate scanning [24], real-time peak force detection [25], or a later artificial intelligence processing of the vast amounts of data acquired during imaging [26][27]. Under vacuum conditions

Ultrafast signatures of magnetic inhomogeneity in Pd_1−xFe_x (x ≤ 0.08) epitaxial thin films

• Andrey V. Petrov,
• Sergey I. Nikitin,
• Lenar R. Tagirov,
• Amir I. Gumarov,
• Igor V. Yanilkin and
• Roman V. Yusupov

Beilstein J. Nanotechnol. 2022, 13, 836–844, doi:10.3762/bjnano.13.74

• supercomputing, big-data processing, artificial intelligence, and neuromorphic computing [1][2][3][4][5][6][7]. The highlight features of superconducting data processing techniques, for example, RSFQ logic [1][2][3][4][5][6][7][8][9], are the high speed and unprecedental energy efficiency [2][3][10][11][12][13

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

• system entities. Using the abovementioned information as data input in artificial intelligence systems, such as neural networks, in order to identify and/or predict materials with special properties, should be explored. Conclusion Magn3t software aims to provide a free, open source solution for the most

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

• learning is a subfield of artificial intelligence. It was defined by Arthur Samuel of IBM in 1959 as the “Field of study that gives computers the ability to learn without being explicitly programmed” [1]. This broad definition includes a variety of tasks including, but not limited to, classification

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

• , Swansea University, Swansea, SA1 8EN, UK 10.3762/bjnano.12.54 Abstract Self-powered sensors can provide energy and environmental data for applications regarding the Internet of Things, big data, and artificial intelligence. Nanogenerators provide excellent material compatibility, which also leads to a

• ][44][45][46], waste milk carton [15], and skin [47][48][49]. Thus, low-cost self-powered sensors can be deployed on a large scale and are a good candidate for data sources for the Internet of things (IoT), big data, and artificial intelligence (AI). NGs can be used as both pressure sensors and as

• increases with pressure. The output voltage reaches a saturation value after a certain number of sensor actuations, as shown in Figure 2e. This intelligent neuromorphic sensor that mimics synaptic enhancement and memory can be used as a human skin tactile sensing solution, providing rich data for artificial

A review on the green and sustainable synthesis of silver nanoparticles and one-dimensional silver nanostructures

• Sina Kaabipour and
• Shohreh Hemmati

Beilstein J. Nanotechnol. 2021, 12, 102–136, doi:10.3762/bjnano.12.9

• , continuous green and sustainable synthesis of silver nanostructures adaptable for in situ characterization, and incorporation of artificial intelligence (AI) in green silver nanostructures synthesis are discussed. 2 Physical and chemical synthesis methodologies of silver nanoparticles In this section

Piezotronic effect in AlGaN/AlN/GaN heterojunction nanowires used as a flexible strain sensor

• Jianqi Dong,
• Liang Chen,
• Yuqing Yang and
• Xingfu Wang

Beilstein J. Nanotechnol. 2020, 11, 1847–1853, doi:10.3762/bjnano.11.166

• investigated and systematically analyzed under compressive and tensile strain. Here, we describe a strain sensor that shows a great application potential in wearable integrated circuits, in health-monitoring devices, and in artificial intelligence. Keywords: AlGaN/AlN/GaN nanowires; flexible; piezotronic

• into electrical signals. They exhibit a potential for application in health-monitoring and motion-monitoring devices, and in artificial intelligence, for example [20][21][22]. However, high sensitivity (gauge factor ≥20) is key to detect a very small deformation of a given material [23][24]. Therefore

• describes the fabrication of a highly sensitive and a highly stable strain sensor based on a new AlGaN/AlN/GaN NW structure, which has a great potential to be applied in wearable integrated circuits, health-monitoring devices, artificial intelligence, among other fields. Results and Discussion The epitaxial

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

• -06CH11357. Funding This work has received funding from the Academy of Finland via the Artificial Intelligence for Microscopic Structure Search (AIMSS) project No. 316601 and the Flagship programme: Finnish Center for Artificial Intelligence FCAI, and from the Emil Aaltonen Foundation.

Multiwalled carbon nanotube based aromatic volatile organic compound sensor: sensitivity enhancement through 1-hexadecanethiol functionalisation

• Nadra Bohli,
• Meryem Belkilani,
• Juan Casanova-Chafer,
• Eduard Llobet and
• Adnane Abdelghani

Beilstein J. Nanotechnol. 2019, 10, 2364–2373, doi:10.3762/bjnano.10.227

• selectivity. It was also shown to improve the sensor response dynamics. These results combined with previous results [22][32] could be interesting for the development of functionalised multisensor arrays combined with an artificial intelligence algorithm for selectivity enhancement. Synoptic structure of the

Wearable, stable, highly sensitive hydrogel–graphene strain sensors

• Jian Lv,
• Chuncai Kong,
• Chao Yang,
• Lu Yin,
• Itthipon Jeerapan,
• Fangzhao Pu,
• Xiaojing Zhang,
• Sen Yang and
• Zhimao Yang

Beilstein J. Nanotechnol. 2019, 10, 475–480, doi:10.3762/bjnano.10.47

• the field of bioelectronics, artificial intelligence, and soft robotics [1][2]. Among these sensors, strain sensors can translate an external applied tensile force into electrical signal, hence attracting numerous research efforts for health monitoring, biomechanics studies and artificial skin for

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

• application in the fields of artificial intelligence and machine learning [1]. The future of cellular and satellite communications, radar systems, deep sea and space exploration will likely be closely related to the capability of ANNs to provide effective solutions to problems such as classification and

An analytical approach to evaluate the performance of graphene and carbon nanotubes for NH₃ gas sensor applications

• Elnaz Akbari,
• Vijay K. Arora,
• Aria Enzevaee,
• Mohamad. T. Ahmadi,
• Mehdi Saeidmanesh,
• Mohsen Khaledian,
• Hediyeh Karimi and
• Rubiyah Yusof

Beilstein J. Nanotechnol. 2014, 5, 726–734, doi:10.3762/bjnano.5.85

• Elnaz Akbari Vijay K. Arora Aria Enzevaee Mohamad. T. Ahmadi Mehdi Saeidmanesh Mohsen Khaledian Hediyeh Karimi Rubiyah Yusof Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia Faculty of Electrical Engineering, Universiti Teknologi

Analytical development and optimization of a graphene–solution interface capacitance model

• Hediyeh Karimi,
• Rasoul Rahmani,
• Reza Mashayekhi,
• Leyla Ranjbari,
• Amir H. Shirdel,
• Niloofar Haghighian,
• Parisa Movahedi,
• Moein Hadiyan and
• Razali Ismail

Beilstein J. Nanotechnol. 2014, 5, 603–609, doi:10.3762/bjnano.5.71

• Hediyeh Karimi Rasoul Rahmani Reza Mashayekhi Leyla Ranjbari Amir H. Shirdel Niloofar Haghighian Parisa Movahedi Moein Hadiyan Razali Ismail Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia Malaysia Japan International Ins. Of Technology