A review of metal-organic frameworks and polymers in mixed matrix membranes for CO₂ capture

• Charlotte Skjold Qvist Christensen,
• Nicholas Hansen,
• Mahboubeh Motadayen,
• Nina Lock,
• Martin Lahn Henriksen and
• Jonathan Quinson

Beilstein J. Nanotechnol. 2025, 16, 155–186, doi:10.3762/bjnano.16.14

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

• relationship (QSPR/QSFR) modelling, read-across, and deep learning models. Mikolajczyk et al. [16] implemented a consensus nano-QSPR scheme for the prediction of the ZP of metal oxide nanoparticles (NPs) based on the size and a quantum mechanical descriptor encoding the energy of the highest occupied molecular

• 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

• dealing with multiscale physical models) and support GPU computing (for deep learning but also for advanced visualization) [61]. The large amount of materials data involved in typical development processes often requires high-performance and high-end storage systems (>100 TB) and high-performance networks

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

• multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In

A wearable nanoscale heart sound sensor based on P(VDF-TrFE)/ZnO/GR and its application in cardiac disease detection

• Yi Luo,
• Jian Liu,
• Jiachang Zhang,
• Yu Xiao,
• Ying Wu and
• Zhidong Zhao

Beilstein J. Nanotechnol. 2023, 14, 819–833, doi:10.3762/bjnano.14.67

• energy harvesting [15]. Applying machine learning classification algorithms in the domain of human physiological signal detection is presently a prominent area of research. A notable study by R. Guo et al. [16] successfully integrated deep learning techniques with frictional hydrogel sensors to achieve

Effect of lubricants on the rotational transmission between solid-state gears

• Huang-Hsiang Lin,
• Jonathan Heinze,
• Alexander Croy,
• Rafael Gutiérrez and
• Gianaurelio Cuniberti

Beilstein J. Nanotechnol. 2022, 13, 54–62, doi:10.3762/bjnano.13.3

• ], which cannot be captured by a Lennard-Jones plane as used in our simulations. To further investigate those open questions, a more powerful pair potential such as the reactive force field (ReaxFF) [66] or a deep learning force field [67] approach might be suitable to address the problem. Finally, we hope

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

• , various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently

• using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is

• , convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data. Keywords: atomic force microscopy (AFM); deep learning; machine learning; neural networks; scanning probe microscopy (SPM); Review Introduction: traditional machine learning vs deep learning Machine

Reducing molecular simulation time for AFM images based on super-resolution methods

• Zhipeng Dou,
• Jianqiang Qian,
• Yingzi Li,
• Rui Lin,
• Jianhai Wang,
• Peng Cheng and
• Zeyu Xu

Beilstein J. Nanotechnol. 2021, 12, 775–785, doi:10.3762/bjnano.12.61

• also been used as training data. However, the simulation is incredibly time consuming. In this paper, we apply super-resolution methods, including compressed sensing and deep learning methods, to reconstruct simulated images and to reduce simulation time. Several molecular simulation energy maps under

• in computer vision. Super-resolution methods could be used to reconstruct a high-resolution image from a low-resolution image. There are a variety of methods in the field of super resolution. Compressed sensing (CS) and deep learning methods are two typical methods with excellent imaging performance

• of deep learning in super-resolution methods many other models have been proposed, such as VGG [49], Res Net [50], GAN [51], and VDSR [52]. The application of deep learning in super-resolution methods has been increasing in recent years, and it is also used in AFM to speed up imaging acquisition [31