Missing links in nanomaterials research impacting productivity and perceptions

• Santosh K. Tiwari and
• Nannan Wang

Beilstein J. Nanotechnol. 2025, 16, 2168–2176, doi:10.3762/bjnano.16.149

• productive technologies like generative AI, machine learning, and related progress, nanotechnology has not achieved autonomous societal integration. The author argues that without a unified, transparent, and science-driven global regulatory framework, the transformative potential of nanotechnology will

• as generative AI, machine learning, and related progress. The latter areas are inherently multidisciplinary, encompassing multiple research domains, with nanotechnology often representing only a component. Nevertheless, from a user‑oriented perspective, generative AI, machine learning, and Internet

• technological revolutions such as generative AI, machine learning, Internet of Things, and related progress, nanotechnology falls short in terms of commercial and practical outcomes. It is understandable that nanotechnology is still an emerging field that is inherently more exploratory in nature. In other words

Microplastic pollution in Himalayan lakes: assessment, risks, and sustainable remediation strategies

• Sameeksha Rawat,
• S. M. Tauseef and
• Madhuben Sharma

Beilstein J. Nanotechnol. 2025, 16, 2144–2167, doi:10.3762/bjnano.16.148

• strategies being pursued for MP remediation, which strike a balance between efficiency, operational feasibility, and environmental sustainability. While physical and chemical methods are dominant, the use of advanced technologies such as electrochemical systems, machine-learning-based sensing, and

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

• -dispersive X-ray spectroscopy (EDX); focused ion beam (FIB); focused ion beam-induced deposition (FIBID); machine learning; scanning electron microscopy (SEM); Introduction A variety of nanomanufacturing techniques, such as optical and electron-beam lithography, nanoimprint lithography, atomic layer

• (SEM EDX) together with machine learning-based hyperspectral data processing, which uses non-negative matrix factorization (NMF) to separate the EDX signals of structures from the ones of the substrate. As already shown, this type of analysis greatly enhances the applicability of SEM EDX for the

• C, O, F, and Si, as well as the Ag Lα map (see Supporting Information File 1, Figure S2–S4 for other precursors). In all cases, the maps show that the formed structures are enriched in metal. In the next step, the EDX data were processed by machine learning NMF as described in details in Jany and

Current status of using adsorbent nanomaterials for removing microplastics from water supply systems: a mini review

• Nguyen Thi Nhan and
• Tran Le Luu

Beilstein J. Nanotechnol. 2025, 16, 1837–1850, doi:10.3762/bjnano.16.127

• ]. Finally, along with the development of information technology, the application of artificial intelligence (AI) and machine learning (ML) can transform water treatment. Leveraging pattern detection, ML simplifies MP classification and enhances nanomaterial identification, improving research efficiency and

Ambient pressure XPS at MAX IV

• Mattia Scardamaglia,
• Ulrike Küst,
• Alexander Klyushin,
• Rosemary Jones,
• Jan Knudsen,
• Robert Temperton,
• Andrey Shavorskiy and
• Esko Kokkonen

Beilstein J. Nanotechnol. 2025, 16, 1677–1694, doi:10.3762/bjnano.16.118

• between fundamental surface studies and application-relevant complexity. Another promising frontier is the coupling of APXPS with advanced data acquisition and analysis, such as machine learning for spectral interpretation, real-time kinetic modeling, and high-throughput experimentation. These approaches

Nanotechnology-based approaches for the removal of microplastics from wastewater: a comprehensive review

• Nayanathara O Sanjeev,
• Manjunath Singanodi Vallabha and
• Rebekah Rubidha Lisha Rabi

Beilstein J. Nanotechnol. 2025, 16, 1607–1632, doi:10.3762/bjnano.16.114

• synthesis, hybrid system integration, and machine learning optimization. Together, these approaches aim to establish a comprehensive, scalable, and environmentally safe solution for the remediation of MPs in wastewater systems. Keywords: artificial intelligence; membrane technology; microplastic

• nanoparticles, enhanced through machine learning (ML) optimization, enable dual removal of MPs and organic pollutants with efficient magnetic recovery. Despite their potential, concerns remain regarding synthesis complexity, environmental safety of nanomaterials, and real-water applicability. These emerging

• recovery and high adsorption selectivity due to imine surface groups. Machine learning was applied to optimize operational parameters, enhancing removal efficiency while minimizing sorbent use. The system demonstrated stable performance across multiple reuse cycles, highlighting its potential as a smart

Nanomaterials for biomedical applications

• Iqra Zainab,
• Zohra Naseem,
• Syeda Rubab Batool,
• Filippo Pierini,
• Seda Kizilel and
• Muhammad Anwaar Nazeer

Beilstein J. Nanotechnol. 2025, 16, 1499–1503, doi:10.3762/bjnano.16.105

• treatments. The research is still in its early stages, and the results are encouraging so far. In the future, machine learning, biocompatible materials, and tailored therapies will all be included for patient-specific care [35]. Nanomaterials are also widely used in medical devices and prosthetics

Laser processing in liquids: insights into nanocolloid generation and thin film integration for energy, photonic, and sensing applications

• Akshana Parameswaran Sreekala,
• Pooja Raveendran Nair,
• Jithin Kundalam Kadavath,
• Bindu Krishnan,
• David Avellaneda Avellaneda,
• M. R. Anantharaman and
• Sadasivan Shaji

Beilstein J. Nanotechnol. 2025, 16, 1428–1498, doi:10.3762/bjnano.16.104

The role of biochar in combating microplastic pollution: a bibliometric analysis in environmental contexts

• Tuan Minh Truong Dang,
• Thao Thu Thi Huynh,
• Guo-Ping Chang-Chien and
• Ha Manh Bui

Beilstein J. Nanotechnol. 2025, 16, 1401–1416, doi:10.3762/bjnano.16.102

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

• spectroscopic measurements. The ability to automate this process is a key aim in development of high resolution scanning probe materials characterisation. In this paper, we assess the feasibility of automating the assessment of imaging quality, and spectroscopic tip quality, via both machine learning (ML) and

• characteristic feature corresponding to the surface state, which appears as a step function around a specific bias value, which for the Au(111) surface appears at around −0.48 V [13][14]. One notable attempt to automate this classification using machine learning (ML) was carried out by Wang et al. [15]. This

• number of spectra in each category after the labelling step. For ML training, the data were split into training, validation, and test sets at a ratio of 70:10:20. This left 1823 spectra for training, 260 for validation, and 521 for final testing. Classification methods Machine learning classifier With

Enhancing the therapeutical potential of metalloantibiotics using nano-based delivery systems

• Alejandro Llamedo,
• Marina Cano,
• Raquel G. Soengas and
• Francisco J. García-Alonso

Beilstein J. Nanotechnol. 2025, 16, 1350–1366, doi:10.3762/bjnano.16.98

• delivery platforms, as well as the investigation of synergistic effects through combination therapies. In-depth in vivo studies will be essential to validate preclinical findings and ensure translational potential. Moreover, the integration of computational modelling and machine learning may expedite the

Electronic and optical properties of chloropicrin adsorbed ZnS nanotubes: first principle analysis

• Prakash Yadav,
• Boddepalli SanthiBhushan and
• Anurag Srivastava

Beilstein J. Nanotechnol. 2025, 16, 1184–1196, doi:10.3762/bjnano.16.87

• composites with other materials, such as carbon nanotubes, to optimize performance [26]. Furthermore, the flexible synthesis of ZnS NTs with controlled morphology and size allows for tailoring their sensing capabilities. External stimuli combined with machine learning can further enhance their sensitivity

Crystalline and amorphous structure selectivity of ignoble high-entropy alloy nanoparticles during laser ablation in organic liquids is set by pulse duration

• Robert Stuckert,
• Felix Pohl,
• Oleg Prymak,
• Ulrich Schürmann,
• Christoph Rehbock,
• Lorenz Kienle and
• Stephan Barcikowski

Beilstein J. Nanotechnol. 2025, 16, 1141–1159, doi:10.3762/bjnano.16.84

• on density functional theory calculations of binding energies and machine learning algorithms for an efficient catalyst design [15][17][19]. The synthesis of HEA NPs has been realized by many methods, including carbothermal shock synthesis (CTS) [20][21], chemical reduction [22][23], fast-moving bed

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

• to map the surface to scales below the measured image feature size by “erosion” [14]. It is also important to note that recently machine-learning based methods have been applied to blind reconstruction to reconstruct true surface images from AFM images experimentally broadened by the tip [15

Soft materials nanoarchitectonics: liquid crystals, polymers, gels, biomaterials, and others

• Katsuhiko Ariga

Beilstein J. Nanotechnol. 2025, 16, 1025–1067, doi:10.3762/bjnano.16.77

Time-resolved probing of laser-induced nanostructuring processes in liquids

• Maximilian Spellauge,
• David Redka,
• Mianzhen Mo,
• Changyong Song,
• Heinz Paul Huber and
• Anton Plech

Beilstein J. Nanotechnol. 2025, 16, 968–1002, doi:10.3762/bjnano.16.74

• substantial production of single-pulse data, which awaits prompt online analysis to facilitate practical implementation of the investigation. Such streamlined management of large data sets can be supported by implementing machine learning algorithms. We anticipate a more pronounced utilization of deep

Recent advances in photothermal nanomaterials for ophthalmic applications

• Jiayuan Zhuang,
• Linhui Jia,
• Chenghao Li,
• Rui Yang,
• Jiapeng Wang,
• Wen-an Wang,
• Heng Zhou and
• Xiangxia Luo

Beilstein J. Nanotechnol. 2025, 16, 195–215, doi:10.3762/bjnano.16.16

• machine learning to accelerate material development, exploring more types of photothermal nanomaterials, exploring more diverse composite photothermal material formulations, utilizing advanced characterization techniques, and collaborating with multidisciplinary researchers, more advanced and effective

• development cycles and high cost. The rapid advancement in AI and machine learning is revolutionizing material design and screening processes [219]. Machine learning has achieved significant success in predicting various material properties, including morphology, toxicity, photothermal characteristics

• , synthesis methods, and activity [220]. Developing machine learning models for ophthalmic photothermal nanomaterials will expedite the development of high-performance target materials and alleviate the burden of extensive experimental work. Advanced characterization tools, theoretical simulations, and high

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

• (1) mechanisms of CO2 sorption in MOFs, (2) considerations related to the integration of MOFs in MMMs, (3) CO2 capture performance of MOF-based MMMs, (4) advancements in MOF-based MMM materials design through machine learning, and (5) considerations for the implementation of MOF-based MMMs in large

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

• underlying models. Using a publicly available dataset, four research groups (NovaMechanics Ltd. (NovaM)-Cyprus, National Technical University of Athens (NTUA)-Greece, QSAR Lab Ltd.-Poland, and DTC Lab-India) built five distinct machine learning (ML) models for the in silico prediction of the zeta potential

• ]. Another example of an IATA is the combination of predictions from two or more individual models under a consensus framework. Consensus models combine outputs from several individual models built upon different sets of descriptors and/or machine learning (ML) algorithms, leading to more trustworthy results

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

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

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

• Indrasis Dasgupta,
• Totan Das,
• Biplab Das and
• Shovanlal Gayen

Beilstein J. Nanotechnol. 2024, 15, 909–924, doi:10.3762/bjnano.15.75

• governing the cellular uptake of ENMOs. The study will direct scientists in the design of ENMOs of higher cellular uptake efficiency for better therapeutic response. Keywords: Bayesian classification; cellular uptake; machine learning; nanoparticles (NPs); Introduction In recent years, the rapid

• uptake in the PaCa2 cell line [22][23][24][25][26][27]. In the current study, we have performed a distinctive approach by developing nano-QSAR machine learning-based classification models that encompass not only the cellular uptake data of the PaCa2 cell line but also the two additional cell lines HUVEC

• using the fivefold cross-validation procedure. Additionally, the model’s quality was evaluated by looking at the receiver operating characteristic (ROC) plot as well as specificity, sensitivity, and accuracy values [40][41][42]. Development of other machine learning models Calculation of descriptors and

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

• N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset

• serve as valuable tools in the design of drug delivery systems for neurosciences. Keywords: artificial neural network (ANN); linear discriminant analysis (LDA); machine learning; nanoparticle; neurodegenerative diseases; Introduction Over time, there has been a significant shift in global dietary

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

• proteins using the same fragment-based approach. To avoid the need to run a time-consuming parameterization protocol based on metadynamics simulations, we produce PMFs for the glucose bead using a machine-learning technique (PMFPredictor) trained on previous metadynamics results [38]. For the lactose

• 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