Size-dependent electronic properties of nanomaterials: How this novel class of nanodescriptors supposed to be calculated?

The use of a chemiluminescence in the assessment of the nanomaterials antioxidant activity

Nanomaterials are one of the most promising classes of advanced materials with fine-tuned biological activities. This is evidenced by the presence of redox activity of a number of nanoparticles aimed at inhibiting free radicals and/or mimicking the functions of enzymes. At the same time, it is impossible to study the expression of these biological properties without the use of well-standardized, representative techniques that provide availability, high precision, sensitivity, and selectivity of the measured characteristics. A method that satisfies these requirements is chemiluminescence analysis, which is widely used both in clinical analysis and to characterize the antioxidant activity of substances of natural or synthetic origin. Recently, a trend of using chemiluminescence analysis to study the biological activity of nanomaterials has appeared as a suitable alternative to spectroscopic and electrochemical techniques. This review briefly describes the examples of successful applications of chemiluminescence methods to study radical-binding and enzyme-like activities of nanomaterials. We discuss the data about the effect of the used reagents (radical-generating systems, chemiluminescence activators) and experimental conditions on the obtained values characterizing the nanomaterials activity.

Graphical Abstract

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Toward size-dependent thermodynamics of nanoparticles from quantum chemical calculations of small atomic clusters: a case study of (B2O3)n

We present a method for obtaining canonical partition functions and, accordingly, temperature-dependent thermodynamics of arbitrary-sized (nano) particles from electronic structure calculations of the corresponding small size atomic clusters. The guiding idea here is to extrapolate the basic properties underlying the thermochemistry of clusters (electronic energies, rotational constants, and vibrational frequencies) rather than the thermodynamic functions themselves. The thus obtained scaling dependences for these basic properties expressed in a simple analytical form provide an efficient tool for fast evaluation of the size-selected thermochemical data for particles of any nuclearity. To exemplify the performance of the methodology, neutral stoichiometric boron oxide clusters are considered. To this end, the geometry and various physical properties of the energetically lowest-lying (B₂O₃)_n (n = 1,…,8) structures are found using density functional theory and the authors' multistage hierarchical procedure customized for global optimization of quite large cluster structures. With these data and based on the physically consistent scaling regularities for the principal cluster properties, the size-selected thermodynamic functions of boron oxide particles in the gas phase, such as enthalpy, entropy, and specific heat capacity, are derived. The variation of these characteristics with increasing cluster size is discussed in detail as well. To facilitate handling of the temperature and size dependences we have found here in further chemical kinetic and equilibrium modeling, the tabulated thermodynamic functions of interest are fitted for n = 1,…,1000 to the standard seven-parameter Chemkin polynomials.

Photochemical Synthesis of Gold and Silver Nanoparticles-A Review

Nanomaterials have supported important technological advances due to their unique properties and their applicability in various fields, such as biomedicine, catalysis, environment, energy, and electronics. This has triggered a tremendous increase in their demand. In turn, materials scientists have sought facile methods to produce nanomaterials of desired features, i.e., morphology, composition, colloidal stability, and surface chemistry, as these determine the targeted application. The advent of photoprocesses has enabled the easy, fast, scalable, and cost- and energy-effective production of metallic nanoparticles of controlled properties without the use of harmful reagents or sophisticated equipment. Herein, we overview the synthesis of gold and silver nanoparticles via photochemical routes. We extensively discuss the effect of varying the experimental parameters, such as the pH, exposure time, and source of irradiation, the use or not of reductants and surfactants, reagents' nature and concentration, on the outcomes of these noble nanoparticles, namely, their size, shape, and colloidal stability. The hypothetical mechanisms that govern these green processes are discussed whenever available. Finally, we mention their applications and insights for future developments.

Designing of Nanomaterials-Based Enzymatic Biosensors: Synthesis, Properties, and Applications

Among the many biological entities employed in the development of biosensors, enzymes have attracted the most attention. Nanotechnology has been fostering excellent prospects in the development of enzymatic biosensors, since enzyme immobilization onto conductive nanostructures can improve characteristics that are crucial in biosensor transduction, such as surface-to-volume ratio, signal response, selectivity, sensitivity, conductivity, and biocatalytic activity, among others. These and other advantages of nanomaterial-based enzymatic biosensors are discussed in this work via the compilation of several reports on their applications in different industrial segments. To provide detailed insights into the state of the art of this technology, all the relevant concepts around the topic are discussed, including the properties of enzymes, the mechanisms involved in their immobilization, and the application of different enzyme-derived biosensors and nanomaterials. Finally, there is a discussion around the pressing challenges in this technology, which will be useful for guiding the development of future research in the area.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).

Ultrasound-Assisted Hydrazine Reduction Method for the Preparation of Nickel Nanoparticles, Physicochemical Characterization and Catalytic Application in Suzuki-Miyaura Cross-Coupling Reaction

In the experimental work leading to this contribution, the parameters of the ultrasound treatment (temperature, output power, emission periodicity) were varied to learn about the effects of the sonication on the crystallization of Ni nanoparticles during the hydrazine reduction technique. The solids were studied in detail by X-ray diffractometry, dynamic light scattering, thermogravimetry, specific surface area, pore size analysis, temperature-programmed CO₂/NH₃ desorption and scanning electron microscopy. It was found that the thermal behaviour, specific surface area, total pore volume and the acid-base character of the solids were mainly determined by the amount of the nickel hydroxide residues. The highest total acidity was recorded over the solid under low-power (30 W) continuous ultrasonic treatment. The catalytic behaviour of the nanoparticles was tested in a Suzuki-Miyaura cross-coupling reaction over five samples prepared in the conventional as well as the ultrasonic ways. The ultrasonically prepared catalysts usually performed better, and the highest catalytic activity was measured over the nanoparticles prepared under low-power (30 W) continuous sonication.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

Universal nanohydrophobicity predictions using virtual nanoparticle library

To facilitate the development of new nanomaterials, especially nanomedicines, a novel computational approach was developed to precisely predict the hydrophobicity of gold nanoparticles (GNPs). The core of this study was to develop a large virtual gold nanoparticle (vGNP) library with computational nanostructure simulations. Based on the vGNP library, a nanohydrophobicity model was developed and then validated against externally synthesized and tested GNPs. This approach and resulted model is an efficient and effective universal tool to visualize and predict critical physicochemical properties of new nanomaterials before synthesis, guiding nanomaterial design.

Development of a nano-QSPR model to predict band gaps of spherical metal oxide nanoparticles

Antibacterial activities and cytotoxicity of metal oxide nanoparticles are determined by their special band structures, which also influence their potential ecological risks. Traditional experimental determination of the band gap is time-consuming, while the accuracy of theoretical computation depends on the selected algorithm, for which higher precision algorithms, being more expensive, can give a more accurate band gap. Therefore, in this study, a quantitative structure–property relationship (QSPR) model, highlighting the influence of crystalline type and material size, was developed to predict the band gap of metal oxide nanoparticles rapidly and accurately. The structural descriptors for metal oxide nanoparticles were generated via quantum chemistry computations, among which heat of formation and beta angle of the unit cell were the most important parameters influencing band gaps. The developed model shows great robustness and predictive ability (R² = 0.848, RMSE = 0.378 eV, RMSE_CV = 0.478 eV, Q_EXT² = 0.814, RMSE_P = 0.408 eV). The current study can assist in screening the ecological risks of existing metal oxide nanoparticles and may act as a reference for newly designed materials.

Antibacterial activities and cytotoxicity of metal oxide nanoparticles are determined by their special band structures, which also influence their potential ecological risks.

Fe-Doped ZnO nanoparticle toxicity: assessment by a new generation of nanodescriptors

In the search for novel tools to combat cancer, nanoparticles (NPs) have attracted a lot of attention. Recently, the controlled release of cancer-cell-killing metal ions from doped NPs has shown promise, but fine tuning of dissolution kinetics is required to ensure specificity and minimize undesirable toxic side-effects. Theoretical tools to help in reaching a proper understanding and finally be able to control the dissolution kinetics by NP design have not been available until now. Here, we present a novel set of true nanodescriptors to analyze the charge distribution, the effect of doping and surface coating of whole metal oxide NP structures. The polarizable model of oxygen atoms enables light to be shed on the charge distribution on the NP surface, allowing the in detail study of the factors influencing the release of metal ions from NPs. The descriptors and their capabilities are demonstrated on a Fe-doped ZnO nanoparticle system, a system with practical outlook and available experimental data.

Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework

The European market for pesticides is currently legislated through the well-developed Regulation (EC) No. 1107/2009. This regulation promotes the competitiveness of European agriculture, recognizing the necessity of safe pesticides for human and animal health and the environment to protect crops against pests, diseases and weeds. In this sense, nanotechnology can provide a tremendous opportunity to achieve a more rational use of pesticides. However, the lack of information regarding nanopesticides and their fate and behavior in the environment and their effects on human and animal health is inhibiting rapid nanopesticide incorporation into European Union agriculture. This review analyzes the recent state of knowledge on nanopesticide risk assessment, highlighting the challenges that need to be overcame to accelerate the arrival of these new tools for plant protection to European agricultural professionals. Novel nano-Quantitative Structure-Activity/Structure-Property Relationship (nano-QSAR/QSPR) tools for risk assessment are analyzed, including modeling methods and validation procedures towards the potential of these computational instruments to meet the current requirements for authorization of nanoformulations. Future trends on these issues, of pressing importance within the context of the current European pesticide legislative framework, are also discussed. Standard protocols to make high-quality and well-described datasets for the series of related but differently sized nanoparticles/nanopesticides are required.