Scanning electron microscopy image representativeness: morphological data on nanoparticles

Antioxidant-related enzymes and peptides as biomarkers of metallic nanoparticles (eco)toxicity in the aquatic environment

Increased awareness of the impact of human activities on the environment has emerged in recent decades. One significant global environmental and human health issue is the development of materials that could potentially have negative effects. These materials can accumulate in the environment, infiltrate organisms, and move up the food chain, causing toxic effects at various levels. Therefore, it is crucial to assess materials comprising nano-scale particles due to the rapid expansion of nanotechnology. The aquatic environment, particularly vulnerable to waste pollution, demands attention. This review provides an overview of the behavior and fate of metallic nanoparticles (NPs) in the aquatic environment. It focuses on recent studies investigating the toxicity of different metallic NPs on aquatic organisms, with a specific emphasis on thiol-biomarkers of oxidative stress such as glutathione, thiol- and related-enzymes, and metallothionein. Additionally, the selection of suitable measurement methods for monitoring thiol-biomarkers in NPs' ecotoxicity assessments is discussed. The review also describes the analytical techniques employed for determining levels of oxidative stress biomarkers.

Green synthesis silver nanoparticles Bougainvillea glabra Choisy/LED light with high catalytic activity in the removal of methylene blue aqueous solution

In this study, we evaluated, in a pioneering way, the influence of wavelengths from the decomposition of white light on the production and physicochemical properties of silver nanoparticles (AgNPs). Bearing in mind a process of green synthesis, an extract of the bracts of Bougainvillea glabra Choisy (BgC) was used, a species native to tropical and subtropical regions and frequently used in ornamentation, possessing in its photochemical composition, biomolecules capable of acting as reducing agents for convert Ag⁺ to Ag⁰. We used light-emitting diodes (LED) to obtain the desired wavelengths (violet, blue, green, yellow, orange, and red) in the test called rainbow, and we evaluated the obtaining of AgNPs compared to white LED light, nature, and absence of light. In the rainbow assay, we obtained a gradual increase in the intensity of the plasmonic band resonance from the red wavelength (0.124 ± 0.067 a.u.) to violet (0.680 ± 0.199 a.u.), indicating a higher reaction yield in obtaining AgNPs. Smaller hydrodynamic sizes (approximately 150 nm) at more energetic wavelengths (violet, blue, and green) about less energetic wavelengths (yellow, orange, and red) (approximately 400 nm) were obtained. Analysis by SEM microscopy, FTIR spectroscopy, and X-ray diffraction indicates the presence of silver nanoparticles in all LED colors used together with white LED light and Laboratory light (natural light). Due to the high environmental demand to remove pollutants from water sources, including textile dyes, we applied AgNPs/BgC to remove methylene blue (MB) dye from an aqueous solution. A minimum removal percentage greater than 65%, with emphasis on formulations synthesized by the colors of violet LED (84.27 ± 2.65%) and orange LED (85.91 ± 1.95%), was obtained.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Semi-Quantitative Method of Assessing the Thrombogenicity of Biomaterials Intended for Long-Term Blood Contact

Biomaterials used in cardiosurgical implants and artificial valves that have long-term contact with blood pose a great challenge for researchers due to the induction of thrombogenicity. So far, the assessment of the thrombogenicity of biomaterials has been performed with the use of highly subjective descriptive methods, which has made it impossible to compare the results of various experiments. The aim of this paper was to present a new semi-quantitative method of thrombogenicity assessment based on scanning electron microscope (SEM) images of an adhered biological material deposited on the surfaces of prepared samples. The following biomaterials were used to develop the proposed method: Bionate 55D polyurethane, polyether-ether ketone, Ti6Al7Nb alloy, sintered yttria-stabilized zirconium oxide (ZrO₂ + Y₂O₃), collagen-coated glass, and bacterial cellulose. The samples were prepared by incubating the biomaterials with platelet-rich plasma. In order to quantify the thrombogenic properties of the biomaterials, a TR parameter based on the fractal dimension was applied. The obtained results confirmed that the use of the fractal dimension enables the quantitative assessment of thrombogenicity and the proper qualification of samples in line with an expert's judgment. The polyurethanes showed the best thrombogenic properties of the tested samples: Bionate 55D (TR = 0.051) and PET-DLA 65% (average TR = 0.711). The ceramics showed the worst thrombogenic properties (TR = 1.846). All the tested materials were much less thrombogenic than the positive control (TR = 5.639).

Prediction models on biomass and yield of rice affected by metal (oxide) nanoparticles using nano-specific descriptors

The use of in silico tools to investigate the interactions between metal (oxide) nanoparticles (NPs) and plant biological responses is preferred because it allows us to understand molecular mechanisms and improve prediction efficiency by saving time, labor, and cost. In this study, four models (C5.0 decision tree, discriminant function analysis, random forest, and stepwise multiple linear regression analysis) were applied to predict the effect of NPs on rice biomass and yield. Nano-specific descriptors (size-dependent molecular descriptors and image-based descriptors) were introduced to estimate the behavior of NPs in plants to appropriately represent the wide space of NPs. The results showed that size-dependent molecular descriptors (e.g., E-state and connectivity indices) and image-based descriptors (e.g., extension, area, and minimum ferret diameter) were associated with the behavior of NPs in rice. The performance of the constructed models was within acceptable ranges (correlation coefficient ranged from 0.752 to 0.847 for biomass and from 0.803 to 0.905 for yield, while the accuracy ranged from 64% to 77% for biomass and 81% to 89% for yield). The developed model can be used to quickly and efficiently evaluate the impact of NPs under a wide range of experimental conditions and sufficient training data.

Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review

Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.

Shape-Dependent Toxicity of Silver Nanoparticles on Freshwater Cnidarians

Silver nanoparticles (AgNPs) are increasingly used in various consumer products, leading to their inadvertent release in aquatic ecosystems. The toxicity of AgNPs could be associated with the leaching of ionic Ag but also with the size, shape and surface properties. The purpose of this study was to test the null hypothesis that toxicity of AgNPs was independent of shape in the invertebrate Hydra vulgaris. The hydranths were exposed to increasing concentrations of ionic Ag and AgNPs of three different shapes (spherical, cubic and prismatic) with the same size and coating (polyvinylpyrrolidone). The data revealed that between 68% and 75% of total Ag remained in solution after the 96 h exposure period, while 85−90% of ionic Ag remained in solution. The 96 h lethal concentration (LC50) was lower with ionic (4 µg/L) and spherical AgNPs (56 µg/L), based on irreversible morphological changes such as loss of tentacles and body disintegration. Cubic and prismatic AgNPs were not toxic at a concentration of <100 µg/L. The sublethal toxicity was also determined at 96 h based on characteristic morphological changes (clubbed and/or shortened tentacles) and showed the following toxicity: ionic (2.6 µg/L), spherical (22 µg/L) and prismatic (32.5 µg/L) AgNPs. The nanocube was not toxic at this level. The data indicated that toxicity was shape-dependent where nanoparticles with a low aspect ratio in addition to high circularity and elongation properties were more toxic at both the lethal and sublethal levels. In conclusion, the shape of AgNPs could influence toxicity and warrants further research to better understand the mechanisms of action at play.

Crystal structure, Hirshfeld and electronic transition analysis of 2-[(1H-benzimidazol-1-yl)meth-yl]benzoic acid

In the title compound, C15H12N2O2, the benzimidazole ring system is inclined to the benzene ring by 78.04 (10)°. The crystal structure features O-H⋯N and C-H⋯O hydrogen bonding and C-H⋯π and π-π inter-actions, which were investigated using Hirshfeld surface analysis.

Quantitative SEM characterisation of ceramic target prior and after magnetron sputtering: a case study of aluminium zinc oxide

Till now electron microscopy techniques have not been used to evaluate the plasma-target interactions undergone during the magnetron sputtering process. The destructive nature of this interaction severely alters the target microstructure. Utilising quantitative microscopy techniques can shed light on the complex plasma and solid-state processes involved which can ultimately lead to improved functional thin film deposition. As a representative functional material, aluminium-doped-zinc oxide (AZO) is an upcoming alternative to conventional transparent electrode wherein the process optimisation is of great importance. In this paper, we evaluate the pre- and post-sputter field emission scanning electron microscopy (FESEM) data for ceramic AZO target fabricated at three final sintering temperatures (1100°C, 1200°C and 1300°C). In all cases, grain boundaries are merged in addition to a visible reduction in the secondary phases which makes segmentation-based image analysis challenging. Through surface statistics (i.e. fractal dimension, autocorrelation length, texture aspect ratio and entropy) as a function of magnification we can quantify the electron microscopy image of the microstructure. We show that the plasma-microstructure interaction leads to an increase in autocorrelation length, texture aspect ratio and entropy for the optimum AZO ceramic sputtering target sintered at 1200°C. Furthermore, a maximum reduction in fractal dimension span (as determined by exponential regression) is also observed for 1200°C. In addition to the evaluation of plasma effects on sintering, our approach can provide a window towards understanding the underlying thin film growth mechanisms. We believe that this technique can be applied to the defect characterisation of a wide range of polycrystalline ceramic sputtering targets (e.g. ITO, CZTS, GAZO and so on) with the ultimate goal of improving the magnetron sputtering process and the resulting functional thin film. LAY DESCRIPTION: Magnetron sputtering allows scientists to make functional thin films on the order of the nanoscale. In this technique, atoms are plucked from a 'target' then placed onto a substrate forming a thin nanometric film: all thanks to magnets, a special power supply and the fourth state of matter (plasma). Understanding what is going on and how to make a 'good' thin film is important for making better light emitting diodes, solar cells and light sensors. Scientists use electron microscopy to see what is going on in the microstructure of the sputtered thin films to fine tune the sputtering recipe. Here, for the first time, we have applied electron microscopy to see the surface of the microstructure before and after magnetron sputtering. This will help us understanding the plasma-microstructure interaction allowing us to make more informed decisions when fine-tuning the sputtering process to get improved thin films. This is a case study of aluminium-doped zinc oxide (AZO) target that could potentially replace indium tin oxide (ITO), which is widely used as a transparent electrode in devices involving light and electricity. In this case, improved characteristics would be lower electrical resistivity and higher transmission of light. We show that it is possible to use a mathematical description (e.g. the fractal dimension) of the scanning electron microscopy picture to show a link between the target surface and the functional properties. Simple explanation of fractal dimensions by Sixty Symbols ○ https://www.youtube.com/watch?v=cmBljeC79Ls Experimental demonstration of magnetron sputtering by The Thought Emporium ○ https://www.youtube.com/watch?v=Cyu7etM-0Ko Introductory video on magnetron sputtering by Applied Science ○ https://www.youtube.com/watch?v=9OEz_e9C4KM Demonstration of AZO target fabrication and sputtering by Pradhyut Rajjkumar ○ https://www.youtube.com/watch?v=kTLaTJfNX3c Simple explanation of a DIY SEM by Applied Science ○ https://www.youtube.com/watch?v=VdjYVF4a6iU.

Automated Single-Particle Reconstruction of Heterogeneous Inorganic Nanoparticles

Single-particle reconstruction can be used to perform three-dimensional (3D) imaging of homogeneous populations of nano-sized objects, in particular viruses and proteins. Here, it is demonstrated that it can also be used to obtain 3D reconstructions of heterogeneous populations of inorganic nanoparticles. An automated acquisition scheme in a scanning transmission electron microscope is used to collect images of thousands of nanoparticles. Particle images are subsequently semi-automatically clustered in terms of their properties and separate 3D reconstructions are performed from selected particle image clusters. The result is a 3D dataset that is representative of the full population. The study demonstrates a methodology that allows 3D imaging and analysis of inorganic nanoparticles in a fully automated manner that is truly representative of large particle populations.

Role of Artificial Intelligence and Machine Learning in Nanosafety

Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time-consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long-term reproductive toxicity assays over multiple generations.

Mapping the underlying mechanisms of fibrinogen benzothiazole drug interactions using computational and experimental approaches

Three-dimensional conformational crystallographic binding-modes are of paramount importance to understand the docking mechanism of protein-ligand interactions and to identify potential "leading drugs" conformers towards rational drugs-design. Herein, we present an integrated computational-experimental study tackling the problem of multiple binding modes among the ligand 3-(2-Benzothiazolylthio)-propane sulfonic acid (BTS) and the fibrinogen receptor (E-region). Based on molecular docking simulations, we found that the free energy of binding values for nine of different BTS-docking complexes (i.e., BTS-pose_1-9) were very close. We have also identified a docking-mechanism of BTS-interaction mainly based on non-covalent hydrophobic interactions with H-bond contacts stabilizing the fibrinogen-BTS docking complexes. Interestingly, the different BTS-poses_1-9 were found to be able to block the fibrinogen binding site (E-region) by inducing local perturbations in effector and allosteric residues, reducing the degree of collectivity in its flexibility normal modes. As such, we theoretically suggest that the BTS-binding modes can significantly affect the physiological condition of the unoccupied fibrinogen protein structure by bringing global and local perturbations in the frequency domain spectra. The proposed theoretical mechanisms, the interactions involved and the conformational changes suggested, were further corroborated by different experimental techniques such as isothermal titration calorimetry (ITC), zeta potential, UV-vis, fluorescence and small angle X-ray scattering (SAXS). The combined results shall open new avenues towards the application of complex supra-molecular information in rational drugs-design.

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.