Scanning electron microscopy image representativeness: morphological data on nanoparticles

Odziomek, Katarzyna; Ushizima, Daniela; Oberbek, Przemysław; Kurzydłowski, Krzysztof J.; Puzyn, Tomasz; Harańczyk, Maciej

doi:10.1111/jmi.12461

Cited by 30 publications

(15 citation statements)

References 48 publications

(61 reference statements)

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“…[ 14 ] Among the different types of descriptors used in predictive modeling approaches, image descriptors resulting from the analysis of electronic images of ENMs have been employed successfully. [ 15,16 ]…”

Section: Introductionmentioning

confidence: 99%

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

Karatzas

Melagraki

Ellis

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

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

Karatzas

Melagraki

Ellis

et al. 2020

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“…These descriptors encode the geometric properties of distributions of nanoparticles and zeta potential models trained on them could predict zeta potential with r 2 values > 0.9. A very similar image processing approach to calculate geometric descriptors was reported by Odziomek et al [39] Mac Fhionnlaoich and Guldin used information entropy to characterize distributions of nanoparticles, providing a better estimate of the properties of distributions. [40] Novel "universal" descriptors for nanoparticles were also reported by Yan et al and used to generate ML models of gold nanoparticle properties using random forest and k-nearest neighbor (kNN) algorithms.…”

Section: Inadequacy Of Nanospecific Descriptors To Represent Nanomatementioning

confidence: 99%

Role of Artificial Intelligence and Machine Learning in Nanosafety

Winkler

2020

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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.

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“…After TiO 2 NP synthesis, the presence and size of the nanoparticles were determined through scanning electron microscopy (SEM) (Odziomek et al, 2017). This process was carried out in an Electronic Microscope MIRA3 FEG 650 Tesco brand Electronic Microscope MIRA3 FEG 650, which provided the data with "high vacuum mode" using a backscattered electron detector (BSED).…”

Section: Nanoparticle Characterizationmentioning

confidence: 99%

Antifungal evaluation of TiO2 nanoparticles that inhibit Fusarium solani in African oil palm

Salcedo

Torres

Kopytko

et al. 2020

International Journal of Agriculture and Natural Resources

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Herrera-Barros. 2020. Antifungal evaluation of TiO 2 nanoparticles that inhibit Fusarium solani in African oil palm. Int. J. Agric. Nat. Resour. 126-133. The ability of titanium dioxide nanoparticles (TiO 2 NPs) to attenuate and control several microorganisms was studied, especially for a disease that affects the health and productiveness of crops. For this purpose, the antifungal capacity of TiO 2 nanoparticles obtained from two synthesis procedures was evaluated: the first procedure involved green chemistry using an aqueous extract prepared from African palm oil (Elaeis guineensis Jacq.) leaves; the second procedure followed the Pechini sol-gel method. The nanoparticles obtained by the green chemistry method and the Pechini sol-gel technique had average sizes of 14.60±0.44 nm and 12.30±0.54 nm as determined by scanning electron microscopy (SEM), respectively. The antifungal properties of TiO 2 NPs were evaluated on the fungus Fusarium solani Mart. isolated on Sabouraud culture medium. A factorial experimental design was implemented with two variables: (1) exposure time ranging from 24, 48, and 72 h; (2) nanoparticle concentration between 100 and 400 mg L-1. All measurements were performed in quintuplicate. The results show that the TiO 2 nanoparticles synthesized by the Pechini method inhibited the fungus by 96.16±0.85% with a nanoparticle concentration of 400 mg L-1 , while the TiO 2 nanoparticles obtained from the green chemistry method generated a high inhibitory activity of approximately 98.51±0.02% at the four experimental concentrations.

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Scanning electron microscopy image representativeness: morphological data on nanoparticles

Cited by 30 publications

References 48 publications

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

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

Role of Artificial Intelligence and Machine Learning in Nanosafety

Antifungal evaluation of TiO2 nanoparticles that inhibit Fusarium solani in African oil palm

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