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Monodisperse iron oxide nanoparticles as reference material candidate for particle size measurements
(2020)
In order to utilize and rationally design materials at the nanoscale the reliable characterization of their physico-chemical properties is highly important, especially with respect to the assessment of their environmental or biological impact. Furthermore, the European Commission’s REACH Regulations require the registration of nanomaterials traded in quantities of at least 1 ton. Powders or dispersions where 50% (number distribution) of the constituent particles have sizes ≤ 100 nm in at least one dimension are defined as nanomaterials. This creates a need for industrial manufacturers and research or analytical service facilities to reliably characterize potential nanomaterials. Currently, BAM is developing reference nanoparticles, which shall expand the scarce list of worldwide available nano reference materials certified for particle size distribution and will also target other key parameters like shape, structure, porosity or functional properties. In this respect, materials like iron oxide or titanium dioxide are considered as candidates to complement the already available silica, Au, Ag, and polystyrene reference nanoparticles.
The thermal decomposition of iron oleate precursors in high boiling organic solvents can provide large quantities of iron oxide nanoparticles that can be varied in size and shape.[1, 2] The presence of oleic acid or other hydrophobic ligands as capping agents ensures stable dispersion in nonpolar solvents. Such monodisperse, spherical particles were synthesized at BAM and pre-characterized by electron microscopy (TEM, SEM including the transmission mode STEM-in-SEM) and dynamic light scattering comparing cumulants analysis and frequency power spectrum.
1. REACH regulations and nanosafety concerns create a strong need for nano reference materials with diverse properties.
2. Iron oxide nanoparticles are under development as new candidate reference material at BAM.
3. Narrow particle size distribution confirmed by light scattering and electron microscopy.
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.
Controlling thickness and tightness of surface passivation shells is crucial for many applications of core–shell nanoparticles (NP). Usually, to determine shell thickness, core and core/shell particle are measured individually requiring the availability of both nanoobjects. This is often not fulfilled for functional nanomaterials such as many photoluminescent semiconductor quantum dots (QD) used for bioimaging, solid state lighting, and display technologies as the core does not show the applicationrelevant functionality like a high photoluminescence (PL) quantum yield, calling for a whole nanoobject approach. By combining high-resolution transmission electron microscopy (HR-TEM) and X-ray photoelectron spectroscopy (XPS), a novel whole nanoobject approach is developed representatively for an ultrabright oleic acid-stabilized, thick shell CdSe/CdS QD with a PL quantum yield close to unity. The size of this spectroscopically assessed QD, is in the range of the information depth of usual laboratory XPS. Information on particle size and monodispersity were validated with dynamic light scattering (DLS) and small angle X-ray scattering (SAXS) and compared to data derived from optical measurements. In addition to demonstrating the potential of this novel whole nanoobject approach for determining architectures of small nanoparticles, the presented results also highlight challenges faced by different sizing and structural analysis methods and method-inherent uncertainties.
Background, Motivation and Objective
Nanomaterials are at the core of some of the 21st century’s most promising technologies. In order to utilize and rationally design materials at the nanoscale the reliable characterization of their physico-chemical properties is highly important. Furthermore, the European Commission has taken measures via the REACH Regulations to control the classification of nanomaterials. REACH Annexes which entered into force in January 2020 require manufacturers to register nanomaterials that are traded in larger quantities (at least 1 ton). Every powder or dispersion where 50% (number distribution) of the constituent particles have sizes ≤ 100 nm in at least one dimension are defined as a nanomaterial. This creates a need for both industrial manufacturers and research and analytical service facilities to reliably characterize potential nanomaterials. Currently, BAM is working on developing reference nanoparticles, which shall expand the scarce list of worldwide available nano reference materials certified for particle size distribution, but also targeting other key parameters such as shape, structure (including porosity) and functional properties. Thus, candidate materials are considered to complement the already available spherical and monodisperse silica, Au and polystyrene reference nanoparticles, e.g. iron oxide and titanium oxide, with an average atomic number between those of silica and gold. Particularly for the imaging by electron microscopies, new nanoparticles of well-defined size in the range of 10 nm are decisive for the accurate particle segmentation by setting precise thresholds.
Statement of Contribution/Methods
Synthesis: Highly monodisperse iron oxide nanoparticles can be synthesized in large quantities by thermal decomposition of iron oleate or iron acetylacetonate precursors in high boiling solvents such as octadecene or dioctyl ether in the presence of oleic acid and oleylamine as capping agents.
Scanning Electron Microscope: An SEM of type Supra 40 from Zeiss has been used including the dedicated measurement mode transmission in SEM (STEM-in-SEM) with a superior material contrast for the nanoparticle analysis. The software package ImageJ has been used for the analysis of the STEM-in-SEM images and to determine the particle size distribution.
Dynamic Light scattering (DLS): Particles in suspension were measured in comparison by means of Zetasizer Nano (Malvern Panalytical; cumulants analysis) and NanoFlex (Microtrac; frequency power spectrum).
Results/Discussion
In this study iron oxide nanoparticles synthesized at BAM and pre-characterized by DLS, SEM (including the transmission mode STEM-in-SEM) are presented. The particles are spherical and highly monodisperse with sizes slightly larger than 10 nm.
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.
We studied the dissolution behavior of β NaYF4:Yb(20%), Er(2%) UCNP of two different sizes in biologically relevant media i.e., water (neutral pH), phosphate buffered saline (PBS), and Dulbecco’s modified Eagle medium (DMEM) at different temperatures and particle concentrations. Special emphasis was dedicated to assess the influence of different surface functionalizations, particularly the potential of mesoporous and microporous silica shells of different thicknesses for UCNP stabilization and protection. Dissolution was quantified electrochemically using a fluoride ion selective electrode (ISE) and by inductively coupled plasma optical emission spectrometry (ICP OES). In addition, dissolution was monitored fluorometrically. These experiments revealed that a thick microporous silica shell drastically decreased dissolution. Our results also underline the critical influence of the chemical composition of the aqueous environment on UCNP dissolution. In DMEM, we observed the formation of a layer of adsorbed molecules on the UCNP surface that protected the UCNP from dissolution and enhanced their fluorescence. Examination of this layer by X ray photoelectron spectroscopy (XPS) and mass spectrometry (MS) suggested that mainly phenylalanine, lysine, and glucose are adsorbed from DMEM. These findings should be considered in the future for cellular toxicity studies with UCNP and other nanoparticles and the design of new biocompatible surface coatings.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.
This dataset accompanies the following publication, first published in Scientific Reports (www.nature.com/articles/s41598-021-84287-6):
B. Ruehle, J. Krumrey, V.-D. Hodoroaba, Scientific Reports, Workflow towards Automated Segmentation of Agglomerated, Non-Spherical Particles from Electron Microscopy Images using Artificial Neural Networks, DOI: 10.1038/s41598-021-84287-6
It contains electron microscopy micrographs of TiO2 particles, the corresponding segmentation masks, and their classifications into different categories depending on their visibility/occlusion. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.com/BAMresearch/automatic-sem-image-segmentation
The publicly available document encapsulates the first version of the Catalogue of Services of the future EC4Safenano Centre (CoS 2019).
The CoS 2019 is structured in 12 Service Categories and 27 Service Topics, for each of the 12 categories considered. This architecture configures a 12 x 27 matrix that allows ordering the potential EC4Safenano offer in 324 types of services/groups of services.
Each type of service/group of services is described, in a simple and friendly way, by means of a specific service sheet: the EC4Safenano - Service Data Sheet (EC4-SDS). These EC4-SDSs allow structuring and summarizing the information of each service, providing the customer with a concise view of characteristics of the service and also the contact details with the service provider.
The CoS 2019 deploys a map of services consisting of a set of 100 EC4-SDSs, covering 7 of the 12 Service Categories and 17 of the 27 Service Topics.
The harmonization of services is visualized as a future necessary step in EC4Safenano, in order to strengthen the offer and provide added value to customers with a growing offer of harmonized services in future versions of the CoS.
The information contained in this document is structured in 3 main sections, as follows:
• Catalogue structure. This section describes in short the main characteristics of the CoS 2019.
• Catalogue content. This section represents the core part of the document and encapsulates the set of 100 SDSs displaying the offer proposed by the CoS 2019.
• Online Catalogue. This section describes the resources implemented by EC4Safenano to facilitate the on-line consultation of the CoS 2019 by customers and other interested parties.