Filtern
Dokumenttyp
- Zeitschriftenartikel (7)
- Corrigendum (1)
Sprache
- Englisch (8)
Schlagworte
- Characterisation (2)
- (Quantitative) Structure-Active Relationships (1)
- 2D materials (1)
- ACEnano (1)
- Aqueous media (1)
- Automation (1)
- Benchmarking (1)
- Concentration (1)
- Data management (1)
- Dispersion of nanomaterials (1)
Organisationseinheit der BAM
As part of the European Horizon ACCORDs project, advanced methods are being developed for the image-based characterisation of 2D nanomaterials. Given the complexity of this task, robust nd wellorganised data management is critical to ensuring high-quality outcomes. To support this, we have established a knowledge infrastructure that serves as the central repository for protocols, images and experimental data which are stored in a standardised, harmonised manner and in accordance with the FAIR principles – Findable, Accessible, Interoperable and Reusable and open science. This machine-readable framework enables the systematic and computationally automated correlation of image features with experimental descriptors, facilitating accurate material characterisation and transparent reporting which is all integrated in the ACCORDs KI.
The ACCORDs project, funded through Horizon Europe, is pioneering a novel approach to investigate Graphene Family Materials (GFMs) through image analysis. Our aim is to unveil how these materials might influence health and the environment. To achieve this, we are developing a platform designed for the easy retrieval, access, sharing, and utilisation of GFM data and the coordination between biological and physico-chemical data formats. Integral to this platform is an OMERO-based library for image storage, alongside data collection forms and image analysis tools. Efforts are underway to streamline the process for researchers to upload and disseminate their findings, manage information within a database and navigate the data with ease. Adhering to REMBI guidelines, which set the standard for annotating biological images with metadata, we ensure our data collection is comprehensive and adheres to established best practices. Initially, we are employing thresholding and basic machine learning techniques for image segmentation, laying the groundwork for advanced analysis through deep learning to gain more profound insights. Upon completion, the project will deliver a comprehensive platform facilitating efficient data and image management concerning GFMs. This platform will enable the straightforward discovery and use of protocols and results, all organised in accordance with the FAIR principles – Findable, Accessible, Interoperable, and Reusable.
This initiative is poised to significantly impact materials science, enhancing our comprehension of the safety and environmental implications of 2D materials.
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.
ACEnano is an EU-funded project which aims at developing, optimising and validating methods for the detection and characterisation of nanomaterials (NMs) in increasingly complex matrices to improve confidence in the results and support their use in regulation. Within this project, several interlaboratory comparisons (ILCs) for the determination of particle size and concentration have been organised to benchmark existing analytical methods. In this paper the results of a number of these ILCs for the characterisation of NMs are presented and discussed. The results of the analyses of pristine well-defined particles such as 60 nm Au NMs in a simple aqueous suspension showed that laboratories are well capable of determining the sizes of these particles. The analysis of particles in complex matrices or formulations such as consumer products resulted in larger variations in particle sizes within technologies and clear differences in capability between techniques. Sunscreen lotion sample analysis by laboratories using spICP-MS and TEM/SEM identified and confirmed the TiO2 particles as being nanoscale and compliant with the EU definition of an NM for regulatory purposes. In a toothpaste sample orthogonal results by PTA, spICP-MS and TEM/SEM agreed and stated the TiO2 particles as not fitting the EU definition of an NM. In general, from the results of these ILCs we conclude that laboratories are well capable of determining particle sizes of NM, even in fairly complex formulations.
The sonication process is commonly used for de-agglomerating and dispersing nanomaterials in aqueous based media, necessary to improve homogeneity and stability of the suspension. In this study, a systematic step-wise approach is carried out to identify optimal sonication conditions in order to achieve a stable dispersion. This approach has been adopted and shown to be suitable for several nanomaterials (cerium oxide, zinc oxide, and carbon nanotubes) dispersed in deionized (DI) water. However, with any change in either the nanomaterial type or dispersing medium, there needs to be optimization of the basic protocol by adjusting various factors such as sonication time, power, and sonicator type as well as temperature rise during the process. The approach records the dispersion process in detail. This is necessary to identify the time Points as well as other above-mentioned conditions during the sonication process in which there may be undesirable changes, such as damage to the particle surface thus affecting surface properties. Our goal is to offer a harmonized approach that can control the Quality of the final, produced dispersion. Such a guideline is instrumental in ensuring dispersion quality repeatability in the nanoscience community, particularly in the field of nanotoxicology.
Nanosafety is becoming increasingly important as nanomaterials are widely used in industrial processes and consumer products. For nanotoxicity measurements prior sterilization of the samples is necessary, but as structure activity relationships are made with properties of pristine particles, the question arises, if the sterilization process has an impact on the physico-chemical properties of nanoparticles and thus on the biological behavior.
This question will be addressed in this talk. For this purpose, results from SEM and EDS measurements are combined with those of a novel lab-based HAXPES spectrometer in order to obtain a more complete picture. At the end, an influence of sterilization will be evident, which indicates a restructuring of the nanoparticles owing to sterilization.
Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by au-tomation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been de-veloped, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air–liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.
