Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Eingeladener Vortrag
- nein (9)
Both essential aspects of the surface of solid matter, its morphology and chemistry, are studied traditionally at BAM starting in the 60’s with different cyclical research focus areas, mostly related either to applicative research or method development. In the recent years, the focus has shifted almost exclusively to the nano-analytics of advanced materials such as complex nanoparticles, (ultra)thin films/coatings, nanocomposites, 2D materials, energy materials, etc. This is also the reason why BAM has established recently the new Competence Center nano@BAM (www.bam.de/Navigation/DE/Themen/Material/Nanotechnologie/sichere-nanomaterialien.html) with the five sub-fields nanoCharacterisation, nanoMaterial, nanoSafety, nanoData and nanoTechnology. The link to the BAM central guidelines to the safety in technology and chemistry is given by the development of reference products such as reference measurement procedures, reference (nano)materials, and newly reference data sets. Thus, an internationally well-networked group in surface analysis has been established @BAM, with regular contributions to integral analytical characterization with metrological and standardization background.
Examples of newly developed methodical approaches will be given with an emphasis on correlative nano-analysis of morphology and chemistry of nanomaterials. Correlative imaging by STEM-in-SEM with high-resolution SEM and EDX, and further with AFM or the new technique TKD (Transmission Kikuchi Diffraction) will be explained on various examples of nanostructures, both as starting materials and embedded/functionalized nanoparticles in products. The unique analytical benefits of the Auger electron probe as a veritable nano-tool for surface chemistry will be highlighted. The panoply of advanced surface characterization methods @BAM is completed by discussing examples of hybrid analysis of the bulk of nanomaterials by X-ray Spectroscopy with the highest surface-sensitive methods X-ray Photoelectron Spectroscopy (XPS) and Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS). Particularly for the analysis of the surface chemistry of nanostructures, such as the completeness of the shells of core-shell nanoparticles or in-depth and lateral gradients of chemistry within mesoporous thin layers, the latter methods are inherent.
Other special developments like approaches for the quantitative determination of the roughness of particle surface by electron microscopy or for the quantitative determination of the porosity of thin mesoporous layers by electron probe microanalysis (EPMA) with SEM will be presented.
The main outcomes of the EMPIR project nPSize are presented and the suitability of the new capabilities, e.g. reference materials, sample preparation protocols, measurement procedures, and data analysis, to be standardized and implemented in accredited analytical laboratories is discussed. Complementation and/or filling gaps of published and ongoing standardisation projects on size, shape and number concentration measurements under ISO/TC 229/JWG 2 are offered.
The two VAMAS inter-laboratory comparisons resulted from the nSPize project and just started under TWA 34 Nanoparticle Populations (Projects #15 and #16) of bipyramidal TiO2 anatase and bimodal SiO2 nanoparticles are presented in detail.
he main objective of the nPSize project is to improve the measurement capabilities for nanoparticle size based on both measurement methods traceable to SI units and new reference materials. Two basic approaches have been used in order to develop measurement procedures resulting in traceable results of the nanoparticle size distribution: physical modelling for the methods used in the project (TSEM, SEM, AFM and SAXS) and machine learning.
Physical modelling: In this part, the physical models associated with different shape measurements for the techniques TSEM, SEM, AFM and SAXS have been collected and further developed with the aim to simulate the resulting signal as measured by the individual methods. Uncertainties and traceability associated with each model were investigated and evaluated. In the following, the progress on these physical models is reported for each individual method.
Machine Learning modelling: The aim of this part is to use machine learning to enable automatic measurement of nanoparticle shape from expert a-priori information only. No physical model will be used as a-priori information in this task.
The accuracy and traceability of the size results obtained by each technique will be analyzed and compared with the physical modelling. A machine learning database will then be used to create automatic detection algorithms.
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.
The Competence Centre nano@BAM is presenting a new X-ray Photoelectron Spectrometer – the HAXPES (XPS at hard energy) – for researching advanced materials at the nanoscale. With HAXPES detailed chemical information can now be gained not only from the first outermost nanometres of the sample surface, but also from deeper regions.
Top international experts will share their knowledge and key findings on how to utilise HAXPES for exploring the surface of various advanced materials. Following the lectures, we will show you a short demo of the new instrument and answer your questions.
The roughness as a property of core–shell (CS) microparticles plays a key role in their functionality. Quantitative evaluation of the roughness of CS microparticles is, however, a challenging task with approaches using electron microscopy images being scarce and showing pronounced differences in terms of methodology and results. This work presents a generalized method for the reliable roughness determination of nonplanar specimens such as CS particles from electron microscopic images, the method being robust and reproducible with a high accuracy. It involves a self-written software package (Python) that analyzes the recorded images, extracts corresponding data, and calculates the roughness based on the deviation of the identified contour. Images of single particles are taken by a dual mode scanning electron microscopy (SEM) setup which permits imaging of the same field-of-view of the sample with high resolution and surface sensitive in SE InLens mode as well as in transmission mode (TSEM). Herein, a new type of polystyrene core–iron oxide shell–silica shell particles is developed to serve as a set of lower micrometer-sized study objects with different surface roughness; the analysis of their images by the semiautomatic workflow is demonstrating that the particles’ profile roughness can be quantitatively obtained.