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Outline
- maging ellipsometry: oncept and setup
- Basic theory: ellipsometry on tilted/curved surfaces
- Geometric considerations
- Tilt-induced cross-polarization
- Application: coating analysis on microlensarray
- Mueller-Matrix Imaging
- Conversion to Delta-Psi Image
- Layer-thickness of ITO coating
NMR is gaining increasing interest in civil engineering applications for the use of microstructure characterization as e.g. pore size determination and monitoring of moisture transport in porous materials. In this study, the use of NMR as a tool for pore size characterization was investigated. For our study we used screed and synthetic materials at partial and full saturation. A successful determination could be achieved when having a reference or calibration method, although partly diffusion effects have been registered. Due to these diffusion effects, for the determination of pore size distributions of synthetic materials another NMR device was needed. Finally, the determination of the surface relaxivity of screed (50 μm/s) led to a higher value than first expected from literature.
Understanding the interaction between boehmite and epoxy and the formation of their interphases with different mechanical and chemical structures is crucial to predict and optimize the properties of epoxy-boehmite nanocomposites. Probing the interfacial properties with atomic force microscopy (AFM)-based methods, especially particle-matrix long-range interactions, is challenging. This is due to size limitations of various analytical methods in resolving nanoparticles and their interphases, the overlap of interphases, and the effect of buried particles that prevent the accurate interphase property measurement. Here, we develop a layered model system in which the epoxy is cured in contact with a thin layer of hydrothermally synthesized boehmite. Different microscopy methods are employed to evaluate the interfacial properties. With intermodulation atomic force microscopy (ImAFM) and amplitude dependence force spectroscopy (ADFS), which contain information about stiffness, electrostatic, and van der Waals forces, a soft interphase was detected between the epoxy and boehmite. Surface potential maps obtained by scanning Kelvin probe microscopy (SKPM) revealed another interphase about one order of magnitude larger than the mechanical interphase. The AFM-infrared spectroscopy (AFM-IR) technique reveals that the soft interphase consists of unreacted curing agent. The long-range electrical interphase is attributed to the chemical alteration of the bulk epoxy and the formation of new absorption bands.
Hydrogen production via formate photoreforming on TiO2 is characterized by marked dependence on the ratio between {101} and {001} surfaces for anatase nanoparticles. We observed higher rates of hydrogen Evolution with the increase of the {101} facets presence, owing to their reductive nature.
This helps the Pt photodeposition in the early stages of Irradiation and, then, the hydrogen ion reduction reaction. The selective photodeposition of 2 nm Pt nanoparticles on {101} facets was confirmed by transmission electron microscopy (TEM) micrographs. The results are confirmed also by experiments carried out without the use of Pt as cocatalyst and by photoelectrochemical measurements.
The work also explains the marginal effect of the fluorination on the H2 evolution.
Ellipsometry has become a powerful measurement tool in semiconductor industry since the sixties of the last century.
Early standardization activities focused exclusively on SiO2/Si (ASTM F 576-01, SEMI 3624). The first generic standard dealing with ellipsometry is DIN 50989-1:2018 Ellipsometry – Part 1: Principles. Standardization is a prerequisite for accreditation according to DIN EN ISO/IEC 17025 and the evaluation of uncertainty budgets.
In the last years, the implementation of imaging ellipsometry in the variety of optical characterization techniques has shown tremendous potential to analyze the topology of surfaces in the lateral dimension. In the later studies, this contrast-rich surface images were affiliated with changes of the refraction indices, Absorption bands or layer thicknesses. However, it was realized that additional factors like curvature or scattering can have a great Impact on the ellipsometric readout of the analyzed system. In this study, we focus on the systematic evaluation of structural anomalies and inhomogenities of several Basic systems. This includes spherical particles as a model for microscopic curved surfaces in a range of 0.25 to 25 μm in diameter. In the macroscopic regime several conventional convex lenses were analyzed. Additional affords were made to generate microscopic concave model systems by applying nanoindentation with a spherical indentation unit. With this method calottes with a depth of 0.04 to 2 μm and radius of 2.5 μm were made. The macroscopic counterpart is delivered by conventional concave lens systems. For all systems, a variety of different bulk materials was investigated. This includes metal oxides, metals and polymers as well as combinations of each by applying coatings on the bulk materials with different layer thicknesses.
To analyze these structural anomalies and inhomogenities properly, Mueller-Matrix imaging ellipsometry is the method of choice to address cross- and depolarization effects that occur due to the curved surfaces.
Supplementary methods were used for an independent characterization of the topological properties of all structural anomalies and inhomogenities. This includes AFM and SEM for the microscopic samples (microparticles and nanoindented holes) and white light interferometry for the macroscopic lenses.
This study results in a systematic screening of different coated and uncoated material systems with a topology that does not fit into conventional ellipsometry and thus is analyzed by Muller-Matrix imaging ellipsometry. This will help in quality control and is a contribution to the understanding of the polarizing effects of non-ideal Systems analyzed by ellipsometry.
Zinc is an essential trace element and is ingested daily by humans, partly in dissolved form. The first contact is with saliva, in which many ions are dissolved whose solubility product with zinc can be low. This could result in compounds forming, possibly in nanoparticular form, which could have different effects on the organism than pure zinc ions.
In this study, we report on the saliva stage of the artificial digestion of zinc chloride as a model substance for zinc ions. To facilitate in situ measurements, the sample is continuously passed through a small-angle x-ray scattering (SAXS) system. This custom-made machine is capable of measuring over a wide q-range and thereby able to resolve structures from around 250 nm down to the crystal structure. It is thus an excellent tool for investigating both the particle size distribution and the atomic structure of the sample.
By curve fitting, we found that shortly after addition of zinc chloride to saliva, small particles with a mean radius of 1.9 ± 0.1 nm and a distribution width of 0.6 ± 0.1 nm formed. These particles are aggregated to compact mass fractals with a fractal aggregate size of 14.7 ± 0.1 nm and a fractal dimension of 2.96 ± 0.02. Approximately 7200 single particles form each mass fractal, whose radius of gyration was found to be 36 ± 1 nm. The growth of these structures continues over the course of several weeks. To determine the compound that was formed, infrared spectroscopy was used in addition to the SAXS measurements, and zinc phosphate was identified as the product.
Zinc oxide (ZnO) nanoparticles find manifold applications, most prominently in photovoltaics, where their unique optical properties are exploited. Particularly important are their band gap energy of 3.37 eV, which can be widely tuned through doping, and a large exciton binding energy of 60 mV. As a wide-bandgap II-VI semiconductor, the optical band gap energy and fluorescence energy become size-dependent when moving to particle radii of a few nanometers.
To gain a deeper insight into this issue, we report on a microwave-assisted, size-selective synthesis of pure ZnO nanoparticles. By hydrolysis of the metal precursor in presence of a strong base at temperatures exceeding the solvent’s boiling point, the reaction is dramatically accelerated, and narrowly dispersed, spherical particles are yielded within seconds – instead of hours at lower temperatures. The determination of their size distributions in high resolution using small-angle x-ray scattering (SAXS) allows for a precise mapping of the optical properties (UV/Vis absorption and fluorescence) to particle size.
We observed that the mean particle radii increase from 2.6 ± 0.1 nm with increasing synthesis temperature from 125 °C to 200 °C. This is accompanied by a red shift of the optical band gap and the fluorescence energies, the latter of which can be seen in Figure 1. Thus, undoped ZnO nanoparticles with narrow size distributions and pre-defined size as well as optical properties can be obtained through a microwave-assisted synthesis.
Comparing Nontargeted LC-MS Methods by Co-visualizing Linear Dynamic Range and Chemical Coverage
(2019)
INTRODUCTION
Biological and environmental samples contain thousands of small molecule species that all vary in chemical properties and concentration range. Identifying and quantifying all these chemical entities remains a long-term goal in metabolomics and related systems approaches. Due to its broad selectivity, nontargeted LC-MS is usually the method of choice for broad chemical screening. Optimizing nontargeted LC-MS methods, however, is less straightforward than for targeted methods where sensitivity, specificity, linearity etc. serve as well-established performance criteria. We therefore investigated linear dynamic range (LDR) and chemical classification as alternative performance criteria to guide nontargeted method development.
EXPERIMENTAL METHODS
LDR was defined as the linear portion of a feature’s response curve over multiple concentration levels. Comparing the LDR of features across methods can be expected to be significantly more robust than comparing signal intensities for a single concentration. To determine LDR for all features, a computational workflow was implemented in the R programming language. For estimating the linear portion of a curve, several mathematical approaches including linear, non-linear and piecewise linear regression were evaluated. Chemical classification was based on ClassyFire, which computes chemical classes for a given structure. To avoid false classifications for incorrectly annotated compounds, we took the following statistical approach. For each compound, multiple likely annotation hypotheses were derived using a recently described workflow[2]. All annotation hypotheses were submitted to ClassyFire and obtained classifications were ranked by frequency. The most frequently suggested class was kept for further analysis. Finally, LDR and chemical classes were visualized together on a molecular network, which was constructed using the well-established MS/MS similarity approach.
RESULTS AND DISCUSSION
For technical validation of the workflow, several hundred curve fits obtained from the different regression models were reviewed visually. Piecewise linear regression performed the most reliably with respect to the heterogeneous curve shapes of ‘real-life’ features. Validation of chemical classification was performed against a compound library, which showed that 90% of ~450 library compounds were correctly classified using the described approach. Two liquid chromatography methods (HILIC, RPC) as well as two electrospray ionization variants (low/high-temperature ESI) applied to urinary metabolomics were exemplarily studied to test the workflow. Molecular network visualization indicated that of all analytical setups, HILIC/high temperature ESI performed best in terms of high LDR achieved over a wide range of compound classes. Despite one order of magnitude lower sensitivity, HILIC/low temperature ESI showed similar chemical coverage, except for organic nitrogen compounds that were underrepresented compared to high-temperature ESI. Both RPC setups were inferior to the HILIC setups in terms of high-LDR features, supporting previous findings for the given matrix. The higher relative representation of benzenoids and lipids in RPC demonstrated that the workflow successfully captured expected selectivity differences between chromatographies.
CONCLUSION
When comparing nontargeted LC-MS methods for optimization purposes, ideally all available quantitative and qualitative information should be integrated. The present workflow follows this idea. Visualizing LDR and chemical classes of all features on a molecular network quickly indicated differences in method selectivity that were otherwise difficult to spot. As an automated approach, it is easily applied to repeated optimization steps, enabling effective optimization strategies.
Nontargeted high-resolution mass spectrometry (HRMS) is widely used for small molecule screening in biotic or abiotic samples. However, HRMS approaches like metabolomics or environmental nontarget screening currently still lack confidence in chemical annotation, i.e. computational structure assignment to all measured mass signals. As a crucial step within the annotation pipeline, molecular weight inference (MWI) deduces a compound’s intact mass from diagnostic mass differences between MS1 peaks, allowing precise database queries in subsequent steps. As the common practice of considering all possible ionization products such as adducts, multimers, multiple charges etc. in MWI suffers from high false positive rates, we aimed at selecting candidate ionization products in a chemically sensitive way. Generally, electrospray ionization produces different types of adducts depending on chromatographic system and sample matrix, necessitating application-specific optimization for optimum MWI performance. To avoid, however, the tedious and potentially biased manual data curation connected to optimization, we established an R-based workflow for automating this task. The workflow consists of two parts. Part 1 creates an MS1 spectral library by performing peak detection, spectral deconvolution and target peak assignment based on density estimation. Part 2 analyzes ion relationships within the library and returns a list of detected ionization products ranked by their frequency. We applied the workflow to a commercial 634-compound library that was acquired for two chromatographic methods (reverse phase, RP; hydrophilic liquid interaction chromatographic, HILIC) and the two ESI modes (positive, negative). As expected, different frequency distributions of ionization products were found for the two chromatographies. Interestingly, however, some of the differences were expected in terms of solvent chemistries (e.g. [M+NH4]+ in ammonium formate-buffered HILIC) while others indicated more complex ion competition (e.g. abundant [M+K]+, [M+2K-H]+ in HILIC). This demonstrated the relevance of this empirical approach. We further show that MWI accuracy clearly benefitted from derived optimized adduct lists – by adding filters or weighting terms – and present FDR calculations supporting this observation. We conclude that chemistry-aware compound annotation based on the combination of high-throughput library acquisition and statistical analysis holds significant potential for further improvements in nontargeted small molecule HRMS.