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Due to its large strength-to-weight ratio and excellent biocompatibility, titanium materials are of paramount importance for medical applications, e.g. as implant material for protheses. In this work, the evolution of various types of laser-induced micro- and nanostructures emerging on titanium or titanium alloys upon irradiation by near-infrared ultrashort laser pulses (925 fs, 1030 nm) in air environment is studied for various laser fluence levels, effective number of pulses and at different pulse repetition rates (1 – 400 kHz). The morphologies of the processed surfaces were systematically characterized by optical and scanning electron microscopy (OM, SEM). Complementary white-light interference microscopy (WLIM) revealed the corresponding surface topographies. Chemical and structural changes were analysed through depth-profiling time-of-flight secondary ion mass spectrometry (TOF-SIMS) and X-ray diffraction (XRD) analyses. The results point towards a remarkable influence of the laser processing parameters on the surface topography, while simultaneously altering the near-surface chemistry via laser-induced oxidation effects. Consequences for medical applications are outlined.
The modern economy is dependent on catalysis, which is main efforts to create environmentally and energy-friendly technologies. The storage of excess electrical energy into chemical energy by splitting water into hydrogen and oxygen is a feasible solution to this energy demand. Due to their abundance on Earth and inherent stability in alkaline solution, transition-metal oxides have become one of several viable alternatives to noble-metal catalysts. Since NiFe oxide is one of the most active oxygen evolution reaction (OER) electrocatalysts for alkaline water electrolysis, it has been the subject of extensive research.
In this work, NiFe2O4 nanoparticles (NPs) of various sizes, specific stoichiometric and non-stoichiometric Fe:Ni surface ratios are synthesized. we will use a combination of ultra-high vacuum surface analysis techniques, such as time-of-flight secondary ion mass spectrometry (TOF-SIMS) and X-ray photoelectron spectroscopy (XPS), to obtain the detailed characterization of the OER electrocatalysts top-surface layer, which is required to identify the rate-limiting step intermediates, and surface morphological changes at the electrolyte/catalyst.
Electrocatalysis is and will continue to play a central role in the development of a new and modern sustainable economy, especially for chemicals and fuels. The storage of excess electrical energy into chemical energy by splitting water into hydrogen and oxygen is a feasible solution in this economic sector. A major drawback of electrical energy lies in the storage. Therefore, hydrogen is discussed as promising alternative. Fortunately, this issue can be effectively addressed through the implementation of chemical storage mechanisms. Due to their abundance on Earth and inherent stability in alkaline solutions, transition-metal oxides have become one of several viable alternatives to conventional noble-metal catalysts. Since FeNi oxide is one of the most active oxygen evolution reaction (OER) electrocatalysts for alkaline water electrolysis, it has been the subject of extensive research.
A series of different types of FeNi oxide nanoparticles (NPs) with atomic ratios covering a broad range, and various sizes with specific stoichiometric and non-stoichiometric iron and nickel ratios was synthesized and characterized by the combination of surface analysis techniques, such as time-of-flight secondary ion mass spectrometry (ToF-SIMS) and X-ray photoelectron spectroscopy (XPS). The morphology was studied using scanning electron microscopy (SEM) and transmission electron microscopy (TEM), which revealed the coexistence of mixed and unmixed iron and nickel NPs with comparable sizes in the range of 30–40 nm across all ratios. The synthesis technique displayed control over the iron-nickel ratio, as evidenced by energy dispersive X-ray spectroscopy (EDS) data. The presence of magnetite (Fe3O4) was detected in all samples investigated by X-ray diffraction (XRD). Furthermore, the existence of nickel ferrite (NiFe2O4) was shown in the Fe2Ni by XRD analysis. For the cyclic voltammetry (CV) measurements, the NPs were deposited onto glassy carbon electrodes using Nafion® as an ionomer, and 1 M KOH was employed as the electrolyte. Subsequently, the NPs/Nafion® electrode was transferred into the ToF-SIMS chamber to allow surface analysis and depth profiling.
The ToF-SIMS analysis revealed distinct peaks corresponding to Fe, Ni, and other peaks associated with Nafion®, whereas a straightforward correlation between the Ni.Fe ratio and the SIMS peak pattern is not possible.
The catalytic activity towards OER was evaluated through CV measurements, where the Fe2Ni3 ratio exhibited the most favorable performance, displaying a lower overpotential.
Electrocatalysis is and will continue to play a central role in the development of a new and modern sustainable economy, especially for chemicals and fuels. The storage of excess electrical energy into chemical energy by splitting water into hydrogen and oxygen is a feasible solution in this economic sector. A major drawback of electrical energy lies in the storage. Therefore, hydrogen is discussed as promising alternative. Fortunately, this issue can be effectively addressed through the implementation of chemical storage mechanisms. Due to their abundance on Earth and inherent stability in alkaline solutions, transition-metal oxides have become one of several viable alternatives to conventional noble-metal catalysts. Since FeNi oxide is one of the most active oxygen evolution reaction (OER) electrocatalysts for alkaline water electrolysis, it has been the subject of extensive research.
A series of different types of FeNi oxide nanoparticles (NPs) with atomic ratios covering a broad range, and various sizes with specific stoichiometric and non-stoichiometric iron and nickel ratios was synthesized and characterized by the combination of surface analysis techniques, such as time-of-flight secondary ion mass spectrometry (ToF-SIMS) and X-ray photoelectron spectroscopy (XPS). The morphology was studied using scanning electron microscopy (SEM) and transmission electron microscopy (TEM), which revealed the coexistence of mixed and unmixed iron and nickel NPs with comparable sizes in the range of 30–40 nm across all ratios. The synthesis technique displayed control over the iron-nickel ratio, as evidenced by energy dispersive X-ray spectroscopy (EDS) data. The presence of magnetite (Fe3O4) was detected in all samples investigated by X-ray diffraction (XRD). Furthermore, the existence of nickel ferrite (NiFe2O4) was shown in the Fe2Ni by XRD analysis. For the cyclic voltammetry (CV) measurements, the NPs were deposited onto glassy carbon electrodes using Nafion® as an ionomer, and 1 M KOH was employed as the electrolyte. Subsequently, the NPs/Nafion® electrode was transferred into the ToF-SIMS chamber to allow surface analysis and depth profiling.
The ToF-SIMS analysis revealed distinct peaks corresponding to Fe, Ni, and other peaks associated with Nafion®, whereas a straightforward correlation between the Ni.Fe ratio and the SIMS peak pattern is not possible.
The catalytic activity towards OER was evaluated through CV measurements, where the Fe2Ni3 ratio exhibited the most favorable performance, displaying a lower overpotential.
The analysis of nanomaterials is current an important task - especially in case of risk assessment, as the properties of these material class are not well understood currently. The rather high surface area of these objects renders their interactions significantly different to their corresponding bulk. Thus, the surfaces chemical composition has to be investigated to get a better understanding and prediction of the nanomaterials' behavior. ToF-SIMS has proven as a powerful tool to determine said chemical composition. Its superior surface sensitivity allows us to study mainly the utmost atomic layer and therefore gives us an idea of the interactions involved. Here, we show first result from the validation of the method for the analysis of polystyrene and gold nanoparticles. ToF-SIMS will be compared to other methods like XPS, T-SEM or REM. Furthermore, principle component analysis (PCA) will be used to detect the influence of different sample preparation performed by an innovative microfluidic device. ToF-SIMS imaging is desired to be implemented for single particle detection as well.
The analysis of nanomaterials is currently an important task - especially in case of risk assessment – as the properties of these material class are not well understood. The rather high surface area of these objects renders their interactions significantly different to their corresponding bulk. Thus, the surface’s chemical composition must be investigated to get a better understanding and prediction of the nanomaterials’ behavior. ToF-SIMS and XPS have proven to be powerful tools to determine the general chemical composition. The superior surface sensitivity of ToF-SIMS furthermore allows us to study mainly the utmost atomic layer and thus gives us an idea of the interactions involved. Here, we present initial data on the analysis of Hyflon®-polystyrene core-shell nanoparticles which can be used as a model system due to the known preparation and a rather good chemical as well as physical separation of core and shell. Furthermore, principle component analysis (PCA) will be used to detect the influence of sample preparation and for a better separation of different samples. ToF-SIMS imaging is desired to be implemented for single particle detection as well.
BAM and Division 6.1
(2018)
The analysis of nanomaterials is currently an important task - especially in case of risk assessment – as the properties of these material class are not well understood. The rather high surface area of these objects renders their interactions significantly different to their corresponding bulk. Thus, the surface’s chemical composition must be investigated to get a better understanding and prediction of the nanomaterials’ behavior. ToF-SIMS and XPS have proven to be powerful tools to determine the general chemical composition. The superior surface sensitivity of ToF-SIMS furthermore allows us to study mainly the utmost atomic layers and thus gives us an idea of the interactions involved.
Here, we present initial data on the analysis of Hyflon®-polystyrene core-shell nanoparticles which can be used as a model system due to the known preparation and a rather good chemical as well as physical separation of core and shell. Furthermore, results on Au nanoparticles with and without an antibody shell are presented. Principle component analysis (PCA) will be used to detect the influence of sample preparation and for a better separation of different samples. ToF-SIMS imaging is desired to be implemented for single particle detection as well.
The analysis of nanomaterials is currently an important task - especially in case of risk assessment – as the properties of these material class are not well understood and their growing use in everyday life. The rather high surface area of these objects renders their interactions significantly different to their corresponding bulk. Thus, the surface’s chemical composition must be investigated to get a better understanding and prediction of the nanomaterials’ behaviour. ToF-SIMS and XPS have proven to be powerful tools to determine the general chemical composition. The superior surface sensitivity of ToF-SIMS furthermore allows us to study mainly the utmost atomic layers and thus gives us an idea of possible interactions involved. Supported by multivariate data analysis such as principal component analysis (PCA), the method can also be used for sub-classification of different materials using slight differences in surface chemistry.
Here, we present data of the analysis of Hyflon®-polystyrene core-shell nanoparticles which can be used as a model system due to the known preparation and a rather good chemical as well as physical separation of core and shell. Principle component analysis (PCA) will be used to detect the influence of sample preparation and for a better separation of different samples. This is achieved by measurement of a statistically relevant set of samples for every particle sample. We acquired surface spectra under static SIMS conditions with Bi32+ and analysed the resulting spectra by PCA. The carefully selected and refined peaks allow a reasonable categorization and further a reliable allocation of blank feeds. In detail, the fluorine containing, organic fragments are an indication for a heterogeneous shell that has errors. Furthermore, results on Au nanoparticles with and without an antibody shell are presented. ToF-SIMS imaging is desired to be implemented for single particle detection as well.
Due to the growing number of engineered nanomaterials (NM) the need for a reliable risk assessment for these materials is today bigger than ever before. Especially the nanomaterial’s surface or shell directly interacts with its environment and therefore is a crucial factor for NM’ toxicity or functionality.
Especially, titania is one of the NM with the greatest technological importance. It is used for a large number of applications and can be found in food, cosmetics, glasses, mirrors, paints to mention only a few. In 2012, experts estimate[d] the annual European nano-titania production or utilization at an amount of more than 10,000 t.
Great progress has been achieved in the area of NM investigation and characterization during the past decade. A variety of publications provide information about technological innovation as well as hazard potential, which means the potential risk on human health and ecosystems. However, enhanced data harmonization and well-defined standards for nanomaterial analysis, could significantly improve the reliability of such studies which often suffers from varying methods, parameters and sample preparations. To develop a suitable approach for the NM’s risk assessment, the ACEnano project aims at establishing a toolbox of verified methods. The size of this well-structured European project allows to handle even those big challenges like data harmonization and standardization.
Due to its powerful combination of superior surface sensitivity and lateral resolution down to the Nano regime, ToF-SIMS could become one of these toolbox methods. Supported by multivariate data analysis such as principal component analysis (PCA), the method can be used for sub-classification of nanomaterial families using slight differences in surface chemistry.
Here, we show a PCA supported classification of titania nanoparticles from various sources (NIST, JRC, BAM) with ToF-SIMS. Parameters like size, shell, pre-preparation and crystal system cause variance in the data and allow us to distinguish the species from each other. Moreover, this variance in the data also occurs and can be used for investigation when we compare our measurements of particle ensembles with those of grown titania films. The carefully selected and refined peaks allow a reasonable particle categorization and further a reliable allocation of blank feeds, which introduces a promising approach for NM characterization in the context of NM risk assessment.