Bioactivity and corrosion behavior of magnesium barrier membranes

In the current research, magnesium and its alloys have been intensively studied as resorbable implant materials. Magnesium materials combine their good mechanical properties with bioactivity, which make them interesting for guided bone regeneration and for the application as barrier membranes. In this study, the in vitro degradation behavior of thin magnesium films was investigated in cell medium and simulated body fluid. Three methods were applied to evaluate corrosion rates: measurements of (i) the gaseous volume evolved during immersion, (ii) volume change after immersion, and (iii) polarization curves. In this comparison, measurements of H2 development in Dulbecco's modified Eagle's medium showed to be the most appropriate method, exhibiting a corrosion rate of 0.5 mm·year−1. Observed oxide and carbon contamination have a high impact on controlled degradation, suggesting that surface treatment of thin foils is necessary. The bioactivity test showed positive results; more detailed tests in this area are of interest

X‐ray microscopy and automatic detection of defects in through silicon vias in three‐dimensional integrated circuits

Through silicon vias (TSVs) are a key enabling technology for interconnection and realization of complex three-dimensional integrated circuit (3D-IC) components. In order to perform failure analysis without the need of destructive sample preparation, x-ray microscopy (XRM) is a rising method of analyzing the internal structure of samples. However, there is still a lack of evaluated scan recipes or best practices regarding XRM parameter settings for the study of TSVs in the current state of literature. There is also an increased interest in automated machine learning and deep learning approaches for qualitative and quantitative inspection processes in recent years. Especially deep learning based object detection is a well-known methodology for fast detection and classification capable of working with large volumetric XRM datasets. Therefore, a combined XRM and deep learning object detection workflow for automatic micrometer accurate defect location on liner-TSVs was developed throughout this work. Two measurement setups including detailed information about the used parameters for either full IC device scan or detailed TSV scan were introduced. Both are able to depict delamination defects and finer structures in TSVs with either a low or high resolution. The combination of a 0.4 objective with a beam voltage of 40 kV proved to be a good combination for achieving optimal imaging contrast for the full-device scan. However, detailed TSV scans have demonstrated that the use of a 20 objective along with a beam voltage of 140 kV significantly improves image quality. A database with 30,000 objects was created for automated data analysis, so that a well-established object recognition method for automated defect analysis could be integrated into the process analysis. This RetinaNet-based object detection method achieves a very strong average precision of 0.94. It supports the detection of erroneous TSVs in both top view and side view, so that defects can be detected at different depths. Consequently, the proposed workflow can be used for failure analysis, quality control or process optimization in R&D environments

Electron Beam-Induced Writing of Nanoscale Iron Wires on a Functional Metal Oxide

Electron beam-induced surface activation (EBISA) has been used to grow wires of iron on rutile TiO2(110)-(1 × 1) in ultrahigh vacuum. The wires have a width down to ∼20 nm and hence have potential utility as interconnects on this dielectric substrate. Wire formation was achieved using an electron beam from a scanning electron microscope to activate the surface, which was subsequently exposed to Fe(CO)5. On the basis of scanning tunneling microscopy and Auger electron spectroscopy measurements, the activation mechanism involves electron beam-induced surface reduction and restructuring

Untersuchungen zur elektronenstrahlinduzierten Oberflächenaktivierung als Werkzeug für die Herstellung wohldefinierter Nanostrukturen: Die Rolle von katalytischen Prozessen, Substraten und Präkursoren.

In the course this thesis, fundamental aspects of nanostructure fabrication using electron beam induced surface activation (EBISA) and electron beam induced deposition (EBID) were investigated in detail. EBISA is a recently (2010) discovered process that makes use of a focused electron beam to activate a surface towards the decomposition of precursor molecules, which are supplied to form deposits at the pre-irradiated areas in a subsequent processing step. In the closely related EBID technique, precursor molecules are directly decomposed by the impact of an electron beam. As the probe size of the electron beam is in the nanometer regime, both processes can be exploited to fabricate nanostructures on surface. The EBISA concept could be successfully expanded to titania (single crystal, rutile TiO2(110)-1×1) and quite different “organic” surfaces (2H- and Co-tetraphenylporphyrin layers on Ag(111)). On both types of substrates, nanostructure fabrication following the EBISA protocol with iron pentacarbonyl was performed and revealed that EBISA is not limited to SiOx surfaces and the activation mechanism is not restricted to generation of oxygen vacancies. The second part focusses on the behavior of various precursors in the context of nanofabrication using EBID and EBISA. Iron pentacarbonyl, Fe(CO)5, has been identified as a viable precursor already during the initial experiments that lead to the development of the EBISA protocol. In order to broaden the range of applicable precursors, cobalt tricarbonyl nitrosyl, Co(CO)3NO, and dicobalt octacarbonyl, Co2(CO)8, were used to conduct EBISA related experiments.Im Verlauf der vorliegenden Arbeit wurden grundlegende Aspekte der Nanostrukturherstellung mittels elektronenstrahlinduzierter Oberflächenaktivierung (Electron Beam Induced Surface Activation, EBISA) und elektronenstrahlinduzierter Abscheidung (Electron Beam Induced Deposition, EBID) untersucht. EBISA bezeichnet hierbei einen kürzlich (2010) entdeckten Prozess, bei dem ein fokussierter Elektronenstrahl genutzt wird, um eine Oberfläche so zu aktivieren, dass die aktivierten Bereiche in einem zweiten Schritt die Zersetzung von bestimmten Molekülen, den sogenannten Präkursoren, initiieren können und so eine Abscheidung erzeugen. Im verwandten EBID-Prozess werden solche Präkursoren direkt durch den Elektronenstrahl zersetzt. Der Durchmesser des verwendeten Elektronenstrahls liegt im einstelligen Nanometer-Bereich, weshalb beide Prozesse genutzt werden können, um Nanostrukturen auf Oberflächen herzustellen. Es konnte gezeigt werden, dass das EBISA-Konzept auf andere Substrate übertragen werden kann. Auf zwei neuen Typen von Substraten (TiO2(110)-Einkristalle und dünne Tetraphenylporphyrin-Schichten auf Silber(111)-Einkristallen) wurden, ausgehend vom Präkursor Eisenpentacarbonyl, Fe(CO)5, erfolgreich Nanostrukturen mittels EBISA hergestellt. Dabei zeigt sich, dass das EBISA-Konzept nicht auf SiOx-Oberflächen beschränkt ist und dass auch andere Aktivierungsmechanismen, die nicht auf der strahlinduzierten Erzeugung von Sauerstofffehlstellen beruhen, möglich sind. Der zweite Teil der vorliegenden Arbeit beschäftigt sich mit dem Verhalten verschiedener Präkursoren im Kontext von EBID und EBISA. Eisenpentacarbonyl wurde bereits zu Beginn der EBISA-Forschung als geeigneter Präkursor identifiziert. Um die Bandbreite an verwendbaren Präkursoren und damit auch abscheidbaren Materialien für EBISA zu erhöhen, wurden die potentiellen Cobalt-Präkursoren Cobalttricarbonylnitrosyl, Co(CO)3NO, und Dicobaltoktacarbonyl, Co2(CO)8, hinsichtlich ihrer Eignung untersucht

Controlled Electron-Induced Fabrication of Metallic Nanostructures on 1 nm Thick Membranes

Preischl C, Le HL, Bilgilisoy E, Vollnhals F, Gölzhäuser A, Marbach H. Controlled Electron-Induced Fabrication of Metallic Nanostructures on 1 nm Thick Membranes. Small . 2020;16(45): 2003947.Functional hybrids comprising metallic nanostructures connected and protected by nonmetallic 2D materials are envisioned as miniaturized components for applications in optics, electronics, and magnetics. A promising strategy to build such elements is the direct writing of metallic nanostructures by focused electron beam induced processing (FEBIP) onto insulating 2D materials. Carbon nanomembranes (CNMs), produced via electron-induced crosslinking of self-assembled monolayers (SAMs), are ultrathin and flexible films; their thickness as well as their mechanical and electrical properties are determined by the specific choice of self-assembling molecules. In this work, functionalized CNMs are produced via electron beam induced deposition of Fe(CO)5 onto terphenylthiol SAMs. Clean iron nanostructures of arbitrary size and shape are deposited on the SAMs, and the SAMs are then crosslinked into CNMs. The functionalized CNMs are then transferred onto either solid substrates or onto grids to obtain freestanding metal/CNM hybrid structures. Iron nanostructures with predefined shapes on top of 1 nm thin freestanding CNMs are realized; they stay intact during the fabrication procedures and remain mechanically stable. Combining the ease and versatility of SAMs with the flexibility of FEBIP thus leads to a route for the fabrication of functional hybrid nanostructures. © 2020 The Authors. Published by Wiley-VCH GmbH

Additive fabrication of nanostructures with focused soft X-rays

We report on a novel technique for the fabrication of metallic nanostructures via soft X-ray irradiation of precursor molecules supplied from the gas phase. With this technique we were able to produce localized Co nanostructures with a growth rate and purity competitive with electron beam induced deposition. We demonstrate that our approach exhibits significant selectivity with respect to incident photon energy leading to enhanced growth for resonant absorption energy of the precursor molecule. Based on this finding we propose a unique new pathway of selective deposition from precursor mixtures. Furthermore, we investigated the growth rate with respect to precursor pressure and growth time and discuss the potential resolution limits of this new technique

Electron-beam induced deposition and autocatalytic decomposition of Co(CO)3NO

The autocatalytic growth of arbitrarily shaped nanostructures fabricated by electron beam-induced deposition (EBID) and electron beam-induced surface activation (EBISA) is studied for two precursors: iron pentacarbonyl, Fe(CO)5, and cobalt tricarbonyl nitrosyl, Co(CO)3NO. Different deposits are prepared on silicon nitride membranes and silicon wafers under ultrahigh vacuum conditions, and are studied by scanning electron microscopy (SEM) and scanning transmission X-ray microscopy (STXM), including near edge X-ray absorption fine structure (NEXAFS) spectroscopy. It has previously been shown that Fe(CO)5 decomposes autocatalytically on Fe seed layers (EBID) and on certain electron beam-activated surfaces, yielding high purity, polycrystalline Fe nanostructures. In this contribution, we investigate the growth of structures from Co(CO)3NO and compare it to results obtained from Fe(CO)5. Co(CO)3NO exhibits autocatalytic growth on Co-containing seed layers prepared by EBID using the same precursor. The growth yields granular, oxygen-, carbon- and nitrogen-containing deposits. In contrast to Fe(CO)5 no decomposition on electron beam-activated surfaces is observed. In addition, we show that the autocatalytic growth of nanostructures from Co(CO)3NO can also be initiated by an Fe seed layer, which presents a novel approach to the fabrication of layered nanostructures

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro- and nanoplastic particles in water and tissue samples