122 research outputs found
Application of disposable chiral plasmonics for biosensing and Raman spectroscopy
This thesis explores the capabilities of disposable chiral plasmonic metafilm assays, termed Disposable Plasmonic Assays, as a promising platform for biosensing and surface-enhanced Raman spectroscopy. The sensing and Raman properties of these metafilms arise from the excitation of surface plasmons when exposed to incident light. These plasmonic properties strongly depend on the geometric characteristics of the constituent nanostructures found in the metafilms. Specifically, the primary nanostructure employed throughout this research is the chiral 'shuriken' star, which generates chiral electromagnetic fields exhibiting greater chiral asymmetry than circularly polarized light.
Monitoring changes in the resonance positions of the characteristic optical rotatory dispersion spectra produced by the Disposable Plasmonic Assays allows for the observation of surface binding events. By measuring resonance shift data and through the utilisation of various gold film functionalisation techniques, these assays are demonstrated as versatile, label-free biosensing platforms capable of specifically detecting a wide range of target proteins and virus particles from complex solutions. Furthermore, the multiplexing performance of these assays is showcased, enabling the detection of multiple different antigens and virions in a single experiment. These results highlight the potential of plasmonic metafilms as rapid and disposable point-of-care immunoassays for diagnostic applications.
In addition to biosensing, the chiral geometry of Disposable Plasmonic Assays is exploited for the chiral discrimination of metal nanoparticles and small molecules using Surface Enhanced Raman Spectroscopy (SERS). By linking helicoid shaped gold nanoparticles to the metafilm surface via a dithiol linker, the chiral properties of both nanoparticles and metafilms combine, resulting in the creation of differential electromagnetic 'hotspot' regions based on their symmetry combinations. The electromagnetic intensity in these regions corresponds to the SERS signal obtained from the achiral dithiol linker molecule, facilitating a deeper understanding of the chirally dependent SERS phenomenon. These findings serve to validate and explain the differential SERS data obtained enantiomers of biomolecules and drug molecules from silver modified Disposable Plasmonic Assays
Understanding and Engineering Interfacial Adhesion in Solid-State Batteries with Metallic Anodes
Funding Information: The authors acknowledge funding for this work from the Engineering and Physical Sciences Research Council (EP/R002010/1, EP/R024006/1 and EP/P003532/1), Shell Global Solutions International B.V., the Spanish government (TED2021‐129254B‐C22) and Horizon Europe HORIZON‐CL5‐2021‐D2‐01 “SEATBELT” 101069726.Peer reviewedPublisher PD
Conditions for the stable adsorption of lipid monolayers to solid surfaces
Lipid monolayers are ubiquitous in biological systems and have multiple roles in biotechnological applications, such as lipid coatings that enhance colloidal stability or prevent surface fouling. Despite the great technological importance of surface-adsorbed lipid monolayers, the connection between their formation and the chemical characteristics of the underlying surfaces has remained poorly understood. Here, we elucidate the conditions required for stable lipid monolayers nonspecifically adsorbed on solid surfaces in aqueous solutions and water/alcohol mixtures. We use a framework that combines the general thermodynamic principles of monolayer adsorption with fully atomistic molecular dynamics simulations. We find that, very universally, the chief descriptor of adsorption free energy is the wetting contact angle of the solvent on the surface. It turns out that monolayers can form and remain thermodynamically stable only on substrates with contact angles above the adsorption contact angle, θads. Our analysis establishes that θads falls into a narrow range of around 60∘–70∘ in aqueous media and is only weakly dependent on the surface chemistry. Moreover, to a good approximation, θads is roughly determined by the ratio between the surface tensions of hydrocarbons and the solvent. Adding small amounts of alcohol to the aqueous medium lowers θads and thereby facilitates monolayer formation on hydrophilic solid surfaces. At the same time, alcohol addition weakens the adsorption strength on hydrophobic surfaces and results in a slowdown of the adsorption kinetics, which can be useful for the preparation of defect-free monolayers
Substoichiometric Phases of Hafnium Oxide with Semiconducting Properties
Since the dawn of the information age, all developments that provided a significant improvement in information processing and data transmission have been considered as key technologies. The impact of ever new data processing innovations on the economy and almost all areas of our daily lives is unprecedented and a departure from this trend is unimaginable in the near future. Even though the end of Moore's Law has been predicted all too often, the steady exponential growth of computing capacity remains unaffected to this day, due to tremendous commercial pressure. While the minimum physical size of the transistor architecture is a serious constraint, the steady evolution of computing effectiveness is not limited in the predictable future. However, the focus of development will have to expand more strongly to other technological aspects of information processing. For example, the development of new computer paradigms which mark a departure from the digitally dominated van Neumann architecture will play an increasingly significant role. The category of so-called next-generation non-volatile memory technologies, based on various physical principles such as phase transformation, magnetic or ferroelectric properties or ion diffusion, could play a central role here. These memory technologies promise in part strongly pronounced multi-bit properties up to quasi-analog switching behavior. These attributes are of fundamental importance especially for new promising concepts of information processing like in-memory computing and neuromorphic processing. In addition, many next-generation non-volatile memory technologies already show advantages over conventional media such as Flash memory. For example, their application promises significantly reduced energy consumption and their write and especially read speeds are in some cases far superior to conventional technology and could therefore already contribute significant technological improvements to the existing memory hierarchy. However, these alternative concepts are currently still limited in terms of their statistical reliability, among other things. Even though phase change memory in the form of the 3D XPoint, for example, has already been commercialized, the developments have not yet been able to compete due to the enormous commercial pressure in Flash memory research. Nevertheless, the further development of alternative concepts for the next and beyond memory generations is essential and the in-depth research on next-generation non-volatile memory technologies is therefore a hot and extremely important scientific topic.
This work focuses on hafnium oxide, a key material in next-generation non-volatile memory research. Hafnium oxide is very well known in the semiconductor industry, as it generated a lot of attention in the course of high-k research due to its excellent dielectric properties and established CMOS compatibility. However, since the growing interest in so-called memristive memory, research efforts have primarily focused on the value of hafnium oxide in the form of resistive random-access memory (RRAM) and, with the discovery of ferroelectricity in HfO₂, ferroelectric resistive random-access memory (FeRAM). RRAM is a next-generation non-volatile memory technology that features a simple metal-insulator-metal (MIM) structure, excellent scalability, and potential 3D integration. In particular, the aforementioned gradual to quasi-continuous switching behavior has been demonstrated on a variety of RRAM systems. A significant change of the switching properties is achievable, for example, by the choice of top and bottom electrodes, the introduction of doping elements, or by designated oxygen deficiency. In particular, the last point is based on the basic physical principle of the hafnium oxide-based RRAM mechanism, in which local oxygen ions are stimulated to diffuse by applying an electrical potential, and a so-called conducting filament is formed by the remaining vacancies, which electrically connects the two electrode sides. The process is characterized by the reversibility of the conducting filament which can be dissolved by a suitable I-V programming (e.g., reversal of the voltage direction).
In the literature there are some predictions of sub-stoichiometric hafnium oxide phases, such as Hf₂O₃, HfO or Hf₆O, which could be considered as conducting filament phases, but there is a lack of conclusive experimental results. While there are studies that assign supposed structures in oxygen-deficient hafnium oxide thin films, these assignments are mostly based on references from various stoichiometric hafnium oxide high-temperature phases such as tetragonal t-HfO₂ (P4₂/nmc) or cubic c-HfO₂ (Fm-3m), or high-pressure phases such as orthorhombic o-HfO₂ (Pbca).
Furthermore, the structural identification of such thin films proves to be difficult, as they are susceptible to arbitrary texturing and reflection broadening in X-ray diffraction. In addition, such thin films are usually synthesized as phase mixtures with monoclinic hafnium oxide. A further challenge in property determination is given by their usual arrangement in MIM configuration, which is determined by the quality of top and bottom electrodes and their interfaces to the active material. It is therefore a non-trivial task to draw conclusions on individual material properties such as electrical conductivity in such (e.g., oxygen-deficient) RRAM devices.
To answer these open questions, this work is primarily devoted to material properties of oxygen-deficient hafnium oxide phases. Therefore, in the first comprehensive study of this work, Molecular-Beam Epitaxy (MBE) was used to synthesize hafnium oxide phases over a wide oxidation range from monoclinic to hexagonal hafnium oxide. The hafnium oxide films were deposited on c-cut sapphire to achieve effective phase selection and identification by epitaxial growth, taking into account the position of relative lattice planes. In addition, the choice of a substrate with a high band gap and optical transparency enabled the direct investigation of both optical and electrical properties by means of UV/Vis transmission spectroscopy and Hall effect measurements. With additional measurements via X-ray diffraction (XRD), X-ray reflectometry (XRR), X-ray photoelectron spectroscopy (XPS) and high-resolution transmission electron microscopy (HRTEM), the oxygen content-dependent changes in crystal as well as band structure could be correlated with electrical properties. Based on these results, a comprehensive band structure model over the entire oxidation range from insulating HfO₂ to metallic Hf was established, highlighting the discovered intermediate key structures of rhombohedral r-HfO₁.₇ and hexagonal hcp-HfO₀.₇.
In the second topic of this work, the phase transition from stoichiometric monoclinic to oxygen-deficient rhombohedral hafnium oxide was complemented by DFT calculations in collaboration with the theory group of Prof. Valentí (Frankfurt am Main). A detailed comparison between experimental results and DFT calculations confirms previously assumed mechanisms for phase stabilization. In addition, the comparison shows a remarkable agreement between experimental and theoretical results on the crystal- and band stucture. The calculations allowed to predict the positions of oxygen ions in oxygen-deficient hafnium oxide as well as the associated space group. Also, the investigations provide information on the thermodynamic stability of the corresponding phases. Finally, the orbital-resolved hybridization of valence states influenced by oxygen vacancies is discussed.
Another experimental study deals with the reproduction and investigation, of the aforementioned substoichiometric hafnium oxide phases in MIM configuration which is typical for RRAM devices. Special attention was given to the influence of surface oxidation effects. Here, it was found that the oxygen-deficient phases r-HfO₁.₇ and hcp-HfO₀.₇ exhibit high ohmic conductivity as expected, but stable bipolar switching behavior as a result of oxidation in air. Here, the mechanism of this behavior was discussed and the role of the r-HfO₁.₇ and hcp-HfO₀.₇ phases as novel electrode materials in hafnium oxide-based RRAM in particular.
In collaboration with the electron microscopy group of Prof. Molina Luna, the studied phases, which have been characterized by rather macroscopic techniques so far, have been analyzed by wide-ranging TEM methodology. The strong oxygen deficiency in combination with the verified electrical conductivity of r-HfO₁.₇ and hcp-HfO₀.₇ shows the importance of the identification of these phases on the nanoscale. Such abilities are essential for the planned characterization of the "conducting-filament" mechanism. Here, the ability to distinguish m-HfO₂, r-HfO₁.₇, and hcp-HfO₀.₇ using high-resolution transmission electron microscopy (HRTEM), Automated Crystal Orientation and Phase Mapping (ACOM), and Electron Energy Loss Spectroscopy (EELS), is demonstrated and the necessity of combined measurements for reliable phase identification was discussed.
Finally, a series of monoclinic to rhombohedral hafnium oxide was investigated in a cooperative study with FZ Jülich using scanning probe microscopy. Since recent studies in particular highlight the significance of the microstructure in stoichiometric hafnium oxide-based RRAM, the topological microstructure in the region of the phase transition to strongly oxygen deficient rhombohedral hafnium oxide was investigated. Special attention was given to the correlation of microstructure and conductivity. In particular, the influences of grain boundaries on electrical properties were discussed.
In summary, this work provides comprehensive insights into the nature and properties of sub-stoichiometric hafnium oxide phases and their implications on the research of hafnium oxide-based RRAM technology. Taking into account a wide range of scientific perspectives, both, the validity of obtained results and the wide range of their application is demonstrated. Thus, this dissertation provides a detailed scientific base to the understanding of hafnium oxide-based electronics
Machine Learning Accelerated Discovery of Corrosion-resistant High-entropy Alloys
Corrosion has a wide impact on society, causing catastrophic damage to
structurally engineered components. An emerging class of corrosion-resistant
materials are high-entropy alloys. However, high-entropy alloys live in
high-dimensional composition and configuration space, making materials designs
via experimental trial-and-error or brute-force ab initio calculations almost
impossible. Here we develop a physics-informed machine-learning framework to
identify corrosion-resistant high-entropy alloys. Three metrics are used to
evaluate the corrosion resistance, including single-phase formability, surface
energy and Pilling-Bedworth ratios. We used random forest models to predict the
single-phase formability, trained on an experimental dataset. Machine learning
inter-atomic potentials were employed to calculate surface energies and
Pilling-Bedworth ratios, which are trained on first-principles data fast
sampled using embedded atom models. A combination of random forest models and
high-fidelity machine learning potentials represents the first of its kind to
relate chemical compositions to corrosion resistance of high-entropy alloys,
paving the way for automatic design of materials with superior corrosion
protection. This framework was demonstrated on AlCrFeCoNi high-entropy alloys
and we identified composition regions with high corrosion resistance. Machine
learning predicted lattice constants and surface energies are consistent with
values by first-principles calculations. The predicted single-phase formability
and corrosion-resistant compositions of AlCrFeCoNi agree well with experiments.
This framework is general in its application and applicable to other materials,
enabling high-throughput screening of material candidates and potentially
reducing the turnaround time for integrated computational materials
engineering
Data-driven and data-oriented methods for materials science and technologies
The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials.
However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process.
This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results.
Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques.
This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns
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