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Graz University of Technology

Graz University of Technology

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307 Projects, page 1 of 62
  • Funder: European Commission Project Code: 636069
    Overall Budget: 1,494,250 EURFunder Contribution: 1,494,250 EUR

    Energy storage is undeniably amongst the greatest societal challenges. Batteries will be key enablers but require major progress. Battery materials that promise a step-change in energy density compared with current Li-ion batteries rely on fundamentally different reactions to store charge, e.g. Si alloying or O2 reduction instead of intercalation. They have in common high volume changes on cycling and poor conductivity. For the active component of a battery electrode to function it must be simultaneously in contact with ionic and electronic pathways to electrolyte and current collector. State-of-the-art conducting additives and binders in the composite electrodes cannot ensure ideal contact for such materials and fail to exploit their full potential. In this project I directly target these fundamental challenges of high-energy batteries by replacing now used conducting additives and binders with flexible organic mixed ion and electron conductors that follow volume changes to ensure at any stage intimate contact with ions and electrons. This requires progress with the fundamental science of such conductors, for which to achieve we develop and combine synthetic, electroanalytic and spectroscopic methods, aided by theory. Mixed conducting polymer gels, designed for the particular storage material, shall be elaborated for two ultra-high capacity electrodes, the O2 cathode and the Si anode. The significant advantage, next to intimate contact, is that the packing density of active material can be maximized. This boosts energy stored by total electrode mass and volume by rigorously cutting the amount of non-active materials compared with current approaches. The expected overriding scientific impact includes improved understanding of mixed conductors concerning synthesis, structure, conductivity and their behaviour in the complex battery environment. This opens up new perspectives for the realm of high-capacity battery materials that demand such a breakthrough to succeed

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  • Funder: European Commission Project Code: 254944
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  • Funder: European Commission Project Code: 771834
    Overall Budget: 1,996,320 EURFunder Contribution: 1,996,320 EUR

    Metal-Organic Frameworks (MOFs) are nanoporous crystalline solids with narrow pore distributions and high accessible surface areas. MOFs are typically prepared in a polycrystalline form via the self-assembly of inorganic (nodes) and organic (links) building units. This bottom-up approach allows for properties such as, pore size, topology and chemical functionality to be precisely tailored. Such synthetic control has identified MOFs as promising platform material for device fabrication in the areas of microelectronics, photonics, sensing. However, current methods for fabricating MOF films and patterns cannot generate precisely oriented crystals on commercially relevant scales (i.e. cm). Thus, limiting access to applications that require anisotropic functional properties (e.g. optics, electronics, separation). POPCRYSTAL will enable the fabrication of films and patterns composed of precisely oriented MOF crystals by exploiting crystalline ceramics to guide the aligned growth of MOF crystals. Remarkably, the scale of these heteroepitaxially grown MOFs is solely determined by the ceramic precursor which can be easily synthesized on areas covering mm2 to cm2. POPCRYSTAL will advance a proof of concept study by addressing the following important research aims: the basic understanding of the formation mechanism and rules governing the heteroepitaxial relationship (WP1), the extension to different ceramic-MOF systems (WP2), the control over crystalline porous film and pattern features (WP3) and the fabrication of a proof-of-concept that will highlight the importance of aligned pores for separation (WP4). In summary, by exploiting the heteroepitaxial growth mechanism between ceramics and MOFs POPOCRYSTAL will fabricate unprecedented crystalline MOF films and patterns with precisely oriented nanopores and nanochannels. Thus POPCRYSTAL intercrosses and connects nanoscale chemistry, controlled self-assembly on a macroscale and nanoporous-based device fabrication.

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  • Funder: European Commission Project Code: 640156
    Overall Budget: 1,473,520 EURFunder Contribution: 1,473,520 EUR

    Since more than 50 years, computer vision has been a very active research field but it is still far away from the abilities of the human visual system. This stunning performance of the human visual system can be mainly contributed to a highly efficient three-layer architecture: A low-level layer that sparsifies the visual information by detecting important image features such as image gradients, a mid-level layer that implements disocclusion and boundary completion processes and finally a high-level layer that is concerned with the recognition of objects. Variational methods are certainly one of the most successful methods for low-level vision. However, it is very unlikely that these methods can be further improved without the integration of high-level prior models. Therefore, we propose a unified mathematical framework that allows for a natural integration of high-level priors into low-level variational models. In particular, we propose to represent images in a higher-dimensional space which is inspired by the architecture for the visual cortex. This space performs a decomposition of the image gradients into magnitude and direction and hence performs a lifting of the 2D image to a 3D space. This has several advantages: Firstly, the higher-dimensional embedding allows to implement mid-level tasks such as boundary completion and disocclusion processes in a very natural way. Secondly, the lifted space allows for an explicit access to the orientation and the magnitude of image gradients. In turn, distributions of gradient orientations – known to be highly effective for object detection – can be utilized as high-level priors. This inverts the bottom-up nature of object detectors and hence adds an efficient top-down process to low-level variational models. The developed mathematical approaches will go significantly beyond traditional variational models for computer vision and hence will define a new state-of-the-art in the field.

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  • Funder: European Commission Project Code: 101116212
    Overall Budget: 1,499,890 EURFunder Contribution: 1,499,890 EUR

    One of the fundamental problems of using optimization models that represent complex systems – e.g. power systems on their path towards achieving net-zero emissions – is the trade-off between model accuracy and computational tractability. Many applied optimization models that use different time series as data input have become increasingly challenging to solve due to the large time horizons they span and the high complexity of technical constraints with short- and long-term time dynamics. To overcome computational intractability of these optimization models, the dimension of input data and model size is commonly reduced through time series aggregation (TSA) methods. However, applying TSA for optimization models that are governed by varying time dynamics simultaneously is quite challenging. TSA methods mostly focus on short-term dynamics, and rarely include long-term dynamics due to the inherent limitations of TSA. As a result, longer-term dynamics are not captured well by aggregated models, which is imperative for reliably modelling many complex systems. Moreover, traditional TSA methods are based on the common belief that the clusters that best approximate the input data also lead to the aggregated model that best approximates the full model, while the metric that really matters –the resulting output error in optimization results – is not well addressed. This belief is mainly based on the lack of theoretical underpinning relating inputs and output error, rendering existing methods trial-and-error heuristics at best. We plan to challenge this belief by discovering the currently unknown relation between input and output error, and to overcome existing TSA shortcomings by developing the novel theoretical TSA framework for optimization models with varying time dynamics, thereby tapping into unprecedented potential of computational efficiency and accuracy. If this project is successful, it would have untangled the Gordian knot of data aggregation in optimization.

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