Accurate time interval measurement and synchronization between two or more pulses are highly desirable in different fields of science and technology. Electronic components-based approaches can not measure the time difference between two events precisely below two-digit picosecond scale. Additionally, the use of highly expensive electronic components and temperature increase due to the heat generated within the system are other drawbacks, limiting its long-term use and performance. To address these issues, we propose to develop a novel, extremely high precision, low energy, all-optical timekeeping methodology and Timekeeper device with epsilon-near-zero (ENZ) metamaterials. Besides timekeeping, the technologies developed in this project can also become a powerful toolbox or an optical metamaterials technology platform, contributing to the creation of low-cost, accurate, practically light-based computing processes in future devices. Our work will have substantial implications on various areas of science and technology, including time and frequency metrology, geodesy, and astronomy. The developed all-optical time interpolation will enable extremely high precision of time events up to femtosecond scale using ultra-low power and exclude the high-speed electronics and high-temperature complications. The project results will apply novel metasurface-enhanced epsilon-near-zero (ENZ) materials to realize all-optical time-to-frequency converters and optical switches that can be used to realize low-cost, high-resolution (femtosecond) time-interval counters and optical gates.

Previous efforts to raise living standards have been based on relentlessly increasing combustion, causing environmental destruction at all scales. In addition to climate-warming CO2, fossil fuel combustion also produces a large number of organic compounds and particulate matter, which deteriorate air quality. The atmosphere is cleansed from such pollutants by gas-phase oxidation reactions, which are invariably mediated by peroxy radicals (RO2). Oxidation transforms initially volatile and water-insoluble hydrocarbons into water-soluble forms (ultimately CO2), enabling scavenging by liquid droplets. A minor but crucially important alternative oxidation pathway leads to oxidative molecular growth, and formation of atmospheric aerosols. Aerosols impart a huge influence on the atmosphere, from local air quality issues to global climate forcing, yet their formation mechanisms and structures of organic aerosol precursors remains elusive. In a paradigm change, RO2 was recently found to undergo autoxidation, enabling rapid aerosol precursor formation even at sub-second time-scales – in stark contrast to the long processing times (days - weeks) previously assumed to be necessary. We have shown how abundant biogenic hydrocarbons (BVOC) autoxidize, but due to key structural differences, the same pathways are not available for anthropogenic hydrocarbons (AVOC), and thus they were not expected to autoxidize. My preliminary experiments reveal that AVOCs do autoxidize, but the mechanism enabling this remain unknown. Crucially, the co-reactants shown to inhibit BVOC seem to enforce AVOC autoxidation – potentially explaining the recent mysterious discovery of new-particle formation in polluted megacities. In ADAPT, I will use a combination of novel mass spectrometric detection methods fortified by theoretical calculations, to solve the mechanism of AVOC autoxidation. This will directly assist both air quality management, and the design of cleaner fuels and engines.

WHAT: MULTIMODAL will develop sensory-motorized material systems that perceive several coupled environmental stimuli and respond to a combination of these via controlled motor functions, shape-change or locomotion. The sensory-motorized materials will be “trained” to strengthen upon repetitive action, they can “heal” upon injury, and mechanically adapt to different environments. They will be utilized in the design of soft robots with autonomous and interactive functions. HOW: We will utilize shape-changing liquid crystal networks (LCNs) that undergo controlled untethered motions in response to photochemical, (photo)thermal, and humidity-triggered activation. Coupling between these stimuli will allow for gated control strategies over the shape changes. I expect that the gated control strategies, in combination with stimuli-induced diffusion from surface to bulk of the LCN, will enable advanced robotic functionalities. The diffusion process will be used for supramolecular crosslinking and formation of interpenetrated dynamic polymer networks with the LCN, to allow for trainable gaiting for versatile locomotion control. We will also make mechanically adaptable amphibious grippers for autonomous object recognition. WHY: Technological disruptions are often due to new materials and fabrication technologies. Paradigm changes on how materials are perceived have profound effects on our society, well-being, and the ways we see the world. Here, we strive for a paradigm change in robotic materials. By taking inspiration from biological sensory-motor interactions, we will develop MULTIMODAL materials with autonomous and interactive features. These features go far beyond the capabilities of conventional stimuli-responsive materials, allowing us to take inanimate, shape-changing materials one ambitious step closer to motor functions of living species.

Forecasting cryptocurrency volatility is a topic of interest in quantitative finance. A growing number of studies argue that compared to equty price cryptocurrency prices are to a large and perhaps abnormal degree driven by sentiments. However, econometric studies focus on forcing conditional volatility models developed for equity return volatility to fit on cryptocurrency data despite being aware that estimation techniques developed for analyzing equity price or commodity price volatility lack robustness and do not work as intended. Is it possible to propose solutions to deal with the mentioned shortcomings? Is it possible to suggest a new family of models? If so, how? The purpose of New, realistic and robust models for cryptocurrency volatility is to answer these questions by suggesting new and more realistic conditional volatility models accompanied with reliable cross-disciplinary estimation techniques to forecast cryptocurrency price volatility. What is novel and innovative about the suggested framework is that contrary to the current literature our point of departure is the empirical features observed in cryptocurrency prices combined with a useful tool, namely, artificial neural networks used to measure sentiments. Our aim is to build a machine that produces discrete sentiment phases each day using news articles and internet search data. Once we have identified the number of phases and determined, which phase an observation at a given time-period belongs to following neural network estimation, we can estimate the model parameters, jumps and filter out the continuous conditional volatility process contemporaneously using particle filtering techniques. Besides academics, this proposal is also relevant for regulators and investors as they can learn a great deal by understanding how cryptocurrency volatility actually behaves. Regulators can use sentiment labels from the neural network to design policies to contrast and overcome financial crises in the future.

About 30 % of all the electrical power generated passes through a power electronic converter, and the proportion is expected to rise to 80 % in 10-15 years. The amount of electricity annually wasted due to the losses in such systems in the EU corresponds to at least billions of euros. A major part of these losses arises in passive magnetic components, such as inductors and transformers, which are also the largest and heaviest components of a power electronic device. The physical phenomena related to the power losses in the magnetic cores of these components are not properly understood at the moment. In addition, the engineering community is currently lacking efficient modeling tools for analyzing the losses in the windings of such components at high frequencies. Improvement of high-frequency magnetic components would require accurate understanding of the power loss mechanisms. However, the device-level losses are affected by physical effects taking place in the microscopic grain and domain structures and very thin conductors, which are often subject to geometrical uncertainties. Accurate geometrical models cannot be used for analyzing the devices due to the impossibly large computational burden. In MULTIMAG, we will address these challenges by establishing a set of new multiscale numerical modeling tools, which will provide insight into the origin of the power losses and make it possible to perform statistical analysis of the electromagnetic behaviour of such components. The application potential of these new numerical tools will be demonstrated by designing working prototypes of emerging power electronic devices, such as a solid-state transformer and a wireless power transfer system. We will also develop inverse problem approaches for identifying the models from available catalog data, lowering the threshold for adopting the models into use. As the outcome, new means for improving the energy efficiency and power density of power electronic devices will arise.