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THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

Country: United Kingdom

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

1,523 Projects, page 1 of 305
  • Funder: EC Project Code: 629887
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  • Funder: EC Project Code: 101002652
    Overall Budget: 2,446,740 EURFunder Contribution: 2,446,740 EUR

    Type Ia supernovae (SNe Ia) are used as “standardiseable candles”: their peak luminosities can be inferred from their optical light curve shapes and colours, so their distances can be estimated from their apparent brightnesses. SN Ia distances with high precision and small systematic error are essential to accurate constraints on the cosmic expansion history, local measurements of the Hubble constant, and the properties of the dark energy driving the acceleration, in particular, its equation-of-state parameter w. The current global sample used for cosmology has grown to over a thousand SNe Ia. Future surveys will boost that number by orders of magnitude. However, the constraints on dark energy with the current optical sample are already limited, not by statistical uncertainties from the numbers of SNe, but by systematic errors. Near-infrared (NIR) observations of SN Ia are a route to more precise and accurate distances and significantly enhance their cosmological utility. SNe Ia are excellent standard candles in the NIR, and are less vulnerable to absorption by dust in the host galaxies. These good NIR properties are not exploited by the conventional optical models currently used for cosmological SN Ia analysis. Furthermore, the present useful sample of SN Ia with NIR data is relatively small compared to the growing nearby or distant optical samples. In this Project, we will leverage our involvement in new SN surveys using the Hubble Space Telescope and ground-based observatories to build a ~10X larger sample of SNe Ia with high-quality optical and NIR data. We will develop the next-generation probabilistic model for SN Ia spectral energy distributions (SEDs) in the optical-to-NIR, accounting properly for the variabilities and uncertainties inherent in the data by fusing advanced hierarchical Bayesian modelling and functional data analysis techniques. We will apply our state-of-the-art model to our new SN datasets and LSST to obtain robust cosmological inferences.

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  • Funder: EC Project Code: 701825
    Overall Budget: 195,455 EURFunder Contribution: 195,455 EUR

    Dynamic polymers, which are able to break and reform due to the dynamic nature of their linkages, can be considered as a new class of smart materials, since they can be altered and/or respond to different external stimuli. We propose a new class of dynamic, conjugated polymers based on the reversible iminoboronate ester bonding motif. Simple monomers containing aldehyde, amine, and boronic acid functionalities will polymerize together with diols to produce iminoboronate ester polymers. The presence of orthogonal dynamic imine and boronate ester bonds will allow us to develop easily functionalizable, and thus tuneable, multicomponent polymer materials that possess the ability to reorganise or adapt in response to various external stimuli. The incorporation of electronically distinct diols, will permit us to tune the photophysical and chemical properties of the conjugated polyimine polymer backbone, enabling the possibility to attain the desired material properties. These multifunctional materials will be investigated as fluorescent polyreceptors for specific diols and as controllably crosslinkable or decrosslinkable materials with the addition or displacement of tetrols. Moreover, development of these high-value smart materials will lay the groundwork for the next generation of multifunctional devices, such as blue-light-emitting polymers with superior efficiency and electrochemical stability to the state-of-the-art poly(fluorene) and GaN (O)LEDs.

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  • Funder: EC Project Code: 291280
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  • Funder: EC Project Code: 322621
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