The growing interest for the integration of renewable energy sources, as solar energy, in the global energy mix, increases the need of developing of new methods that will assist on the up-scaling and demonstration of efficient energy storage and conversion technologies. In this regard, advanced modelling methods can be an indispensable tool towards this effort. SHINE aims at developing a holistic numerical methodology – by using in-house codes coupled with commercial software– that will boost the cost-efficient and sustainable electricity production and storage at unprecedented ultra-high temperatures (> 1000 oC). The stepping stone for the modelling activities will be a compact latent heat thermophotovoltaic device recently patented in UPM targeted for energy storage and production at ultra-high temperatures. The core components in such a device are the latent heat thermal energy storage system and the thermophotovoltaic device. The modelling methodology will integrate rigorous multi-physics models (fluid dynamics, heat transfer and optoelectronics) targeted at a component level into a reduced order model (ROM) by using multi-variable polynomial functions. Key in the proposed methodology is the validation of the rigorous models through in-house measurements at ultra-high temperatures that will be undertaken at the host organisation. Key as well is the production of the multi-variable polynomials through artificial neural networks that will be undetaken during the Secondment phase. The whole project is highly interdisciplinary because it integrates highly interrelated diverse disciplines (physics, engineering, optoelectronics, thermo- and fluid-dynamics, photovoltaics and thermal storage, and artificial intelligence-AI) as well as know-how from experiments is a single holistic approach. Once developed the ROM will be used to predict the whole system's performance as being part of a solar-to-heat-to-power and a power-to-heat-to-power concepts.

The goal of “SDGine for Healthy People and Cities” will be to deliver a world-class training programme at Universidad Politécnica de Madrid (UPM) for 12 ESRs mainly in the STEAM disciplines (Science, Technology, Engineering, Architecture, and Applied Mathematics), with a strong focus on Sustainable Development Goals (SDGs). SDGine's fundamental objectives will be to develop technologies and tools that accelerate the compliance with SDGs related to climate change and social transformations needed in urban contexts. ESRs will complete Industrial and International Doctorates (through UPM’s Doctoral Division & International Doctorate School and the existing 45 Doctoral Programmes) connected with the notion of innovation platform, a systemic approach aligned with major international programs in which UPM participates (EIT Digital, EIT Health, EIT Raw Materials and Climate Knowledge Innovation Community) and Horizon Europe Missions. SDGine will enhance mobility between countries, sectors and disciplines. All trained ESRs will become highly (self)-employable for future research positions in academia and non-academia. SDGine will follow the Seven EU principles on Doctoral Training and will contemplate the Triple-“i” dimension through: i) International secondments; ii) Intersectoral collaboration agreements with industry; and iii) Interdisciplinary PhD supervision. Moreover, the innovation & entrepreneurship dimension of the PhD thesis projects will be enhanced. SDGine will be aligned with the "Charter & Code" for researchers, since UPM has received the HR Excellence in Research award, and will include excellent training in research, complementary and transferable skills (cross-cutting disciplinary seminars, summer schools, writing days...). The programme will have high societal impact through SDGs as well as a positive impact not only in ESRs’ career development and future job opportunities, but also in the UPM, the region of Madrid, Spain, the EU and beyond the EU.

In this project I will develop an integrated multi-module platform in which a breast cancer ecosystem-on-a-chip (BCE-on-a-chip) bioreactor module, connected to an optical biosensing module based on multiplexed Resonant Nanopillars (R-NPs) transducers, will be developed for biomarkers and anti-breast cancer drug real-time monitoring. Currently attrition rates in clinical trials for new anti-cancer drugs and personalized treatments are higher than all other therapeutic areas. Mainly due to the great reliability on conventional 2D and 3D scaffolds in-vitro culture methodologies in preclinical studies. Those cell-based models are limited by their inability to conserve the patient tumor features and do not accurately show drug response, observed later in clinical trials. Due to the increasing number of cancer diagnoses, an engineered system that allows an accurate prediction of patient tumor response to anti-cancer drug, is urgently needed. In Bitform Project I will develop a BCE-on-a-chip bioreactor that intends to conserve the cancer tissue characteristics with high reliability. Breast cancer cell secreted biomarkers will be monitored in real-time with a multiplexed biosensing module based on R-NPs. In order to assess the capability of Bitform platform, an anti-cancer drug demonstrator (Paclitaxel) will be tested. By delivering Paclitaxel to the BCE-on-a-chip, monitoring of different cell secreted target biomarkers will permit to evaluate the effect of this drug, and thus to demonstrate the performance of the platform. The Bitform platform will be suitable for different organ-on-chip culture models and biomolecules monitoring secreted by the cells. This will lead to a new methodology for testing anti-cancer therapies on-a-chip. This is a relevant milestone to study the further potential of the Bitform Platform in personalized medicine, which can have a transformative impact not only on the outcome but also on the costs of treatments by avoiding expensive failures.