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The optical and photovoltaic properties of Si NCs / SiC multilayers (MLs) are investigated using a membrane-based solar cell structure. By removing the Si substrate in the active cell area, the MLs are studied without any bulk Si substrate contribution. The occurrence is confirmed by scanning electron microscopy and light-beam induced current mapping . Optical characterization combined with simulations allows us to determine the absorption within the ML absorber layer, isolated from the other cell stack layers. The results indicate that the absorption at wavelengths longer than 800 nm is only due to the SiC matrix. The measured short-circuit current is significantly lower than that theoretically obtained from absorption within the ML absorber, which is ascribed to losses that limit carrier extraction. The origin of these losses is discussed in terms of the material regions where recombination takes place. Our results indicate that carrier extraction is most efficient from the Si NCs themselves, whereas recombination is strongest in SiC and residual a-Si domains . Together with the observed onset of the external quantum efficiency (EQE) at 700-800 nm, this fact is an evidence of quantum confinement in Si NCs embedded in SiC on device level.

The aggravating global problems of energy crisis, rising atmospheric greenhouse gas concentrations and accumulation of persistent waste have attracted the attention of scientists, policy-makers and global organisations to come up with effective and expeditious solutions to address these challenges. In this context, the development of sustainable technologies driven by renewable energy sources for the production of clean fuels and commodity chemicals from diverse waste feedstocks is an appealing approach towards creating a circular economy. Over the years, semiconductor photocatalysts based on TiO₂, CdS, carbon-nitrides (CNx) and carbon dots (CDs) have been widely used for the photocatalytic reforming (PC reforming) of pre-treated waste substrates to organic products, accompanied with clean hydrogen (H₂) generation. However, these conventional solar-driven processes suffer from major drawbacks such as low production rates, poor product selectivity, CO₂ release, challenging process and catalyst optimisation, and harsh waste pre-treatment conditions, which limit their commercial applicability. These challenges are tackled in this thesis with the introduction of new and efficient photoelectrochemical (PEC) and chemoenzymatic processes for reforming a diverse range of waste feedstocks to sustainable fuels. Solar-driven PEC reforming based on halide perovskite light-absorber is first developed as an attractive alternative to PC reforming. The PEC systems consist of a perovskite|Pt photocathode for clean H₂ production and a Cu-Pd alloy anode for reforming diverse waste streams, including pre-treated cellulosic biomass, polyethylene terephthalate (PET) plastics, and industrial by-product glycerol into industrially-relevant, value-added chemicals (gluconic acid, glycolic acid and glyceric acid) without any externally applied bias or voltage. Additionally, the single light-absorber PEC systems can also convert the airborne waste stream and greenhouse gas CO₂ to diverse products with the simultaneous reforming of PET plastics with no applied voltage. The perovskite-based photocathode enables the integration of different CO₂ reduction catalysts such as a molecular cobalt porphyrin, a Cu-In alloy and formate dehydrogenase enzyme, which produce CO, syngas and formate, respectively. The versatile PEC systems, which can be assembled in either a ‘two-compartment’ or standalone ‘artificial leaf’ configurations achieve 60‒90% oxidation product selectivity (with no over-oxidation) and >100 µmol cm‾² h‾¹ product formation rates, corresponding to 10²‒10⁴ times higher activity than conventional PC reforming systems. In addition to developing PEC platforms, this thesis also explores avenues for circumventing the harsh alkaline pre-treatment strategies (pH >13, 60‒80 ºC) adopted for photoreforming waste substrates. For this purpose, a chemoenzymatic pathway is introduced whereby PET and polycaprolactone plastics were deconstructed using functional enzymes under benign conditions (pH 6‒8, 37‒65 ºC), followed by PC reforming using Pt loaded TiO₂ (TiO₂|Pt) or Ni₂P loaded carbon-nitride (CNx|Ni₂P) photocatalysts. The chemoenzymatic reforming process demonstrates versatility in upcycling polyester films and nanoplastics for H₂ production at high yields reaching ∼10³‒10⁴ µmol gsub‾¹ and activities at >500 µmol gcat‾¹ h‾¹. The utilisation of enzyme pre-treated plastics also allowed the coupling of plastic reforming with photocatalytic CO₂-to-syngas conversion using a phosphonated cobalt bis(terpyridine) co-catalyst immobilised on TiO₂ (TiO₂|CotpyP). Finally, moving beyond solar-driven systems, a bio-electrocatalytic flow process is demonstrated for the conversion of microbe pre-treated food waste to ethylene (an important feedstock in the chemical industry) on graphitic carbon electrodes via succinic acid as the central intermediate. In conclusion, with its focus on improving efficiencies, achieving selective product formation, building versatile platforms, diversifying substrate and product scope, and reducing carbon footprint and economic strain, this thesis aims to bring sustainable waste-to-fuel technologies a step closer to commercial implementation.

The accurate training of a wind power forecasting (WPF) model for a newly built wind farm is difficult because of limited historical data. This study established a multitask learning architecture wherein the WPF in different wind farms represents an independent task. Subsequently, a novel short-term WPF model based on a multitask learning architecture was proposed. In this model, a multitask Gaussian process is used to capture the intertask conjunction, which contributed to the training of each task. The proposed methodological framework employs dependencies from other tasks wherein older wind farms contain substantial historical data to enhance the performance of tasks in which there is a newly built wind farm. Several numerical experiments were conducted using datasets from seven independent wind farms in Australia. The results show that the proposed scheme not only obtains improved point forecasting results but also produces better probabilistic forecasting results, thus demonstrating the superiority of the proposed method.

The stability of winter wheat-flowering-date is crucial for ensuring consistent and robust crop performance across diverse climatic conditions. However, the impact of climate change on wheat-flowering-dates remains uncertain. This study aims to elucidate the influence of climate change on wheat-flowering-dates, predict how projected future climate conditions will affect flowering date stability, and identify the most stable wheat genotypes in the study region. We applied a multi-locus genotype-based (MLG-based) model for simulating wheat-flowering-dates, which we calibrated and evaluated using observed data from the Northern China winter wheat region (NCWWR). This MLG-based model was employed to project flowering dates under different climate scenarios. The simulated flowering dates were then used to assess the stability of flowering dates under varying allelic combinations in projected climatic conditions. Our MLG-based model effectively simulated flowering dates, with a root mean square error (RMSE) of 2.3 days, explaining approximately 88.5 % of the genotypic variation in flowering dates among 100 wheat genotypes. We found that, in comparison to the baseline climate, wheat-flowering-dates are expected to shift earlier within the target sowing window by approximately 11 and 14 days by 2050 under the Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 climate scenarios, respectively. Furthermore, our analysis revealed that wheat-flowering-date stability is likely to be further strengthened under projected climate scenarios due to early flowering trends. Ultimately, we demonstrate that the combination of Vrn and Ppd genes, rather than individual Vrn or Ppd genes, plays a critical role in wheat-flowering-date stability. Our results suggest that the combination of Ppd-D1a with winter genotypes carrying the vrn-D1 allele significantly contributes to flowering date stability under current and projected climate scenarios. These findings provide valuable insights for wheat breeders and producers under future climatic conditions.

Revolutionary catalyst protection by single layer graphene capping, tremendous catalyst lifetime longevity and activity enhancement towards oxygen reduction reaction.

This master thesis explores the integration of Artificial Intelligence (AI) in Environmental, Social, and Governance (ESG) reporting, focusing on its potential to enhance the accuracy, transparency, and efficiency of ESG disclosures. It investigates how AI can transform ESG reporting from a reactive to a proactive tool, enabling companies to better assess and mitigate ESG-related risks. The study examines the perceptions of industry professionals regarding AI's role in ESG, considering factors such as age, education, and professional background. It highlights the significant impact AI can have on sectors like finance, energy, and healthcare, while addressing challenges and ethical concerns. The findings suggest that AI can be a powerful enabler of ESG transparency, but its adoption requires sector-specific adaptations and careful ethical considerations. Future research is recommended to explore long-term effects and deeper ethical issues in AI-driven ESG reporting. Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura Objectius de Desenvolupament Sostenible::16 - Pau, Justícia i Institucions Sòlides

Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.6 - Per a 2030, reduir l’impacte ambiental negatiu per capita de les ciutats, amb especial atenció a la qualitat de l’aire, així com a la gestió dels residus municipals i d’altre tipus 2n Premi Domènec Valero 2024 Award-winning