Doctoral School

Reading date: 02/06/2025

• AGRELO LESTÓN, ASIER: Development of metal-enhanced TiO2-based photocatalysts for hydrogen production
  
  Author: AGRELO LESTÓN, ASIER
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING
  Department: Department of Chemical Engineering (EQ)
  Mode: Normal
  Deposit date: 02/04/2025
  Reading date: 02/06/2025
  Reading time: 11:00
  Reading place: Campus Diagonal Besòs, Edifici I (EEBE)Sala Polivalent, Edifici I - I.01Av. Eduard Maristany, 16 08019 Barcelonahttps://eebe.upc.edu/ca/lescola/com
  Thesis director: LLORCA PIQUE, JORDI | SOLER TURU, LLUIS
  Thesis abstract: Human activity has led to rising greenhouse gas levels, altering climate patterns and intensifying weather events. Therefore, a transition to a decarbonized energy system is needed, with hydrogen as a promising energy vector alongside solar and wind energy. However, current hydrogen production methods, such as steam methane reforming, generate significant CO2 emissions. Sunlight-driven water splitting offers a sustainable alternative, though efficiency improvements are required for industrial viability.This PhD thesis focuses on developing novel TiO₂-based catalysts for photocatalytic hydrogen production.Chapter 3 was conducted with the SYMAC team from Université Toulouse 3-Paul Sabatier. A TiO₂ catalyst was decorated with Cu nanoparticles stabilized by quinidine and compared to a sample prepared via incipient wetness impregnation (IWI) using L-ascorbic acid. The quinidine-stabilized sample exhibited 5 times superior activity under UV, as well as activity enhancement under Uv-visible irradiation. UV-vis spectroscopy revealed a plasmonic band relative to Cu, and a decrease in the bandgap was confirmed by Tauc plots. XRD confirmed Cu deposition and predominant anatase phase of the TiO2. TEM confirmed presence of Cu nanoparticles that XAS and XPS identified predominant metallic nature with minor oxide contributions.Chapter 4 was carried out with the Supra- and Nanostructured Systems group at Universitat de Barcelona (UB). Hybrid TiO₂ photocatalysts were prepared with Au(I) complexes and Au(0) systems were developed as co-catalysts. Three catalyst series incorporating coumarin-based ligands were evaluated under light and heat. Two (1 wt.% co-catalyst) were prepared via IWI and ball milling (BM), while a third (0.25 wt.% Au) was synthesized by IWI. IWI-prepared samples showed superior activity, achieving up to 2.7 times the H₂ production of conventional Au/TiO₂. UV-vis spectroscopy confirmed plasmonic bands relatives to Au and Tauc plots revealed bandgap narrowing. TEM, HAADF-STEM, and XPS confirmed the presence of Au nanoparticles with a predominant metallic nature.Chapter 5 focused on Pt/TiO₂ photocatalysts synthesized by BM, optimized through a design of experiments (DoE) approach. The most active sample was 1.4 times more efficient than an IWI-prepared Pt/TiO2 reference under UV light irradiation. HAADF-STEM-EDX revealed Pt atoms dispersed on TiO₂, with post-reaction growth into nanoparticles while there was presence of some Pt atoms dispersed. XPS confirmed partial Pt reduction during the reaction.Chapter 6 explored bimetallic PdCu photocatalysts with a total metal loading of 1 wt.%. A Pd:Cu atomic ratio of 1:2 was chosen after a screening from 3:1 to 1:3. The bimetallic sample outperformed theoretical activity of the combination of thus metals under UV light by 27%, and Cu incorporation enhanced H₂ production under UV-vis irradiation. BM-prepared samples were 1.2 times more active than IWI ones. Pd stability was improved with Cu incorporation, as seen in long-term tests, with less activity loss compared to monometallic Pd. Raman spectroscopy indicated strong metal-support interactions. UV-vis spectroscopy and Tauc plots showed enhanced visible absorption and bandgap narrowing, respectively. HAADF-STEM-EDX revealed bimetallic PdCu nanoparticles in BM samples, whereas IWI samples had separate Pd and Cu nanoparticles. BM also constrained Pd growth, as Pd nanoparticles in the monometallic sample grew 3.5 times during the reaction. XPS showed Pd reduction in both samples, with complete reduction in BM-prepared catalysts, further supported by H₂-TPR.
  

Reading date: 03/06/2025

• BLANC BLOCQUEL DI MARCO, AUGUSTO: Derivatives and risks
  
  Author: BLANC BLOCQUEL DI MARCO, AUGUSTO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN STATISTICS AND OPERATIONS RESEARCH
  Department: Department of Statistics and Operations Research (EIO)
  Mode: Normal
  Deposit date: 13/03/2025
  Reading date: 03/06/2025
  Reading time: 11:00
  Reading place: Aula de Teleensenyament, edifici B3, Campus UPC Barcelona Nord
  Thesis director: ORTIZ GRACIA, LUIS
  Thesis abstract: This thesis aims to provide solutions to real world problems by the use and development ofstate-of-the-art quantitative finance techniques. The first part of this work tackles the challenge of digital options hedging, particularly, at the-money digital options near maturity. The problem stems from the fact that a digitaloption has a discontinuous payoff at the strike price and has a huge delta and gamma nearexpiration. This problem is well-known among practitioners and academics. In this work, weconsider a general setting for hedging at-the-money digital options near maturity by meansof a bull spread. We solve different optimization problems, with the aim of minimizing theprobability of sub-hedging the digital option at maturity, considering transaction costs andilliquidity issues. Our contribution consists in the fact that we determine the compositionof the bull spread that minimizes the probability of sub-hedging a digital option given thatthe cost of hedging is below a certain threshold. We consider traditional and state-of-the-artmodels for driving the dynamics of the underlying asset. We also introduce the modelingof the illiquidity issue in the optimization problem, and solve that optimization problem.Finally, we calibrate one model to real market data and solve the optimization problem withtransaction costs with the calibrated model.In the second part of this work we intend to create financial tools to fight against climatechange. Over the last five years there have been increasing concerns about the impact ofcryptocurrency mining on climate. One of the main effects of climate change is its impacton agriculture and food production. In addition, climate change has clear consequencesfor human health. We propose novel bitcoin-denominated derivatives contracts on carbonbonds to address this problematic. This paper creates novel financial products which couldhelp the regulatory authorities impact the climate in an indirect fashion, agglutinating twodesired financial outcomes (hedging and volatility transfer) in a single financial instrument.Particularly, the instrument can be used by governments to hedge against climate change andinfluence the prices of carbon bonds and cryptocurrencies. In order to price this product, wedevelop novel parameter estimation techniques based on Shannon wavelets.The third part of this work also revolves around climate change, finance and mathematics.In this work we put forward a methodology to calculate the impact of an increase of the earth’sglobal surface temperature on the probability of default of a company from the agriculturesector. Extreme temperatures have a negative impact on asset prices in all sectors. Weperform a regression of firm’s stock value with predictors S&P 500 and temperature anomaliesand observe that an increase of temperature anomalies has a negative impact on the stockof the firm considered in this work. When modelling temperature anomalies time series it isimportant to identify points in time where a significant change occurs in the behaviour of thedata. These points are called breakpoints. Then, we model the time series of temperatureanomalies by means of segmented linear regression, where the breakpoints are estimatedby means of a wavelet analysis. We calibrate a Merton model with real stock data of thecompany and estimate the probability of default based on the probability that the assetvalue of the firm is below the liabilities level. We proceed to use the regression model toforecast future values of the firm’s stock influenced by the predicted temperature anomaliesand estimate a new probability of default.
  

Reading date: 05/06/2025

• LAUT TURÓN, SERGI: Architecture-aware Sparse Patterns to Accelerate Inverse Preconditioning
  
  Author: LAUT TURÓN, SERGI
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN COMPUTER ARCHITECTURE
  Department: Department of Computer Architecture (DAC)
  Mode: Normal
  Deposit date: 10/04/2025
  Reading date: 05/06/2025
  Reading time: 11:00
  Reading place: Sala C6-E101
  Thesis director: CASAS GUIX, MARC | BORRELL POL, RICARD
  Thesis abstract: This work focuses on improving the efficiency of iterative methods for solving large and sparse linear systems.These problems arise in many fields, including climate modeling, molecular and fluid dynamics, among others.To solve them, iterative methods such as the Conjugate Gradient (CG) and Generalized Minimal Residual (GMRES) methods are widely employed.Their efficiency heavily depends on the choice of preconditioners, which accelerate convergence by improving the numerical properties of the system.Sparse Approximate Inverse (SAI) preconditioners, and their factorized variant (FSAI) for symmetric positive definite systems, are particularly appealing due to their parallel-friendly nature and straightforward application via Sparse Matrix-Vector (SpMV) operations.State-of-the-art SAI and FSAI approaches define their sparsity patterns primarily based on numerical considerations.This work introduces novel architecture-aware preconditioners designed to enhance performance by optimizing the sparse pattern selection process.The first contribution presents the Factorized Sparse Approximate Inverse with Pattern Extension (FSAIE) preconditioner, an optimized version of FSAI tailored for shared memory CPU architectures.FSAIE introduces a cache-aware algorithm that extends sparsity patterns, improving both the numerical effectiveness of FSAI and its computational efficiency.Additionally, a filtering-out strategy is proposed to remove numerically insignificant entries, reducing computational cost without compromising convergence.These techniques enhance data locality in the SpMV kernel by ensuring that extended sparse patterns align with cache-line-sized memory access patterns.The second contribution extends FSAIE to distributed memory CPU environments, introducing the Communication-aware Factorized Sparse Approximate Inverse with Pattern Extension (FSAIE-Comm) preconditioner.FSAIE-Comm incorporates communication-awareness to ensure that the sparse pattern extension does not introduce unnecessary inter-process communication overhead.To prevent load imbalance, an innovative strategy is developed to distribute computational workload more evenly.The third contribution focuses on GPU execution by introducing the GPU-aware Factorized Sparse Approximate Inverse (GFSAI) preconditioner.By structuring the sparse pattern to enhance coalesced memory accesses and exploit GPU-specific architectural characteristics, GFSAI significantly accelerates FSAI computations on GPUs.The final contribution generalizes the architecture-aware preconditioning strategies beyond FSAI by introducing the Communication-aware Sparse Approximate Inverse with Pattern Extension (SAIE-Comm) preconditioner.This approach optimizes SAI for distributed memory environments, similar to FSAIE-Comm, but is adapted for general linear systems where the GMRES solver is preferable over CG.SAIE-Comm highlights the versatility and flexibility of the proposed optimizations, demonstrating that architecture- and communication-aware pattern extensions can be effectively integrated into different preconditioning strategies and solver frameworks.By integrating hardware-aware considerations into preconditioner design, this research advances the state of the art in iterative solvers and contributes to the development of scalable and high-performance numerical methods.The proposed methods achieve substantial improvements in time-to-solution across diverse High-Performance Computing (HPC) architectures, with reductions ranging from 12.94% to 26.43% on five different CPU architectures—Intel Skylake, Power9, Zen 2, A64FX, and Intel Sapphire Rapids—and from 23.83% to 26.07% on two GPU architectures—Volta and Vega20—when applied to representative sparse matrix benchmarks.These results underscore the impact of architecture-aware preconditioning strategies in modern HPC applications, paving the way for more efficient and scalable iterative solvers.
  

• PASTOR LÓPEZ, EDWARD JAIR: Nature-based solutions to reduce the spread of antibiotics and antimicrobial resistance genes in aquatic ecosystem
  
  Author: PASTOR LÓPEZ, EDWARD JAIR
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Article-based thesis
  Deposit date: 14/04/2025
  Reading date: 05/06/2025
  Reading time: 11:00
  Reading place: Place: ETSECCPBUPC, Campus NordBuilding C2. Classroom: 212C/Jordi Girona, 1-308034 Barcelona
  Thesis director: MATAMOROS MERCADAL, VÍCTOR | ESCOLÀ CASAS, MÒNICA
  Thesis abstract: Antibiotics (ABs) are antimicrobial agents whose production and consumption have increased exponentially since the discovery of penicillin in 1929. The overuse of ABs has driven the emergence of antibiotic resistance, leading the frequent detection of ABs and antimicrobial resistance genes (ARG) in aquatic environments, primarily due to wastewater treatment plants (WWTP) effluent discharge despite regulatory efforts. Additionally, prolonged extreme weather conditions, such as droughts, intensify this issue by reducing water availability, threatening aquatic ecosystems and human health. Although advanced water treatment technologies, such as ozonation or membrane-based systems, can remove these pollutants from wastewater, their high cost of construction and maintenance, limited their widespread implementation. Alternatively, Nature-Based Solutions (NBS) have emerged as a potential option due to their cost-effectiveness and their potential capacity to remove a wide range of pollutants. However, studies on the reduction of ABs and ARGs in full-scale on NBS applied to wastewater treatment or river streams remain limited.This PhD dissertation is presented as a compendium of publications and evaluates the effectiveness of NBS in reducing ABs and ARGs in wastewater. First, a review study explored the capacity of NBS to reduce the presence of ABs, ARGs and pathogens across diverse aquatic environments spanning secondary wastewater treatment to estuarine areas and saltmarshes (Chapter II – DOI: 10.1016/j.scitotenv.2024.174273). Second, the performance of two full-scale configurations of constructed wetlands (CW) as tertiary wastewater treatment systems were monitored during the summer and the winter seasons to assess the reduction of ABs and ARGs (Chapter III – DOI: 10.1016/j.watres.2024.122038). Their effectiveness were compared with a conventional tertiary wastewater treatment technology system. Third, the impact of wastewater effluent-dominated stream renaturalization on the reduction of ABs and ARGs across seasonal variations was assessed by monitoring a vegetated and less vegetated stream during both warm and cold periods (Chapter IV – DOI: 10.1016/j.envres.2025.120910).The findings presented in this doctoral research project demonstrate that NBSs are potential alternatives for water treatment management in river basins. CWs as tertiary wastewater-treatment systems have shown the capacity to improve the general water quality parameters and remove ABs and ARGs. In addition, unlike conventional systems, those systems promote a shift in microbial composition towards a more natural profile and reduce the ecotoxicological and resistance selection risks more than a conventional tertiary WWTP. Furthermore, vegetated streams with meanders have shown to increase the degradation kinetics of ABs and the attenuation of ARGs, foster gradual changes in bacterial community structures and decrease the ecotoxicological and resistance selection risks, especially during the warm period. Further research should keep focusing on evaluating novel full-scale NBS configurations, identification of the transformation products (TPs) as well as other aquatic micropollutants, quantification of diverse ARGs and assessing the ecological status of the water.
  

• SETIEN UGALDE, IÑAKI: Enhanced Inherent Strain Modelling for Powder-Based Metal Additive Manufacturing
  
  Author: SETIEN UGALDE, IÑAKI
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CIVIL ENGINEERING
  Department: Barcelona School of Civil Engineering (ETSECCPB)
  Mode: Article-based thesis
  Deposit date: 10/04/2025
  Reading date: 05/06/2025
  Reading time: 11:00
  Reading place: Campus Nord - UPCCIMNE - Edifici C1 C/ Gran Capità,s/nSala OCZ (Zienkiewicz)08034, Barcelona https://meet.google.com/nvd-fqgp-gsc
  Thesis director: CHIUMENTI, MICHELE | SAN SEBASTIÁN ORMAZABAL, MARÍA
  Thesis abstract: Metal additive manufacturing (MAM), particularly powder bed fusion using a laser beam (PBF-LB), has transformed manufacturing by enabling the production of intricate and optimised metal components directly from digital designs. This process offers major advantages such as material efficiency, high geometric flexibility, and the ability to produce lightweight, customised parts. However, its industrial adoption is hindered by challenges such as large residual stresses and distortions resulting from steep temperature gradients and rapid thermal cycles inherent in layer-by-layer manufacturing process. These issues compromise dimensional accuracy and structural integrity, posing barriers to the broader implementation of the technology.High-fidelity thermo-mechanical finite element (FE) simulations can predict these phenomena but their high computational cost makes them impractical for large-scale industrial applications. The inherent strain method (ISM) has emerged as an efficient alternative, condensing complex thermal and mechanical phenomena into an inherent strain tensor applied in simplified elastic simulations. While ISM significantly reduces computational time, conventional implementations often lack robustness, requiring extensive recalibration for different geometries and scanning strategies and failing to capture spatial and temporal variations in thermal histories.This thesis addresses these limitations by developing an enhanced inherent strain method (EISM) for powder bed fusion (PBF), improving ISM's predictive accuracy and extending its applicability to complex industrial geometries. By integrating a macro-scale thermal analysis into ISM, the method dynamically refines the precomputed inherent strain tensor based on part-scale temperature evolution. This enhancement better accounts for geometry- and boundary-specific thermal effects, improving distortion predictions compared to conventional ISM.Additionally, this work tackles the fundamental challenge of determining inherent strain tensors necessary for ISM-based methodologies. Two complementary approaches are proposed: (1) an empirical calibration strategy using twin-cantilever beam coupons, where distortions measured after partial cutting are used to determine best-fit inherent strain tensors via inverse engineering, and (2) a numerical approach employing a meso-scale thermo-mechanical model within a multi-scale framework, computing local inherent strains and homogenising them to obtain macro-scale inherent strain tensors.Comprehensive experimental calibration and validation were conducted using Ti-6Al-4V components manufactured via PBF-LB. Temperature histories were recorded with embedded thermocouples, while distortion and residual stress data were acquired using coordinate measuring machines (CMM), 3D scanning, and incremental hole-drilling, respectively. The empirical and numerical methodologies for inherent strain tensor determination, along with EISM, were validated across multiple geometries, including twin-cantilever beams, a non-symmetric bridge, and an industrial aerospace component (the Steady Blowing Actuator). The results demonstrated that EISM significantly improved distortion predictions while maintaining computational efficiency, reducing errors by more than half compared to conventional ISM.In conclusion, this thesis presents two methods for calculating the inherent strain tensor (empirical and numerical) and introduces the EISM methodology for distortion prediction, improving the accuracy of distortion prediction in PBF-LB. In this way, the dependence on trial-and-error-based experimental testing is reduced, moving towards an optimised simulation-based design and facilitating the industrial adoption of MAM.