Doctoral School

Reading date: 10/09/2026

• COLLADO CIPRÉS, VERÓNICA: Hot deformation behaviour of WC–Co cemented carbides
  
  Author: COLLADO CIPRÉS, VERÓNICA
  Programme: DOCTORAL DEGREE IN MATERIALS SCIENCE AND ENGINEERING
  Department: Department of Materials Science and Engineering (CEM)
  Mode: Article-based thesis
  Deposit date: 03/06/2026
  Reading date: 10/09/2026
  Reading time: 11:30
  Reading place: ESCOLA D'ENGINYERIA BARCELONA ESTC/Eduard Maristany, 16 (08019 Barcelona)EDIFICI A planta 0, SALA D'ACTEShttps://meet.google.com/pcm-czdv-rxr
  Thesis director: LLANES PITARCH, LUIS MIGUEL | GARCÍA, JOSÉ LUIS
  Thesis abstract: This PhD thesis presents a systematic approach to describe, model, and understand the hot deformation behaviour of cemented carbides—composite materials based on tungsten carbide (WC) and cobalt (Co), commonly known as hardmetals—under service-like conditions. The work aims to bridge the gap between traditional room-temperature studies and the complex mechanical demands encountered at elevated temperatures.Hardmetals are used in applications requiring extreme mechanical performance, such as cutting tools, mining equipment, and forming dies. Historically, research has concentrated on their room-temperature properties, mainly hardness and fracture toughness. However, such studies do not capture the complex mechanical behaviour at elevated temperatures, where plastic deformation becomes a dominant wear mechanism.Previous investigations into high-temperature deformation have primarily relied on creep testing, which is conducted under very low strain rates and constant stress. While these studies have helped to identify deformation regimes and mechanisms such as grain boundary sliding and binder infiltration, they do not reflect the dynamic, high-strain-rate conditions experienced during actual service, such as in machining operations. Similarly, research on hot hardness has shown that fine-grained WC structures tend to retain higher hardness at lower temperatures but may soften more rapidly at elevated temperatures—highlighting the need for a deeper understanding of microstructural effects under thermal stress.This thesis addresses these critical knowledge gaps by systematically studying the hot deformation behaviour of WC–Co cemented carbides under service-like conditions, with strain rates ranging from 0.0005 to 0.1 s⁻¹ and temperatures from 700 °C up to 1000 °C. The research unfolds in three interconnected stages. The first study investigates sintered cobalt, the binder phase in WC–Co. Through hot compression testing, the deformation mechanisms were characterised, revealing a creep exponent of n = 5 and an activation energy consistent with self-diffusion in face-centered cubic Co. These findings indicate that deformation is governed by dislocation glide and climb.A physically based constitutive equation was developed to describe the peak stress in WC–Co as the sum of three components: stress carried by the binder, stress accommodated by the WC, and stress arising from the interaction between the two phases. The WC phase exhibited an activation energy of 585 kJ/mol, attributed to W pipe diffusion, and a high stress exponent (n = 19), indicating minimal sensitivity to temperature and strain rate—typical of ceramic materials. The interaction term was modelled using the binder mean free path and carbide skeleton stiffness, revealing its significant influence on overall mechanical resistance.The final study incorporated WC grain size into the constitutive model. Experimental results demonstrated that fine-grained WC structures exhibit higher resistance to plastic deformation at lower temperatures due to grain boundary strengthening. However, this advantage diminishes rapidly at elevated temperatures, where grain boundary sliding and binder infiltration become dominant deformation mechanisms. Coarser WC grains, while less resistant at room temperature, maintain structural integrity more effectively under thermal stress. Microstructural analysis via electron back-scattered diffraction confirmed these trends, highlighting the role of grain boundaries, phase transformations, and interface behaviour.These studies provide a framework for understanding and optimizing the high-temperature mechanical behaviour of WC–Co cemented carbides. By bridging the gap between microstructural characterisation and physical modelling, this thesis advances the field beyond traditional room-temperature and creep-based analyses, offering practical tools for designing hardmetals capable of withstanding extreme service conditions.
  

• PLANA RIU, JOSEP: Marching faster in time: advanced time-integration for incompressible flows
  
  Author: PLANA RIU, JOSEP
  Programme: DOCTORAL DEGREE IN THERMAL ENGINEERING
  Department: Department of Heat Engines (MMT)
  Mode: Normal
  Deposit date: 22/07/2026
  Reading date: 10/09/2026
  Reading time: 15:00
  Reading place: Sala de Conferències de TR5
  Thesis director: PEREZ SEGARRA, CARLOS DAVID
  Thesis abstract: The computational cost of scale-resolving simulations of incompressible flows, such as Direct Numerical Simulation (DNS) and Large-Eddy Simulation (LES), is largely dictated by the temporal integration of the incompressible Navier-Stokes equations. Traditional timestepping strategies often rely on conservative stability criteria, such as the standard Courant-Friedrichs-Lewy (CFL) condition, which typically underestimates the maximum stable timestep by neglecting the complex interplay between convective and diffusive scales. This thesis addresses the challenge of "marching faster in time" by developing a framework that integrates stability-aware adaptive timesteppting with hardware-optimized sparse linear algebra techniques. The first part of this thesis focuses on the development and extension of self-adaptive time integration methods based on the spectral analysis of the governing operators. By implementing algorithms such as AlgEigCD on staggered grids, it is shown that the maximum stable timestep can be accurately determined by tracking the evolution of the operators’ eigenvalues, rather than relying on the conservative CFL condition. This approach allows for significantly larger timesteps without compromising stability or accuracy, leading to substantial reductions in computational time for incompressible flow simulations, without sacrificing the fidelity of the results. Furthermore, this thesis extends these concepts to the realm of reduced-order modeling (ROM). Using Poincaré’s separation theorem for singular values, a theoretical foundation is established to prove that the stability limits of the reduced convective operators are bounded by those of the full-order system. The resulting RedEigCD algorithm enables ROMs to sustain larger timesteps, effectively leveraging their lower dimensionality to further reduce total wall-clock time. Finally, the research addresses the hardware-level bottleneck inherent in modern CFD solvers, which are frequently limited by memory bandwidth during sparse algebraic operations. By exploiting the repeated matrix structure found in parallel-in-time ensemble averaging simulations, sparse matrix-vector products are replaced by sparse matrix-matrix products. This transformation increases the arithmetic intensity of the computations, enabling higher performance on modern hardware architectures. The effectiveness of these combined mathematical and computational strategies is validated through a series of academic and industrial test cases, demonstrating that significant speedups can be achieved without compromising the accuracy or stability of the simulations. This work provides a practical pathway for accelerating CFD simulations in real-world applications, while also contributing to the theoretical understanding of stability in numerical methods for incompressible flows.
  

• PRELAT OLIVARES, LEILA ROCIO: Free-electron interaction with nanophotonic excitations
  
  Author: PRELAT OLIVARES, LEILA ROCIO
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 07/07/2026
  Reading date: 10/09/2026
  Reading time: 10:00
  Reading place: ICFO Auditorium i https://teams.microsoft.com/meet/322181711925000?p=OtJ34OjGTLMTdTXLs6
  Thesis director: GARCÍA DE ABAJO, JAVIER
  Thesis abstract: Free electrons provide a powerful platform to control optical excitations at the nanoscale because they carry electromagnetic fields that are tightly localized and contain large evanescent wave-vector components inaccessible to propagating light. Although this property makes electron beams uniquely suited to address confined optical modes, their full potential for technological applications is still being actively developed. This Thesis aims to contribute to this effort by exploring novel phenomena that arise when electron beams are incorporated into different optical systems.As an introduction to the main concepts underlying this Thesis, Chapter 1 summarizes the theoretical frameworks used to describe electromagnetic excitations, with emphasis on linear and nonlinear optical phenomena, surface waves such as polaritons and waveguide modes, and electron beams.Chapter 2 explores electron-driven excitation of surface polaritons through resonant scatterers placed near polariton-supporting materials. The passing electron polarizes a small resonant particle, which then launches surface modes with a spectrum determined by the particle’s response. Our semi-analytical model reveals an optimum scatterer-surface separation that maximizes polariton emission. This approach is extended to periodic arrays of scatterers, leading to a polaritonic analog of the Smith-Purcell effect, in which surface polaritons are emitted directionally into diffraction orders controlled by the array period, electron velocity, and polariton dispersion relation. Hexagonal boron nitride nanodisks coupled to graphene plasmons are identified as a realistic mid-infrared implementation for small resonant scatterers, with efficiency comparable to resonant lossless particles.In Chapter 3, we introduce wave-mixing cathodoluminescence as a nonlinear spectromicroscopy technique for detecting low-frequency excitations through visible-range optical readout. In this mechanism, the evanescent field of a swift electron mixes with an external optical pump through the second-order nonlinear response of a specimen, generating sum- and difference-frequency photons. The method up-converts far-infrared spectral fingerprints into the visible range, avoiding the need for low-frequency light sources or detectors. Calculations for retinal-coated silver nanorods show that molecular vibrational signatures can be accessed with nanometer-scale spatial resolution under external illumination and visible-range detection.Chapter 4 explores electrostatic control of electron trajectories as a means of tuning coupling to guided modes in silicon waveguides. By deflecting electrons into grazing trajectories using a static electron-repulsive field, the minimum electron-waveguide separation becomes a controllable parameter that governs both coupling strength and modal selectivity. In particular, we consider three doped silicon waveguides placed on a sapphire substrate, with the two side elements acting as lateral gating structures. Including image-force effects and collision thresholds, the analysis predicts voltage-tunable photon yields reaching several photons per electron in realistic integrated photonic geometries.In Chapter 5 cylindrical waveguides are studied as mediators between free electrons and nanoscale absorbers. A gate-controlled grazing electron launches a guided wave packet that subsequently drives a nearby resonant particle. This waveguide-mediated channel concentrates the broadband electron field spectrally and spatially, producing strong absorption enhancements relative to direct bare-electron excitation.In summary, this Thesis establishes free-electron-nanophotonic interactions as a versatile platform for nanoscale excitation, spectroscopy, and control of optical, polaritonic, and guided modes, with potential applications in integrated photonics, molecular sensing, and quantum nanophotonics.
  

Reading date: 14/09/2026

• GARÍ GALÍNDEZ, JON: Developing Artificial Intelligence Strategies to Accelerate Scientific Research: Application to Thin-Film Photovoltaic Technologies
  
  Author: GARÍ GALÍNDEZ, JON
  Programme: DOCTORAL DEGREE IN COMPUTATIONAL AND APPLIED PHYSICS
  Department: Department of Physics (FIS)
  Mode: Article-based thesis
  Deposit date: 13/07/2026
  Reading date: 14/09/2026
  Reading time: 10:00
  Reading place: Edifici A, 1a planta, aula A1.07Campus Diagonal-Besòs – UPCAv. d'Eduard Maristany, 1608930 Sant Adrià de Besòs (Barcelona) https://meet.google.com/qmy-zzfx-xnx
  Thesis director: IZQUIERDO ROCA, VICTOR | GUC, MAXIM
  Thesis abstract: The development cycle of new materials-based technologies typically spans 15-25 years due to the constantly increasing complexity of experimental research processes. Accelerating this process is essential to enable large-scale implementation of sustainable technologies that support the green energy transition and secure the electrification of society. Photovoltaics (PV) play a central role in this transition, particularly novel thin-film photovoltaic (TFPV) technologies, which promise to significantly broaden the application of solar modules in everyday life. Therefore, accelerating their development and transfer is key to achieving this goal. In this context, recent advances in artificial intelligence (AI), explainable artificial intelligence (XAI), robotics, and automation have enabled a new paradigm: the data-driven materials science, capable of accelerating materials discovery and optimization. However, despite the increasing use of machine learning (ML) in materials science, the application of XAI approaches to real experimental characterization datasets remains limited, and the translation of ML model outputs into physically meaningful insights is still a major challenge.The present work addresses this challenge by exploring and developing AI-driven strategies for the analysis and interpretation of experimental characterization data, following a proposed interpretability framework aimed at both accelerating research and enhancing explainability of ML models. The work begins by implementing and optimizing different training strategies for restricted Boltzmann machines (RBMs) using benchmark datasets. This study is aligned with the first level of the interpretability framework (Level 1: Using AI) and establishes the basis for the implementation of ML algorithms in experimental characterization datasets. Subsequently, an initial approach to XAI-driven strategies for extracting valuable information from experimental characterization data of kesterite-based TFPV devices is developed (Level 2: Using XAI), demonstrating the potential of interpretable ML models for identifying correlations between the physicochemical properties of materials and the PV performance of devices.Finally, building on this progression, an integrated XAI-driven methodology is implemented to analyze multimodal datasets obtained from holistic characterization of kesterite-based solar cells. It integrates the entire research cycle, from synthesis and characterization to data analysis, interpretation, and extraction of valuable feedback for further technology optimization. Particular emphasis is placed on interpretability, where the combination of intrinsic XAI models with post-hoc XAI techniques enables the identification of the physicochemical properties that govern device performance and reproducibility, linking the relevant spectral zones with the corresponding physical properties. This strategy reaches the third level of the interpretability framework (Level 3: Using XAI+) and provides meaningful feedback to guide subsequent research cycles. This feedback is validated through modified fabrication processes, resulting in improved device performance. Furthermore, it substantially reduces research cycle times, accelerating material optimization from timescales exceeding one year to less than a week. These results contribute to the development of self-driving laboratory (SDL) approaches and the advancement of data-driven materials research, ultimately supporting the green energy transition.
  

Reading date: 18/09/2026

• ERBS, FLORENCE AMANDINE: AI-enhanced Passive Acoustic Monitoring of Tropical Freshwater Environments
  
  Author: ERBS, FLORENCE AMANDINE
  Programme: DOCTORAL DEGREE IN MARINE SCIENCES
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Article-based thesis
  Deposit date: 28/05/2026
  Reading date: 18/09/2026
  Reading time: 14:00
  Reading place: Place: ETSECCPBUPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: ANDRE SANCHEZ, MICHEL
  Thesis abstract: Tropical freshwater ecosystems, particularly in the Amazon basin, are among the world’s most vulnerable environments, facing escalating pressures from human activities such as overexploitation, pollution, and habitat degradation. Effective conservation efforts are constrained by the scarcity of long-term monitoring data, as traditional visual survey methods are often unsuitable for the high turbidity of Amazonian waters and the complex floodplain habitat mosaics where seasonally inundated lakes, channels, and forests make species detection challenging. This dissertation addresses these challenges by developing an innovative and scalable monitoring framework using Artificial Intelligence (AI)-enhanced Passive Acoustic Monitoring (PAM) focused on three ecological indicator species: the pink river dolphin (Inia geoffrensis), the tucuxi (Sotalia fluviatilis), and the Amazonian manatee (Trichechus inunguis).The research is presented as a compendium of two scientific publications that integrates longterm autonomous acoustic monitoring with Deep Learning techniques, specifically Convolutional Neural Networks (CNNs), to automate species detection in complex and noisy freshwater soundscapes. The first study focuses on Amazonian river dolphins, exploiting their near-continuous production of echolocation clicks for presence detection. The developed CNN classifier achieved a high average precision of 0.95 and successfully discriminated between dolphin clicks and impulsive interferences like rain and boat engine noise. This automation enabled the tracking of synchronized seasonal movements into floodplain bay and river channel habitats following the annual flood pulse. Furthermore, the study provided rare insights into the seasonal use of flooded forests revealing regular dolphin presence during high-water periods. Additionally, it quantified the spatio-temporal overlap between dolphins and boat traffic, establishing a baseline for assessing anthropogenic noise exposure in core habitats.The second study applied a similar AI-enhanced approach for detecting the Amazonian manatee, a cryptic species characterized by inconspicuous surfacing behavior and a previously poorly described vocal repertoire in the wild. By training a CNN model on wild vocalizations, the research provided the first detailed characterization of the wild Amazonian manatee’s vocal repertoire, identifying four distinct call types, and leading to new insights into the vocal behavior of the species in the floodplains. The analysis of vocal parameters, including repetition rates and frequency characteristics, revealed that Mamirau´a Lake, where the species was presumed absent, serves as a critical nursery habitat.This dissertation demonstrates that the integration of PAM and machine learning offers a noninvasive, cost-effective, and performant tool for monitoring biodiversity in remote tropical freshwater environments. The transition from manual acoustic analysis to automated processing allows for efficient conversion of high-volume acoustic datasets into ecological insights, facilitating the identification of critical habitats, supporting the understanding of floodplain ecological processes, and advancing the assessment of anthropogenic impacts and ecosystem health. These findings provide a robust foundation for a standardized Amazonian monitoring framework to support evidence-based conservation for these endangered species and their ecosystems.