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Margarida Barroso

Publications and source records attributed to Margarida Barroso.

4 recordsLinked to original sources

Controlling nitrogen incorporation in (Eu,Ba,Ca)TiO$_3$-derived perovskite-type oxynitrides through Fermi Level Engineering

Defect states directly influence not only functional properties, but also oxynitride formation during ammonolysis. Along with structural stability and charge balance, these defects can cause non-uniform nitrogen incorporation. To address this, the recently proposed Fermi Level Engineering concept provides a powerful alternative of rationalising oxynitride synthesis. By using the electron chemical potential, i.e., the Fermi level, as a unique descriptor, this approach can predict, understand, and manipulate defect formation enthalpies and charge transition levels, subsequently allowing to incorporate defect-property correlations to the concept. This work combines theoretical modelling and experimental characterisation of barium titanium oxynitrides to explain an intrinsic, self-limiting nitridation process. Progressive nitrogen incorporation causes a Fermi level downward shift, leaving further nitrogen substitution on oxygen sites energetically unfavourable, up to 0.03 N per unit formula. We demonstrate that A-site cation substitution in Eu$_{1-x}$Ba$_x$TiO$_3$- and Eu$_{1-x}$Ca$_x$TiO$_3$-derived oxynitrides can be applied to control compensation of the additional negative charge from N$^{3-}$ and keep the Fermi level high. The Eu$^{2+/3+}$ redox couple enables substitution of oxygen by nitrogen through pinning of the Fermi level at the europium charge transition level, at $\sim$ 2.9 eV above the VBM. This allows for higher nitrogen uptake of up to 0.65 per formula unit (taken from hot gas extraction) through a more controlled process. Further incorporation is then limited by an intra-Ti(O,N)$_6$ octahedra distortion due to internal bond-angle variance. These findings demonstrate that Fermi Level Engineering provides a pathway to predict and overcome intrinsic synthetic and thermodynamic limitations in oxynitride synthesis.

cond-mat.mtrl-sci↗

PyFLI: A Python Library for Simulation, Parameter Estimation, and Benchmarking in Fluorescence Lifetime Imaging

Fluorescence lifetime imaging (FLI) measures the temporal decay of fluorescence after excitation and provides quantitative information about a fluorophore's local environment and molecular interactions. Depending on the fluorophore and experimental design, lifetime can report changes associated with pH, oxygenation, cellular metabolism, and Forster resonance energy transfer. These properties make FLI useful across microscopy, biophysics, biomedical optics, and preclinical imaging, where the same type of molecular contrast can be studied across different biological scales. FLI measurements, however, are acquired with instruments that record fluorescence in different ways. Intensified charge-coupled device (ICCD) cameras, single-photon avalanche diode (SPAD) arrays, and time-correlated single-photon counting (TCSPC) systems differ in temporal sampling, data organization, detector noise, instrument response, and file format. Lifetime-estimation methods also make different assumptions about the recorded decay, while learning-based approaches require realistic training and validation data for which the underlying parameters are known. PyFLI is an open-source framework for FLI processing and standardized data simulation. It imports measurements from acquisition systems, simulates labeled data under configurable acquisition and noise conditions, and provides complementary approaches for parameter estimation. These include nonlinear least-squares fitting (NLSF), maximum-likelihood estimation (MLE), phasor analysis, rapid lifetime determination (RLD), Laguerre-based estimation, and optional Bayesian and deep-learning inference. CPU and GPU processing support image-scale analysis, while reconstruction, visualization, statistical analysis, and cross-software comparison provide tools for evaluating results. PyFLI includes compressed-sensing reconstruction for single-pixel hyperspectral FLI.

q-bio.QM↗

Compositional amortized inference for large-scale hierarchical Bayesian models

Amortized Bayesian inference (ABI) with neural networks has emerged as a powerful simulation-based approach for estimating complex mechanistic models. However, extending ABI to hierarchical models, a cornerstone of modern Bayesian analysis, has been a major hurdle due to the need to simulate and process massive datasets. Our study tackles these challenges by extending compositional score matching (CSM), a divide-and-conquer strategy for Bayesian updating using diffusion models. We develop a new error-damping estimator to address previous stability issues of CSM when aggregating large numbers of data points. We first verified the numerical stability with up to 100,000 data points on a controlled benchmark. We then evaluated our method on a hierarchical AR model, achieving competitive performance to direct ABI baselines on smaller problem sizes while using less than one full model simulation for larger problem sizes. Finally, we address a large-scale inverse problem in advanced microscopy with over 750,000 parameters, demonstrating its relevance to real scientific applications.

q-bio.QM↗

Roadmap on Deep Learning for Microscopy

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

physics.optics↗