Brazilian Center for Research in Energy and Materials

We present an extensive investigation on the properties of Eu 3+ optical emissions associated with deformation of ZnO host applying an external hydrostatic pressure, combining in situ synchrotron X-ray diffraction and photoluminescence spectroscopy with first-principles calculations. A pressure-induced phase transition from the hexagonal wurtzite to the cubic rocksalt structure near 10 GPa is accompanied by complete quenching of the 5 D 0 → 7 F J emissions near the threshold, followed by a partial but reproducible recovery at higher pressures, likely associated with the emergence of structural disorder. Concurrently, as the crystal field strength increases, the Stark components of the emissions exhibit a systematic redshift (∼0.40 ± 0.02meV/GPa) and pressure-induced broadening (∼0.55 ± 0.02 meV/GPa). The first-principles calculations support the observed pressure-induced shifts in the Eu-4f states and emphasize the influence of lattice symmetry on their electronic environment. These findings establish hydrostatic pressure as a powerful tool for tuning rare-earth optical emissions through symmetry-driven and local-environment modifications, laying the foundation for pressure-engineered photonic functionalities and luminescent devices. • Pressure-driven wurtzite-to-rocksalt transition modulates Eu 3+ luminescence in ZnO. • The structural transition initiates at ∼5.8 GPa, with cubic dominance above 10GPa. • Redshift and Stark splitting due to enhanced lattice symmetry and crystal field. • DFT reveals symmetry-dependent reshaping of electronic states under compression. • Lattice symmetry control governs RE optical activity under extreme conditions.

Multi-technique high resolution X-ray mapping enhanced by the recent advent of 4th generation synchrotron facilities can produce colossal datasets, challenging traditional analysis methods. Such difficulty is clearly materialized when probing the crystal structure of inhomogeneous samples, where the number of diffraction patterns quickly increases with map resolution, making the identification of crystal phases within a vast collection of reflections unfeasibly challenging by direct human inspection. Here we develop an analysis approach based on a physics-informed similarity measure and unsupervised clustering algorithms for identifying independent phases within a diffraction spatial map, which allowed us to identify the material distribution across a high-pressure cerium hydride. By investigating the specific compound, we also contribute to the understanding of synthesis inhomogeneities among the superhydrides, a prominent superconductor class in condensed matter physics whose characterization is highly challenging even for state-of-the-art materials techniques. The analysis framework we present may be readily extended to any correlated set of curves whose features are tied to specific entities such as structural phases.

Abstract Conventional two-dimensional (2D) histology relies upon destructive sample preparation and stereological estimation, frequently leading to sampling bias and loss of critical spatial context required for understanding renal structure relationships. Here, we detail a novel pipeline for high-resolution 3D histology of ex vivo murine kidneys using X-ray micro-computed tomography (micro-CT) at the high flux of a synchrotron light source, the architecture of the nephron and associated microvasculature necessitates three-dimensional (3D) analysis to accurately characterize its complexity. Soft-tissue contrast was optimized through an established phosphotungstic acid (PTA) staining protocol, enabling robust mapping of macro and microstructures via absorption contrast. Multi-scale imaging was performed, providing whole-organ context at resolutions around 3 μm and achieving sub-micron detail (down to 400 nm) in targeted regions of interest (ROI) of the renal cortex. Utilizing machine learning segmentation pipelines optimized for large volumetric datasets, we extracted crucial 3D quantitative morphometric data. The results presented herein demonstrate accuracy and morphological insight achievable through synchrotron-based 3D imaging, establishing a robust method for quantitative preclinical research.

The clinical success of an implant largely depends on its ability to promote bone repair and facilitate osteointegration. While significant advancements have been made in designing new scaffolds, a thorough evaluation of implant success at various bone length scales has not been sufficiently investigated. Bone is a hierarchically organized tissue, with an intricate structure that ranges from the organization of collagen fibrils and their mineralization at the nanometer and micrometer scales to the macroscopic arrangement of compact and cancellous bone. This hierarchical organization is crucial for ensuring bone function and mechanical performance. In this context, a comprehensive assessment of the effects of biomaterials on bone repair at multiple length scales is essential for evaluating the effectiveness and safety of implanted materials. However, preparing samples for integrated multimodal analyses poses a technical challenge. In this work, we present a protocol designed to evaluate bone regeneration across various hierarchical levels and analyze the interface between newly formed bone and the implanted scaffold. This approach provides a robust method for assessing the effectiveness of scaffolds in inducing bone repair. The combination of selected 3D and 2D imaging techniques is crucial for a proper evaluation of the complex mineralized tissue that forms during the bone healing process.

Transformation of agro-industrial products into value-added products, such as prebiotic oligosaccharides, is a key element of the emerging bioeconomy. Here, we characterized a new GH30_8 glucuronoxylanase from Bacillus pumilus (BpXyn30_8A) for its potential in producing xylooligosaccharides (XOS). BpXyn30_8A showed tolerance to ethanol and NaCl and released both linear and branched XOS containing MeGlcA at the penultimate nonreducing end residue. Its X-ray structure, determined at 2.16 Å resolution, revealed high similarity to other glucuronoxylanases. Furthermore, BpXyn30_8A achieved higher xylan conversion yields from corn cob and Eucalyptus sawdust than Ruminococcus champanellensisRcXyn30A. Finally, fermentation assays showed that Bifidobacterium adolescentis metabolized neutral XOS to acetate and lactate, whereas acidic XOS were poorly utilized. These results highlight the potential of BpXyn30_8A as a valuable enzyme for the green transformation of plant biomass into prebiotic oligosaccharides with promising applications in human and animal nutrition, health, and biotechnology.

The intelligent discovery of novel biosensors is often hampered by slow, trial‐and‐error experimental cycles. To overcome this gap, we introduce and validate a computationally‐guided discovery pipeline that synergizes molecular simulation, synthetic biology, electrochemical engineering, and machine learning guided analysis for the rational design of high‐performance sensors. We demonstrate the power of this approach by tackling the urgent challenge of detecting micro‐ and nanoplastics (MNPs), which are potential harmful agents, causing respiratory, cardiovascular, and oncological disorders. Our pipeline begins with computational screening, using molecular dynamics simulations to evaluate the thermal stability and binding affinity of a candidate protein for recognition element, the carbohydrate‐binding module of Bacillus anthracis ( Ba CBM2). Building upon the positive computational results, the protein was synthesized and integrated onto a custom‐fabricated electrochemical platform. The computationally‐informed protein was experimentally verified to have a superior analytical performance when covalently tethered to gold surfaces of on‐chip electrodes, which acted as label‐free electrochemical biosensors. To date, this system implied reductions in the root mean square error in MNP quantification and measurement standard deviation by 92.24% and 24.83%, respectively, compared to unmodified devices, and its integration with multivariate analysis (Sure Independence Screening and Sparsifying Operator) overcame device‐to‐device variability. This work not only delivers a promising biosensor for MNP monitoring, but, more importantly, establishes a validated workflow for the intelligent discovery of functional and specific proteins. This pipeline provides a direct pathway for future integration with high‐throughput virtual screening and machine learning models, enabling the inverse design of next‐generation environmental and diagnostic sensor technologies.

This study introduces a framework to identify opinion leaders on Telegram, adapting a methodology developed for Twitter. The work is motivated by the need to examine influence dynamics on hybrid platforms like Telegram, where platform affordances can potentially facilitate the spread of disinformation and polarizing discourse. Specifically, the traceability of forwarded messages provides analytical possibilities for understanding these complex communication networks. The analysis involves a case study conducted with data extracted from public groups supporting Jair Bolsonaro's presidential re-election campaign. Using network analysis techniques, the method maps key roles including influencers, conversation starters, active engagers, and network creators. Results highlight these actors' roles in shaping information circulation within Telegram's public groups and channels. The discussion emphasizes the framework's systematic approach to analyzing opinion leadership. This research contributes to understanding how hybrid social platforms facilitate influence and information dissemination, offering practical tools for researchers studying emerging social media ecosystems.