Despite being the archetypal thermoelectric material, still today some of the most exciting advances in the efficiency of these materials are being achieved by tuning the properties of PbTe. Its inherently low lattice thermal conductivity can be lowered to its fundamental limit by designing a structure capable of scattering phonons over a wide range of length scales. Intrinsic defects, such as vacancies or grain boundaries, can and do play the role of these scattering sites. Here we assess the effect of these defects by means of molecular dynamics simulations. For this we purposely parametrize a Buckingham potential that provides an excellent description of the thermal conductivity of this material over a wide temperature range. Our results show that intrinsic point defects and grain boundaries can reduce the lattice conductivity of PbTe down to a quarter of its bulk value. By studying the size dependence we also show that typical defect concentrations and grain sizes realized in experiments normally correspond to the bulk lattice conductivity of pristine PbTe.

The growing need for efficient battery reuse and recycling requires rapid, reliable methods to assess the state of health (SoH) of lithium-ion cells. Conventional SoH estimation based on full charge–discharge cycling is slow, energy-intensive, and unsuitable for dismantled cells with unknown histories. This work presents an automated diagnostic approach using Electrochemical Impedance Spectroscopy (EIS) combined with Electrical Equivalent Circuit (EEC) modeling for fast, non-invasive SoH estimation. A correlation between fitted EIS parameters and cell degradation stages was established through controlled aging tests on NMC-based lithium-ion cells. The methodology was implemented in custom software (BaterurgIA) integrated into a robotic testing bench, enabling automatic EIS acquisition, data fitting, and SoH determination. The system achieves SoH estimation with 5–10% accuracy for cells in intermediate and advanced degradation stages, while additional parameters improve sensitivity during early aging. Compared to conventional cycling methods, the proposed approach reduces diagnostic time from hours to minutes, minimizes energy consumption, and offers predictive insights into internal degradation mechanisms. This enables fast and reliable cell grading for reuse, reconditioning, or recycling, supporting the development of scalable solutions for battery second-life applications and circular economy initiatives.

In a scenario in which the manufacturing of high‐performance, safe batteries on an unprecedented large scale is crucial for the energy transition and fight against climate change, research laboratories and cell production industries are facing challenges due to the lack of efficient data management and training tools. In this context, the use of intelligent devices plays an important role on the path towards the optimization of the manufacturing process and the enhancement of the battery performance while reducing production costs. In this Concept, we present an innovative Mixed Reality tool for efficient data collection and training in real‐time in battery research laboratories and battery manufacturing pilot lines, which runs on Microsoft HoloLens 2 glasses. We report a deep analysis on its ergonomic and usability aspects, and describe how we solved the problems found during its development. Thanks to this tool, users can collect data while keeping their hands free and receive advice in real time to design and build batteries with tailored properties. This optimizes data management in battery manufacturing environments. Now, thanks to our Mixed Reality application, users can collect data in the place of work, save this data automatically on a server and exploit it to receive advice and feedback to support their decision‐making and learning of the manufacturing process.

Magnesium (Mg) is one of the most abundant metallic elements in nature and presents attractive mechanical properties in the industry. Particularly, it has a low density and relatively high strength/weight and stiffness/weight ratios, which make it one of the most attractive lightweight metals. However, the huge potential of Mg is restricted by its low ductility, associated with its hexagonal close packed (hcp) structure. This problem can be solved if Mg adopts the body centered cubic (bcc) structure, which is stable at high pressure or in confinement with stiff bcc metals like Nb. Molecular dynamics method is a magnificent tool to study material’s structure and deformation mechanisms at the atomic level, however, requiring accurate interatomic potentials. The majority of the interatomic potentials available in the literature for Mg have only been fitted to the properties of its stable hcp phase. In the present work, we perform systematic study of applicability of currently available Mg potentials to modeling the properties of metastable bcc polymorph of Mg, taking into account cohesive energy curves, elastic constants, stacking fault energies, and phonon dispersion curves. We conclude that the modified embedded atom method (MEAM) potentials are the most suitable for investigating bcc Mg in Mg/Nb nano-composites, while the properties of high-pressure bcc Mg would be better modeled by neural network interatomic potentials after different local atomic environments corresponding to bcc Mg being included into the fitting database.

The growing need for efficient battery reuse and recycling requires rapid, reliable methods to assess the State of Health (SoH) of lithium-ion cells. Conventional SoH estimation based on full charge-discharge cycling is slow, energy-intensive, and unsuitable for dismantled cells with unknown histories. This work presents an automated diagnostic approach using Electrochemical Impedance Spectroscopy (EIS) combined with Electrical Equivalent Circuit (EEC) modeling for fast, non-invasive SoH estimation. A correlation between fitted EIS parameters and cell degradation stages was established through controlled aging tests on NMC-based lithium-ion cells. The methodology was implemented in custom software (BaterurgIA) integrated into a robotic testing bench, enabling automatic EIS acquisition, data fitting, and SoH determination. The system achieves SoH estimation with 5–10% accuracy for cells in intermediate and advanced degradation stages, while additional parameters improve sensitivity during early aging. Compared to conventional cycling methods, the proposed approach reduces diagnostic time from hours to minutes, minimizes energy consumption, and offers predictive insights into internal degradation mechanisms. This enables fast and reliable cell grading for reuse, reconditioning, or recycling, supporting the development of scalable solutions for battery second-life applications and circular economy initiatives.