arXiv subjects

Sabrina Baha

Publications and source records attributed to Sabrina Baha.

3 recordsLinked to original sources

arXiv2026

From Deposition Stress to Surface Reactivity: Strain-Dependent Hydrogen Evolution on Sputtered Platinum Thin Films

Strain has emerged as a promising approach for tuning electrocatalytic properties, yet its role in sputter-deposited thin films remains poorly understood. In this work, magnetron-sputtered platinum (Pt) thin films with different stress states were prepared by varying the sputter pressure. The resulting changes in microstructure, residual strain, and hydrogen evolution reaction (HER) activity were investigated using complementary characterization techniques and density functional theory (DFT) calculations. Structural analysis reveals a transition of (111)-textured Pt thin films from dense and smooth films at low pressures, to more porous microstructures with increased roughness at higher pressures. Electrochemical measurements show that films deposited at low sputter pressure exhibit the highest HER activity, while higher sputter pressures lead to reduced activity despite increased surface area. DFT calculations demonstrate that lattice strain alters hydrogen adsorption energetics and surface coverage on Pt(111), providing a mechanistic explanation for the observed activity trends. Overall, the results highlight that HER activity in sputtered Pt thin films is governed by the interplay of residual strain, microstructure, and hydrogen coverage.

cond-mat.mtrl-sci↗

arXiv2026

Influence of Ru content on electrocatalytic activity and defect formation of Au-Pd-Pt-Ru compositionally complex solid solution thin films

Compositionally complex solid solutions (CCSSs) consist of a randomly mixed single phase with the potential to enhance electrocatalytic activity through their polyelemental surface atom arrangements. However, microstructural complexity originating from multiple principal elements influences local structure, chemistry, and lattice strain, which might also affect electrocatalytic activity. Here, we investigate the effect of Ru content on electrochemistry and defect formation in Au-Pd-Pt-Ru CCSS thin films. Such defects could provide active sites when terminating at the CCSS surface or modify surface composition through preferential segregation. A thin-film material library covering a wide composition range was fabricated by room-temperature combinatorial co-sputtering. High-throughput compositional, structural and functional characterization, including electron microscopy equipped with energy dispersive X-ray spectroscopy, X-ray diffraction, and electrochemical screening, were used to correlate composition and microstructural features with catalytic activity. Three representative compositions selected from the library - Au68Pd13Pt15Ru4, Au27Pd24Pt23Ru26, and Au9Pd21Pt18Ru52 - were examined in detail. The three samples exhibit face-centered cubic structures, with lattice contraction occurring with increasing Ru content. In addition, with increasing Ru content, a transition from a high density of nanotwins to high-density, atomic-layer stacking faults was observed. Moreover, the hydrogen evolution reaction activity improves with higher Ru content. Atom probe tomography reveals local compositional fluctuations, including element-specific enrichment and depletion at grain boundaries. The findings provide a new insight into surface atom arrangement design in the CCSS electrocatalysts with enhanced performance.

cond-mat.mtrl-sci↗

arXiv2023

Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements

High-throughput experimentation enables efficient search space exploration for the discovery and optimization of new materials. However, large search spaces of, e.g., compositionally complex materials, require decreasing characterization times significantly. Here, an autonomous measurement algorithm was developed, which leverages active learning based on a Gaussian process model capable of iteratively scanning a materials library based on the highest uncertainty. The algorithm is applied to a four-point probe electrical resistance measurement device, frequently used to obtain indications for regions of interest in materials libraries. Ten materials libraries with different complexities of composition and property trends are analyzed to validate the model. By stopping the process before the entire library is characterized and predicting the remaining measurement areas, the measurement efficiency can be improved drastically. As robustness is essential for autonomous measurements, intrinsic outlier handling is built into the model and a dynamic stopping criterion based on the mean predicted covariance is proposed. A measurement time reduction of about 70-90% was observed while still ensuring an accuracy of above 90%.

cond-mat.mtrl-sci↗