Akihiko Kudo | Searcharxiv

arXiv2026

Imaging nanoscale photocarrier traps in solar water-splitting catalysts

Defects trap photocarriers and hinder solar water splitting. The nanoscale photocarrier transport, trapping, and recombination mechanisms are usually inferred from ensemble-averaged measurements and remain elusive. Because an individual high-performing nanoparticle photocatalyst may outperform the ensemble average, design rules that would otherwise enhance catalytic efficiency remain unclear. Here, we introduce photomodulated electron energy-loss spectroscopy (EELS) in an optically coupled scanning transmission electron microscope (STEM) to map photocarrier localization. Using rhodium-doped strontium titanate (SrTiO3:Rh) solar water-splitting nanoparticles, we directly image the carrier densities concentrated at oxygen-vacancy surface trap states. This is achieved by separating photothermal heating from photocarrier populations through experimental and computational analyses of low-loss spectra. Photomodulated STEM-EELS enables angstrom-scale imaging of defect-induced photocarrier traps and their impact on photocatalytic efficiency.

physics.chem-ph↗

arXiv2025

Covariance Linkage Assimilation method for Unobserved Data Exploration

This study proposes a materials search method combining a data assimilation technique based on a multivariate Gaussian distribution with Bayesian optimization. The efficiency of the search using this method was demonstrated using a pair of example functions. By combining Bayesian optimization with the data assimilation technique, the maximum value of the example function was found more efficiently compared to ordinary Bayesian optimization without the data assimilation. A practical demonstration was also conducted by constructing a data assimilation model for the bandgap of (Sr$_{1-x_{1}-x_{2}}$La$_{x_{1}}$Na$_{x_{2}}$)(Ti$_{1-x_{1}-x_{2}}$Ga$_{x_{1}}$Ta$_{x_{2}}$)O$_{3}$. The concentration dependence of the bandgap was analyzed, and synthesis was performed with chemical compositions in the sparse region of the training data points to validate the predictions.

cond-mat.mtrl-sci↗