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Masashi Fukuhara

Publications and source records attributed to Masashi Fukuhara.

2 recordsLinked to original sources

Alkaline-Earth Rare-Earth Fluoride Nanoparticle Superlattices for Ultrafast, Radiation Stable Scintillators

Radioluminescent nanostructures provide a pathway to the fabrication of next-generation scintillators with tunability in composition, size, and morphology, and spectral and temporal properties, as well as scalable processing. Here we create a 3D millimeter-scale solid-state scintillators from SrLuF Ce3+, Pr3+ (SrLuF) core-shell nanostructures, integrating nanoscale building blocks into self-assembled macroscopic crystals. These scintillators exhibit single-digit nanosecond decay times, linear response, resistance to radiation-induced degradation, and optical emission yields within an order of magnitude of YAG Ce3+. We select a SrLuF host lattice owing to its high effective atomic number, wide band gap, and low phonon energy, which together support efficient 4f-5d radiative transitions from Ce3+ and Pr3+ activators while suppressing afterglow. We create a library of core-shell nanoscintillators with undoped SrLuF shells and cores spanning compositions from undoped SrLuF to fully doped SrCeF or SrPrF. Time-resolved and steady-state X-ray excited optical luminescence (XEOL) reveal broadband emission at 310 nm (Ce3+) and 335 nm (Pr3+) with biexponential decays in the sub-nanosecond (100-500 ps) and sub-15 ns (4-13 ns) regimes, demonstrating tunable radiative efficiency and ultrafast dynamics. Ensemble performance of the mm-scale superlattices is characterized under both continuous-wave and femtosecond high-intensity excitation, revealing high light yield, linear response, and radiation hardness under extreme irradiation of ultrafast 50fs X-ray pulses up to 5mJ per mm2 corresponding to a peak intensity of 1013 W per cm2. Together, these results establish a design framework for stable, bright, and tunable scintillation platforms with applications in precision health, space exploration and hard X-ray imaging at next-generation free-electron laser facilities.

cond-mat.mtrl-sci

Feature visualization of Raman spectrum analysis with deep convolutional neural network

We demonstrate a recognition and feature visualization method that uses a deep convolutional neural network for Raman spectrum analysis. The visualization is achieved by calculating important regions in the spectra from weights in pooling and fully-connected layers. The method is first examined for simple Lorentzian spectra, then applied to the spectra of pharmaceutical compounds and numerically mixed amino acids. We investigate the effects of the size and number of convolution filters on the extracted regions for Raman-peak signals using the Lorentzian spectra. It is confirmed that the Raman peak contributes to the recognition by visualizing the extracted features. A near-zero weight value is obtained at the background level region, which appears to be used for baseline correction. Common component extraction is confirmed by an evaluation of numerically mixed amino acid spectra. High weight values at the common peaks and negative values at the distinctive peaks appear, even though the model is given one-hot vectors as the training labels (without a mix ratio). This proposed method is potentially suitable for applications such as the validation of trained models, ensuring the reliability of common component extraction from compound samples for spectral analysis.

cs.CV