Takaaki Harada

Physical, health, and environmental hazards of chemical substances are classified into hazard classes in the Globally Harmonized System of Classification and Labeling of Chemicals (GHS). Within each hazard class in the GHS classification, the degree of chemical hazard is mostly expressed as a category. The chemical hazard information is in high demand for risk assessment and accident prevention; however, the hazards of most chemical substances are undetermined yet. Investigations on chemical hazards involve a time-consuming step such as in vitro and in vivo studies, whereas more and more novel chemical substances are synthesized on a daily basis. It is essential to accelerate investigations into the identification of chemical hazards. Herein, we use deep learning models to predict the hazard classes and categories of chemical substances in the GHS classification. The chemical structure is expressed as the Simplified Molecular Input Line Entry System (SMILES) notation. A hazard class prediction model is trained on a data set of chemical structures in SMILES that are labeled as to whether they are classified into the hazard class or not. Similarly, a hazard category prediction model is trained on a data set of SMILES labeled with a hazard category within the hazard class. The average accuracy values of hazard class and category prediction models are 89.2 and 70.4%, respectively. Test-time augmentation is employed to gain the ability of robust prediction regardless of the input chemical structure in SMILES. The atoms of a molecule that contribute to the correct prediction are determined by calculating their attribution scores, which provide insight into our prediction models. Our chemical hazard prediction models can be used as a tool for risk assessment and prioritization of physicochemical studies on novel chemical substances.

Recently, ultrafast lasers exhibiting high peak powers and extremely short pulse durations have created a new paradigm in materials processing. The precision and minimal thermal damage provided by ultrafast lasers in the machining of metals and dielectrics also suggests a novel application in obtaining precise cross-sections of fragile, combustible paint layers in artwork and cultural heritage property. Cross-sections of paint and other decorative layers on artwork provide critical information into its history and authenticity. However, the current methodology which uses a scalpel to obtain a cross-section can cause further damage, including crumbling, delamination, and paint compression. Here, we demonstrate the ability to make controlled cross-sections of paint layers with a femtosecond pulsed laser, with minimal damage to the surrounding artwork. The femtosecond laser cutting overcomes challenges such as fragile paint disintegrating under scalpel pressure, or oxidation by the continuous-wave (CW) laser. Variations in laser power and translational speed of the laser while cutting exhibit different benefits for cross-section sampling. The use of femtosecond lasers in studying artwork also presents new possibilities in analyzing, sampling, and cleaning of artwork with minimal destructive effects.

The hot-phonon bottleneck effect in lead-halide perovskites (APbX₃) prolongs the cooling period of hot charge carriers, an effect that could be used in the next-generation photovoltaics devices. Using ultrafast optical characterization and first-principle calculations, four kinds of lead-halide perovskites (A=FA⁺/MA⁺/Cs⁺, X=I⁻/Br⁻) are compared in this study to reveal the carrier-phonon dynamics within. Here we show a stronger phonon bottleneck effect in hybrid perovskites than in their inorganic counterparts. Compared with the caesium-based system, a 10 times slower carrier-phonon relaxation rate is observed in FAPbI₃. The up-conversion of low-energy phonons is proposed to be responsible for the bottleneck effect. The presence of organic cations introduces overlapping phonon branches and facilitates the up-transition of low-energy modes. The blocking of phonon propagation associated with an ultralow thermal conductivity of the material also increases the overall up-conversion efficiency. This result also suggests a new and general method for achieving long-lived hot carriers in materials.

Transfer of electrons through semiconductor heterojunctions is the key process in all electronics. Here we make movies of electron dynamics in InSe/GaAs through time and spectrally-resolved photoemission electron microscopy, bringing insights to this fundamental process.