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BODIPY derivatives are promising scaffolds for triplet photosensitizers because of their strong absorption and tunable absorption range, but their dominant fluorescence decay and weak spin–orbit coupling hinder efficient intersystem crossing (ISC). Here, we report iodine-substituted BODIPY photosensitizers bearing anthracene, pyrene, and perylene units (BI-Ant, BI-Pyr, and BI-Per), designed to combine the heavy-atom effect with the spin–orbit charge-transfer ISC (SOCT-ISC) process. Spectroscopic and computational analyses revealed charge-transfer character and efficient triplet formation in all three compounds. Transient absorption measurements showed that BI-Ant and BI-Pyr undergo an ISC process to BODIPY-centered triplet state, whereas BI-Per exhibits intermediate strong charge-transfer-state evolution and the fastest ISC process. Solvent-dependent results further indicate that charge-transfer-state stabilization accelerates ISC process, supporting an additional SOCT contribution. These findings provide an effective strategy for developing highly efficient metal-free triplet photosensitizers.

Nanographenes, finite models of graphene sheets, are endowed with intriguing optical, electronic, and spintronic features. So-called heteroatom-doping, where one or more carbon is replaced by non-carbon light atoms has been proved effective in tuning the properties of nanographenes. Here we extend the concept of heteroatom nanographene doping to include metal centers. The method employed involves the use of a dipyrromethene fragment as an auxiliary ligand that is directly linked to the bay area of the model nanographene hexa-peri-hexabenzocoronene (HBC) to give a dipyrromethene-fused nanographene-type hybrid ligand (HBCP). HBCP has a corrole-like trianionic core that is capable of coordinating group 11 metal cations, including trivalent Cu, Ag and Au. These cations are introduced into the cavity with atomic precision to give metal complexes (HBCP-M; M = Cu, Ag, Au). The electronic structure and photophysical properties of HBCP and its metal complexes are investigated by steady-state and fs-transient spectroscopies, as well as DFT calculations. The ligand and metal complexes are also characterized via single crystal X-ray diffraction analyses. This work paves the way towards the precise metal doping of nanographenes within the carbon network, as opposed to the synthetic appendage of an independent chelating group, such as a fused tetrapyrrolic moiety. Nanographenes, finite models of graphene sheets, are endowed with intriguing optical, electronic, and spintronic features which can be tuned by replacing carbon via heteroatom-doping. Here the authors extend the concept of heteroatom nanographene doping to include metal centers.

Deep learning models perform well when there is enough data available for training, but otherwise the performance deteriorates rapidly owing to the so-called data shortage problem. Recently, model-agnostic meta-learning (MAML) was proposed to alleviate this problem by embedding common prior knowledge from different tasks into the initial parameters of the target model. Data shortages are very common in regional influenza predictions, and MAML also often struggles with regional influenza forecasting, especially when region-specific knowledge, such as peak timing or intensity, varies. In this paper, we propose a novel MAML-based parameter adjustment scheme for influenza forecasting, called MARAPAS. The fundamental idea of our scheme is to adjust the initial parameters obtained from common knowledge to a target region by using adjustment variables. We experimentally show that MARAPAS outperforms other MAML-based methods, in terms of root mean square error and Pearson correlation coefficient. Particularly, this scheme improves the forecasting performance by up to 34 % compared with that of the state-of-the-art schemes. We also show the robust forecasting accuracy of our scheme and demonstrate its applicability by performing zero-shot COVID-19 forecasting.

In this paper, we review the development of plasma engineering technology that improves dramatically the production efficiency of OLED (organic light-emitting diode) displays and semiconductor manufacturing by utilizing a process monitoring methodology based on the physical domain knowledge. The domain knowledge consists of plasma-heating and sheath physics, plasma chemistry and plasma-material surface reaction kinetics, and plasma diagnostics. Based on this, a plasma information-based virtual metrology (PI-VM) algorithm was developed drastically enhanced process prediction performance by parameterizing plasma information (PI) which can trace the states of processing plasmas. PI-VM has superior process prediction accuracy compared to the classical statistics-based virtual metrologies. The developed PI-VM algorithms adopted for practical processing issues such as the control and management of the OLED-display mass production demonstrated savings of approximately 25% of the yield loss over the past 5 years. This improvement was achieved with the development of FDC (fault detection and classification) and APC (advanced process control) logic, which can be developed through the analysis of the physical characteristics of the feature parameters used in PI-VM with the evaluation of their contributions and their correlations to the processing results. PI-VM provides leverage that can be applied in the development of process equipment and factory automation technologies.