Search

Three different iridium(III) complexes, labelled as Ir1–Ir3, each bearing a unique anchoring moiety (diethyl [2,2′-bipyridine]-4,4′-dicarboxylate, tetraethyl [2,2′-bipyridine]-4,4′-diylbis(phosphonate), or [2,2′-biquinoline]-4,4′-dicarboxylic acid), were synthesized to serve as photosensitizers. Their electrochemical and photophysical characteristics were systematically investigated. ERP measurements were employed to elucidate the impact of the anchoring groups on the photocatalytic hydrogen generation performance of the complexes. The novel iridium(III) complexes were integrated with platinized TiO2 (Pt–TiO2) nanoparticles and tested for their ability to catalyze hydrogen production under visible light. A H2 turnover number (TON) of up to 3670 was obtained upon irradiation for 120 h. The complexes with tetraethyl [2,2′-bipyridine]-4,4′-diylbis(phosphonate) anchoring groups were found to outperform those bearing other moieties, which may be one of the important steps in the development of high-efficiency iridium(III) photosensitizers for hydrogen generation by water splitting. Additionally, toxicological analyses found no significant difference in the toxicity to luminescent bacteria of any of the present iridium(III) complexes compared with that of TiO2, which implies that the complexes investigated in this study do not pose a high risk to the aquatic environment compared to TiO2.

People often think about how they are perceived by others, but their perceptions (described as metaperceptions) are frequently off-target. Speakers communicating in their first language demonstrate a robust phenomenon, called the liking gap, where they consistently underestimate how much they are liked by their interlocutors. We extended this research to second language (L2) speakers to determine whether they demonstrate a similar negative bias and if it predicts willingness to engage in future interactions. We paired 76 English L2 university students with a previously unacquainted student to carry out a 10 min academic discussion task in English. After the conversation, students rated each other’s interpersonal liking, speaking skill, and interactional behavior, provided their metaperceptions for their partner’s ratings of the same dimensions, and assessed their willingness to engage in future interaction. We found a reliable interpersonal liking gap for all speakers, along with speaking skill and interaction behavior gaps for female speakers only. Only the female speakers (irrespective of their partner’s gender) seemed to factor metaperceptions into their willingness to engage in future communication. We discuss the implications of these initial findings and call for further work into the role of metaperception in L2 communication.

We report a rare case of isolated neurofibroma arising in the left mandible without family history of neurofibromatosis type 1. The tumor was histopathologically analyzed and an immunohistochemical panel comprising S-100 protein was performed. Vim, HMB45, and HHF35 were negative. The tumor cells were fusiform in shape, arranged in a plexiform manner, and grew actively. Features typical of neurofibroma, including an enlarged nerve fascicle composed of elongated nuclei and scant cytoplasmic cells, were identified. Our study suggest that neurofibroma can occur in the jaw as an isolated benign tumor in patients without other features of neurofibromatosis type 1.

Multi-modal hashing supports efficient multimedia retrieval well. However, existing methods still suffer from two problems: (1) Fixed multi-modal fusion. They collaborate the multi-modal features with fixed weights for hash learning, which cannot adaptively capture the variations of online streaming multimedia contents. (2) Binary optimization challenge. To generate binary hash codes, existing methods adopt either two-step relaxed optimization that causes significant quantization errors or direct discrete optimization that consumes considerable computation and storage cost. To address these problems, we first propose a Supervised Multi-modal Hashing with Online Query-adaption method. A self-weighted fusion strategy is designed to adaptively preserve the multi-modal features into hash codes by exploiting their complementarity. Besides, the hash codes are efficiently learned with the supervision of pair-wise semantic labels to enhance their discriminative capability while avoiding the challenging symmetric similarity matrix factorization. Further, we propose an effi- cient UnsupervisedMulti-modal Hashing with Online Query-adaption method with an adaptive multi-modal quantization strategy. The hash codes are directly learned without the reliance on the specific objective formulations. Finally, in both methods, we design a parameter-free online hashing module to adaptively capture query variations at the online retrieval stage. Experiments validate the superiority of our proposed methods. [ABSTRACT FROM AUTHOR]