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A nanographene-fused expanded carbaporphyrin (5) and its BF2 complex (6) were synthesized. Single-crystal X-ray structures revealed that 5 and 6 are connected by two hexa-peri-hexabenzocoronene (HBC) units and two dipyrromethene or BODIPY units, respectively. As prepared, 5 and 6 both show nonaromatic character with figure-of-eight carbaoctaphyrin (1.1.1.0.1.1.1.0) cores and adopt tweezers-like conformations characterized by a partially confined space between the two constituent HBC units. The distance between the HBC centers is >10 Å, while the dihedral angles between the two HBC planes are 30.5 and 35.2° for 5 and 6, respectively. The interactions between 5 and 6 and fullerene C60 were studied both in organic media and in the solid state. Proton NMR spectral titrations of 5 and 6 with C60 revealed a 1:1 binding mode for both macrocycles. In toluene-d8, the corresponding binding constants were determined to be 1141 ± 17 and 994 ± 10 M−1 for 5 and 6, respectively. Single-crystal X-ray diffraction structural analyses confirmed the formation of 1:1 fullerene inclusion complexes in the solid state. The C60 guests in both complexes are found within triangular pockets composed of two HBC units from the tweezers-like receptor most closely associated with the bound fullerene, as well as an HBC unit from an adjacent host. Femtosecond transient absorption measurements revealed subpicosecond ultrafast charge separation between 5 (and 6) and C60 in the complexes. To the best of our knowledge, the present report provides the first example wherein a nanographene building block is incorporated into the core of a porphyrinic framework. 

Abstract How can we evaluate the performance of a disambiguation method implemented on big bibliographic data? This study suggests that the open researcher profile system, ORCID, can be used as an authority source to label name instances at scale. This study demonstrates the potential by evaluating the disambiguation performances of Author-ity2009 (which algorithmically disambiguates author names in MEDLINE) using 3 million name instances that are automatically labeled through linkage to 5 million ORCID researcher profiles. Results show that although ORCID-linked labeled data do not effectively represent the population of name instances in Author-ity2009, they do effectively capture the ‘high precision over high recall’ performances of Author-ity2009. In addition, ORCID-linked labeled data can provide nuanced details about the Author-ity2009’s performance when name instances are evaluated within and across ethnicity categories. As ORCID continues to be expanded to include more researchers, labeled data via ORCID-linkage can be improved in representing the population of a whole disambiguated data and updated on a regular basis. This can benefit author name disambiguation researchers and practitioners who need large-scale labeled data but lack resources for manual labeling or access to other authority sources for linkage-based labeling. The ORCID-linked labeled data for Author-ity2009 are publicly available for validation and reuse. 

Chinese author names are known to be more difficult to disambiguate than other ethnic names because they tend to share surnames and forenames, thus creating many homonyms. In this study, we demonstrate how using Chinese characters can affect machine learning for author name disambiguation. For analysis, 15K author names recorded in Chinese are transliterated into English and simplified by initialising their forenames to create counterfactual scenarios, reflecting real-world indexing practices in which Chinese characters are usually unavailable. The results show that Chinese author names that are highly ambiguous in English or with initialised forenames tend to become less confusing if their Chinese characters are included in the processing. Our findings indicate that recording Chinese author names in native script can help researchers and digital libraries enhance authority control of Chinese author names that continue to increase in size in bibliographic data. 

Abstract Understanding of interactions among molecules is essential to elucidate the binding of pharmaceuticals on receptors, the mechanism of protein folding and self-assembling of organic molecules. While interactions between two aromatic molecules have been examined extensively, little is known about the interactions between two antiaromatic molecules. Theoretical investigations have predicted that antiaromatic molecules should be stabilized when they stack with each other by attractive intermolecular interactions. Here, we report the synthesis of a cyclophane, in which two antiaromatic porphyrin moieties adopt a stacked face-to-face geometry with a distance shorter than the sum of the van der Waals radii of the atoms involved. The aromaticity in this cyclophane has been examined experimentally and theoretically. This cyclophane exhibits three-dimensional spatial current channels between the two subunits, which corroborates the existence of attractive interactions between two antiaromatic π-systems. 

Abstract In several author name disambiguation studies, some ethnic name groups such as East Asian names are reported to be more difficult to disambiguate than others. This implies that disambiguation approaches might be improved if ethnic name groups are distinguished before disambiguation. We explore the potential of ethnic name partitioning by comparing performance of four machine learning algorithms trained and tested on the entire data or specifically on individual name groups. Results show that ethnicity‐based name partitioning can substantially improve disambiguation performance because the individual models are better suited for their respective name group. The improvements occur across all ethnic name groups with different magnitudes. Performance gains in predicting matched name pairs outweigh losses in predicting nonmatched pairs. Feature (e.g., coauthor name) similarities of name pairs vary across ethnic name groups. Such differences may enable the development of ethnicity‐specific feature weights to improve prediction for specific ethic name categories. These findings are observed for three labeled data with a natural distribution of problem sizes as well as one in which all ethnic name groups are controlled for the same sizes of ambiguous names. This study is expected to motive scholars to group author names based on ethnicity prior to disambiguation.