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Despite the various strategies for achieving metal–nitrogen–carbon (M–N–C) single-atom catalysts (SACs) with different microenvironments for electrochemical carbon dioxide reduction reaction (CO 2 RR), the synthesis–structure–performance correlation remains elusive due to the lack of well-controlled synthetic approaches. Here, we employed Ni nanoparticles as starting materials for the direct synthesis of nickel (Ni) SACs in one spot through harvesting the interaction between metallic Ni and N atoms in the precursor during the chemical vapor deposition growth of hierarchical N-doped graphene fibers. By combining with first-principle calculations, we found that the Ni-N configuration is closely correlated to the N contents in the precursor, in which the acetonitrile with a high N/C ratio favors the formation of Ni-N 3 , while the pyridine with a low N/C ratio is more likely to promote the evolution of Ni-N 2 . Moreover, we revealed that the presence of N favors the formation of H-terminated edge of sp 2 carbon and consequently leads to the formation of graphene fibers consisting of vertically stacked graphene flakes, instead of the traditional growth of carbon nanotubes on Ni nanoparticles. With a high capability in balancing the *COOH formation and *CO desorption, the as-prepared hierarchical N-doped graphene nanofibers with Ni-N 3 sites exhibit a superior CO 2 RR performance compared to that with Ni-N 2 and Ni-N 4 ones. 

Emotions at work have long been identified as critical signals of work motivations, status, and attitudes, and as predictors of various work-related outcomes. When more and more employees work remotely, these emotional signals of workers become harder to observe through daily, face-to-face communications. The use of online platforms to communicate and collaborate at work provides an alternative channel to monitor the emotions of workers. This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers. In particular, we present how the developers on GitHub use emojis in their work-related activities. We show that developers have diverse patterns of emoji usage, which can be related to their working status including activity levels, types of work, types of communications, time management, and other behavioral patterns. Developers who use emojis in their posts are significantly less likely to dropout from the online work platform. Surprisingly, solely using emoji usage as features, standard machine learning models can predict future dropouts of developers at a satisfactory accuracy. Features related to the general use and the emotions of emojis appear to be important factors, while they do not rule out paths through other purposes of emoji use. 

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e.g., more English texts are labeled than texts in any other languages, which creates a considerable inequality in the quality of related information services received by users speaking different languages. To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i.e., the source language, usually English) to another language with fewer labels (i.e., the target language). The source and the target languages are usually bridged through off-the-shelf machine translation tools. Through such a channel, cross-language sentiment patterns can be successfully learned from English and transferred into the target languages. This approach, however, often fails to capture sentiment knowledge specific to the target language, and thus compromises the accuracy of the downstream classification task. In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns. We propose a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language. The learned representations are then integrated to facilitate cross-lingual sentiment classification. The proposed method demonstrates state-of-the-art performance on benchmark datasets, which is sustained even when sentiment labels are scarce.