A liquid-phase stripping method was used to strip the graphite under the action of mechanical shear force to prepare graphene nanosheets (GNSs) on a large scale. Given the multicomponent composite conductive particles formed by GNSs with acid-treated MWCNTs (f-MWCNTs) and carbon black (CB), the three-dimensional (3D) intercalation electrothermal composite of GNSs/MWCNTs/CB with excellent conductivity and mechanical properties was prepared with water-based acrylic resin as a connector. Carbon particles (16.97 wt %) are found in the composite and the sheet resistance (R s) is only 4 Ω sq–1 as f-MWCNTs and CB intercalations form a more stable 3D conducting medium between the GNSs. The flexible electrothermal film (2.5 cm × 2.5 cm) printed with the 3D intercalation GNSs/MWCNTs/CB composite had a saturation temperature (T s) of 175 °C with an input of 3 V and lower power consumption (249.87 cm2 W–1). It only takes 10 s to reach T s and the electrical performance is still intact under the pressure of 1 × 105 kPa. After being bent 2500 times (bending radius is 5 mm), the electrothermal performance of the flexible electrothermal film remained stable.

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A hyperbole is an intentional and creative exaggeration not to be taken literally. Despite its ubiquity in daily life, the computational explorations of hyperboles are scarce. In this paper, we tackle the under-explored and challenging task: sentence-level hyperbole generation. We start with a representative syntactic pattern for intensification and systematically study the semantic (commonsense and counterfactual) relationships between each component in such hyperboles. Next, we leverage the COMeT and reverse COMeT models to do commonsense and counterfactual inference. We then generate multiple hyperbole candidates based on our findings from the pattern, and train neural classifiers to rank and select high-quality hyperboles. Automatic and human evaluations show that our generation method is able to generate hyperboles creatively with high success rate and intensity scores.

A writable ultrafine water-based conductive ink with excellent stability and no harmful gas emission was prepared. The mass fraction of carbon conductive particles was 4.95%, and the mean median particle size (D50) was as low as 5 μm. The size of conductive carbon particles in the ink was analyzed with continuous milling, and the phenomenon of reagglomeration of conductive particles with a small particle size and large specific surface area was explained. Moreover, the sheet resistance of the writable graphene nanosheets/multiwalled carbon nanotubes (MWCNTs)/carbon black nanocomposite conductive ink was 11.1 Ω•sq −1 . With a centrifugal speed of 3000 r/min for 15 min, the performance of the ink remained stable. Additionally, a stable conductive ballpoint pen was developed based on the writable conductive ink, and the excellent electrical and mechanical properties of the directly handwritten paper-based flexible circuit and the electrochemical properties of the directly handwritten paper-based chemical battery were tested.

Perspective differences exist among different cultures or languages. A lack of mutual understanding among different groups about their perspectives on specific values or events may lead to uninformed decisions or biased opinions. Automatically understanding the group perspectives can provide essential background for many downstream applications of natural language processing techniques. In this paper, we study colingual groups 1 and use language corpora as a proxy to identify their distributional perspectives. We present a novel computational approach to learn shared understandings, and benchmark our method by building culturally-aware models for the English, Chinese, and Japanese languages. On a held out set of diverse topics including marriage, corruption, democracy, our model achieves high correlation with human judgements regarding intra-group values and inter-group differences.