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We use density functional theory to predict and evaluate 10 novel covalent organic frameworks (COFs), labeled (X4Y)(BDC)3, (X = C/Si; Y = C, Si, Ge, Sn, and Pb), with topology based on metal organic framework isoreticular metal-organic framework (IRMOF-1), but with new elements substituted for the corner atoms. We show that these new materials are stable structures using frequency calculations. For two structures, (C4C and Si4C) molecular dynamics simulations were performed to demonstrate stability of the systems up to 600 K for 10 ps. This demonstrates the remarkable stability of these systems, some of which may be experimentally accessible. For the C4C material, we also explored the stability of isolated corners and linkers and vacuum and started to build the structure from these pieces. We discuss the equilibrium lattice parameters, formation enthalpies, electronic structures, chemical bonding, and mechanical and optical properties. The predicted bulk moduli of these COFs range from 18.9 to 23.9 GPa, larger than that of IRMOF-1 (ca. 15.4 GPa), and larger than many existing 3D COF materials. The band gaps range from 1.5 to 2.1 eV, corresponding to 600-830 nm wavelength (orange through near infrared). The negative values of the formation enthalpy suggest that they are stable and should be experimentally accessible under suitable conditions. Seven materials distort the crystal structure to a lower space group symmetry Fm-3, while three materials maintain the original Fm-3m space group symmetry. All of the new materials are highly luminescent. We hope that this work will inspire efforts for experimental synthesis of these new materials.
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Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. The literature has shown that due to inauthentic actors such as malicious social bots and trolls, not every message is a genuine expression from a legitimate user. However, the prevalence of inauthentic activities in social data streams is still unclear, making it difficult to gauge biases of analyses based on such data. In this article, we aim to close this gap using Twitter data from the 2018 U.S. midterm elections. We propose an efficient and low-cost method to identify voters on Twitter and systematically compare their behaviors with different random samples of accounts. We find that some accounts flood the public data stream with political content, drowning the voice of the majority of voters. As a result, these hyperactive accounts are over-represented in volume samples. Hyperactive accounts are more likely to exhibit various suspicious behaviors and to share low-credibility information compared to likely voters. Our work provides insights into biased voter characterizations when using social media data to analyze political issues.
RESUMO
Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.