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NASA's New Horizons mission unveiled a diverse landscape of Pluto's surface with massive regions being neutral in color, while others like Cthulhu Macula range from golden-yellow to reddish comprising up to half of Pluto's carbon budget. Here, we demonstrate in laboratory experiments merged with electronic structure calculations that the photolysis of solid acetylene – the most abundant precipitate on Pluto's surface – by low energy ultraviolet photons efficiently synthesizes benzene and polycyclic aromatic hydrocarbons via excited state photochemistry thus providing critical molecular building blocks for the colored surface material. Since low energy photons deliver doses to Pluto's surface exceeding those from cosmic rays by six orders of magnitude, these processes may significantly contribute to the coloration of Pluto's surface and of hydrocarbon-covered surfaces of Solar System bodies such as Triton in general. This discovery critically enhances our perception of the distribution of aromatic molecules and carbon throughout our Solar System.

Additive manufacturing is a revolutionary technology that offers a different pathway for material processing and design. However, innovations in either new materials or new processing technologies can seldom be successful without a synergistic combination. We demonstrate an in situ design approach to make alloys spatially modulated in concentration by using laser-powder bed fusion. We show that the partial homogenization of two dissimilar alloy melts—Ti-6Al-4V and a small amount of 316L stainless steel—allows us to produce micrometer-scale concentration modulations of the elements that are contained in 316L in the Ti-6Al-4V matrix. The corresponding phase stability modulation creates a fine scale–modulated β + α′ dual-phase microstructure that exhibits a progressive transformation-induced plasticity effect, which leads to a high tensile strength of ~1.3 gigapascals with a uniform elongation of ~9% and an excellent work-hardening capacity of >300 megapascals. This approach creates a pathway for concentration-modulated heterogeneous alloy design for structural and functional applications.

Extractive text summarization aims at extract- ing the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sen- tence embedding plays an important role. Re- cent studies have leveraged graph neural net- works to capture the inter-sentential relation- ship (e.g., the discourse graph) to learn con- textual sentence embedding. However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity & natural connection), nor model intra-sentential relationships (e.g, semantic & syntactic relationship among words). To ad- dress these problems, we propose a novel Mul- tiplex Graph Convolutional Network (Multi- GCN) to jointly model different types of rela- tionships among sentences and words. Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extrac- tive text summarization. Finally, we evaluate the proposed models on the CNN/DailyMail benchmark dataset to demonstrate the effec- tiveness of our method.