Transformation of greenhouse gas CO2 and renewable H2 into fuels and commodity chemicals is recognized as a promising route to store fluctuating renewable energy. Although several C1 chemicals, olefins, and gasoline have been successfully synthesized by CO2 hydrogenation, selective conversion of CO2 and H2 into aromatics is still challenging due to the high unsaturation degree and complex structures of aromatics. Here we report a composite catalyst of ZnAlOx and H-ZSM-5 which yields high aromatics selectivity (73.9%) with extremely low CH4 selectivity (0.4%) among the carbon products without CO. Methanol and dimethyl ether, which are synthesized by hydrogenation of formate species formed on ZnAlOx surface, are transmitted to H-ZSM-5 and subsequently converted into olefins and finally aromatics. Furthermore, 58.1% p-xylene in xylenes is achieved over the composite catalyst containing Si-H-ZSM-5. ZnAlOx&H-ZSM-5 suggests a promising application in manufacturing aromatics from CO2 and H2.

Quantum key distribution provides secure keys resistant to code-breaking quantum computers. The continuous-variable version of quantum key distribution offers the advantages of higher secret key rates in metropolitan areas, as well as the use of standard telecom components that can operate at room temperature. However, the transmission distance of these systems (compared with discrete-variable systems) are currently limited and considered unsuitable for long-distance distribution. Herein, we report the experimental results of long distance continuous-variable quantum key distribution over 202.81 km of ultralow-loss optical fiber by suitably controlling the excess noise and employing highly-efficient reconciliation procedures. This record-breaking implementation of the continuous-variable quantum key distribution doubles the previous distance record and shows the road for long-distance and large-scale secure quantum key distribution using room-temperature standard telecom components.

A primary goal of metabolomics is to identify all biologically important metabolites. One powerful approach is liquid chromatography-high resolution mass spectrometry (LC-MS), yet most LC-MS peaks remain unidentified. Here, we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. We consider all experimentally observed ion peaks together, and assign annotations to all of them simultaneously so as to maximize a score that considers properties of peaks (known masses, retention times, MS/MS fragmentation patterns) as well network constraints that arise based on mass difference between peaks. Global optimization results in accurate peak assignment and trackable peak-peak relationships. Applying this approach to yeast and mouse data, we identify a half-dozen novel metabolites, including thiamine and taurine derivatives. Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to annotate untargeted metabolomics data, revealing novel metabolites.