21The Mesoproterozoic Era (1,600-1,000 million years ago; Ma) has long been considered a 22 period of relative environmental stasis, with persistently low levels of atmospheric oxygen. 23 There remains much uncertainty, however, over the evolution of ocean chemistry during this 24 time period, which may have been of profound significance for the early evolution of 25 eukaryotic life. Here, we present rare earth element, iron speciation and inorganic carbon 26 isotope data to investigate the redox evolution of the 1,600-1,550 Ma Yanliao Basin, North 27 China Craton. These data confirm that the ocean at the start of the Mesoproterozoic was 28 dominantly anoxic and ferruginous. Significantly, however, we find evidence for a 29 progressive oxygenation event starting at ~1,570 Ma, immediately prior to the occurrence of 30 complex multicellular eukaryotes in shelf areas of the Yanliao Basin. Our study thus 31 demonstrates that oxygenation of the Mesoproterozoic environment was far more dynamic 32 and intense than previously envisaged, and establishes an important link between rising 33 oxygen and the emerging record of diverse, multicellular eukaryotic life in the early 34 Mesoproterozoic. 35 36The earliest definitive evidence for the evolution of eukaryotes occurs in late Paleoproterozoic 37 marine sediments 1,2 , but the subsequent Mesoproterozoic has traditionally been perceived as a 38 period of relative evolutionary stasis 2 . However, emerging evidence from several early 39 Mesoproterozoic localities 3,4,5 increasingly supports a relatively high abundance and diversity of 40 eukaryotic organisms by this time. Moreover, decimeter-scale, multicellular fossils have recently 41 been discovered in early Mesoproterozoic (~1,560 Ma) shelf sediments from the Gaoyuzhuang 42 Formation of the Yanliao Basin, North China Craton 6 . Although their precise affinity is unclear, 43the Gaoyuzhuang fossils most likely represent photosynthetic algae, and provide the strongest 44 3 evidence yet for the evolution of complex multicellular eukaryotes as early as the 45 Mesoproterozoic 6 . 46While molecular oxygen is required for eukaryotic synthesis 7 , the precise oxygen requirements 47 of early multicellular eukaryotes, including the Gaoyuzhuang fossils, are unclear. This is 48 exacerbated by the fact that recent reconstructions of oxygen levels across the Mesoproterozoic 49 are highly variable, which has reignited the debate over the role of oxygen in early eukaryote 50 evolution 8,9,10,11 . Thus, in addition to providing insight into the affinity of the Gaoyuzhuang fossils, 51 a detailed understanding of the environmental conditions that prevailed in the Yanliao Basin 52 would also inform on the nature of Earth surface oxygenation through the Mesoproterozoic. 53 Over recent years, understanding of Mesoproterozoic ocean chemistry has converged on a 54 scenario whereby the deep ocean remained predominantly anoxic and iron-rich (ferruginous) 55 beneath oxic surface waters, with widespread euxinic (anoxic and sulphidic) conditions b...

Metrics & MoreArticle Recommendations CONSPECTUS: Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it experimentally reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse experimental data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor.We propose three broad categories of data in chemistry: data on fundamental properties, data on reaction outcomes, and data on reaction mechanics. We highlight flexible, automated platforms that can be deployed to acquire and leverage these data. The first platform combines solid-and liquid-dosing modules with computer vision to automate solubility screening, thereby gathering fundamental data that are necessary for almost every experimental design. Using computer vision offers the additional benefit of creating a visual record, which can be referenced and used to further interrogate and gain insight on the data collected. The second platform iteratively tests reaction variables proposed by a ML algorithm in a closed-loop fashion. Experimental data related to reaction outcomes are fed back into the algorithm to drive the discovery and optimization of new materials and chemical processes. The third platform uses automated process analytical technology to gather real-time data related to reaction kinetics. This system allows the researcher to directly interrogate the reaction mechanisms in granular detail to determine exactly how and why a reaction proceeds, thereby enabling reaction optimization and deployment.

New organometallic wires were obtained from two diruthenium units bridged by E-hex-3-ene-1,5-diyn-dyl.

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