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Abstract Converting CO₂into industrially useful products is an appealing strategy for utilization of an abundant chemical resource. Electrochemical CO₂reduction (eCO₂R) offers a pathway to convert CO₂into CO and ethylene, using renewable electricity. These products can be efficiently copolymerized by organometallic catalysts to generate polyketones. However, the conditions for these reactions are very different, presenting the challenge of coupling microenvironments typically encountered for the transformation of CO₂into highly complex but desirable multicarbon products. Herein, we present a system to produce polyketone plastics entirely derived from CO₂and water, where both the CO and C₂H₄intermediates are produced by eCO₂R. In this system, a combination of Cu and Ag gas diffusion electrodes is used to generate a gas mixture with nearly equal concentrations of CO and C₂H₄, and a recirculatory CO₂reduction loop is used to reach concentrations of above 11% each, leading to a current‐to‐polymer efficiency of up to 51% and CO₂utilization of 14%. 

Samarium diiodide (SmI₂) is a privileged, single-electron reductant deployed in diverse synthetic settings. However, generalizable methods for catalytic turnover remain elusive because of the well-known challenge associated with cleaving strong Sm^III–O bonds. Prior efforts have focused on the use of highly reactive oxophiles to enable catalyst turnover. However, such approaches give rise to complex catalyst speciation and intrinsically limit the synthetic scope. Herein, we leveraged a mild and selective protonolysis strategy to achieve samarium-catalyzed, intermolecular reductive cross-coupling of ketones and acrylates with broad scope. The modularity of our approach allows rational control of selectivity based on solvent, pK_a(whereK_ais the acid dissociation constant), and the samarium coordination sphere and provides a basis for future developments in catalytic and electrocatalytic lanthanide chemistry. 

The premise of identifiable and causal representation learning is to improve the current representation learning paradigm in terms of generalizability or robustness. Despite recent progress in questions of identifiability, more theoretical results demonstrating concrete advantages of these methods for downstream tasks are needed. In this paper, we consider the task of intervention extrapolation: predicting how interventions affect an outcome, even when those interventions are not observed at training time, and show that identifiable representations can provide an effective solution to this task even if the interventions affect the outcome non-linearly. Our setup includes an outcome variable Y , observed features X, which are generated as a non-linear transformation of latent features Z, and exogenous action variables A, which influence Z. The objective of intervention extrapolation is then to predict how interventions on A that lie outside the training support of A affect Y . Here, extrapolation becomes possible if the effect of A on Z is linear and the residual when regressing Z on A has full support. As Z is latent, we combine the task of intervention extrapolation with identifiable representation learning, which we call Rep4Ex: we aim to map the observed features X into a subspace that allows for non-linear extrapolation in A. We show that the hidden representation is identifiable up to an affine transformation in Z-space, which, we prove, is sufficient for intervention extrapolation. The identifiability is characterized by a novel constraint describing the linearity assumption of A on Z. Based on this insight, we propose a flexible method that enforces the linear invariance constraint and can be combined with any type of autoencoder. We validate our theoretical findings through a series of synthetic experiments and show that our approach can indeed succeed in predicting the effects of unseen interventions. 

Abstract The leaf economics spectrum 1,2 and the global spectrum of plant forms and functions 3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species 2 . Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities 4 . However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability 4,5 . Here we derive a set of ecosystem functions 6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems 7,8 .