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By 2035, the need for battery-grade lithium is expected to quadruple. About half of this lithium is currently sourced from brines and must be converted from a chloride into lithium carbonate (Li2CO3) through a process called softening. Conventional softening methods using sodium or potassium salts contribute to carbon emissions during reagent mining and battery manufacturing, exacerbating global warming. This study introduces an alternative approach using carbon dioxide (CO2(g)) as the carbonating reagent in the lithium softening process, offering a carbon capture solution. We employed an active learning-driven high-throughput method to rapidly capture CO2(g) and convert it to lithium carbonate. The model was simplified by focusing on the elemental concentrations of C, Li, and N for practical measurement and tracking, avoiding the complexities of ion speciation equilibria. This approach led to an optimized lithium carbonate process that capitalizes on CO2(g) capture and improves the battery metal supply chain's carbon efficiency.

Research challenges encountered across science, engineering, and economics can frequently be formulated as optimization tasks. In chemistry and materials science, recent growth in laboratory digitization and automation has sparked interest in optimization-guided autonomous discovery and closed-loop experimentation. Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials. However, the experiment planning strategy that is most suitable to a scientific discovery task is a priori unknown while rigorous comparisons of different strategies are highly time and resource demanding. As optimization algorithms are typically benchmarked on low-dimensional synthetic functions, it is unclear how their performance would translate to noisy, higher-dimensional experimental tasks encountered in chemistry and materials science. We introduce Olympus, a software package that provides a consistent and easy-to-use framework for benchmarking optimization algorithms against realistic experiments emulated via probabilistic deep-learning models. Olympus includes a collection of experimentally derived benchmark sets from chemistry and materials science and a suite of experiment planning strategies that can be easily accessed via a user-friendly python interface. Furthermore, Olympus facilitates the integration, testing, and sharing of custom algorithms and user-defined datasets. In brief, Olympus mitigates the barriers associated with benchmarking optimization algorithms on realistic experimental scenarios, promoting data sharing and the creation of a standard framework for evaluating the performance of experiment planning strategies, Comment: 15 pages, 4 figures, 4 tables (with SI: 22 pages, 11 figures, 15 tables). Changes: minor fixes to text and references. Two paragraphs added in Sec. III

Discovering and optimizing commercially viable materials for clean energy applications typically takes over a decade. Self-driving laboratories that iteratively design, execute, and learn from material science experiments in a fully autonomous loop present an opportunity to accelerate this research. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies., Comment: 43 pages, 9 figures

Carbonaceous chondrites contain numerous indigenous organic compounds and could have been an important source of prebiotic compounds required for the origin of life on Earth or elsewhere. Extraterrestrial amino acids have been reported in five of the eight groups of carbonaceous chondrites and are most abundant in CI, CM, and CR chondritesbut are also present in the more thermally altered CV and CO chondrites. We report the abundance, distribution, and enantiomeric and isotopic compositions of simple primary amino acids in six metal-rich CH and CB carbonaceous chondrites that have not previously been investigated for amino acids: Allan Hills (ALH) 85085 (CH3), Pecora Escarpment(PCA) 91467 (CH3), Patuxent Range (PAT) 91546 (CH3), MacAlpine Hills (MAC) 02675(CBb), Miller Range (MIL) 05082 (CB), and Miller Range (MIL) 07411 (CB). Amino acid abundances and carbon isotopic values were obtained by using both liquid chromatography time-of-flight mass spectrometry and fluorescence, and gas chromatography isotope ratiomass spectrometry. The (delta D, delta C-13, delta N-15) ratios of multiple amino acids fall outside of the terrestrial range and support their extraterrestrial origin. Extracts of CH chondrites were found to be particularly rich in amino acids (1316 parts per million, ppm) while CB chondrite extracts had much lower abundances (0.22 ppm). The amino acid distributions of the CH and CB chondrites were distinct from the distributions observed in type 2 and 3 CM and CR chondrites and contained elevated levels of beta-, gamma-, and delta-amino acids compared to the corresponding alpha-amino acids, providing evidence that multiple amino acid formation mechanisms were important in CH and CB chondrites.