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1.
J Am Chem Soc ; 143(24): 9244-9259, 2021 Jun 23.
Artigo em Inglês | MEDLINE | ID: mdl-34114812

RESUMO

The rational solid-state synthesis of inorganic compounds is formulated as catalytic nucleation on crystalline reactants, where contributions of reaction and interfacial energies to the nucleation barriers are approximated from high-throughput thermochemical data and structural and interfacial features of crystals, respectively. Favorable synthesis reactions are then identified by a Pareto analysis of relative nucleation barriers and phase selectivities of reactions leading to the target. We demonstrate the application of this approach in reaction planning for the solid-state synthesis of a range of compounds, including the widely studied oxides LiCoO2, BaTiO3, and YBa2Cu3O7, as well as other metal oxide, oxyfluoride, phosphate, and nitride targets. Pathways for enabling the retrosynthesis of inorganics are also discussed.

2.
J Chem Inf Model ; 61(8): 3908-3916, 2021 08 23.
Artigo em Inglês | MEDLINE | ID: mdl-34288678

RESUMO

Surface adsorption is a crucial step in numerous processes, including heterogeneous catalysis, where the adsorption of key species is often used as a descriptor of efficiency. We present here an automated adsorption workflow for semiconductors which employs density functional theory calculations to generate adsorption data in a high-throughput manner. Starting from a bulk structure, the workflow performs an exhaustive surface search, followed by an adsorption structure construction step, which generates a minimal energy landscape to determine the optimal adsorbate-surface distance. An extensive set of energy-based, charge-based, geometric, and electronic descriptors tailored toward catalysis research are computed and saved to a personal user database. The application of the workflow to zinc telluride, a promising CO2 reduction photocatalyst, is presented as a case study to illustrate the capabilities of this method and its potential as a material discovery tool.


Assuntos
Semicondutores , Zinco , Adsorção , Propriedades de Superfície , Fluxo de Trabalho
3.
Phys Chem Chem Phys ; 21(45): 25323-25327, 2019 Dec 07.
Artigo em Inglês | MEDLINE | ID: mdl-31701964

RESUMO

Pourbaix diagrams have been used extensively to evaluate stability regions of materials subject to varying potential and pH conditions in aqueous environments. However, both recent advances in high-throughput material exploration and increasing complexity of materials of interest for electrochemical applications pose challenges for performing Pourbaix analysis on multidimensional systems. Specifically, current Pourbaix construction algorithms incur significant computational costs for systems consisting of four or more elemental components. Herein, we propose an alternative Pourbaix construction method that filters all potential combinations of species in a system to only those present on a compositional convex hull. By including axes representing the quantities of H+ and e- required to form a given phase, one can ensure every stable phase mixture is included in the Pourbaix diagram and reduce the computational time required to construct the resultant Pourbaix diagram by several orders of magnitude. This new Pourbaix algorithm has been incorporated into the pymatgen code and the Materials Project website, and it extends the ability to evaluate the Pourbaix stability of complex multicomponent systems.

4.
Nat Mater ; 16(2): 225-229, 2017 02.
Artigo em Inglês | MEDLINE | ID: mdl-27723737

RESUMO

While the search for catalysts capable of directly converting methane to higher value commodity chemicals and liquid fuels has been active for over a century, a viable industrial process for selective methane activation has yet to be developed. Electronic structure calculations are playing an increasingly relevant role in this search, but large-scale materials screening efforts are hindered by computationally expensive transition state barrier calculations. The purpose of the present letter is twofold. First, we show that, for the wide range of catalysts that proceed via a radical intermediate, a unifying framework for predicting C-H activation barriers using a single universal descriptor can be established. Second, we combine this scaling approach with a thermodynamic analysis of active site formation to provide a map of methane activation rates. Our model successfully rationalizes the available empirical data and lays the foundation for future catalyst design strategies that transcend different catalyst classes.

5.
Phys Chem Chem Phys ; 20(5): 3813-3818, 2018 Jan 31.
Artigo em Inglês | MEDLINE | ID: mdl-29349458

RESUMO

The reactivity of solid oxide surfaces towards adsorption of oxygen and hydrogen is a key metric for the design of new catalysts for electrochemical water splitting. In this paper, we report on trends in the adsorption energy of different adsorbed intermediates derived from the oxidation and reduction of water for ternary ABO3 oxides in the cubic perovskite structure. Our findings support a previously reported trend that rationalizes the observed lower bound in oxygen evolution (OER) overpotentials from correlations in OH* and OOH* adsorption energies. In addition, we report hydrogen adsorption energies that may be used to estimate hydrogen evolution (HER) overpotentials along with potential metrics for electrochemical metastability in reducing environments. We also report and discuss trends between atom-projected density of states and adsorption energies, which may enable a design criteria from the local electronic structure of the active site.

6.
Nat Mater ; 16(1): 70-81, 2016 12 20.
Artigo em Inglês | MEDLINE | ID: mdl-27994241

RESUMO

The conversion of sunlight into fuels and chemicals is an attractive prospect for the storage of renewable energy, and photoelectrocatalytic technologies represent a pathway by which solar fuels might be realized. However, there are numerous scientific challenges in developing these technologies. These include finding suitable materials for the absorption of incident photons, developing more efficient catalysts for both water splitting and the production of fuels, and understanding how interfaces between catalysts, photoabsorbers and electrolytes can be designed to minimize losses and resist degradation. In this Review, we highlight recent milestones in these areas and some key scientific challenges remaining between the current state of the art and a technology that can effectively convert sunlight into fuels and chemicals.

7.
Microb Ecol ; 74(3): 507-509, 2017 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-28326437

RESUMO

Knowledge of isotopic discrimination, or fractionation, by denitrifying bacteria can benefit agricultural fertilizer management, wastewater treatment, and other applications. However, the complexity of N transformation pathways in the environment and the sensitivity of denitrification to environmental conditions warrant better isotopic distinction between denitrification and other processes, especially for oxygen isotopes. Here, we present a dataset of δ18O measurements in continuous culture of Paracoccus denitrificans. The authors hope that it will be useful in further studies of N2O in the environment.


Assuntos
Desnitrificação , Óxido Nitroso/metabolismo , Isótopos de Oxigênio/análise , Paracoccus denitrificans/metabolismo
8.
Phys Chem Chem Phys ; 19(24): 15856-15863, 2017 Jun 21.
Artigo em Inglês | MEDLINE | ID: mdl-28585950

RESUMO

In the future, industrial CO2 electroreduction using renewable energy sources could be a sustainable means to convert CO2 and water into commodity chemicals at room temperature and atmospheric pressure. This study focuses on the electrocatalytic reduction of CO2 on polycrystalline Au surfaces, which have high activity and selectivity for CO evolution. We explore the catalytic behavior of polycrystalline Au surfaces by coupling potentiostatic CO2 electrolysis experiments in an aqueous bicarbonate solution with high sensitivity product detection methods. We observed the production of methanol, in addition to detecting the known products of CO2 electroreduction on Au: CO, H2 and formate. We suggest a mechanism that explains Au's evolution of methanol. Specifically, the Au surface does not favor C-O scission, and thus is more selective towards methanol than methane. These insights could aid in the design of electrocatalysts that are selective for CO2 electroreduction to oxygenates over hydrocarbons.

9.
Angew Chem Int Ed Engl ; 55(4): 1450-4, 2016 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-26692282

RESUMO

Oxide-derived copper (OD-Cu) electrodes exhibit unprecedented CO reduction performance towards liquid fuels, producing ethanol and acetate with >50% Faradaic efficiency at -0.3 V (vs. RHE). By using static headspace-gas chromatography for liquid phase analysis, we identify acetaldehyde as a minor product and key intermediate in the electroreduction of CO to ethanol on OD-Cu electrodes. Acetaldehyde is produced with a Faradaic efficiency of ≈5% at -0.33 V (vs. RHE). We show that acetaldehyde forms at low steady-state concentrations, and that free acetaldehyde is difficult to detect in alkaline solutions using NMR spectroscopy, requiring alternative methods for detection and quantification. Our results represent an important step towards understanding the CO reduction mechanism on OD-Cu electrodes.

10.
Phys Chem Chem Phys ; 17(4): 2634-40, 2015 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-25502921

RESUMO

In this work, we present first-principles calculations describing the catalytic activity for of a set of photoelectrocatalysts identified as candidates for total water splitting in a previous screening study for bulk stability and bandgap. Our Density Functional Theory (DFT) calculations of the intermediate energetics for hydrogen evolution and oxygen evolution suggest that none of the proposed materials has the ideal combination of bandgap and surface chemical properties that should allow for total water splitting in a single material. This result suggests that co-catalysts are necessary to overcome the kinetic limitations of the both reactions, although some materials may catalyze one half-reaction, as has been observed in experiment.

11.
Chem Sci ; 15(15): 5660-5673, 2024 Apr 17.
Artigo em Inglês | MEDLINE | ID: mdl-38638212

RESUMO

Exploratory synthesis has been the main generator of new inorganic materials for decades. However, our Edisonian and bias-prone processes of synthetic exploration alone are no longer sufficient in an age that demands rapid advances in materials development. In this work, we demonstrate an end-to-end attempt towards systematic, computer-aided discovery and laboratory synthesis of inorganic crystalline compounds as a modern alternative to purely exploratory synthesis. Our approach initializes materials discovery campaigns by autonomously mapping the synthetic feasibility of a chemical system using density functional theory with AI feedback. Following expert-driven down-selection of newly generated phases, we use solid-state synthesis and in situ characterization via hot-stage X-ray diffraction in order to realize new ternary oxide phases experimentally. We applied this strategy in six ternary transition-metal oxide chemistries previously considered well-explored, one of which culminated in the discovery of two novel phases of calcium ruthenates. Detailed characterization using room temperature X-ray powder diffraction, 4D-STEM and SQUID measurements identifies the structure and composition and confirms distinct properties, including distinct defect concentrations, of one of the new phases formed in our experimental campaigns. While the discovery of a new material guided by AI and DFT theory represents a milestone, our procedure and results also highlight a number of critical gaps in the process that can inform future efforts towards the improvement of AI-coupled methodologies.

12.
BMJ Glob Health ; 8(5)2023 05.
Artigo em Inglês | MEDLINE | ID: mdl-37137535

RESUMO

What, how and why people eat has long been understood to be important for human health, but until recently, has not been recognised as an essential facet of climate change and its effects on planetary health. The global climate change and diet-related health crises occurring are connected to food systems, food environments and consumer food choices. Calls to transform food systems for human and planetary health highlight the importance of understanding individual food choice. Understanding what, how and why people eat the way they do is crucial to successful food systems transformations that achieve both human and planetary health goals. Little is known about how food choice relates to climate. To clarify potential paths for action, we propose that individual food choice relates to climate change through three key mechanisms. First, the sum of individual food choices influences the supply and demand of foods produced and sold in the marketplace. Second, individual food decisions affect type and quantity of food waste at the retail and household level. Third, individual food choices serve as a symbolic expression of concern for human and planetary health, which can individually and collectively stimulate social movements and behaviour change. To meet the dietary needs of the 2050 global population projection of 10 billion, food systems must transform. Understanding what, how and why people eat the way they do, as well as the mechanisms by which these choices affect climate change, is essential for designing actions conducive to the protection of both human and planetary health.


Assuntos
Alimentos , Eliminação de Resíduos , Humanos , Dieta , Características da Família
13.
Oecologia ; 169(1): 187-98, 2012 May.
Artigo em Inglês | MEDLINE | ID: mdl-22038059

RESUMO

Maintaining coral reef resilience against increasing anthropogenic disturbance is critical for effective reef management. Resilience is partially determined by how processes, such as herbivory and nutrient supply, affect coral recovery versus macroalgal proliferation following disturbances. However, the relative effects of herbivory versus nutrient enrichment on algal proliferation remain debated. Here, we manipulated herbivory and nutrients on a coral-dominated reef protected from fishing, and on an adjacent macroalgal-dominated reef subject to fishing and riverine discharge, over 152 days. On both reefs, herbivore exclusion increased total and upright macroalgal cover by 9-46 times, upright macroalgal biomass by 23-84 times, and cyanobacteria cover by 0-27 times, but decreased cover of encrusting coralline algae by 46-100% and short turf algae by 14-39%. In contrast, nutrient enrichment had no effect on algal proliferation, but suppressed cover of total macroalgae (by 33-42%) and cyanobacteria (by 71% on the protected reef) when herbivores were excluded. Herbivore exclusion, but not nutrient enrichment, also increased sediment accumulation, suggesting a strong link between herbivory, macroalgal growth, and sediment retention. Growth rates of the corals Porites cylindrica and Acropora millepora were 30-35% greater on the protected versus fished reef, but nutrient and herbivore manipulations within a site did not affect coral growth. Cumulatively, these data suggest that herbivory rather than eutrophication plays the dominant role in mediating macroalgal proliferation, that macroalgae trap sediments that may further suppress herbivory and enhance macroalgal dominance, and that corals are relatively resistant to damage from some macroalgae but are significantly impacted by ambient reef condition.


Assuntos
Antozoários/fisiologia , Recifes de Corais , Cadeia Alimentar , Herbivoria , Animais , Antozoários/crescimento & desenvolvimento , Biomassa , Eutrofização , Sedimentos Geológicos , Nitrogênio/metabolismo , Phaeophyceae/crescimento & desenvolvimento , Phaeophyceae/fisiologia , Dinâmica Populacional , Clima Tropical
14.
Sci Data ; 9(1): 302, 2022 Jun 14.
Artigo em Inglês | MEDLINE | ID: mdl-35701432

RESUMO

We report a dataset of 96640 crystal structures discovered and computed using our previously published autonomous, density functional theory (DFT) based, active-learning workflow named CAMD (Computational Autonomy for Materials Discovery). Of these, 894 are within 1 meV/atom of the convex hull and 26826 are within 200 meV/atom of the convex hull. The dataset contains DFT-optimized pymatgen crystal structure objects, DFT-computed formation energies and phase stability calculations from the convex hull. It contains a variety of spacegroups and symmetries derived from crystal prototypes derived from known experimental compounds, and was generated from active learning campaigns of various chemical systems. This dataset can be used to benchmark future active-learning or generative efforts for structure prediction, to seed new efforts of experimental crystal structure discovery, or to construct new models of structure-property relationships.

15.
J Chem Theory Comput ; 18(4): 2737-2748, 2022 Apr 12.
Artigo em Inglês | MEDLINE | ID: mdl-35244397

RESUMO

Three-dimensional atomic-level models of polymers are the starting points for physics-based simulation studies. A capability to generate reasonable initial structural models is highly desired for this purpose. We have developed a python toolkit, namely, polymer structure predictor (psp), to generate a hierarchy of polymer models, ranging from oligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input. This toolkit allows users to tune several parameters to manage the quality and scale of models and computational cost. The output structures and accompanying force field (GAFF2/OPLS-AA) parameter files can be used for downstream ab initio and molecular dynamics simulations. The psp package includes a Colab notebook where users can go through several examples, building their own models, visualizing them, and downloading them for later use. The psp toolkit, being a first of its kind, will facilitate automation in polymer property prediction and design.


Assuntos
Simulação de Dinâmica Molecular , Polímeros , Modelos Estruturais , Polímeros/química
16.
Sci Rep ; 12(1): 4694, 2022 03 18.
Artigo em Inglês | MEDLINE | ID: mdl-35304496

RESUMO

Sequential learning for materials discovery is a paradigm where a computational agent solicits new data to simultaneously update a model in service of exploration (finding the largest number of materials that meet some criteria) or exploitation (finding materials with an ideal figure of merit). In real-world discovery campaigns, new data acquisition may be costly and an optimal strategy may involve using and acquiring data with different levels of fidelity, such as first-principles calculation to supplement an experiment. In this work, we introduce agents which can operate on multiple data fidelities, and benchmark their performance on an emulated discovery campaign to find materials with desired band gap values. The fidelities of data come from the results of DFT calculations as low fidelity and experimental results as high fidelity. We demonstrate performance gains of agents which incorporate multi-fidelity data in two contexts: either using a large body of low fidelity data as a prior knowledge base or acquiring low fidelity data in-tandem with experimental data. This advance provides a tool that enables materials scientists to test various acquisition and model hyperparameters to maximize the discovery rate of their own multi-fidelity sequential learning campaigns for materials discovery. This may also serve as a reference point for those who are interested in practical strategies that can be used when multiple data sources are available for active or sequential learning campaigns.


Assuntos
Aprendizagem
17.
Sci Rep ; 11(1): 16200, 2021 08 10.
Artigo em Inglês | MEDLINE | ID: mdl-34376772

RESUMO

Small pigmented eukaryotes (⩽ 5 µm) are an important, but overlooked component of global marine phytoplankton. The Amazon River plume delivers nutrients into the oligotrophic western tropical North Atlantic, shades the deeper waters, and drives the structure of microphytoplankton (> 20 µm) communities. For small pigmented eukaryotes, however, diversity and distribution in the region remain unknown, despite their significant contribution to open ocean primary production and other biogeochemical processes. To investigate how habitats created by the Amazon river plume shape small pigmented eukaryote communities, we used high-throughput sequencing of the 18S ribosomal RNA genes from up to five distinct small pigmented eukaryote cell populations, identified and sorted by flow cytometry. Small pigmented eukaryotes dominated small phytoplankton biomass across all habitat types, but the population abundances varied among stations resulting in a random distribution. Small pigmented eukaryote communities were consistently dominated by Chloropicophyceae (0.8-2 µm) and Bacillariophyceae (0.8-3.5 µm), accompanied by MOCH-5 at the surface or by Dinophyceae at the chlorophyll maximum. Taxonomic composition only displayed differences in the old plume core and at one of the plume margin stations. Such results reflect the dynamic interactions of the plume and offshore oceanic waters and suggest that the resident small pigmented eukaryote diversity was not strongly affected by habitat types at this time of the year.


Assuntos
Clorofila/metabolismo , Ecossistema , Fitoplâncton/fisiologia , Rios/química , Estações do Ano , Água do Mar/análise , Clorófitas/fisiologia , Diatomáceas/fisiologia , Dinoflagellida/fisiologia
18.
Sci Rep ; 11(1): 8888, 2021 04 26.
Artigo em Inglês | MEDLINE | ID: mdl-33903606

RESUMO

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material's structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal-organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25-30% decrease in root-mean-squared-deviation and an average increase of 40-50% in R2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure-property relationships for materials design.

19.
Mar Pollut Bull ; 164: 112076, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33529879

RESUMO

Following the Deepwater Horizon oil spill of 2010, large amounts of biodegraded oil (petrocarbon) sank to the seafloor. Our objectives were to 1) determine post-spill isotopic values as the sediments approached a new baseline and 2) track the recovery of affected sediments. Sediment organic carbon δ13C and Δ14C reached a post-spill baseline averaging -21.2 ± 0.9‰ (n = 129) and -220 ± 66‰ (n = 95). Spatial variations in seafloor organic carbon baseline isotopic values, 13C and 14C, were influenced by river discharge and hydrocarbon seepage, respectively. Inverse Distance Weighting of surface sediment Δ14C values away from seep sites showed a 50% decrease in the total mass of petrocarbon, from 2010 to 2014. We estimated a rate of loss of -2 × 109 g of petrocarbon-C/year, 2-11% of the degradation rates in surface slicks. Despite the observed recovery in sediments, lingering residual material in the surface sediments was evident seven years following the blowout.


Assuntos
Poluição por Petróleo , Poluentes Químicos da Água , Monitoramento Ambiental , Sedimentos Geológicos , Golfo do México , Hidrocarbonetos/análise , Poluição por Petróleo/análise , Poluentes Químicos da Água/análise
20.
Sci Adv ; 7(52): eabj5505, 2021 Dec 24.
Artigo em Inglês | MEDLINE | ID: mdl-34936439

RESUMO

In materials discovery efforts, synthetic capabilities far outpace the ability to extract meaningful data from them. To bridge this gap, machine learning methods are necessary to reduce the search space for identifying desired materials. Here, we present a machine learning­driven, closed-loop experimental process to guide the synthesis of polyelemental nanomaterials with targeted structural properties. By leveraging data from an eight-dimensional chemical space (Au-Ag-Cu-Co-Ni-Pd-Sn-Pt) as inputs, a Bayesian optimization algorithm is used to suggest previously unidentified nanoparticle compositions that target specific interfacial motifs for synthesis, results of which are iteratively shared back with the algorithm. This feedback loop resulted in successful syntheses of 18 heterojunction nanomaterials that are too complex to discover by chemical intuition alone, including extremely chemically complex biphasic nanoparticles reported to date. Platforms like the one developed here are poised to transform materials discovery across a wide swath of applications and industries.

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