Trapping Aluminum Hydroxide Clusters with Trisilanols during Speciation in Aluminum(III)–Water Systems: Reproducible,Large Scale Access to Molecular Aluminate Models

To gain molecular level insights into the properties of certain functions and units of extended oxides/hydroxides, suitable molecular model compounds are needed. As an attractive route to access such compounds the trapping of early intermediates during the hydrolysis of suitable precursor compounds with the aid of stabilizing ligands is conceivable, which was tested for the aluminum(III)/water system. Indeed, trisilanols proved suitable trapping reagents: their presence during the hydrolysis of AlⁱBu₂H in dependence on the amount of water used allowed for the isolation of tri‐ and octanuclear aluminum hydroxide cluster complexes [Al₃(μ₂‐OH)₃(THF)₃(PhSi(OSiPh₂O)₃)₂] ( 1 ) and [Al₈(μ₃‐OH)₂(μ₂‐OH)₁₀(THF)₃(p‐anisylSi(OSiPh₂O)₃)₄] ( 2 ). 1 can be regarded as the Al(OH)₃ cyclic trimer, where six protons have been replaced by silyl residues. While 2 features a unique [Al₈(μ₃‐OH)₂(μ₂‐OH)₁₀]¹²⁺ core. In contrast to most other known aggregates of this type, 1 and 2 can be readily prepared at reasonable scales, dissolve in common solvents, and retain an intact framework even in the presence of excessive amounts of water. This finding paves the way to future research addressing the reactivity of the individual functional groups.     

Palladium-catalyzed selective C–C bond cleavage and stereoselective alkenylation between cyclopropanol and 1,3-diyne: one-step synthesis of diverse conjugated enynes

The stereoselective synthesis of 1,3-enynes from 1,3-diynes is demonstrated by palladium-catalyzed selective C–C bond cleavage of cyclopropanol. Exclusive formation of mono-alkenylated adducts was achieved by eliminating the possibility of di-functionalization with high stereoselectivity. Indeed, this protocol worked very well with electronically and sterically diverse substrates. Several studies, including deuterium labeling experiments and intermolecular competitive experiments, were carried out to understand the mechanistic details. The atomic-level mechanism followed in the catalytic process was also validated using DFT calculations, and the rate-controlling states in the catalytic cycle were identified. Furthermore, preliminary mechanistic investigations with radical scavengers revealed the non-involvement of the radical pathway in this transformation.

Palladium-catalyzed tandem activation and functionalization of readily accessible cyclopropanols have been demonstrated to access valuable conjugated enynes from 1,3-diynes with high stereo-selectivity.     

Advances in rapid and effective break-in/conditioning/recovery of automotive PEMFC stacks

Automotive proton exchange membrane fuel cell stacks need to meet manufacturer specified rated beginning-of-life (BOL) performance before being assembled into vehicles and shipped off to customers. The process of “breaking-in” of a freshly assembled stack is often referred to as “conditioning.” It has become an intensely researched area especially in automotive companies, where imminent commercialization of fuel cell electric vehicles (FCEVs) demands a short, energy- and cost-efficient, and practical conditioning protocol. Significant advances in reducing the conditioning time from 1 to 2 days to as low as 4h or less, in some cases without the use of additional inert gases such as nitrogen, and with minimal use of hydrogen, and specialized test stations will be discussed.     

13C and 1H NMR analysis of the nitration products of 2-amino-4-oxo-(3H)-5-trifluoromethylquinazoline and 2-amino-4-oxo-(3H)-5-fluoroquinazoline

Nitration of 2-amino-4-oxo-(3H)-5-trifluoromethylquinazoline is shown to occur exclusively at C⁶ as determined from an analysis of long range ¹H and ¹⁹F scalar couplings to ring carbons. Nitration of 2-amino-4-oxo-(3H)-5-fluoroquinazoline is found to occur both at C⁶ and C⁸ as evident from an analysis of the ¹⁹F and ¹H couplings of the ring protons.     

Linear diacetylene polymers containing bis(dimethylsilyl) phenyl and/or bis-(tetramethyldisiloxane) carborane residues: Their synthesis,characterization and thermal and oxidative properties

A series of inorganic-organic linear diacetylenic hybrid polymers ( 5a–e ) were prepared by the polycondensation reaction of 1,4-dilithiobutadiyne with 1,4-bis(dimethylchlorosilyl)benzene and/or 1,7-bis(tetramethylchlorodisiloxane)-m-carborane. Their structures were characterized using FTIR, and ¹³C and ¹H NMR spectroscopies, and their thermal and oxidative properties were evaluated by DSC and TGA analyses. The hybrid polymers exhibited solubility in common organic solvents and were viscous liquids or low melting solids at room temperature. Broad prominent exotherms, attributed to reaction of the diacetylenic units, were observed by DSC in the 306°C to 354°C temperature range. When 5a–e were analyzed by TGA to 1000°C under nitrogen, weight retention between 79 and 86% were obtained. Ageing studies, performed at elevated temperatures in air on a thermoset and a ceramic obtained from polymer 5b , showed this system to have excellent thermal and oxidative stability. © 1997 John Wiley & Sons, Inc. J Polym Sci A: Polym Chem 35 : 2387–2391, 1997     

Ag NPs decked GO composite as a competent and reusable catalyst for ‘ON WATER’ chemoselective synthesis of pyrano[2,3-c:6,5-c′]dipyrazol]-2-ones

We have synthesized and characterized Ag NPs decked GO composite and studied its role as reusable catalyst for the ‘ON WATER’ chemoselective synthesis of pyranodipyrazolones via the reaction of different carbonyl compounds with 3-methyl-1-phenyl-1H-pyrazol-5(4H)-one. This method illustrates significant selectivity for pyranodipyrazolones over arylmethylene bispyrazolols and arylmethylenepyrazolones. Synergistic effect of heterogenic nature of water with reactants and Ag NPs/GO had profuse outcome on reaction as indicated by high TOF (18.03 × 10^?5 mol g^?1 min^?1). Furthermore, catalyst was recycled for 7-times without significant loss of activity.     

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules

A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (−1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model''s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.

Prediction of bond dissociation energies for charged molecules with a graph neural network enabled by global molecular features and reaction difference features between products and reactants.     

A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation

Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B → C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI – or of any complex chemical process – through selective control of reactivity.

A chemically consistent graph architecture enables autonomous identification of novel solid-electrolyte interphase formation pathways from a massive reaction network.     

An Anthracene-Based Metal-Organic Framework for Selective Photo-Reduction of Carbon Dioxide to Formic Acid Coupled with Water Oxidation

A Zr-based metal-organic framework has been synthesized and employed as a catalyst for photochemical carbon dioxide reduction coupled with water oxidation. The catalyst shows significant carbon dioxide reduction property with concomitant water oxidation. The catalyst has broad visible light as well as UV light absorption property, which is further confirmed from electronic absorption spectroscopy. Formic acid was the only reduced product from carbon dioxide with a turn-over frequency (TOF) of 0.69 h⁻¹ in addition to oxygen, which was produced with a TOF of 0.54 h⁻¹. No external photosensitizer is used and the ligand itself acts as the light harvester. The efficient and selective photochemical carbon dioxide reduction to formic acid with concomitant water oxidation using Zr-based MOF as catalyst is thus demonstrated here.