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.     

The effect of rapid rotation on a vertical bridgman furnace at large Rayleigh number

In quasi-steady operation, convection currents in a Bridgmandevice, used for producing a semi-conductor crystal, createinhomogeneities that may make the crystal unusable. It has oftenbeen suggested that additional forces due to rotation or magnetismmight be efficacious in reducing the segregation of the elementsof the alloy. It has been found that, over a wide range of rotationrates, there is no improvement in performance due to rotationabout the vertical axis. However, numerical results that havebeen obtained previously (Lee & Pearlstein, J. Crys. Growth240, 2002) indicate that, when effects of centrifugal buoyancyare introduced, a substantial reduction in segregation is achieved.In the work reported here, by contrast, in which we extend previouslarge-Rayleigh-number asymptotic analysis to include centrifugalbuoyancy, we find no improvement in radial segregation, butrather increasing segregation with increasing rotation rate.     

Sulfonylcarbamimidic azides from sulfonyl chlorides and 5-aminotetrazole

Treatment of o-nitrobenzenesulfonyl chloride ( 3 ) with 5-aminotetrazole (5-AT) gave [(2-nitrophenyl)-sulfonyl]carbamimidic azide ( 6 ), a ring-opened isomer of the expected N-(1H-tetrazol-5-yl)-2-nitrobenzenesulfonamide ( 4 ). Sulfonylcarbamimidic azide 6 was converted to 2-amino-N-(aminoiminomethyl)benzene-sulfonamide ( 7 ) with ethanolic stannous chloride, and to 3-amino-1,2,4-thiadiazine 1,1-dioxide ( 8 ) with sodium dithionite. Methanesulfonyl chloride ( 9 ) and 5-AT gave 2-(methylsulfonyl)carbamimidic azide ( 10 ), which isomerized to 5-[(methylsulfonyl)amino]-1H-tetrazole ( 11 ) in warm ethanol. Attempted cycloaddition of 2-(phenylsulfonyl)carbamimidic azide ( 13 ) and ethyl vinyl ether led only to alkylated tetrazole products. In addition, other tetrazole-alkylating reactions are described. Isomers produced from these alkylations were differentiated with ¹³C nmr spectroscopy.     

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.     

Reactions of 3-chloro-6-hydrazinopyridazine with michael-retro-michael reagents

Although pyrazole formation results from treatment of 3-chloro-6-hydrazinopyridazine ( 2 ) with both ethoxymethylenemalononitrile and ethyl (ethoxymethylene)cyanoacetate, 6-chlorotriazolo[4,3-b]pyridazine ( 5 ) was produced (75% yield) when 2 was treated with diethyl ethoxymethylenemalonate. Treatment of 2 with diethyl acetylmalonate ( 8 ) gave both 6-chloro-3-methyltriazolo[4,3-b]pyridazine ( 10 ) and 5-hydroxy-3-methyl-1-(6-chloro-3-pyridazinyl)-1H-pyrazole-4-carboxylic acid ethyl ester ( 12 ). Pyrazole 12 was initially isolated as a salt of triazolopyridazine 10 .     

Preparation of a DSCG-like bisbenzofuran

The preparation of 6,6′-[(2-hydroxy-1,3-propanediyl)]bis(oxy)]bis(3-hydroxy)]-2-benzofurancarboxylic acid) diethyl ester trisodium salt trihydrate ( 6 ), a compound structurally related to disodium cromoglycate (DSCG), is described.     

Synthesis of benzofuro[3,2-b] quinolin-6(11H) one and derivatives

A new and efficient synthesis of benzof'uro[3,2-b]quinolin-6(11H) one ( 3 ) is reported, by treatment of 2- {[(phenoxy)acetyl]amino} benzoic acid ( 6a ) with polyphosphoric acid. An intermediate in the conversion of 3 to 6a , namely, 2-(3-benzofuranylamino)benzoic acid ( 7 ), was defined. An improved method for the synthesis of 6a is also described, which was used to prepare analogs ( 6b-n ) of 6a . In addition, an 11-alkoxy derivative ( 8 ) and 11-dialkylamino derivatives ( 10 and 11 ) of benzofuro [3,2-b]quinoline were prepared from 3 .