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.     

Experimental investigation for stability and surface properties of TiO2 and Al2O3 water-based nanofluids

Journal of Thermal Analysis and Calorimetry - Nanofluids have gained recent attention because of their potential applications in diverse engineering fields like enhancing thermal transport,...     

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.     

Synthesis and characterization of novel thermoresponsive‐co‐biodegradable hydrogels composed of N‐isopropylacrylamide,poly(L‐lactic acid), and dextran

A series of novel multifunctional hydrogels that combined the merits of both thermoresponsive and biodegradable polymeric materials were designed, synthesized, and characterized. The hydrogels were copolymeric networks composed of N‐isopropylacrylamide (NIPAAM) as a thermoresponsive component, poly(L‐lactic acid) (PLLA) as a hydrolytically degradable and hydrophobic component, and dextran as an enzymatically degradable and hydrophilic component. The chemical structures of the hydrogels were characterized by an attenuated total reflection–Fourier transform infrared spectroscopy (ATR–FTIR) technique. The hydrogels were thermoresponsive, showing a lower critical solution temperature (LCST) at approximately 32 °C, and their swelling properties strongly depended on temperature changes, the balance of the hydrophilic/hydrophobic components, and the degradation of the PLLA component. The degradation of the hydrogels caused by hydrolytic cleavage of ester bonds in the PLLA component was faster at 25 °C below the LCST than at 37 °C above the LCST, determined by the ATR–FTIR technique. Due to their multifunctional properties, the designed hydrogels show great potential for biomedical applications, including drug delivery and tissue engineering. © 2004 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 42: 5054–5066, 2004     

Bounds on the inverses of nonnegative triangular matrices with monotonicity properties

Some bounds on the entries and on the norm of the inverse of triangular matrices with nonnegative and monotone entries are found. All the results are obtained by exploiting the properties of the fundamental matrix of the recurrence relation which generates the sequence of the entries of the inverse matrix. One of the results generalizes a theorem contained in a recent article of one of the authors about Toeplitz matrices.     

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.     

Substituted m-phenylene bridges as strong ferromagnetic couplers for Cu(II)-bridge-Cu(II) magnetic interactions: new perspectives

Based on a combined theoretical-experimental study, we propose that substituted m-phenylene ligands (m-N-Phi-N) can act as tuneable strong ferromagnetic couplers connecting Cu(II) ions; a new complex presenting that bridge with J close to +15 cm(-1) has been suggested and synthesized.     

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.