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A FORTRAN computer program was developed for analyzing the performance of phosphoric acid fuel cell power plant systems. Energy mass and electrochemical analysis in the reformer, the shaft converters, the heat exchangers, and the fuel cell stack were combined to develop a mathematical model for the power plant for both atmospheric and pressurized conditions, and for several commercial fuels.
An integrated 5 kW power system based upon methanol fuel and a phosphoric acid fuel cell operating at about 473 K is described. Description includes test results of advanced fuel cell catalysts, a semiautomatic acid replenishment system and a completed 5 kW methanol/system reformer. The results of a preliminary system test on a reformer/stack/inverter combination are reported. An initial design for a 25 kW stack is presented. Experimental plans are outlined for data acquisition necessary for design of a 50 kW methanol/steam reformer. Activities related to complete mathematical modelling of the integrated power system, including wasteheat utilization, are described.
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The initial rates of heat release produced by the reactions of hydrazine and unsymmetrical dimethylhydrazine with nitric acid were determined in a bomb calorimeter under conditions of forced mixing. Fuel-oxidant weight ratio and injection velocity were varied. The rate of heat release apparently depended on the interfacial area between the propellants. Above a narrow range of injection velocities representing a critical amount of interfacial area, the rates reached a maximum and were almost constant with injection velocity. The maximum rate for hydrazine was about 70 percent greater than that for unsymmetrical dimethylhydrazine. The total heat released did not vary with mixture ratio over the range studied.
All-vanadium sulfate redox flow battery systems have a catholyte and an anolyte comprising an aqueous supporting solution including chloride ions and phosphate ions. The aqueous supporting solution stabilizes and increases the solubility of vanadium species in the electrolyte, allowing an increased vanadium concentration over a desired operating temperature range. According to one example, the chloride ions are provided by MgCl2, and the phosphate ions are provided by (NH4)2HPO4.
All-vanadium sulfate redox flow battery systems have a catholyte and an anolyte comprising an aqueous supporting solution including chloride ions and phosphate ions. The aqueous supporting solution stabilizes and increases the solubility of vanadium species in the electrolyte, allowing an increased vanadium concentration over a desired operating temperature range. According to one example, the chloride ions are provided by MgCl 2 , and the phosphate ions are provided by (NH 4 ) 2 HPO 4 .
A technology development and commercial feasibility evaluation is presented for phosphoric acid fuel cells (PAFCs) applicable to electric utility operations. The correction of identified design deficiencies in the control card and water treatment subsystems is projected to be able to substantially increase average powerplant availability from the 63 percent achieved in recent field tests of a PAFC system. Current development work is proceeding under NASA research contracts at the output levels of a multimegawatt facility for electric utility use, a multikilowatt on-site integrated energy generation facility, and advanced electrocatalysts applicable to PAFCs.
Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.
Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.
The development of a battery comprised of bipolar lead acid modules is discussed. The battery is designed to satisfy the requirements of the Advanced Launch System (ALS). The battery will have the following design features: (1) conventional lead acid chemistry; (2) thin electrode/active materials; (3) a thin separator; (4) sealed construction (gas recombinant); and (5) welded plastic frames for the external seal.
Building and optimizing biosynthetic pathways in engineered cells holds promise to address societal needs in energy, materials, and medicine, but it is often time-consuming. Cell-free synthetic biology has emerged as a powerful tool to accelerate design-build-test-learn cycles for pathway engineering with increased tolerance to toxic compounds. However, most cell-free pathway prototyping to date has been performed in extracts from wildtype cells which often do not have sufficient flux towards the pathways of interest, which can be enhanced by engineering. Here, in this study, to address this gap, we create a set of engineered Escherichia coli and Saccharomyces cerevisiae strains rewired via CRISPR-dCas9 to achieve high-flux toward key metabolic precursors; namely, acetyl-CoA, shikimate, triose-phosphate, oxaloacetate, α-ketoglutarate, and glucose-6-phosphate. Cell-free extracts generated from these strains are used for targeted enzyme screening in vitro. As model systems, we assess in vivo and in vitro production of triacetic acid lactone from acetyl-CoA and muconic acid from the shikimate pathway. The need for these platforms is exemplified by the fact that muconic acid cannot be detected in wildtype extracts provided with the same biosynthetic enzymes. We also perform metabolomic comparison to understand biochemical differences between the cellular and cell-free muconic acid synthesis systems (E. coli and S. cerevisiae cells and cell extracts with and without metabolic rewiring). While any given pathway has different interfaces with metabolism, we anticipate that this set of pre-optimized, flux enhanced cell extracts will enable prototyping efforts for new biosynthetic pathways and the discovery of biochemical functions of enzymes.
Grid-level energy storage systems are needed to enable intermittent renewables. Li-ion, Pb-acid battery systems have been implemented but pose safety and environmental risks. Successful grid storage must be safe, reliable, and low-cost.
Grid-level energy storage systems are needed to enable intermittent renewables. Li-ion, Pb-acid battery systems have been implemented but pose safety and environmental risks. Successful grid storage must be safe, reliable, and low-cost.
A simple model of interacting complex systems of species is tested to assess the binding behavior of monomeric nucleic acid and protein components during evolution. Nine representative amino acids are immobilized by the formation of an amide linkage on a prepared chromatographic support. Selective binding of ribonucleoside 5-phosphates in these amino acids is achieved under standardized conditions, and a site-binding model is derived to characterize the binding. It is shown that the binding behavior of the reactants during nucleic acid-protein interactions depends on the nature of the base and the amino acid. The results of the study are assessed as useful for the interpretation of more complex nucleic acid-protein systems and of their role in the evolution of the cell.
Humic acid (HA) is a relatively stable product of organic matter decomposition and thus accumulates in environmental systems. Humic acid might benefit plant growth by chelating unavailable nutrients and buffering pH. We examined the effect of HA on growth and micronutrient uptake in wheat (Triticum aestivum L.) grown hydroponically. Four root-zone treatments were compared: (i) 25 micromoles synthetic chelate N-(4-hydroxyethyl)ethylenediaminetriacetic acid (C10H18N2O7) (HEDTA at 0.25 mM C); (ii) 25 micromoles synthetic chelate with 4-morpholineethanesulfonic acid (C6H13N4S) (MES at 5 mM C) pH buffer; (iii) HA at 1 mM C without synthetic chelate or buffer; and (iv) no synthetic chelate or buffer. Ample inorganic Fe (35 micromoles Fe3+) was supplied in all treatments. There was no statistically significant difference in total biomass or seed yield among treatments, but HA was effective at ameliorating the leaf interveinal chlorosis that occurred during early growth of the nonchelated treatment. Leaf-tissue Cu and Zn concentrations were lower in the HEDTA treatment relative to no chelate (NC), indicating HEDTA strongly complexed these nutrients, thus reducing their free ion activities and hence, bioavailability. Humic acid did not complex Zn as strongly and chemical equilibrium modeling supported these results. Titration tests indicated that HA was not an effective pH buffer at 1 mM C, and higher levels resulted in HA-Ca and HA-Mg flocculation in the nutrient solution.
Analyzer and sampling system continuously monitors nitrogen dioxide concentrations in the feed and tail gas streams of a facility recovering nitric acid. The system, using a direct calorimetric approach, makes use of readily available equipment and is flexible and reliable in operation.
The kinetics of formation of the N-blocked dipeptide, N-acetylglycylglycine, from N-acetylglycyl adenylate anhydride and glycine in aqueous solution at 25 C, and at various PH's are reported. The reaction is of interest in that over a physiologically relevant pH range (6-8), peptide synthesis proceeds more rapidly than hydrolysis, even at those pH's at which this compound becomes increasingly susceptible to base-catalyzed hydrolysis. Under similar conditions, the corresponding unblocked aminoacyl adenylate anhydrides are considerably more unstable, and undergo appreciable hydrlysis in the presence of free amino acid. Because N-blocked aminoacyl adenylate anhydrides serve as model compounds of peptidyl adenylate anhydrides, these results suggest that primitive amino acid polymerization systems may have operated by cyclic reactivation of the peptidyl carboxyl group, rather than that of the incoming amino acid.