Transient non-isothermal model of a polymer electrolyte fuel cell

In this paper we present a one-dimensional transient model for the membrane electrode assembly of a polymer-electrolyte fuel cell. In earlier work we established a framework to describe the water balance in a steady-state, non-isothermal cathode model that explicitly included an agglomerate catalyst layer component. This paper extends that work in several directions, explicitly incorporating components of the anode, including a micro-porous layer, and accounting for electronic potential variations, gas convection and time dependence. The inclusion of temperature effects, which are vital to the correct description of condensation and evaporation, is new to transient modelling. Several examples of the modelling results are given in the form of potentiostatic sweeps and compared to experimental results. Excellent qualitative agreement is demonstrated, particularly in regard to the phenomenon of hysteresis, a manifestation of the sensitive response of the system to the presence of water. Results pertaining to pore size, contact angle and the presence of a micro-porous layer are presented and future work is discussed

Quantifying Performance of Bipedal Standing with Multi-channel EMG

Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spinal stimulation and rehabilitative robots (such as exoskeletons) in real time feedback. To study the potential for automated electromyography (EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients undergoing electrical spinal cord stimulation using both video and multi-channel surface EMG recordings during spinal stimulation therapy sessions. The quality of standing under different stimulation settings was quantified manually by experienced clinicians. By correlating features of the recorded EMG activity with the expert evaluations, we show that multi-channel EMG recording can provide accurate, fast, and robust estimation for the quality of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis shows that the total number of EMG channels needed to effectively predict standing quality can be reduced while maintaining high estimation accuracy, which provides more flexibility for rehabilitation robotic systems to incorporate EMG recordings

A transient PEMFC model with CO poisoning and mitigation by O2 bleeding and Ru-containing catalyst

In this paper we present a transient, fully two-phase, non-isothermal model of carbon monoxide poisoning and oxygen bleeding in the membraneelectrode assembly of a polymer electrolyte fuel cell. The model includes a detailed description of mass, heat and charge transport, chemisorption,electrochemical oxidation and heterogeneous catalysis (when oxygen is introduced). Example simulation results demonstrate the ability of themodel to qualitatively capture the fundamental features of the poisoning process and the extent of poisoning with respect to channel temperatureand concentration. Further examples show how the multi-step kinetics can interact with other physical phenomena such as liquid-water flooding,particularly in the anode. Carbon monoxide pulsing is simulated to demonstrate that the complicated reaction kinetics of oxygen bleeding canbe captured and even predicted. It is shown that variations in the channel temperature have a convoluted effect on bleeding, and that trends inperformance on relatively short time scales can be the precise opposite of the trends observed at steady state. We incorporate a bi-functionalmechanism for carbon monoxide oxidation on platinum–ruthenium catalysts, demonstrating the marked reduction in the extent of poisoning, theeffect of variations in the platinum–ruthenium ratio and the influence of temperature. Finally, we discuss the implications of the results, extensionsto the model and possible avenues for experimental work

Economic analysis of ethanol production from biomass using a hybrid thermal/biological conversion process

The objective of this case study is to examine the economics of ethanol production using the Waterloo Fast Pyrolysis process integrated with a fermentation step. The raw materials considered are wood and switchgrass. The pyrolytic ethanol process is evaluated in terms of capital costs, operating costs, and ethanol production costs for each type of feedstocks used. Sensitivity analyses are carried out to study the uncertainties of feedstock costs, ethanol production rates and ethanol yields on ethanol production costs. The economics of pyrolytic ethanol is compared to two other widely-known processes: simultaneous saccharification and fermentation, and dilute acid hydrolysis and fermentation. This analysis indicates that the pyrolytic ethanol process is comparable with the other two processes and suggests that it should be considered for further development

Training Set Optimization in an Artificial Neural Network Constructed for High Bandwidth Interconnects Design

In this article, a novel training set optimization method in an artificial neural network (ANN) constructed for high bandwidth interconnects design is proposed based on rigorous probability analysis. In general, the accuracy of an ANN is enhanced by increasing training set size. However, generating large training sets is inevitably time-consuming and resource-demanding, and sometimes even impossible due to limited prototypes or measurement scenarios. Especially, when the number of channels in required design are huge such as graphics double data rate (GDDR) memory and high bandwidth memory (HBM). Therefore, optimizing the training set selection process is crucial to minimizing the training datasets for developing an efficient ANN. According to rigorous mathematical analysis of the uniformity of the training data by probability distribution function, optimization flow of the range selection is proposed to improve accuracy and efficiency. The optimal number of training data samples is further determined by studying the prediction error rates. The performance of the proposed method in terms of accuracy is validated by comparing the scattering parameters of arbitrarily chosen strip and microstrip type GDDR interconnects obtained from EM simulations with those predicted by ANNs using default and the proposed training-set selection methods

FePt nanodot arrays with perpendicular easy axis, large coercivity, and extremely high density

Ordered FePt nanodot arrays with extremely high density have been developed by physical vapor deposition using porous alumina templates as evaporation masks. Nanodot diameter of 18 nm and periodicity of 25 nm have been achieved, resulting in an areal density exceeding 1 x1012 dots/in2. Rapid thermal annealing converts the disordered fcc to L10 phase, resulting in (001)-oriented FePt nanodot arrays with perpendicular anisotropy and large coercivity, without the need of epitaxy. High anisotropy and coercivity, perpendicular easy axis orientation and extremely high density are desirable features for future magnetic data storage media applications

A Network of microRNAs Acts to Promote Cell Cycle Exit and Differentiation of Human Pancreatic Endocrine Cells.

Pancreatic endocrine cell differentiation is orchestrated by the action of transcription factors that operate in a gene regulatory network to activate endocrine lineage genes and repress lineage-inappropriate genes. MicroRNAs (miRNAs) are important modulators of gene expression, yet their role in endocrine cell differentiation has not been systematically explored. Here we characterize miRNA-regulatory networks active in human endocrine cell differentiation by combining small RNA sequencing, miRNA over-expression, and network modeling approaches. Our analysis identified Let-7g, Let-7a, miR-200a, miR-127, and miR-375 as endocrine-enriched miRNAs that drive endocrine cell differentiation-associated gene expression changes. These miRNAs are predicted to target different transcription factors, which converge on genes involved in cell cycle regulation. When expressed in human embryonic stem cell-derived pancreatic progenitors, these miRNAs induce cell cycle exit and promote endocrine cell differentiation. Our study delineates the role of miRNAs in human endocrine cell differentiation and identifies miRNAs that could facilitate endocrine cell reprogramming

Investigation of the effect of double-walled carbon nanotubes on the curing reaction kinetics and shear flow of an epoxy resin

In this article, the effect of combined temperature-concentration and shear rate conditions on the rheology of double-walled carbon nanotubes (DWCNTs)/RTM6-Epoxy suspension was investigated to determine the optimum processing conditions. The rheological behavior and cure kinetics of this nanocomposite are presented. Cure kinetics analysis of the epoxy resin and the epoxy resin filled with DWCNTs was performed using Differential Scanning Calorimeter (DSC) and parameters of the kinetics model were compared. The DWCNTs have an acceleration effect on the reaction rate of the epoxy resin but no significant effect is noted on the glass transition temperature of the epoxy resin. This study reveals that the effect of shear-thinning is more pronounced at high temperatures when DWCNTs content is increased. In addition, the steady shear flow exhibits a thermally activated property above 60°C whereas the polymer fluid viscosity is influenced by the free volume and cooperative effects when the temperature is below 60°C