# All-iron hybrid flow batteries with in-tank rebalancing

A semi-empirical modeling study demonstrating an all-iron battery operating at 100 mA/cm2 in a sealed system with continuous, in-tank rebalancing.

http://jes.ecsdl.org/content/166/10/A1725.full

# ECS Poster (Phoenix, AZ)

A poster made for the 228th ECS meeting in Phoenix, Arizona. This work describes a new approach based on sealed systems and in-tank hydrogen-ferric ion recombination.

# Introduction to the COCO Process Simulator

A presentation introducing the COCO simulator (www.cocosimulator.org) for chemical engineering students. (here). Most examples shown are from the Koretsky textbook, Engineering and Chemical Thermodynamics.

# Animating data in Mathematica

In Mathematica, large sets of data can easily be animated using a few lines of code. One approach is shown below:

rawdata =
Import[“/Documents/nervesignals/nervesignals.atf”,
“Data”];

animationtable =
Table[ListLinePlot[
ParallelTable[
Transpose[{rawdata[[All, 1]][[10000 ;; 130000]],
Standardize[
rawdata[[All, i]][[10000 ;; 130000]]] + (22 i)}], {i, 2,
7}], Frame -> True, Axes -> False, AxesLabel -> False,
FrameTicks -> None,
PlotRange -> {{(j – 2.5), (j + 2.5)}, {20, 165}},
ImageSize -> 750, PlotStyle -> Thick], {j, 3.5, 9.5, 0.08}];

Export[“/animation/myanimation.gif”,
animationtable, “DisplayDurations” -> 0.15]

A similar example for animating cyclic voltammetric data:

# Graphene metal oxide composite supercapacitor electrodes

I was able to take part in this supercapacitor research at NASA Ames, the result of which was published in a special edition of the Journal of Vacuum Science and Technology B. The article can be found here

# Electrochemical Society Meeting Paper

I was a co-author on research carried out at the Ames research center. The abstract can be found here

# BZ Chaos Report

My report about nonlinear physics and chaos in the belousov-zhabotinsky reaction, as well as simulation methods in python using packages in the enthought python distribution

# Flux Modulation with Surfactants

This paper describes some experiments about using surfactants (specifically, CTAB), in order to modify the electrostatic charges at pore walls and therefore affect ion transport

# BZ 3D Attractor Pictures

These were made using a combination of python and gnuplot. Initially it was all python, but I found that gnuplot was much faster and more stable than MayaVi in this case. Not sure if that’s always true.

How these were plotted:

The data points were generated from the BZ-simulation equation shown in my paper. I have tried plotting these huge data sets in MayaVi, Mathematica, and gnuplot. There was no detailed quantitative comparison as to which was the best, and each has its own advantages.

Since the simulation (data crunching) was done in python, MayaVi had the advantage of being fully integrated into the python code. However, it tended to be very slow with the large sets of data.

Mathematica made some very nice-looking plots, but was also pretty slow with large sets and also I was unable to figure out how to remove the 3D frame around the image.

The overall winner in was gnuplot, which excels primarily in 2D plots but has some basic 3D capabilities. Most importantly, it’s probably ten times as fast as the others for very large data sets. Typical gnuplot commands for these plots were as follows:

\$ gnuplot

gnuplot> unset border; unset tics

gnuplot> set key off

gnuplot> unset colorbox

gnuplot> set view

gnuplot> set size 1.2,1.2

gnuplot> set palette rgbformulae 30,31,5

gnuplot> splot ‘mydata.tsv’ using 1:2:3 with points palette pointsize 0.01 pointtype 7

mydata.tsv is just a text file with x,y,z data separated with spaces

# SJSU Thesis Template in LaTeX

This is an un-official work in progress. Believed to meet all the guidelines. Nevertheless, there is obviously no warranty or guarantee that there aren’t any potential formatting issues.

Here are the main files:

the complete project (zip file)

the custom BibTeX style file