ajkoer

A harder avenue that may impress your teacher. 1. Use a solution of AlCl3 and water (need to prepare it, see suggested method below) and insert Magnesium metal in large strips. I have not perform this, but I would suspect that the Mg will replace the Aluminum and deposit it on the metal Magnesium. You may be able to scrap off the Al and get a solution of very thin Aluminum flakes that is much more reactive than any physical method. 2 AlCl3 +3 Mg --> 3 MgCl2 + 2 Al Note: I am suggesting AlCl3 as the MgCl2 formed dissolves into the solution unlike say, Mg(OH)2 or MgCO3. As a student please note that there is ongoing research in the creation of nano-sized aluminum particles given their very special properties. TO PREPARE ALUMINUM CHLORIDE WATER SOLUTION 1. Dissolve an excess of Al as foil in HCl. 2. Remove undissolved Al and carefully add H2O. You should have a solution of mostly AlCl3 in water and some HCl (not an issue as the Mg will convert it to MgCl2, but you can boil the solution to drive off some of the residual HCl to save the Mg).

Hi, came across an old (over a century) process by Lowig described as an easy contact like reaction method in which to prepare NaOH. Heat an excess of Fe2O3 and Na2CO3 to red heat. Dissolve the mass in warm water to recover NaOH. The author noted that the Fe2O3 was regenerated. My attempt at the chemistry: Red Heat Na2CO3 + Fe2O3 + O2 = Fe3O4 + FeCO3 + Na2O Note, the high temperature converts some of the Fe2O3 to Fe3O4 and, in addition, the FeCO3 decomposes and regenerates the iron oxide: 4 FeCO3 + O2 = 2 Fe2O3 + 4 CO2 And finally: Na2O + H20 = 2 NaOH Caution, the reaction may be thermite like in nature (hence "easy"?). Reference is The principles of chemistry – Dmitry Ivanovich Mendeleyev, page 520. a free online Google Book. Link: http://books.google....r&output=reader

OK, I just thought of a possible way (others can comment on the chemistry and perhaps try it). The ingredients are household ammonia and baking soda: NH4OH (in excess) + NaHCO3 = NaOH + NH4HCO3 Let solution react at room temperature in a sealed container as heating in an open vessel may cause loss of NH3 thereby driving the reaction to the left in spite of the use of excess NH4OH. Then, cool the solution and try to precipitate out the ammonium hydrogen carbonate. Evaporate the remaining solution to recover NaOH. Heating the solid residue should removing any NH3HCO3 as at fairly low temperature (under 100C), NH4HCO3 breaks down: NH4HCO3 = NH3 + CO2 + H2O leaving just NaOH (?). If high purity is required, one could heat the household ammonia and pass the NH3 into a solution of baking soda dissolved in distilled water. Good luck.

Yes, Probit Analysis. In essence, the probability of a Bernoulli outcome (like default) is impacted by one or more variables. Not surprisingly, big in the financial literature. See also Reliability Theory, which deals with a variety of stress related failures and appropriate statistical probability density functions.

I am a firm believer in Analysis of Covariance which is basically regression anlysis with continuous and dummy variables. Statistical significant of regression coefficients are hypothesis test on the factors. But why do I favor this approach? Because differences in variables, other than the major one you are testing, can be better detected (meaning a sharper analysis can be performed). Parsimony in variables, is however, important due to confounding and the actual accuracy for which regression coefficients can be estimated. You may wish to build a simulation model using a hypothetical distribution of reputed effects for the variable of interest and ACTUAL DISTRIBUTIONS FOR ALL OTHER VARIABLES IN THE REGRESSION MODEL. This will test and give guidance on the issues of variables construction, number of variables including interaction terms, and assuming a positive effect for the variable of interest, how good is your model design (statistically speaking since you know apriori the strenght of variable of interest hypothetically) in actually uncovering the effect. Talk to some experts in the field to discuss why yield results vary (as a potential variable of interest). For example, sunlight may vary from plot to plot, so create a dummy variable to measure the differences in access to light(or water or wind or nitrogen delivery). Whatever factors you are using MUST be determined in advance (by a prior experiment, for example) and supported by expert opinion. As a final word, which you may of aware of, Analysis of Variance models (ANOVA) can all be transformed into equivalent regression model forms. This is important because if some of the underlying assumptions are invalid (for example, normally distributed error terms), use the regression literature to suggest fixes (for normality issues, Box-Cox Analysis of Transformation) and even Robust Regression models (like Least-Absolute Deviations, Median Regression, etc). Also, from the perspective of Bayesian Regression Analysis, the results go from yes/no in ANOVA to a whole distribution of possible results. Don't be surprised however if this suggestion is resisted by your professor as he may be poorly schooled in regression analysis (and how to fix assumption violations), spreadsheet based skills needed in constructing random variables for your simulation model, and anything about Robust Regression Analysis. By the way, if the simple ANOVA model has good attributes (or weak), your simulation exercise we confirm (or warn you).