Titration of Acids and Bases Scott Buzby home酸和碱史葛芭兹比家居滴定.pptVIP

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Titration of Acids and Bases Scott Buzby home酸和碱史葛芭兹比家居滴定.ppt

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Titration of Acids and Bases Scott Buzby home酸和碱史葛芭兹比家居滴定

Experiment 20 Page 215 Dr. Scott Buzby Ph.D. An acid-base titration is the determination of the concentration of an acid or base by exactly neutralizing the acid/base with an acid or base of known concentration This allows for quantitative analysis of the concentration of an unknown acid or base solution It makes use of the neutralization reaction that occurs between acids and bases and the knowledge of how acids and bases will react if their formulas are known The burette should be rinsed with the standard solution and an Erlenmeyer flask with distilled water Add a known volume of the unknown to the Erlenmeyer flask, along with a small amount of the indicator chosen The known solution should then be allowed out of the burette, into the conical flask. At this stage we want a rough estimate of the amount of this solution it took to neutralize the unknown solution Three more titrations should be performed, this time more accurately, taking into account roughly where the end point will occur. The readings on the burette at the end point should be recorded, and averaged to give a final result. The end point is reached when the indicator just changes color permanently Molarity Molarity of Unknown % KHP in Sample The standard deviation of a statistical population, a data set is the square root of its variance Standard deviation is a widely used measure of variability or dispersion, it shows how much variation there is from the average (mean) A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data are spread out over a large range of values Consider a population consisting of the following values: There are eight data points in total, with a mean (or average) value of 5: To calculate the population standard deviation, first compute the difference of each data point from the mean, and square the result: Next divide the sum of these values by the number of values and take the