Quick quantitative analysis problem

In summary, a 1.00 × 10^-4 M NaF solution can be prepared using two methods. Method 1 involves placing 0.0042 ± 0.0001 g of NaF in a 1000.0 ± 0.3 mL volumetric flask and filling it to the mark with water, resulting in a final concentration of 1.0*10^-7 ± 2. * 10^-9 M with a relative uncertainty of 2%. Method 2 involves placing 0.0420 ± 0.0001 g of NaF in a 100.00 ± 0.08 mL volumetric flask, diluting it by a factor of 100, and filling
  • #1
Xelb
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Homework Statement


A 1.00 × 10^-4 M NaF solution can be prepared in several ways. Two methods are listed below. The formula weight of NaF is 41.9984 ± 0.0003.

For each method use propagation of error methods to determine the absolute and relative uncertainty in the final concentration.

Method 1
0.0042 ± 0.0001 g of NaF is placed in a 1000.0 ± 0.3 mL volumetric flask. The flask is filled to the mark with water

Method 2
0.0420 ± 0.0001 g of NaF is placed in a 100.00 ± 0.08 mL volumetric flask. The flask is filled to the mark with water. This solution is diluted 100:1 by pipeting 1.000± 0.003 mL of the solution to another 100.00 ± 0.08 mL volumetric flask and then filling to the mark with water.

For each method use propagation of error to determine the absolute and relative uncertainty in the final concentration.

Which method has the most uncertainty? Which individual measurement introduces the most uncertainty?



Homework Equations



Molarity = mol/L

moles = mass/MW

Final Concentration = (Initial Concentration)*(Initial Volume)/(Final Volume)


The Attempt at a Solution


Note: When writing the absolute uncertainties, I purposely write "2." because it needed to be just one significant figure, and writing down just "2" means that there are no significant figures.

For method one, I took the grams of NaF and converted it to moles, which turned out to be 1.0*10^-4 ± 2. * 10^-6 when propagation of uncertainty and the correct significant figures were used. Once I found moles, I then converted to molarity using the following: (1.0*10^-4 ± 2. * 10^-6 )/(1.0*10^3 ± 0.3L) and arrived at 1.0 * 10^-7 ± 2. * 10^-9 M.

Is 1.0 * 10^-7 ± 2. * 10^-9 M the final concentration in this case? I know the problem says it needs to be 1.0 * 10 ^-4 M, but it wanted the percent relative uncertainty, which ended up being 2%. I'm not really sure what else to do regarding method one.



For method two, it seemed a bit easier, but I'm not even sure I did it right. I made use of the following formula: Final Concentration = (Initial Concentration)*(Initial Volume)/(Final Volume).

Since I had found the initial concentration from method one, I just plugged that in and multiplied it by the pipet volume and divided it by the volume of the flask. I arrived at a final concentration, again different from the value that the problem originally stated, of 1.0*10^-9 ± 2. * 10^-11 M with a relative uncertainty of 2% as well. Not sure what else to do...
 
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  • #2
As a general remark, the value itself should be given with the same "precision" and the same decimal power as the uncertainty. Don't write 1*10-6 ± 1*10-8, write 100*10-8 ± 1*10-8 or 1.00*10-6 ± 0.01*10-6 or (100±1)*10-8.For method 1, you mixed ml and l, so your result is off by a factor of 1000.
2% as relative uncertainty is right.

Since I had found the initial concentration from method one
You do not have that, at least not with the correct uncertainty. The values are different here.
And there is another factor of 100 wrong here.
 

1. What is quick quantitative analysis?

Quick quantitative analysis is a method used in scientific research to quickly and efficiently collect and analyze data. It involves using statistical techniques to analyze numerical data and draw conclusions about a specific research topic.

2. Why is quick quantitative analysis important?

Quick quantitative analysis is important because it allows scientists to quickly and accurately gather and analyze data, which can help them make informed decisions and draw accurate conclusions about their research. It also allows for comparisons between different variables and can help identify patterns and trends in data.

3. What are the steps involved in quick quantitative analysis?

The steps involved in quick quantitative analysis typically include identifying a research question, selecting a sample, collecting data, analyzing the data using appropriate statistical methods, and interpreting the results. It is important to follow a systematic and structured approach to ensure accurate and reliable results.

4. What are some common statistical methods used in quick quantitative analysis?

Some common statistical methods used in quick quantitative analysis include descriptive statistics, correlation analysis, regression analysis, and hypothesis testing. These methods help to summarize and analyze data, identify relationships between variables, and determine if there are significant differences between groups.

5. How can I ensure the accuracy and reliability of my quick quantitative analysis?

To ensure the accuracy and reliability of quick quantitative analysis, it is important to use appropriate statistical methods, carefully select and measure variables, use a representative sample, and avoid bias in data collection and analysis. It is also important to consider the limitations of the data and discuss any potential sources of error or uncertainty in the results.

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