代写Experiment 2: Gas Stoichiometry帮做Python语言

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Experiment 2: Gas Stoichiometry

Report Sheet

You are welcome to seek guidance on the content of this lab report from your TA during your lab session and from the lab coordinator during lab office hours. You are also welcome to work constructively with your peers on the general content and understanding of the material. However, the work submitted here in this report sheet must be your own. For more details on academic integrity and potential penalties see the Code of Behaviour on Academic Matters.

I certify that this submitted laboratory report represents entirely my own efforts. I have read and understand the University of Toronto policies regarding, and sanctions for, academic honesty.

I regret that I violated the Code of Behaviour on this assessment and would like to admit that now so that I can take responsibility for my mistake.

Portions of this lab report will be auto-graded, so please follow the formatting in this document to ensure your results can be properly graded.

Required Details	Response
Student Name
Student Number
PRA Section Code (e.g. 0101, 0201…)
Demo Group
Workstation Number

Support for completing this report sheet can be found on the “After Lab 2” page on Quercus

1. Write the balanced chemical equation for the reaction between HCl and Mg.

2. Did the pH change as the reaction occurred? Explain this observation. (Hint: What do you think is the limiting reactant?)

3. Write the balanced chemical equation for the reaction that occurred when the hydrogen bubbles were lit on fire.

4. Complete the table below using your experimental data. Some of the numbers in this table will be autograded and so include values only, do not include units, and if you would like to use scientific notation use exponential notation (e.g. 1.2 x 10^-5 is 1.2e-5 with no spaces between the "e" and the numbers).

Temperature (^oC)	Mass of Mg(s) (g)	Volume of 1.0 M HCl (mL)	Volume of H₂(g) (mL)	Moles of H₂(g) (mol)	Percent Yield (%)

Show your calculation for the number of moles of H₂(g):

Show your calculation for percent yield:

5. For the data collected by your lab section, what is the mean and standard deviation of the percent yields of hydrogen gas? The values in this table will be autograded and so include values only, do not include units, and if you would like to use scientific notation use exponential notation (e.g. 1.2 x 10^-5 is 1.2e-5 with no spaces between the "e" and the numbers).

Note: Before you report your values below remember standard deviation is a measure of the spread in the data and so can tell you something about your confidence in the mean value and the number of appropriate significant figures. See the “After Lab 2” page on Quercus for more details.

Mean (%)	Standard Deviation (%)

6. Were some of the yields calculated in your lab section above 100%? This is not physically possible; however when we make these measurements, we are using volume as a measure of the number of moles of gas present. How might temperature confound the relationship between volume and moles of gas in this experimental setup?

7. In the second part of the experiment temperature is less of an issue as the reaction does not exchange as much heat with the solution. If we take temperature out of the equation, the biggest confounding factor is gas loss, either from leaks in the system or the trapping of gas in the tubing that was filled with water. If gas were lost during the experiment, would you expect the calculated atomic mass of the metal to be higher or lower than the actual atomic mass? Explain your answer.

8. Complete the table below using your experimental data. Some of the numbers in this table will be autograded and so include values only, do not include units, and if you would like to use scientific notation use exponential notation (e.g. 1.2 x 10^-5 is 1.2e-5 with no spaces between the "e" and the numbers).

Unknown Letter	Mass of sample used (g)	Volume of CO₂(g) (mL)	Moles of CO₂(g) (mol)	Atomic Mass of unknown metal (g/mol)	Identity of unknown metal

Show your calculation for the number of moles of CO₂(g):

Show your calculation of the atomic mass of the unknown metal:

9. Now that you have identified your unknown, what was the yield for the decomposition reaction of your metal carbonate into carbon dioxide gas? The yield put in the leftmost column will be autograded and so include values only, do not include units, and if you would like to use scientific notation use exponential notation (e.g. 1.2 x 10^-5 is 1.2e-5 with no spaces between the "e" and the numbers).

Yield (%)	Calculation

10. Complete the table below using the pooled data from your lab section. What is the identity of metals A, B, and C? The values in this table will be autograded and so include values only, do not include units, and if you would like to use scientific notation use exponential notation (e.g. 1.2 x 10^-5 is 1.2e-5 with no spaces between the "e" and the numbers).

Unknown	Mean	Standard Deviation	Identity
Metal A Atomic Mass (g/mol)
Metal B Atomic Mass (g/mol)
Metal C Atomic Mass (g/mol)

11. Look at the pooled data and see if there is a value that seems to be drastically changing one of the mean values calculated in question 10. While there might more than one point that is far from the mean, choose a single data point. Remove this value and report the new mean and standard deviation below for this metal (note this should be easy in Excel as you can delete the value and the mean and standard deviation will automatically update).

Value removed (include 2 decimal places)	Assigned metal (A, B, C)	New mean atomic mass (g/mol)	New standard deviation (g/mol)

12. Does the removal of the value in question 11 bring the mean value closer to that of the metal you assigned above?

13. The value removed in question 11 could be an outlier, a value that doesn’t belong in the pool of data being analyzed. An outlier can be the result of an error (either clerical or experimental) or simply a value that lies an abnormal distance away from the mean. There are statistical tests to determine if a value is an outlier which we won’t explore those here. Instead we will reflect on how removing outliers can be a part of a data cleaning protocol to produce data that is ready for further analysis (similar to removing the -999 concentration values from the air quality data set in Lab 1).

An important aspect of any data cleaning protocol is that it is reproducible. Can you explain the thought process that led you to choose to remove the data point in question 11? If so, provide a brief explanation that you think could be followed by another analyst. If not,explain why you feel this approach won’t work for the dataset in question.