Chemiluminescence of Luminol

Introduction

In this experiment, luminol was mixed with hydrogen peroxide (an oxidising agent) resulting in chemiluminescence. The experimentis designed to see how the duration of luminescence is affected by varying temperatures. It has been made to be deliberately inaccurate, so another objective would be to see whether subjective observations can be improved by repetition and averaging.

Chemiluminescence is the production of light from a release of energy in a chemical reaction without the aid of heat. The light produced is due to electrons being given an excess of energy and this energy being released as the electrons revert to their ground state, a luminescent light being given off as a result.

An important use of this reaction is in forensic science where luminol and hydrogen peroxide are used to highlight samples of blood at a crime scene. The two chemicals react readily due to the iron present in haemoglobin, giving off a luminescent glow as a result.

Chemiluminescence occurs even in living organisms where it is termed bioluminescence. Using fireflies as an example, a reaction where luciferin combines with adenosine tri-phosphate (ATP) and reacts with the enzyme luciferase. The action of the enzyme acting on luciferase simulates something similar to luminol and a peroxide being catalysed by a transition metal catalyst, providing electrons with enough energy to reach a higher energy level which then gives off light as they relax back to their ground state.

Reaction pathway of luminol

2H2O22H2O + O2(g) – Decomposition of Hydrogen Peroxide

In this experiment we used luminol and hydrogen peroxide solution. When the two react, a dianion is from the peroxide reacting with the luminol. As hydrogen peroxide reacts, oxygen is evolved which then reacts with the dianion to produce an unstable peroxide; the instability of the peroxide causes it to break down immediately and lose nitrogen, producing 3-aminopthalate. (above)

Experimental

A set of five test tubes were prepared and labelled with a letter(A-E) and their temperatures; 220C (room temperature which varied between groups), 250C, 350C, 60C and 400C. 250C, 350C and 400C were conducted in water baths whereas 60C used an ice bath. Luminol solution (8mL) was measured using a beaker and a measuring cylinder before being placed into the test tubes in their appropriate temperatures. Whilst the test tubes were left to equilibrate to their individual environments volumes of hydrogen peroxide solution (2mL) were measured using a measuring cylinder and beaker.

The temperature of each test tube was ...

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Experimental

The temperature of each test tube was measured before being removed and the hydrogen peroxide added. A stopwatch timer was started as soon as the two solutions came into contact, stopping once the luminescent glow had ceased to show. The time was recorded and the technique repeated for the remainder of the samples.

Results and Discussion

The graph above is a plot of temperature against. Before the anomalies were removed, there raw data was difficult to interpret due to so many results, making it hard to distinguish a trend. Even after all the anomalies were removed, a trend becomes slightly more clear but because the results are so scattered, it is still difficult to determine a definite trend.

As shown in the graph above, the trend is different to what would be expected as the times it takes for the luminescence to fade increases with temperature. It would be expected for time to decrease with temperature as heat is being provided to a chemical reaction (which is not exothermic), increasing the rate of reaction.

The graph above shows a plot of temperature against time using the average values of readings with more than one result. After taking the averages, we can still see that the results are scattered and the correlation is very weak, showing that the results are unreliable and imprecise.

Table showing error values of all data.

Absolute Errors (calculated by – Range ÷ 2)

Absolute errors compare the experimental value to the original value, creating an estimate value at which you would expect your values to lie within. This highlights the physical errors made in the experiment and used to compensate for equipment errors.

Relative Errors (calculated by – (Absolute Error ÷ Mean) x 100)

Relative error compares the error between the observational value and the correct value, expressing the difference in the form of a percentage.

Standard deviation

Standard deviation shows how spread out the results are from the mean. Standard deviation is used to calculate the middle value which can be calculated by using subtracting the smallest value from the largest value, however, this is quite inaccurate. Standard deviation is a more accurate process which also determines how much the data deviates from the mean value.

As mentioned before the experiment is designed to test whether repetition and averaging can overcome subjective observations. Looking at how far the results deviate from each other and looking at the errors shows that the results are highly inaccurate. This is due to several reasons.

The biggest source of error would be individual, subjective error. The experiment was conducted in groups of more than one where each member of the group would have a different perception as to when the glow had stopped, resulting in some groups having values too large and other groups having values too small. For the entire set of results, this would be classed as a systematic error.

Another source of error would be the members of the groups operating the stop watches. They could have started or stopped the watch too late or too soon, affecting the group’s overall accuracy and precision by creating anomalous results. Furthermore, error could have been increased due to equipment functioning incorrectly; however, it is more likely for the inaccuracies to be down to operator error instead of equipment error.

There was also an error in procedure, the test tubes in the water baths in particular. This is because as they were carried over from the water batch to the work bench the loss of heat was not accounted for, so the reading for that particular temperature would be incorrect.

The laboratories also had varying levels of light, resulting in some students having more accurate results due to being in darker parts of the laboratory where the change would be easier to notice, contributing to the pattern of fluctuating data.

After taking all these points and the results of the practical into consideration, this provides evidence that repeating and averaging data is insufficient to offset subjective error.

Conclusion

It can be concluded that the experiment is heavily inaccurate and imprecise mainly due to the absolute errors, relative errors and standard deviation being far too high. These were all calculated by the mean which was the first value to be calculated and because the mean influenced all of the error values based on inaccurate data, inaccurate values were produced as a result.

The standard deviation highlights how inaccurate the experiment was. Deviation values range from the lowest, 4.344, to the highest, 296.784. This shows that the results are far away from the mean and that despite repeating the experiment many times, it did not balance out subjective error.

The relative error is unreliable as it compares, in this case, the mean to the absolute error. Considering both were prone to corruption from anomalous results, this cannot be used to calculate the level of error in the results accurately.

Absolute error measures equipment error. Since all students used similar apparatus, there is no apparatus error because it cancels out between groups, therefore differences occur because of operator error. Absolute error shows that 300C was the most accurate temperature and 10C was the least accurate. The small error value recorded from the 300C shows that the temperature given from the water bath was maintained consistently in between groups whereas the 10C fluctuated.

If the experiment was to be repeated, it would be best to use a machine or computer to determine the absorbance, giving consistent readings not affected by subjective observation. For example, analysing samples with photomultiplier based detection (PMT) single photon counting systems allows the levels of light to be precisely and accurately recorded.

References:

http://www.3dchem.com/molecules.asp?ID=334

Luminol reaction pathway:

Schaum’s A-Z Biology by Bill Indge. Published by Hodder & Stroughton Educational in London.