Chemistry: Data Analysis for the most suitable material for a backpacker's towel
Chemistry Coursework February 2005
Chemistry: Data Analysis for the most suitable material for a backpacker’s towel.
Method
In groups, we tested three different types of material (named pale blue, dark blue and brown) to find out which one would be more suitable as a backpacker’s towel. The criteria that would make a good backpacker’s towel would be aspects such as a low density, high absorpancy, rapid drying etc.
We weighed the fabric before wetting it and weighing it again. We then hung it on some suspended string and let a rotating fan dry them for fifteen minutes. After this time was up, we weighed the materials again in order to work out the drying rate, the absorpancy and the amount of water lost. We then recorded our results in a table (displayed further on) and analysed these to come to a conclusion.
Results
*(my results are shown in the red font)
The formulas I used were as follows:
Absorbency – wet towel mass – dry towel mass/dry towel mass
Water Lost – Wet mass – Dry mass
Drying Rate – (Water Lost/Time [15]) x 60 to give [g/hr]
Density – Mass Dry/Area to give [g/cm squared]
Interpretation
My two scatter graphs show the absorbency and the drying rate of the three fabrics (pale blue, dark blue and brown).
The absorbency graphs show two anomalies, in comparison with the drying rate graph which shows five anomalies.
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*(my results are shown in the red font)
The formulas I used were as follows:
Absorbency – wet towel mass – dry towel mass/dry towel mass
Water Lost – Wet mass – Dry mass
Drying Rate – (Water Lost/Time [15]) x 60 to give [g/hr]
Density – Mass Dry/Area to give [g/cm squared]
Interpretation
My two scatter graphs show the absorbency and the drying rate of the three fabrics (pale blue, dark blue and brown).
The absorbency graphs show two anomalies, in comparison with the drying rate graph which shows five anomalies.
Part of the criteria that adds to what makes a backpacker’s towel is a high absorbency. The graph shows that the Pale Blue fabric had the highest absorbency and no anomalies, indicating accurate results. The least absorbent fabric is the brown.
The Pale blue fabric absorbency ranges from 2.1 to 4.2. The Dark Blue fabric’s absorbency ranges from 1.7 to 3.2 with one higher anomaly and the brown fabric’s absorbency ranges from 1 to 2.9, with one higher anomaly.
What also makes a good backpacker’s towel is rapid drying, i.e. a high drying rate. My graph shows that the fabric with the highest drying rate is, again, the pale blue one. The ranges I have devised on both graphs do not include the anomalies, and I will go further into this in my evaluation. The pale blue data for drying rate shows a very high anomaly and a very low one, with the data taken into consideration ranging from 14 to 52.5. The Dark Blue fabric ranges from 12.5 to 39.5, with two higher anomalies and the brown fabric ranges from 18.5 to 42.5 with 1 anomaly.
To summarise, the brown fabric had the lowest absorbency, then the dark blue and then the pale blue. The dark blue fabric had the lowest drying rate, then the brown fabric and then the pale blue fabric.
To conclude, the pale blue fabric was identified, from interpreting my graphs, as the best fabric for a backpacker’s towel, so at this point the pale blue fabric would probably be better that the dark blue and brown fabrics for a backpacker’s towel.
Evaluation
There are many factors in this investigation that weren’t done accurately, that could be improved and/or repeated in order to find more accurate results.
The variables themselves and the way in which the data was collected plays a major part in the accuracy of the results. The first thing is that not all the fabrics were of the same size – they had different areas. This means that some could hold more water than others and eventually taking longer to dry. This leads me on to the question that I think should be answered in an evaluation of an investigation or experiment – Was this a fair investigation/experiment? In most investigations and experiments, it is very rare that the data collection methods, results and interpretations are all perfect. There is nearly always room for improvement in all/most investigations and experiments. Improvements are made to ensure accuracy or more accuracy to try and achieve the best results. The way in which the problem of the uneven areas of the fabrics can be solved is ensuring beforehand that all fabrics are the same size and area.
A data collection that can be questioned is also how much water is put into the fabrics. We did not put a specific amount of water into the fabrics. We simply wet the fabric and ringed out the water. Some people may not have rung out their fabrics as much as others, and some may have even put in more water than others. This will affect the results in the fact that, the fabrics that were containing more water when they were hung up to dry will take longer to dry, thus lowering the drying rate. This can be resolved by maybe putting the same amount of water in each piece of fabric and the repetition of this process and the investigation. I suggest repetition, not only for accuracy, but because even if you put in the same amount of water into each fabric, when it is rung out, people will have different amounts of water in their fabric because people are of different strengths and techniques (when ringing it out).
The drying of the fabric, the process was not as accurate as it could have been. Firstly, we had to dry them for 15 minutes. However, people were hanging them up to be dried at different times. This, in a few cases, caused confusion between members of the class as to (a) how long we were drying them for; (b) if they had dried them for long enough or too long (i.e. losing track of time) and (c) which fabrics were theirs. To improve this I would say more attention could have been included and again, the repetition of results. Repetition enough times would give us an average which would overall be more accurate than just one result, accurate or not.
The actual method of drying was also not the best way of ensuring accuracy within the results. We used a rotating fan to dry the fabrics that were pegged upon a straight length of string. Due to the fact the fan was rotating, meant that some materials were getting more air than others, thus drying quicker than the others. Some of the fabrics were also closer to the fan so that could have affected the drying rate. The improvement I suggest for the drying of the fabrics is another method – whether that be a different fan that gives the fabrics equal amounts of air or just letting the fabrics dry naturally for longer. However, we are obviously restricted for time within a one-hour lesson.
With regards to the actual results, there can be some improvements made. Firstly, There was not an even ratio of pale blue, to dark blue, to brown fabrics. There were seven pale blue fabrics handed in and eleven of the dark blue and brown ones. This could have affected the results, particularly with the fact that the pale blue fabric contained the best criteria for a backpacker’s towel.
Also, there were some blanks in the data which could have affected the overall averages and results. To improve this, all data should be collected and recorded and repeat readings would increase accuracy.
Conclusion
My graphs clearly show the grouped data for each fabrics and anomalies.
From these graphs, it can be said that the pale blue fabric would be the material best suitable for a backpacker’s towel because it has a high drying rate and a high absorbency. However this is only from one experiment; this experiment, and so it may be found that if the same experiment was done again, different outcomes would occur. So to conclude, repeating the methods and the experiment, with all variables checked will give more accuracy. Repetition is the key.