Are different surfaces (stone tiles, red brick, relatively smooth rubber staircase, bumpy rubber track, grass turf) in a school (Taipei American School) on sunny and rainy days (wet and dry ground) dangerous for high school students in flip-flops?
Independent Variables Type of surface, and whether that surface is wet or dry.
Dependent Variables The friction of a flip-flop on different surfaces and wetness levels. (Fake grass, rubber track, bricks, stone tiles, plastic stairs surface)
Controls Person pulling on the dual range force sensor - one person was in charge of pulling the sensor attached to the flip-flop, as the speed at which the flip-flop is being pulled needs to be approximately the same for all trials. Changing the person would result in a greater change of the speed, therefore effecting the resulting static fiction
Distance the flip-flop travels (1 m) - A meter stick was placed at the side of the flip-flop while being pulled to ensure all trials have the same length of surface for static friction to be recorded.
Length of the string being used to attach the flip-flop to the sensor - This was kept at 50cm. Changing the length of the string would result in inconsistency in the data, as different string lengths could yield different static frictions
Flip-flop - The flip-flop being used to test the friction on different surfaces and levels of wetness was kept constant, to isolate the independent variables from the dependent.
Weight put on the flip-flop - The weight put on the flip-flop was used to create more friction, as the flip-flop itself is too light to yield meaningful results; and by using the same weight, the amount of normal force will also stay constant.
Materials
Flip-flop, size seven (86.056 g)
String (50 cm)
1 weight (1 kg)
Dual Range Force Sensor
Laptop with Vernier Loggerpro Programme
1 Metre stick
Excess tap water (using a bucket, hose, or water bottle)
Procedure
Set up experiment: Tie a string connecting the flip-flop to the dual range force sensor. Hook a 1 kg mass weight to the flip-flop. Set up the Vernier LoggerPro Programme.
Go to the first surface (fake grass, rubber track, bricks, stone tiles, or plastic stairs). Place a metre stick on the ground. Drag the dual range force sensor with the flip-flop and weight for 1 metre, while recording the friction levels with the LoggerPro Programme. Perform 5 trials.
Pour water on the surface that was just tested and perform 5 more trials. Make sure the surface is constantly wet (i.e. if it absorbs water, or if it is at an uneven surface).
Repeat the same process for all surfaces
The grass turf surface
The rubber staircase surface
The stone tiles surface
Sample Graphs
Since the objective of the lab was to find the static friction, the data points we selected were the first instances where a "peak" is formed, as denoted by the red dots (the black circles are simply there to show the points clearly)
Data Table
Static Friction of Different Surfaces
Static Friction (N)
Trial 1
Trial 2
Trial 3
Trial 4
Trial 5
Avg
Trial 1
Trial 2
Trial 3
Trial 4
Trial 5
Avg
Stone Tiles
11.6
11.4
10.9
10.7
8.6
10.6
10.5
12.2
10.6
10.1
10.8
10.8
Red Brick Tiles
9.18
9.32
9.47
8.9
8.9
9.15
7.30
7.32
7.03
7.10
6.54
7.06
Rubber Layered Staircase
8.50
8.5
8.5
8.5
8.5
8.50
3.56
3.56
3.56
3.56
3.56
3.56
Rubber Track
15.1
13.2
15.2
14.4
12.4
14.1
13.00
12.2
11.4
10.5
12.0
11.8
Turf / Fake Grass
6.91
9.15
7.84
7.00
6.49
7.50
8.06
8.3
7.13
8.03
5.67
7.44
Averages of Static Friction Coefficient
Friction Coefficient (N)
Dry
Wet
Stone Tiles
1.00
1.02
Red Brick Tiles
0.86
0.67
Rubber Layered Staircase
0.8
0.34
Rubber Track
1.33
1.12
Turf / Fake Grass
0.71
0.70
1086.1g = 10.6N
Sample Calculations
Normal force = 1 kg mass + 0.086 kg flip flop = 1.086 kg
Coefficient of static friction = static friction / normal force
Possible Errors
The volume of water could have been different in other surfaces.
more than 5 trials were implemented for each variable, because sometimes the data seemed off, but only 5 data trials were used - could have affected the results/conclusion?
Random leaves on the ground under the shoe
Method Limitations
Selective data bias (re-trying data when the results were not reasonable)
Inefficiency - the actual procedure of the experiment only requires a maximum of three experimenters while six people were performing the actual experiment.
The flip-flop was wet after being dragged on a wet surface, then used in a dry surface trial while it was still wet.
Conclusion
According to the data, the lower the coefficient of static friction, the less safe it is for students to walk on that surface. The smooth rubber staircase had the lowest coefficient of static friction when it was wet (0.34 N), while the rubber bumpy track had the highest coefficient of static friction when it was dry (1.33 N). A possible explanation for this might be because the smooth rubber staircase did not absorb any water when it was wet and was relatively smooth surface, thereby creating a film of fluid. On the other hand, the track had an uneven and very porous surface with many small indentations, allowing water to seep into those spaces, retaining its roughness. The grass turf had the smallest difference in coefficient of static friction between wet and dry, perhaps because the small rubber particles under the fake grass absorbed much of the water and cleared the surface (that is walked on) of most of the water, making it less slippery. The surfaces that had more friction were less slippery and more safe to walk on when it was raining or not.Another area where potential errors could occur would be our interpretation of the graph. As the data points were often quite scattered and magnifying the graph proved a bit of a challenge, it was difficult to determine where exactly the first instance of static friction occurred. This could be improved by perhaps using a different, more precise software to graph the data points obtained from Logger Pro, or if we could somehow figure out a way to manipulate the graph for better results.
Independent Variables
Type of surface, and whether that surface is wet or dry.
Dependent Variables
The friction of a flip-flop on different surfaces and wetness levels.
(Fake grass, rubber track, bricks, stone tiles, plastic stairs surface)
Controls
Person pulling on the dual range force sensor - one person was in charge of pulling the sensor attached to the flip-flop, as the speed at which the flip-flop is being pulled needs to be approximately the same for all trials. Changing the person would result in a greater change of the speed, therefore effecting the resulting static fiction
Distance the flip-flop travels (1 m) - A meter stick was placed at the side of the flip-flop while being pulled to ensure all trials have the same length of surface for static friction to be recorded.
Length of the string being used to attach the flip-flop to the sensor - This was kept at 50cm. Changing the length of the string would result in inconsistency in the data, as different string lengths could yield different static frictions
Flip-flop - The flip-flop being used to test the friction on different surfaces and levels of wetness was kept constant, to isolate the independent variables from the dependent.
Weight put on the flip-flop - The weight put on the flip-flop was used to create more friction, as the flip-flop itself is too light to yield meaningful results; and by using the same weight, the amount of normal force will also stay constant.
Materials
Procedure
Sample Graphs
Since the objective of the lab was to find the static friction, the data points we selected were the first instances where a "peak" is formed, as denoted by the red dots (the black circles are simply there to show the points clearly)
Data Table
Static Friction of Different Surfaces
Averages of Static Friction Coefficient
Sample Calculations
Normal force = 1 kg mass + 0.086 kg flip flop = 1.086 kg
Coefficient of static friction = static friction / normal force
Possible Errors
Method Limitations
Conclusion
According to the data, the lower the coefficient of static friction, the less safe it is for students to walk on that surface. The smooth rubber staircase had the lowest coefficient of static friction when it was wet (0.34 N), while the rubber bumpy track had the highest coefficient of static friction when it was dry (1.33 N). A possible explanation for this might be because the smooth rubber staircase did not absorb any water when it was wet and was relatively smooth surface, thereby creating a film of fluid. On the other hand, the track had an uneven and very porous surface with many small indentations, allowing water to seep into those spaces, retaining its roughness. The grass turf had the smallest difference in coefficient of static friction between wet and dry, perhaps because the small rubber particles under the fake grass absorbed much of the water and cleared the surface (that is walked on) of most of the water, making it less slippery. The surfaces that had more friction were less slippery and more safe to walk on when it was raining or not.Another area where potential errors could occur would be our interpretation of the graph. As the data points were often quite scattered and magnifying the graph proved a bit of a challenge, it was difficult to determine where exactly the first instance of static friction occurred. This could be improved by perhaps using a different, more precise software to graph the data points obtained from Logger Pro, or if we could somehow figure out a way to manipulate the graph for better results.