Modelling Radioactive Decay (WJEC GCSE Science (Double Award): Physics): Revision Note

Modelling Radioactive Decay

• It cannot be predicted when a particular unstable nucleus will decay

• This is because radioactive decay is a random process, this means that:

  • There is an equal probability of any nucleus decaying

  • It cannot be known which particular nucleus will decay next

  • It cannot be known at what time a particular nucleus will decay

  • The rate of decay is unaffected by the surrounding conditions

  • It is only possible to estimate the probability of nuclei decaying in a given time period

• For example, a researcher might take some readings of background radiation

• If they were to reset the counter to zero, wait one minute and then take the readings again, they might obtain a set of results such as:

32    11    25    16    28

• The readings don't appear to follow a particular trend

  • This happens because of the randomness of radioactive decay

• The random nature of radioactive decay can be demonstrated by observing count rate with a Geiger-Muller (GM) tube

  • When a GM tube is placed near a radioactive source, the counts are found to be irregular and cannot be predicted

  • Each count represents a decay of an unstable nucleus

  • These fluctuations in count rate on the GM tube provide evidence for the randomness of radioactive decay

Count Rate for a Radioactive Substance

The variation of count rate with time of a radioactive substance. The fluctuations show the randomness of radioactive decay

Modelling Radioactive Decay

• A model, or analogy, is a way of understanding an idea by using a different but similar situation

• Radioactive decay can be modelled using

  • A large collection of dice or coins

  • A computer simulation or spreadsheet programme

• Rolling dice is a good analogy for radioactive decay because it is also a random process

Dice represent the random nature of radioactive decay

A dice roll is a random process because you don't know when you will roll a particular value. However, you can determine the probability of a particular result

• Imagine rolling a dice and hoping to roll a '6'

• Each time you roll the dice, you cannot know what the result will be, but you know there is a 1/6 probability that it will be a '6'

• If you rolled the dice 1000 times, you can expect to roll a '6' around 1000 ÷ 6 ≈ 127 times