Gibbs paradox for a small numbers of particles

In summary, the conversation discusses the Gibbs paradox and the use of Stirling's approximation to solve it. The mixing entropy is found to be non-zero for small numbers of particles, and the question is raised about when the approximation becomes accurate. The Sackur-Tetrode equation is mentioned as a solution, but it also uses Stirling's approximation. The extensive form of the equation is discussed and it is stated that the non-extensive form, which does not use Stirling's approximation, leads to the Gibbs paradox. A suggestion is made to use a more exact form of Stirling's approximation to evaluate the expression.
  • #1
greypilgrim
517
36
Hi.

Trying to solve the Gibbs paradox for two identical volumes of ideal gas with ##N## particles each, I found the mixing entropy to be
$$\Delta S=2N \log(2)-\log((2N)!)+2\log(N!)\enspace .$$
The usual approach now uses Stirling's approximation to the order ##\log (n!)\approx n\log (n)-n## which indeed gives ##\Delta S=0##.

However, this is not zero for small ##N##. I assume this is because in the mixed case, fluctuations where the number of particles is different in the two volumes are still quite dominant for small number of particles, is this correct?

I used Mathematica to plot above function up to ##N=10^{10}## (it stops plotting after that), and it still looks to be monotonically increasing, yet very slowly and only up to about a value of 11. I somehow always assumed Stirling's approximation to be better for much smaller values. At which ##N## will above expression for ##\Delta S## start decreasing?
 
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  • #2
A google of the subject says that the paradox is caused if a non-extensive form is used for the entropy equation. The problem is not because of Stirling's approximation. The google also says the Sackur-Tetrode expression for entropy is an extensive form that corrects this difficulty. The Wikipedia article appears to be a good one for this topic.
 
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  • #3
I am using the (so-called) extensive form, the one that accounts for indistinguishability by dividing the number of microstates by the number of particle permutations ##N!##. That's what gave me the expression for ##\Delta S## in #1, which is only zero using Stirling's approximation. As I wrote in #1, numerically I get a monotonically increasing ##\Delta S## for up to ##N=10^{10}##, and my question is at what ##N## Stirling's approximation seems to "kick in", making this expression decrease.

The Sackur-Tetrode equation on the Wikipedia article on the Gibbs paradox is already simplified with Stirling's approximation, so it's not surprising that it leads to ##\Delta S=0## for any ##N##. This form only gets truly extensive with Stirling's approximation, which seems to be good only for ridiculously large ##N##.
As I stated in #1, it cannot be exactly extensive because there are microstates in the mixed case where the number of particles in the partial volumes differ from the case where the volumes are separated. Stirling's approximation sort of says that these fluctuations become negligible for large ##N##.

The non-extensive form that leads to the Gibbs paradox, where ##\Delta S## is even proportional to ##N##, contains no factorial, so there's no reason to use Stirling's approximation there.
 
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  • #4
greypilgrim said:
I am using the (so-called) extensive form, the one that accounts for indistinguishability by dividing the number of microstates by the number of particle permutations ##N!##. That's what gave me the expression for ##\Delta S## in #1, which is only zero using Stirling's approximation. As I wrote in #1, numerically I get a monotonically increasing ##\Delta S## for up to ##N=10^{10}##, and my question is at what ##N## Stirling's approximation seems to "kick in", making this expression decrease.

The Sackur-Tetrode equation on the Wikipedia article on the Gibbs paradox is already simplified with Stirling's approximation, so it's not surprising that it leads to ##\Delta S=0## for any ##N##. This form only gets truly extensive with Stirling's approximation, which seems to be good only for ridiculously large ##N##.
As I stated in #1, it cannot be exactly extensive because there are microstates in the mixed case where the number of particles in the partial volumes differ from the case where the volumes are separated. Stirling's approximation sort of says that these fluctuations become negligible for large ##N##.

The non-extensive form that leads to the Gibbs paradox, where ##\Delta S## is even proportional to ##N##, contains no factorial, so there's no reason to use Stirling's approximation there.
One other suggestion is to use the more exact Stirling's approximation ## ln(N!)=N ln(N)-N +\frac{1}{2}ln(2 \pi N) ## to evaluate the expression. I'm not sure how exact the expression ## \Delta S=0 ## is supposed to be for the expressions that are being used.
 

1. What is the Gibbs paradox for a small number of particles?

The Gibbs paradox for a small number of particles is a puzzle in statistical mechanics that arises when considering the mixing of two different types of particles, such as gas particles of different masses. It is named after physicist J. Willard Gibbs who first described it in the late 19th century.

2. What is the main issue addressed by the Gibbs paradox?

The main issue addressed by the Gibbs paradox is the apparent contradiction between the predictions of classical thermodynamics and statistical mechanics when it comes to the mixing of two different types of particles. Classical thermodynamics predicts that there should be no change in entropy when the particles are mixed, while statistical mechanics predicts an increase in entropy.

3. How is the Gibbs paradox resolved?

The Gibbs paradox is resolved by considering the particles to be indistinguishable from one another. This means that the number of microstates (ways of arranging the particles) is reduced, leading to a decrease in entropy and reconciling the predictions of classical thermodynamics and statistical mechanics.

4. Are there any real-world implications of the Gibbs paradox?

While the Gibbs paradox is primarily a theoretical puzzle, it does have some real-world implications. For example, it helps to explain the phenomenon of supercooling, where a liquid can remain in a liquid state below its freezing point. It also has applications in fields such as quantum mechanics and information theory.

5. What are some potential criticisms of the resolution to the Gibbs paradox?

Some potential criticisms of the resolution to the Gibbs paradox include the assumption of indistinguishability of particles, which may not hold in certain scenarios, and the fact that the paradox only arises in certain idealized situations. Additionally, some argue that the paradox may not have a definitive resolution and may require further research and understanding of the underlying principles.

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