Chemistry IA · Reactivity 1 · What drives chemical reactions? · topic 15 of 25
Dissolving uphill: entropy and potassium nitrate · IB Chemistry IA idea: guide, sample graph and step-by-step plan
thermodynamics you can see :)Potassium nitrate dissolves even though it takes in heat, so entropy must be doing the work. Can you get both terms out of one plot?
- ✓ you like a model with a payoff
- ✓ you can heat and cool slowly and patiently
- ✓ you enjoy straight-line graphs
The big idea
← this is your RQ hook!Dissolving potassium nitrate cools the water, so enthalpy is against it, and yet it dissolves because entropy increases. At equilibrium in a saturated solution, ΔG = −RT ln K and ΔG = ΔH − TΔS, so a plot of ln K against 1/T should be a straight line. The gradient gives −ΔH/R and the intercept gives ΔS/R. The hard part is that K is only an approximation for such a concentrated solution, and the experiment hides a trap: you set the composition and measure the temperature, not the other way round.
Make it yours: pick one twist
Copying a classic won't show your choices. Pick one of these (or invent your own) and your IA becomes yours.
Swipe for more twists →
Present mass per 100 g of water against temperature before you do any thermodynamics.
→ A simpler first graph that stands alone, with the ln K plot as the deeper analysis.Repeat with ammonium chloride or sodium nitrate.
→ A second line to compare. Different gradients mean different enthalpies, and you can test whether both are 'entropy driven'.Estimate K from molality, then use a literature correction or compare with a simple model.
→ Tests how far the ideal assumption is from reality and gives your evaluation a quantified weakness.Use a temperature probe with a slow, steady heating rate and record the temperature at the last crystal.
→ Smaller temperature uncertainty, and a way to show how the heating rate changes the reading.Repeat a few compositions at a slower rate.
→ Tests whether your saturation temperatures depend on how you measure them, a direct check on the method.Your Chemistry IA research question: run the check
Write your own question first. Then make sure it ticks every box:
- Names the independent variable (temperature, with range) and the dependent variable (solubility), and says how they link to the derived K, ΔH and ΔS.
- Is clear that the composition is set and the saturation temperature is measured, and says how many compositions you will use.
- Says how you decide 'saturated' (the temperature where the last crystal dissolves on slow warming) and why not on cooling.
- Names the controls: water mass, stirring, heating rate, and the way mass is measured.
- Says which quantities you will compare with the literature and which conditions the accepted values apply to.
- Is narrow enough for about 10 hours and has enough compositions for a line (at least 6).
The set-up
One way to set it up. Draw your own version, with real masses and equipment, for the Method section.
How your Chemistry IA could be laid out
The usual order for a topic like this, what each part needs to show, and roughly how much of your 3,000 words it takes. The words are all yours.
Your question; why solubility versus temperature matters (crystallisation, cold packs, fertilisers); what you expect and why.
Dissolving equation and equilibrium constant; ΔG = −RT ln K and ΔG = ΔH − TΔS; the resulting ln K against 1/T line; the approximation made in K. Cite sources.
Independent, dependent and controlled variables, and how each is set and checked.
Apparatus with resolutions; masses; how you heat; how you judge the last crystal; repeats; enough detail to copy.
Specific hazards: potassium nitrate is an oxidiser, hot water bath, glassware. Where the solutions go afterwards.
Masses of salt and water, temperature at the last crystal for each composition, units and resolutions in headers.
Convert to molality, then to K, ln K and 1/T, with one worked example each; propagate uncertainty.
A solubility curve and the ln K against 1/T plot with a fit and error bars; the gradient and intercept with uncertainty and what they mean.
Answer with ΔH and ΔS with uncertainty; compare with literature and with the sign of each term; say when the ideal assumption fails.
Weaknesses ranked by effect on the gradient: the K approximation, judging the last crystal, evaporation, thermometer lag; fixes linked to each.
Literature values with source and conditions, and every other source.
What your Chemistry IA graph could look like
check the sign of the gradient!Made-up data to show the form of a strong graph, not the answer. A straight line of ln K against 1/T, with the temperature uncertainty as horizontal error bars. example only
- The axis is 1000/T so the numbers are easy to read. Remember it when you turn the gradient into ΔH.
- Error bars on both axes: temperature gives horizontal bars, mass and water give vertical bars.
- A positive ΔH means the gradient is negative. Check the sign before you interpret it.
- The intercept is ΔS/R. Check it carries the factor 1000 from the axis if you rescaled.
- Another graph to consider: solubility in g per 100 g of water against temperature, to show the shape before you linearise it.
Which uncertainty matters most?
Rank by the effect on the gradient, and so on ΔH. One systematic assumption is far bigger than any reading error here, and the chart makes that obvious.
Your game plan
slow heating is the whole experimentTick steps off as you go. We'll remember on this device.
0 of 10 done
- Read and predictRevise ΔG = ΔH − TΔS and ΔG = −RT ln K. Predict the sign of ΔH and ΔS, the shape of ln K against 1/T, and what the gradient and intercept should mean.
- Trial run (1 lesson)Make one composition and find its saturation temperature on slow warming. How slowly must you heat to repeat the reading? Does the last crystal vanish sharply? Fix your method now.
- Decide the compositionsChoose at least 6 masses of salt per fixed mass of water, spaced so the saturation temperatures spread over 20 to 70 °C. Write down why.
- Decide repeats and heating rateChoose the heating rate and the number of repeats. Randomise the order of the compositions.
- Collect the dataRecord masses and the temperature at the last crystal, with observations such as how clearly the crystals disappear.
- ProcessConvert to molality, then to K, ln K and 1/T. State clearly how you approximate K and what that ignores.
- Graph and fitPlot ln K against 1/T with error bars, fit a line, and find the gradient and intercept with uncertainties from max and min lines.
- Compare with literatureTurn gradient and intercept into ΔH and ΔS. Compare with accepted values, and say what conditions they apply to.
- EvaluateWhich weakness matters most for the gradient? Rank them, and say how you would test the biggest.
- Write, then checkWrite in the order of the layout, then run the checklist below before your teacher's single read.
Chemistry you'll need
- IB Chemistry data booklet →
- Uncertainties and error bars (physics examples, same rules) →
- IB command terms explained →
Traps to avoid in this Chemistry IA
- Reading the temperature on cooling: crystals appear late because of supercooling, so the saturation temperature comes out too low.
- Heating too fast, so the thermometer and the solution are at different temperatures.
- Using °C in the 1/T axis. It has to be kelvin.
- Treating K as exact in a very concentrated solution without saying it is an approximation.
- Forgetting that the gradient is −ΔH/R, so a positive ΔH gives a negative slope, and that R is in J K⁻¹ mol⁻¹.
- Fixing the temperature and varying the mass in the write-up when you actually did the opposite.
More Chemistry IA ideas like this
- Why do carbonates decompose at different temperatures?●●●
- Do weak acids give off less heat when neutralised?●●○
- Hess's law with copper(II) sulfate hydration●●○
This guide shows how a topic like this is usually approached. It is not an IA. Your question, choices, data, analysis and words must be your own: change something, tell your teacher where the idea came from, and follow your school's rules on outside help.