12 of 12 ideas
B.2 Greenhouse effect: 4 ideas
Solar cell output power against tilt angle under a lamp
Research question. How does the maximum power output of a small silicon solar cell vary with tilt angle θ from 0° to 80° in 10° steps, under a lamp fixed 0.30 m from the cell centre?
- B.2 Greenhouse effect
- SL and HL
- Easy data
- Rarely listed
My take. Very common, so make it yours by finding the true maximum power point at every angle and explaining why the data leave the cosine curve at large angles.
Method, physics and where marks are lost+
- Independent variable
- Angle between the cell normal and the lamp direction, 0° to 80° in 10° steps (9 values), on a rotating protractor mount, 3 repeats each.
- What you measure
- Power P = VI at the maximum power point, found by varying a load resistor (10 Ω to 1 kΩ) with a voltmeter and ammeter. Compare with a cosθ model.
- Controlled variables
- Lamp distance to cell centre, measured by ruler at each angle. Lamp power, supplied from a stabilised source. Cell temperature, limited by a heat filter and short exposure times. Room lighting, blocked by a dark screen.
- Physics and graph
- Intensity on a surface falls as I cosθ, so the power should be P = P0 cosθ. Plot P against cosθ and test for a straight line through the origin. Compare gradient with P0.
- SL and HL
- SL students test the cosine law. Top work looks at deviations at large angles due to reflection (Fresnel-like losses) and heat, and uses a lux meter to convert the power to efficiency.
- Where marks are lost
- Research design: allowing the lamp to heat the cell, which lowers voltage. Data analysis: using power at one fixed load and not the maximum power point. Evaluation: stray light at high angles.
- Data
- Needs a lamp, small cell, load box and two meters; the main uncertainty is angle setting, about ±2°, and heating.
Surface finish and lamp-heated plate temperature
Research question. How does the reflectance of a metal plate's coating, varied from about 0.1 to 0.9 using different paper or paint finishes, affect its equilibrium temperature under a 60 W lamp held 20 cm away?
- B.2 Greenhouse effect
- SL and HL
- Needs care
- Rarely listed
My take. Good link to the greenhouse and albedo theme, but only if you measure reflectance yourself. The twist of comparing visible and infrared behaviour is what makes it worth choosing.
Method, physics and where marks are lost+
- Independent variable
- Surface reflectance: 6 finishes from matt black to white to polished foil, each one measured independently using a light sensor comparing reflected and incident light (about 0.1 to 0.9).
- What you measure
- Equilibrium temperature of the plate, found with a thermocouple taped to the back (±0.1 °C) and logged until it stops rising, about 15 to 20 minutes. Repeat 3 times.
- Controlled variables
- Distance and angle from lamp: fixed with a clamp and a ruler. Plate: same aluminium sheet with a coating on the front only. Room temperature: measured and starting with the plate at the same value. Back of plate: insulated with foam.
- Physics and graph
- At equilibrium, absorbed power (1 − a) I A equals power lost by convection and radiation, roughly h A (T − Troom). Plot (T − Troom) against (1 − a): expected straight line through the origin if heat loss is linear. The gradient links to intensity over the loss coefficient.
- SL and HL
- SL: plot temperature rise against measured absorbed fraction. Top band or HL depth: relate to the Stefan-Boltzmann law and the fact that the lamp emits mostly infrared, so visible colour is not a reliable measure of absorption.
- Where marks are lost
- Research design: judging reflectivity by colour by eye instead of measuring it. Data analysis: not using the temperature rise above the room. Conclusion: ignoring that a lamp's infrared differs from visible light. Evaluation: not commenting on the emissivity of the coating, which also changes the losses.
- Data
- Needs a thermocouple or probe and a light meter; the main uncertainty is that visible reflectance does not equal infrared absorption and that the lamp warms the air.
Temperature rise of coloured cans under a lamp
Research question. How does the initial rate of temperature rise of 100 mL of water in aluminium cans covered with different colours of paper, from white to black, change under a 100 W lamp at 20 cm?
- B.2 Greenhouse effect
- SL and HL
- Easy data
- Rarely listed
My take. Easy but commonplace, so build a quantitative scale. Twist by measuring reflectance with a light meter and comparing visible and infrared behaviour with a thermal camera or a filter.
Method, physics and where marks are lost+
- Independent variable
- Colour of the covering, 6 to 8 colours or grey levels from white to black, each repeated 3 times.
- What you measure
- Water temperature with a probe every 30 s for 10 minutes; initial rate from the gradient of T against t; a light meter can give the reflectance.
- Controlled variables
- Distance from lamp fixed with a clamp. Same starting water temperature, about room temperature. Same can and paper thickness, with matte paper only. Room darkened with no other light source.
- Physics and graph
- Absorbed power P = (1 - albedo) x intensity x area, and rate of temperature rise = P/(mc). Plot initial heating rate against reflectance from a light meter; the line should be straight and decreasing, showing absorption depends on the reflected fraction.
- SL and HL
- SL: rate against colour or grey level with uncertainties. Top band: quantify reflectance with a light meter and check the linear relationship. HL: consider the emission of the can and the lamp spectrum, including infrared, which colour does not test.
- Where marks are lost
- Research design: lamp distance and starting temperature vary. Data analysis: colours not ranked on a scale. Conclusion: no link to reflectance. Evaluation: ignoring that visible colour may not predict infrared absorption.
- Data
- Needs cans, a lamp, a temperature probe and paper; the main uncertainty is heating from the lamp changing during the run.
Testing inverse square fall-off for a bulb and a torch
Research question. How does the illuminance measured by a light sensor change with distance from a small filament bulb and from a lens-focused torch, for distances from 0.20 m to 1.00 m in steps of 0.10 m?
- B.2 Greenhouse effect
- SL and HL
- Easy data
- Rarely listed
My take. Easy to run but common and only loosely tied to the syllabus, so the twist matters. Add the torch comparison and fit the offset to make it yours.
Method, physics and where marks are lost+
- Independent variable
- Distance from source to sensor, 0.20 to 1.00 m in 9 steps, measured with a metre rule fixed on an optical bench; three readings at each distance. Two sources: bare bulb and torch with reflector.
- What you measure
- Illuminance in lux from a light sensor or lux meter, with the background reading in a darkened room subtracted; the calculated quantity is the exponent n in I = k/dn.
- Controlled variables
- Supply voltage to the bulb held with a stabilised power supply and checked on a voltmeter. Room darkness kept constant by blinds and a black cloth. Sensor orientation kept normal to the source using a fixed mount. Warm-up time of 2 minutes before every run.
- Physics and graph
- For a point source I = P/(4πd²). Plot ln I against ln d: the gradient should be -2 for the bulb and different for the torch. Alternatively plot I against 1/d². Correct for the sensor and the bulb not being a true point by measuring from the filament position.
- SL and HL
- SL students compare the two gradients and comment on the shape of the graphs. Top band work adds a distance offset as a fitted parameter, uncertainty on the gradient from a worst-fit line, and a reasoned range over which the point approximation is valid. Note this is really a light intensity topic, so the syllabus link is weak: the inverse square law is best framed as an analogy to field strength.
- Where marks are lost
- Research design: no treatment of stray light, and a weak syllabus link. Data analysis: distance uncertainty ignored when the filament position is unclear. Conclusion: claiming n = 2 exactly without comparing to the gradient uncertainty. Evaluation: not discussing the finite size of the bulb at short distances.
- Data
- Needs a lux meter or light sensor and a bench; the main uncertainty is stray light and the unknown position of the filament.
B.3 Gas laws: 7 ideas
Boyle's law with a syringe and dead volume
Research question. How does the pressure of air trapped in a syringe vary as its volume is reduced from 50 cm³ to 20 cm³, and what dead volume in the tubing does the fit give?
- B.3 Gas laws
- SL and HL
- Needs care
- Common: on 2 sites
My take. A standard experiment, so it needs the dead volume analysis to be worth doing. Twist: compare slow and fast compression to show isothermal versus adiabatic behaviour.
Method, physics and where marks are lost+
- Independent variable
- Syringe reading from 50 cm³ down to 20 cm³ in 5 cm³ steps, giving seven values, and each compressed and released three times.
- What you measure
- Pressure from a digital gas pressure sensor or a Bourdon gauge, in kPa. Calculate 1/(V + V0) and fit for the dead volume V0.
- Controlled variables
- Temperature held constant by moving the plunger slowly and waiting 30 s at each reading. Same amount of trapped air with no leaks, checked by holding a reading. Syringe lubricated with a drop of silicone oil. Same sensor tubing length.
- Physics and graph
- pV = constant at fixed T and amount of gas, and with dead volume p(V + V0) = k. Plot 1/p against V; the intercept on the V axis is −V0 and the gradient is 1/k.
- SL and HL
- SL verifies inverse proportionality with a straight line. Stronger work fits the dead volume V0 and shows the uncorrected graph does not pass through the origin. HL depth could explore adiabatic effects by pushing quickly and estimating the heating.
- Where marks are lost
- Data analysis: plotting p against V and calling the curve inverse without a linearised graph. Evaluation: ignoring dead volume, leaks and friction in the plunger. Conclusion: claiming the law is verified when the graph intercept is not zero.
- Data
- Needs a syringe and a pressure sensor; the main uncertainty is the unknown volume in the connector and slow leaks.
Counting molecules of air in a syringe from p, V and T
Research question. How closely does the number of air molecules calculated from pV = nRT match the number expected from the mass of air in a 60 cm³ syringe, at gas volumes of 20 to 60 cm³ in 5 steps?
- B.3 Gas laws
- SL and HL
- Needs care
- Rarely listed
My take. Fine if the RQ is about agreement between two independent estimates of N and not about proving Avogadro. Twist: compare air with a sample of your own breath.
Method, physics and where marks are lost+
- Independent variable
- Trapped air volume, six values from 20 to 60 cm³ at room temperature, set with a syringe connected to a pressure sensor.
- What you measure
- Pressure from a digital pressure sensor in kPa. Calculate n = pV/RT, then N = n·NA. Compare with N from the air mass measured on a 0.001 g balance by weighing the sealed syringe empty and full, or from repeated volume readings.
- Controlled variables
- Temperature kept constant by compressing slowly and waiting 30 seconds and recording room temperature. Same amount of air sealed each run, with no leaks checked by holding the plunger. Same syringe and sensor with dead volume in the tube measured.
- Physics and graph
- pV = nRT and N = nNA. Plot p against 1/V, gradient = nRT. From the gradient calculate n, then the number of particles N. Compare with N from mass, m/M·NA, using air M = 29 g/mol.
- SL and HL
- SL students can obtain n from the gradient of p against 1/V with uncertainty. For higher marks, include the tube dead volume as the intercept on a V + V₀ axis. HL work could use Boltzmann's constant with pV = NkT to get N directly.
- Where marks are lost
- Research design: 'verify Avogadro's number' as the aim, which is not achievable from one gas sample. Data analysis: ignoring the dead volume in the connection. Evaluation: not discussing heating during compression.
- Data
- Needs a syringe and pressure sensor; the dead volume and slow leaks are the dominant uncertainties.
Measuring the gas constant with a syringe and water bath
Research question. What value of the molar gas constant R is obtained from a fixed sample of air in a sealed syringe heated from 20 °C to 80 °C, and how close is it to 8.31 J mol⁻¹ K⁻¹?
- B.3 Gas laws
- SL and HL
- Needs care
- Rarely listed
My take. Very doable and gives a numerical target. Do not just repeat it; fit V against T and interpret the intercept to give the analysis real depth.
Method, physics and where marks are lost+
- Independent variable
- Temperature of the air sample, 20, 30, 40, 50, 60, 70, 80 °C (7 values) in a water bath, with 3 repeats per value. Pressure is held near atmospheric.
- What you measure
- Volume of air read from the syringe scale. Amount of gas is found from the initial state, and R is found from the gradient of V against T.
- Controlled variables
- Pressure kept constant with a freely moving, lubricated syringe. Amount of gas fixed by sealing the outlet. Thermal equilibrium allowed for 3 min at each step. Atmospheric pressure recorded from a barometer.
- Physics and graph
- pV = nRT, so at constant pressure V = (nR/p)T. Plot V against T in kelvin; the gradient equals nR/p, so R = gradient × p/n. Dead volume in the tip appears as an intercept.
- SL and HL
- SL students calculate R from the gradient and compare with the accepted value. Top band work corrects for dead volume and friction, propagates uncertainty from n and p, and comments on systematic offset.
- Where marks are lost
- Research design: not finding the amount of gas properly. Data analysis: using Celsius on the axis, or forgetting dead volume. Conclusion: comparing to the accepted value without judging whether the difference is within uncertainty. Evaluation: not testing syringe friction or heat loss from the exposed part.
- Data
- Glass or plastic syringe, water bath and thermometer; the main uncertainty is friction of the plunger and the amount of gas.
Pressure of trapped air in water baths and absolute zero
Research question. How does the pressure of a fixed mass of air at constant volume change as its temperature is raised from 0 °C to 90 °C in steps of 10 °C, and what value of absolute zero does the extrapolation give?
- B.3 Gas laws
- SL and HL
- Needs care
- Rarely listed
My take. A classic and often seen, so the personal twist has to come from the analysis, for example the uncertainty of the extrapolated intercept and the dead volume correction. Safe and dependable data.
Method, physics and where marks are lost+
- Independent variable
- Bath temperature: 0 °C (ice water), then about 20, 30, 40, 50, 60, 70, 80, 90 °C (at least 6 values), each measured with a thermometer at the flask (±0.5 °C). Three readings each while heating and cooling.
- What you measure
- Pressure of air in a sealed flask read from a pressure sensor or Bourdon gauge (±0.5 kPa). Absolute zero is found from where the extrapolated line meets P = 0.
- Controlled variables
- Gas amount: flask sealed once with a bung and no leaks. Volume: rigid flask, with tubing volume kept small. Time at each temperature: wait 3 minutes for thermal equilibrium, with stirring. Immersion: the whole flask below the water surface.
- Physics and graph
- P/T = constant at fixed V, so P = kTC + P₀ with T in °C. Plot P against θ (°C): x-intercept at −P₀/k gives absolute zero, about −273 °C. Compare and calculate a percentage difference.
- SL and HL
- SL: plot the graph, extrapolate and compare with −273 °C. Top band or HL depth: use the maximum and minimum gradient lines for the uncertainty on the intercept, and estimate the effect of the connecting tube being at room temperature.
- Where marks are lost
- Research design: leaks and the dead volume of the sensor tube. Data analysis: a very long extrapolation with no uncertainty on the result. Conclusion: not checking whether the result is within the uncertainty range. Evaluation: not addressing the gas not being at the bath temperature.
- Data
- A pressure sensor with a flask and bung is needed; the main uncertainty is a long extrapolation and the gas in the tubing not being at the bath temperature.
Simulated gas temperature and wall collision frequency
Research question. How does the temperature of a simulated ideal gas of 200 particles, varied from 100 K to 500 K, affect the number of wall collisions per second in a fixed box?
- B.3 Gas laws
- SL and HL
- Easy data
- simulation
- Rarely listed
My take. Acceptable but thin as a pure simulation. Writing your own short Python model makes it personal and gives you far more control over the variables.
Method, physics and where marks are lost+
- Independent variable
- Gas temperature in a particle simulation, 100, 200, 300, 400, 500 K (5 values), with 5 runs per value using different random starts.
- What you measure
- Collisions with the walls per second counted by the simulation for a fixed 10 s run. Mean speed and pressure are also recorded and compared with prediction.
- Controlled variables
- Number of particles fixed. Box volume fixed. Particle mass and size identical. Run duration and time step the same.
- Physics and graph
- Average speed scales as √T, so collision rate should be proportional to √T at fixed volume and particle number. Plot collision rate against √T; the gradient depends on particle number and box size. Compare with pressure proportional to T.
- SL and HL
- SL students record collision rates and link them to kinetic theory. Top band work derives the expected √T law, tests it, and discusses how the simulation's finite particle size and time step limit agreement.
- Where marks are lost
- Research design: a simulation with no justification of settings. Data analysis: treating random variation as if it were a real uncertainty. Conclusion: not testing the √T prediction. Evaluation: not discussing model assumptions such as no intermolecular forces.
- Data
- PhET Gas Properties or a short Python script; the main uncertainty is statistical scatter and what the simulation actually counts.
Speed of sound in air from 5 to 50 °C
Research question. How does the speed of sound in air in a closed tube vary with air temperature from 10 °C to 50 °C in seven steps, and does the gradient of v² against T match the ideal-gas value?
- B.3 Gas laws
- SL and HL
- Hard data
- Rarely listed
My take. Interesting because it links waves and gases, but temperature control is the difficulty. Let the air settle for a few minutes before each reading and record the temperature at both ends.
Method, physics and where marks are lost+
- Independent variable
- Air temperature in the tube: about 10, 20, 25, 30, 35, 40, 50 °C (seven values), reached by warming the tube with a water jacket or hairdryer and cooling.
- What you measure
- Speed of sound, from resonance frequency in a closed tube of known length (v = 4Lf, with end correction) using a speaker and a microphone; temperature read with a digital thermometer inside the tube.
- Controlled variables
- Tube length (measured at each temperature, checking expansion is negligible); humidity (same air, tube dry); speaker and microphone positions; mode of resonance (always the first).
- Physics and graph
- v = √(γRT/M), so v² is proportional to T in kelvin. Plot v² against T (K): gradient γR/M, about 402 m²s⁻²K⁻¹ for dry air. Compare the experimental gradient with this.
- SL and HL
- SL can plot v against T and compare with the theory. Top-band work fits v² against T, includes the intercept, and discusses the effect of humidity. HL students can derive v from kinetic theory.
- Where marks are lost
- Research design: temperature gradient along the tube, so the reading does not represent the air. Data analysis: forgetting to convert to kelvin. Evaluation: neglecting humidity and the end correction.
- Data
- Tube, water bath or heater, thermometer and speaker with a microphone; the main uncertainty is non-uniform air temperature in the tube.
Volume of trapped air against temperature at constant pressure
Research question. How does the volume of a fixed sample of air trapped by a mercury-free oil plug or a syringe vary as the temperature is raised from 20 C to 80 C in 10 C steps?
- B.3 Gas laws
- SL and HL
- Needs care
- Rarely listed
My take. Classic and quite common in schools, so depth is what wins marks. Twist by using the extrapolation to absolute zero and analysing what the systematic error in your intercept is.
Method, physics and where marks are lost+
- Independent variable
- Water bath temperature, 7 values from 20 C to 80 C, with each set repeated 3 times.
- What you measure
- Length of the air column in a capillary tube, or syringe reading, with a ruler; volume from cross-sectional area; temperature measured with a digital probe.
- Controlled variables
- Pressure kept at atmospheric with a free moving oil plug. Same mass of air, tube sealed at one end. Wait at least 3 minutes for thermal equilibrium at each step. Tube fully submerged to the same depth.
- Physics and graph
- Charles's law V proportional to T with temperature in kelvin. Plot V against T in kelvin; the line passes through the origin, and extrapolating V against T in Celsius gives absolute zero as an x-intercept near -273 C.
- SL and HL
- SL: linear graph and absolute zero estimate with uncertainty. Top band: assess systematic effects such as air heating unevenly, and compare the estimate with the accepted value. HL: link to kinetic theory and the ideal gas equation.
- Where marks are lost
- Research design: not waiting for equilibrium. Data analysis: extrapolation with no uncertainty range. Conclusion: not comparing intercept with -273 C. Evaluation: ignoring the dead volume in the syringe or moisture in the tube.
- Data
- Needs a capillary tube and water bath or a syringe with a thermometer; the main uncertainty is temperature difference between bath and gas.
B.4 Thermodynamics: 1 idea
Syringe compression speed: isothermal or adiabatic
Research question. How does the time taken to compress air in a 60 ml syringe from 60 ml to 20 ml, varied from 0.2 s to 20 s, affect the effective exponent γ in PVγ = constant?
- B.4 Thermodynamics
- HL topic
- Hard data
- Rarely listed
My take. Interesting and truly HL, but difficult to get clean data. Choose it only if you have a fast sensor; video of the scale is a practical way to get volume.
Method, physics and where marks are lost+
- Independent variable
- Compression time: 6 values from about 0.2 s to 20 s, timed with a data logger and driven by a falling mass or a hand pressed against a ruler. Three repeats each.
- What you measure
- Pressure from a fast pressure sensor connected to the syringe, logged at 100 Hz, with volume from the plunger position on the scale. The exponent is the gradient of ln P against ln V.
- Controlled variables
- Starting amount of air: same 60 ml at room pressure and temperature. Compression ratio: same in every trial. Syringe: same smooth syringe with a seal, tested for leaks. Rest time: wait 2 minutes between trials so the gas returns to room temperature.
- Physics and graph
- Isothermal: PV = constant, so γeff = 1. Adiabatic: PVγ = constant with γ = 1.4 for air. Plot ln P against ln V: gradient is −γeff. Then plot γeff against log of compression time.
- SL and HL
- HL: adiabatic processes are HL syllabus, so it fits HL best. Top band: model the thermal time constant of the syringe wall and explain why even the fast run does not reach 1.4.
- Where marks are lost
- Research design: no way to measure volume at the same moment as the pressure. Data analysis: pressure logged without synchronisation with the volume. Conclusion: not comparing with both limiting cases. Evaluation: friction, leaks and dead volume in the sensor tube.
- Data
- Needs a fast pressure sensor and a way to record plunger position, for example video analysis; the main uncertainty is dead volume and friction.
Ideas were collected from public lists and published IA titles, merged when they are the same investigation, and rewritten from scratch. “On N sites” counts how many public lists carry the same investigation. How this list was made.
Frequently asked questions
What are good IB Physics IA ideas on gas laws, greenhouse effect and thermodynamics?
+
Good starting points with easy data that few sites list are solar cell output power against tilt angle under a lamp, temperature rise of coloured cans under a lamp and testing inverse square fall-off for a bulb and a torch. Each one gives a straight-line graph from school equipment, which is what the Data analysis and Conclusion criteria need.
Which gas laws, greenhouse effect and thermodynamics IA ideas are overdone?
+
None of the 12 ideas on this page appears on three or more public lists, so any of them is less likely to look like a copy.
Can I do a gas laws, greenhouse effect and thermodynamics IA at SL?
+
11 of the 12 ideas use SL physics. The others rely on HL-only content and are marked as HL topics.