ACT Science practice test

Two scientists discussed why sparkling water releases carbon dioxide gas when a sealed bottle is opened.

Scientist 1 argued that carbon dioxide remains dissolved more readily under high pressure and at low temperature. Opening a bottle lowers the pressure above the liquid, allowing gas to leave the water. This scientist predicted that an unshaken warmer bottle would release more gas than an otherwise identical colder bottle.

Scientist 2 agreed that opening provides a route for gas to escape but claimed that temperature has little effect over the tested range. According to Scientist 2, shaking is the main cause of differences in gas release because bubbles begin forming on disturbed surfaces.

Researchers stored identical sealed bottles for 24 hours at 5°C, 15°C, 25°C, or 35°C. Without shaking them, the researchers opened 5 bottles at each temperature and collected the gas released during the next 2 minutes. The mean masses are shown in Table 1 and Figure 1. The bottles contained equal volumes from the same production batch and were opened with the same apparatus.

Gas passed through flexible tubing into a gas-tight collection bag. The researchers determined released mass by weighing each bottle immediately before opening and again after 2 minutes; no liquid left the bottle. A separate unopened bottle at each temperature was weighed twice and showed no measurable mass change. This check indicated that ordinary scale drift was negligible compared with the observed losses.

In an additional treatment, 5 bottles stored at 15°C were shaken for 30 seconds immediately before opening. Those bottles released a mean of 2.1 g of gas. Table 2 compares that shaken treatment with the unshaken 15°C bottles from Table 1. The researchers noted that Table 1 and Figure 1 alone address the temperature predictions because none of those bottles were shaken, while Table 2 provides evidence about shaking.

Researchers sampled red maple leaves in one city park to examine whether distance from a busy road was associated with particle deposits and lead concentration. They selected leaves at 0, 10, 30, and 60 m from the road. Sample sizes differed because fewer intact leaves were available near the road.

All leaves were mature red maple leaves collected on the same dry morning from a height of 1.5 to 2.0 m. At each distance, leaves came from multiple trees, and no single tree supplied more than one-fifth of that distance’s sample. The researchers recorded whether each leaf had visible black particle deposits. They then used a calibrated analyzer to determine the mean lead concentration for leaves at each distance. The same collection tools, storage bags, preparation method, and analyzer settings were used throughout. Table 1 gives the results.

For estimates between sampled distances, assume the local trend is linear only between the two surrounding distances.

Students investigated heat transfer along solid metal rods. They used copper, aluminum, and steel rods that had equal lengths and diameters. One end of each rod was held against the same electric heater. Temperature sensors were attached 5 cm, 10 cm, and 15 cm from the heated end.

At the start of each trial, the rod and room were at 22°C. The heater was switched on, and temperatures were recorded every minute for 9 min. A shield reduced air currents around the apparatus. The same heater setting, sensor type, attachment method, and heating time were used for every metal. Three trials were completed for each condition.

Before heating, the students checked every sensor in the same water bath; all read 22°C within 0.2°C. Rods were allowed to return to room temperature between trials. The heater contacted a cleaned end of each rod, and a clamp maintained equal contact pressure.

Experiment 1 compared the 3 metals. After 6 min of heating, the students recorded the temperature at each sensor. Table 1 reports the mean of the three trials for each metal and distance.

Experiment 2 tested whether the heater, rather than room conditions alone, produced the temperature increases. The students repeated the 10 cm measurements with the heater switched off. The control rods stayed at 22°C after 6 min. Figure 1 compares those control readings with the heated 10 cm readings from Experiment 1.

The students proposed that, under their conditions, temperature at a sensor would continue to decrease as its distance from the heated end increased. They planned to test that proposal with sensors at additional distances.

A laboratory prepared identical 40 g samples of a salt solution initially at 90°C. Solubility of the dissolved salt decreased as the samples were cooled from 90°C to 20°C at constant rates, and crystallization began as cooling started. The cooling rates were 2°C/min, 5°C/min, 10°C/min, and 20°C/min. After each sample reached 20°C, technicians measured the longest dimension of 50 randomly selected crystals and calculated the mean crystal size. The samples had equal composition, initial temperature, container shape, and final temperature. Figure 1 shows the means.

All 3 researchers agreed that faster cooling produced smaller crystals, but they proposed different mechanisms.

Researcher 1

Cooling initiates the formation of crystal nuclei. Faster cooling produces more nuclei during the first minute. Because the fixed amount of dissolved material is divided among more growing crystals, each crystal remains smaller. Slower cooling produces fewer nuclei, so more material is available for each crystal.

Researcher 2

The number of nuclei is approximately the same at every cooling rate. Crystal size instead depends on growth time. During slow cooling, dissolved particles have more time to attach to existing crystals before the sample reaches 20°C. During rapid cooling, that interval is shorter, so crystals stop growing at a smaller size.

Researcher 3

Faster cooling creates steeper temperature differences between the center and edges of a sample. Those differences drive stronger convection currents. The currents break some growing crystals into fragments, and each fragment becomes another growth site. Thus, rapid cooling indirectly creates more growing sites and a smaller mean size.

In a follow-up trial, technicians counted nuclei after the first minute and mapped liquid motion with tracer beads. They could also place thin baffles in a container to reduce convection without changing its cooling rate. All measurements used the same salt solution and microscope procedure.

To check measurement reliability, a second technician independently measured 10 crystals from each sample. The two technicians’ mean estimates differed by less than 0.1 mm, and neither technician knew the assigned cooling rate while measuring them.

Researchers studied whether biochar, a carbon-rich material made by heating plant matter with little oxygen, can remove nitrate from water.

In Study 1, each beaker received 500 mL of water containing 40 mg/L nitrate. The researchers added 0 g, 5 g, 10 g, or 15 g of biochar to separate beakers. The mixtures were stirred for 24 hours at 20°C and then filtered. Four beakers were tested at each biochar mass. Table 1 shows the mean nitrate concentrations remaining.

In Study 2, every beaker received 10 g of biochar, but contact time varied. Remaining nitrate was measured after 1, 4, 12, and 24 hours. The starting water and stirring speed matched those used in Study 1. Table 2 shows the mean nitrate concentrations remaining.

Researcher A proposed that nitrate attaches to binding sites on the biochar surface. Additional biochar supplies more binding sites but encounters progressively less nitrate remaining in the fixed water volume, so each additional gram of biochar should remove less nitrate than the preceding gram. Researcher B proposed that microorganisms carried on the biochar consume nitrate. According to this explanation, longer contact gives the microorganisms more time to act. Both researchers expected that adding more biochar would generally leave less nitrate in the water.

The studies did not distinguish conclusively between these mechanisms. The biochar was not sterilized, and neither the number of binding sites nor the abundance of living microorganisms was measured. As a comparison, the researchers repeated the 24-hour, 10 g treatment with washed gravel instead of biochar (Table 3). They also found that pH changed by less than 0.2 unit in every beaker.

Astronomers measured the percentage of a star’s light blocked when a planet crossed the star. Table 1 lists transit depths at wavelengths of 500, 600, 700, and 800 nm during Epoch 1 and, half a stellar rotation later, during Epoch 2. Figure 1 graphs Epoch 1 transit depth versus wavelength. Measurement uncertainty was $\pm0.02$ percentage point.

The Haze Model proposes that tiny particles in the planet’s atmosphere scatter shorter wavelengths, making blue-light transits deeper. It predicts that the wavelength pattern will remain stable between epochs. The Starspot Model proposes that cool, unblocked regions on the rotating star create the apparent pattern. It also predicts deeper transits at shorter wavelengths, but predicts that at least 1 depth will change by 0.10 percentage point or more after half a stellar rotation because different stellar longitudes will face Earth.

Scientists tested how moths corrected their headings when a white lamp appeared. In Study 1, the lamp was 60° to the right of the moths’ starting direction; in Study 2, it was 60° to the left. Separate groups experienced no lamp or a lamp at 6, 12, or 24 lux. After 4 seconds, the researchers calculated the magnitude of each group’s mean turn toward the lamp. Positive values therefore indicate a correction toward the light in either study. Each group contained 40 moths. The chamber, lamp color, release point, and airflow were unchanged. The no-lamp measurements reflect ordinary turning, so treatment effects can be found by subtracting each study’s own no-lamp value. Each moth was tested only once.