Science Practice Test 2 (Timed) – Outschool

Item 1

In coordination chemistry, the stability of a transition metal complex depends on several factors, including the identity of the central metal ion, the nature of the ligands, and environmental conditions such as temperature and pH. One important concept used to evaluate complex stability is the formation constant (Kₙ), which quantifies the equilibrium between a metal ion and its ligands in solution. Larger Kₙ values indicate more stable complexes. Researchers have proposed that both ligand denticity (the number of donor atoms in a ligand) and ligand geometry significantly influence complex stability through the chelate effect and steric interactions.

Two research groups conducted experiments to investigate how ligand structure affects the stability of complexes formed with copper(II) ions (Cu²⁺) in aqueous solution.

Experiment 1

A group of researchers examined three ligands:

• Ligand A: a monodentate ligand (1 donor atom)
• Ligand B: a bidentate ligand (2 donor atoms)
• Ligand C: a tetradentate ligand (4 donor atoms)

Each ligand was added separately to identical Cu²⁺ solutions at 25°C and neutral pH. The formation constant (log Kₙ) for each resulting complex was measured.

Table 1.

Experiment 2

A second group investigated how ligand geometry and rigidity affect stability. Two bidentate ligands were studied:

• Ligand D: a flexible ligand with a wide range of bond angles
• Ligand E: a rigid ligand with a fixed bond angle optimized for Cu²⁺ coordination

Both ligands were added to Cu²⁺ solutions under the same conditions as Experiment 1. Formation constants were measured at two temperatures: 25°C and 50°C.

Table 2.

Based on the results of Experiment 1, which of the following statements best explains the increase in log Kₙ from Ligand A to Ligand C?

Item 2

In coordination chemistry, the stability of a transition metal complex depends on several factors, including the identity of the central metal ion, the nature of the ligands, and environmental conditions such as temperature and pH. One important concept used to evaluate complex stability is the formation constant (Kₙ), which quantifies the equilibrium between a metal ion and its ligands in solution. Larger Kₙ values indicate more stable complexes. Researchers have proposed that both ligand denticity (the number of donor atoms in a ligand) and ligand geometry significantly influence complex stability through the chelate effect and steric interactions.

Two research groups conducted experiments to investigate how ligand structure affects the stability of complexes formed with copper(II) ions (Cu²⁺) in aqueous solution.

Experiment 1

A group of researchers examined three ligands:

• Ligand A: a monodentate ligand (1 donor atom)
• Ligand B: a bidentate ligand (2 donor atoms)
• Ligand C: a tetradentate ligand (4 donor atoms)

Each ligand was added separately to identical Cu²⁺ solutions at 25°C and neutral pH. The formation constant (log Kₙ) for each resulting complex was measured.

Table 1.

Experiment 2

A second group investigated how ligand geometry and rigidity affect stability. Two bidentate ligands were studied:

• Ligand D: a flexible ligand with a wide range of bond angles
• Ligand E: a rigid ligand with a fixed bond angle optimized for Cu²⁺ coordination

Both ligands were added to Cu²⁺ solutions under the same conditions as Experiment 1. Formation constants were measured at two temperatures: 25°C and 50°C.

Table 2.

A researcher proposes a new tridentate ligand, Ligand F. Based on Experiment 1, the log Kₙ for Ligand F would most likely be:

Item 3

In coordination chemistry, the stability of a transition metal complex depends on several factors, including the identity of the central metal ion, the nature of the ligands, and environmental conditions such as temperature and pH. One important concept used to evaluate complex stability is the formation constant (Kₙ), which quantifies the equilibrium between a metal ion and its ligands in solution. Larger Kₙ values indicate more stable complexes. Researchers have proposed that both ligand denticity (the number of donor atoms in a ligand) and ligand geometry significantly influence complex stability through the chelate effect and steric interactions.

Two research groups conducted experiments to investigate how ligand structure affects the stability of complexes formed with copper(II) ions (Cu²⁺) in aqueous solution.

Experiment 1

A group of researchers examined three ligands:

• Ligand A: a monodentate ligand (1 donor atom)
• Ligand B: a bidentate ligand (2 donor atoms)
• Ligand C: a tetradentate ligand (4 donor atoms)

Each ligand was added separately to identical Cu²⁺ solutions at 25°C and neutral pH. The formation constant (log Kₙ) for each resulting complex was measured.

Table 1.

Experiment 2

A second group investigated how ligand geometry and rigidity affect stability. Two bidentate ligands were studied:

• Ligand D: a flexible ligand with a wide range of bond angles
• Ligand E: a rigid ligand with a fixed bond angle optimized for Cu²⁺ coordination

Both ligands were added to Cu²⁺ solutions under the same conditions as Experiment 1. Formation constants were measured at two temperatures: 25°C and 50°C.

Table 2.

Which of the following conclusions about temperature effects is best supported by the data in Experiment 2?

Item 4

In coordination chemistry, the stability of a transition metal complex depends on several factors, including the identity of the central metal ion, the nature of the ligands, and environmental conditions such as temperature and pH. One important concept used to evaluate complex stability is the formation constant (Kₙ), which quantifies the equilibrium between a metal ion and its ligands in solution. Larger Kₙ values indicate more stable complexes. Researchers have proposed that both ligand denticity (the number of donor atoms in a ligand) and ligand geometry significantly influence complex stability through the chelate effect and steric interactions.

Two research groups conducted experiments to investigate how ligand structure affects the stability of complexes formed with copper(II) ions (Cu²⁺) in aqueous solution.

Experiment 1

A group of researchers examined three ligands:

• Ligand A: a monodentate ligand (1 donor atom)
• Ligand B: a bidentate ligand (2 donor atoms)
• Ligand C: a tetradentate ligand (4 donor atoms)

Each ligand was added separately to identical Cu²⁺ solutions at 25°C and neutral pH. The formation constant (log Kₙ) for each resulting complex was measured.

Table 1.

Experiment 2

A second group investigated how ligand geometry and rigidity affect stability. Two bidentate ligands were studied:

• Ligand D: a flexible ligand with a wide range of bond angles
• Ligand E: a rigid ligand with a fixed bond angle optimized for Cu²⁺ coordination

Both ligands were added to Cu²⁺ solutions under the same conditions as Experiment 1. Formation constants were measured at two temperatures: 25°C and 50°C.

Table 2.

Which of the following ligands would most likely form the most stable complex with Cu²⁺ at 25°C?

Item 5

In coordination chemistry, the stability of a transition metal complex depends on several factors, including the identity of the central metal ion, the nature of the ligands, and environmental conditions such as temperature and pH. One important concept used to evaluate complex stability is the formation constant (Kₙ), which quantifies the equilibrium between a metal ion and its ligands in solution. Larger Kₙ values indicate more stable complexes. Researchers have proposed that both ligand denticity (the number of donor atoms in a ligand) and ligand geometry significantly influence complex stability through the chelate effect and steric interactions.

Two research groups conducted experiments to investigate how ligand structure affects the stability of complexes formed with copper(II) ions (Cu²⁺) in aqueous solution.

Experiment 1

A group of researchers examined three ligands:

• Ligand A: a monodentate ligand (1 donor atom)
• Ligand B: a bidentate ligand (2 donor atoms)
• Ligand C: a tetradentate ligand (4 donor atoms)

Each ligand was added separately to identical Cu²⁺ solutions at 25°C and neutral pH. The formation constant (log Kₙ) for each resulting complex was measured.

Table 1.

Experiment 2

A second group investigated how ligand geometry and rigidity affect stability. Two bidentate ligands were studied:

• Ligand D: a flexible ligand with a wide range of bond angles
• Ligand E: a rigid ligand with a fixed bond angle optimized for Cu²⁺ coordination

Both ligands were added to Cu²⁺ solutions under the same conditions as Experiment 1. Formation constants were measured at two temperatures: 25°C and 50°C.

Table 2.

The difference in log Kₙ values between Ligands D and E at 25°C is most likely due to differences in:

Item 6

In coordination chemistry, the stability of a transition metal complex depends on several factors, including the identity of the central metal ion, the nature of the ligands, and environmental conditions such as temperature and pH. One important concept used to evaluate complex stability is the formation constant (Kₙ), which quantifies the equilibrium between a metal ion and its ligands in solution. Larger Kₙ values indicate more stable complexes. Researchers have proposed that both ligand denticity (the number of donor atoms in a ligand) and ligand geometry significantly influence complex stability through the chelate effect and steric interactions.

Two research groups conducted experiments to investigate how ligand structure affects the stability of complexes formed with copper(II) ions (Cu²⁺) in aqueous solution.

Experiment 1

A group of researchers examined three ligands:

• Ligand A: a monodentate ligand (1 donor atom)
• Ligand B: a bidentate ligand (2 donor atoms)
• Ligand C: a tetradentate ligand (4 donor atoms)

Each ligand was added separately to identical Cu²⁺ solutions at 25°C and neutral pH. The formation constant (log Kₙ) for each resulting complex was measured.

Table 1.

Experiment 2

A second group investigated how ligand geometry and rigidity affect stability. Two bidentate ligands were studied:

• Ligand D: a flexible ligand with a wide range of bond angles
• Ligand E: a rigid ligand with a fixed bond angle optimized for Cu²⁺ coordination

Both ligands were added to Cu²⁺ solutions under the same conditions as Experiment 1. Formation constants were measured at two temperatures: 25°C and 50°C.

Table 2.

If a graph were constructed showing percent of Cu²⁺ bound in complex vs. log Kₙ at 25^oC, which of the following trends would most likely be observed?

Item 7

Efficient cooling of compact electronic devices requires careful optimization of multiple design variables. In forced-convection systems, heat is removed from a central processing unit (CPU) using a heat sink and airflow generated by a fan. The effectiveness of such systems depends on heat sink surface area, fin density (number of fins per unit width), and airflow rate.

While increasing surface area and airflow generally improves cooling, higher fin density can restrict airflow, and increased airflow requires greater energy consumption. Engineers must therefore balance these competing factors to achieve optimal performance.

Three engineering teams conducted experiments to evaluate these variables for a CPU operating at constant power.

Experiment 1

Team 1 investigated the combined effects of surface area and fin density on CPU temperature at a constant airflow rate of 2.0 m/s. Table 1 shows the CPU temperature (^oC) at each surface area and fin density. 

Table 1.

Experiment 2

Team 2 measured the effect of airflow rate on both temperature and fan power consumption using a heat sink with 150 cm² surface area and medium fin density. The results are displayed in Table 2. 

Table 2.

Experiment 3

Team 3 investigated the combined effects of heat sink surface area and airflow rate on CPU temperature using heat sinks with medium fin density. Table 3 shows the CPU temperature (^oC) at each surface area and airflow rate.

Table 3. 

At a surface area of 100 cm², how does increasing fin density from medium to high affect CPU temperature, and what is the most likely explanation?

Item 8

Efficient cooling of compact electronic devices requires careful optimization of multiple design variables. In forced-convection systems, heat is removed from a central processing unit (CPU) using a heat sink and airflow generated by a fan. The effectiveness of such systems depends on heat sink surface area, fin density (number of fins per unit width), and airflow rate.

While increasing surface area and airflow generally improves cooling, higher fin density can restrict airflow, and increased airflow requires greater energy consumption. Engineers must therefore balance these competing factors to achieve optimal performance.

Three engineering teams conducted experiments to evaluate these variables for a CPU operating at constant power.

Experiment 1

Team 1 investigated the combined effects of surface area and fin density on CPU temperature at a constant airflow rate of 2.0 m/s. Table 1 shows the CPU temperature (^oC) at each surface area and fin density. 

Table 1.

Experiment 2

Team 2 measured the effect of airflow rate on both temperature and fan power consumption using a heat sink with 150 cm² surface area and medium fin density. The results are displayed in Table 2. 

Table 2.

Experiment 3

Team 3 investigated the combined effects of heat sink surface area and airflow rate on CPU temperature using heat sinks with medium fin density. Table 3 shows the CPU temperature (^oC) at each surface area and airflow rate.

Table 3. 

Which of the following best describes the relationship between airflow rate and fan power in Table 2?

Item 9

Efficient cooling of compact electronic devices requires careful optimization of multiple design variables. In forced-convection systems, heat is removed from a central processing unit (CPU) using a heat sink and airflow generated by a fan. The effectiveness of such systems depends on heat sink surface area, fin density (number of fins per unit width), and airflow rate.

While increasing surface area and airflow generally improves cooling, higher fin density can restrict airflow, and increased airflow requires greater energy consumption. Engineers must therefore balance these competing factors to achieve optimal performance.

Three engineering teams conducted experiments to evaluate these variables for a CPU operating at constant power.

Experiment 1

Team 1 investigated the combined effects of surface area and fin density on CPU temperature at a constant airflow rate of 2.0 m/s. Table 1 shows the CPU temperature (^oC) at each surface area and fin density. 

Table 1.

Experiment 2

Team 2 measured the effect of airflow rate on both temperature and fan power consumption using a heat sink with 150 cm² surface area and medium fin density. The results are displayed in Table 2. 

Table 2.

Experiment 3

Team 3 investigated the combined effects of heat sink surface area and airflow rate on CPU temperature using heat sinks with medium fin density. Table 3 shows the CPU temperature (^oC) at each surface area and airflow rate.

Table 3. 

What is the relationship between temperature drop and surface area when the airflow is increased from 1.5 to 3.0m/s?

Item 10

Efficient cooling of compact electronic devices requires careful optimization of multiple design variables. In forced-convection systems, heat is removed from a central processing unit (CPU) using a heat sink and airflow generated by a fan. The effectiveness of such systems depends on heat sink surface area, fin density (number of fins per unit width), and airflow rate.

While increasing surface area and airflow generally improves cooling, higher fin density can restrict airflow, and increased airflow requires greater energy consumption. Engineers must therefore balance these competing factors to achieve optimal performance.

Three engineering teams conducted experiments to evaluate these variables for a CPU operating at constant power.

Experiment 1

Team 1 investigated the combined effects of surface area and fin density on CPU temperature at a constant airflow rate of 2.0 m/s. Table 1 shows the CPU temperature (^oC) at each surface area and fin density. 

Table 1.

Experiment 2

Team 2 measured the effect of airflow rate on both temperature and fan power consumption using a heat sink with 150 cm² surface area and medium fin density. The results are displayed in Table 2. 

Table 2.

Experiment 3

Team 3 investigated the combined effects of heat sink surface area and airflow rate on CPU temperature using heat sinks with medium fin density. Table 3 shows the CPU temperature (^oC) at each surface area and airflow rate.

Table 3. 

Based on the results of Experiment 2, which increase in airflow rate provides the greatest cooling efficiency (temperature decrease per watt)?

Item 11

Efficient cooling of compact electronic devices requires careful optimization of multiple design variables. In forced-convection systems, heat is removed from a central processing unit (CPU) using a heat sink and airflow generated by a fan. The effectiveness of such systems depends on heat sink surface area, fin density (number of fins per unit width), and airflow rate.

While increasing surface area and airflow generally improves cooling, higher fin density can restrict airflow, and increased airflow requires greater energy consumption. Engineers must therefore balance these competing factors to achieve optimal performance.

Three engineering teams conducted experiments to evaluate these variables for a CPU operating at constant power.

Experiment 1

Team 1 investigated the combined effects of surface area and fin density on CPU temperature at a constant airflow rate of 2.0 m/s. Table 1 shows the CPU temperature (^oC) at each surface area and fin density. 

Table 1.

Experiment 2

Team 2 measured the effect of airflow rate on both temperature and fan power consumption using a heat sink with 150 cm² surface area and medium fin density. The results are displayed in Table 2. 

Table 2.

Experiment 3

Team 3 investigated the combined effects of heat sink surface area and airflow rate on CPU temperature using heat sinks with medium fin density. Table 3 shows the CPU temperature (^oC) at each surface area and airflow rate.

Table 3. 

Which of the following statements is best supported by the results of the experiments?

Item 12

Efficient cooling of compact electronic devices requires careful optimization of multiple design variables. In forced-convection systems, heat is removed from a central processing unit (CPU) using a heat sink and airflow generated by a fan. The effectiveness of such systems depends on heat sink surface area, fin density (number of fins per unit width), and airflow rate.

While increasing surface area and airflow generally improves cooling, higher fin density can restrict airflow, and increased airflow requires greater energy consumption. Engineers must therefore balance these competing factors to achieve optimal performance.

Three engineering teams conducted experiments to evaluate these variables for a CPU operating at constant power.

Experiment 1

Team 1 investigated the combined effects of surface area and fin density on CPU temperature at a constant airflow rate of 2.0 m/s. Table 1 shows the CPU temperature (^oC) at each surface area and fin density. 

Table 1.

Experiment 2

Team 2 measured the effect of airflow rate on both temperature and fan power consumption using a heat sink with 150 cm² surface area and medium fin density. The results are displayed in Table 2. 

Table 2.

Experiment 3

Team 3 investigated the combined effects of heat sink surface area and airflow rate on CPU temperature using heat sinks with medium fin density. Table 3 shows the CPU temperature (^oC) at each surface area and airflow rate.

Table 3. 

A design team chooses a configuration with maximum surface area (200 cm²), high fin density and an airflow rate of 2.0m/s. Which change would most likely produce the greatest additional decrease in CPU temperature?

Item 13

Populations of many bee species have declined significantly across multiple regions. Bees play a critical role in pollination, so understanding the causes of these declines has become an important area of research. Three scientists present differing perspectives on the most significant factors affecting bee populations. 

Scientist 1

Exposure to neonicotinoid pesticides is the primary cause of bee population decline. These chemicals affect the nervous system, impairing navigation and memory, which reduces bees’ ability to forage and return to their hives. Chronic, low-level exposure leads to reduced colony efficiency and can eventually cause collapse. In addition, pesticide exposure can interfere with reproduction by reducing queen fertility and the viability of eggs and larvae. Laboratory studies demonstrate that even sublethal doses of neonicotinoids can alter bee behavior, decrease foraging success, and increase mortality rates. Field observations confirm that colonies in regions with heavy pesticide use exhibit higher rates of decline than those in areas with minimal exposure.

Scientist 2

The primary driver of declining bee populations is the loss of habitat and plant diversity. Bees rely on a variety of flowering plants to obtain balanced nutrition, including proteins, lipids, and essential micronutrients. When natural landscapes are replaced with monoculture crops or urban environments, bees experience nutritional deficiencies that can weaken individuals and colonies over time. Areas with high plant diversity support larger and more stable bee populations, whereas areas dominated by a single crop often see reduced colony survival. Nutritional stress can also reduce bees’ ability to maintain brood and forage efficiently, leading to slower colony growth and increased vulnerability to environmental fluctuations. Observational studies indicate that maintaining or restoring diverse habitats correlates with higher colony survival rates.

Scientist 3

The primary cause of bee population decline is the spread of pathogens and parasites, especially the Varroa mite. These parasites feed on bee bodily fluids, directly weakening individuals, and transmit viruses that spread rapidly within colonies. Infestations often lead to colony collapse within a single season. Experimental studies show that colonies with heavy Varroa infestations experience high mortality even when environmental conditions are favorable and food sources are abundant. Parasites also reduce foraging efficiency and lifespan, disrupt brood development, and compromise immune responses. Observations indicate that regions with widespread parasitic infestations have consistently lower bee population densities, regardless of other environmental variables.

Based on the explanation given by Scientist 1, if bee exposure to neonicotinoid pesticides increased, how would colony reproduction and their ability to return to their hives be affected?

Item 14

Populations of many bee species have declined significantly across multiple regions. Bees play a critical role in pollination, so understanding the causes of these declines has become an important area of research. Three scientists present differing perspectives on the most significant factors affecting bee populations. 

Scientist 1

Exposure to neonicotinoid pesticides is the primary cause of bee population decline. These chemicals affect the nervous system, impairing navigation and memory, which reduces bees’ ability to forage and return to their hives. Chronic, low-level exposure leads to reduced colony efficiency and can eventually cause collapse. In addition, pesticide exposure can interfere with reproduction by reducing queen fertility and the viability of eggs and larvae. Laboratory studies demonstrate that even sublethal doses of neonicotinoids can alter bee behavior, decrease foraging success, and increase mortality rates. Field observations confirm that colonies in regions with heavy pesticide use exhibit higher rates of decline than those in areas with minimal exposure.

Scientist 2

The primary driver of declining bee populations is the loss of habitat and plant diversity. Bees rely on a variety of flowering plants to obtain balanced nutrition, including proteins, lipids, and essential micronutrients. When natural landscapes are replaced with monoculture crops or urban environments, bees experience nutritional deficiencies that can weaken individuals and colonies over time. Areas with high plant diversity support larger and more stable bee populations, whereas areas dominated by a single crop often see reduced colony survival. Nutritional stress can also reduce bees’ ability to maintain brood and forage efficiently, leading to slower colony growth and increased vulnerability to environmental fluctuations. Observational studies indicate that maintaining or restoring diverse habitats correlates with higher colony survival rates.

Scientist 3

The primary cause of bee population decline is the spread of pathogens and parasites, especially the Varroa mite. These parasites feed on bee bodily fluids, directly weakening individuals, and transmit viruses that spread rapidly within colonies. Infestations often lead to colony collapse within a single season. Experimental studies show that colonies with heavy Varroa infestations experience high mortality even when environmental conditions are favorable and food sources are abundant. Parasites also reduce foraging efficiency and lifespan, disrupt brood development, and compromise immune responses. Observations indicate that regions with widespread parasitic infestations have consistently lower bee population densities, regardless of other environmental variables.

Which of the scientists would most likely agree that bee populations can decline even when various food sources are abundant?

Item 15

Populations of many bee species have declined significantly across multiple regions. Bees play a critical role in pollination, so understanding the causes of these declines has become an important area of research. Three scientists present differing perspectives on the most significant factors affecting bee populations. 

Scientist 1

Exposure to neonicotinoid pesticides is the primary cause of bee population decline. These chemicals affect the nervous system, impairing navigation and memory, which reduces bees’ ability to forage and return to their hives. Chronic, low-level exposure leads to reduced colony efficiency and can eventually cause collapse. In addition, pesticide exposure can interfere with reproduction by reducing queen fertility and the viability of eggs and larvae. Laboratory studies demonstrate that even sublethal doses of neonicotinoids can alter bee behavior, decrease foraging success, and increase mortality rates. Field observations confirm that colonies in regions with heavy pesticide use exhibit higher rates of decline than those in areas with minimal exposure.

Scientist 2

The primary driver of declining bee populations is the loss of habitat and plant diversity. Bees rely on a variety of flowering plants to obtain balanced nutrition, including proteins, lipids, and essential micronutrients. When natural landscapes are replaced with monoculture crops or urban environments, bees experience nutritional deficiencies that can weaken individuals and colonies over time. Areas with high plant diversity support larger and more stable bee populations, whereas areas dominated by a single crop often see reduced colony survival. Nutritional stress can also reduce bees’ ability to maintain brood and forage efficiently, leading to slower colony growth and increased vulnerability to environmental fluctuations. Observational studies indicate that maintaining or restoring diverse habitats correlates with higher colony survival rates.

Scientist 3

The primary cause of bee population decline is the spread of pathogens and parasites, especially the Varroa mite. These parasites feed on bee bodily fluids, directly weakening individuals, and transmit viruses that spread rapidly within colonies. Infestations often lead to colony collapse within a single season. Experimental studies show that colonies with heavy Varroa infestations experience high mortality even when environmental conditions are favorable and food sources are abundant. Parasites also reduce foraging efficiency and lifespan, disrupt brood development, and compromise immune responses. Observations indicate that regions with widespread parasitic infestations have consistently lower bee population densities, regardless of other environmental variables.

Based on Scientist 2’s explanation, a decline in bee populations due to nutritional stress would most likely result from which of the following?

Item 16

Populations of many bee species have declined significantly across multiple regions. Bees play a critical role in pollination, so understanding the causes of these declines has become an important area of research. Three scientists present differing perspectives on the most significant factors affecting bee populations. 

Scientist 1

Exposure to neonicotinoid pesticides is the primary cause of bee population decline. These chemicals affect the nervous system, impairing navigation and memory, which reduces bees’ ability to forage and return to their hives. Chronic, low-level exposure leads to reduced colony efficiency and can eventually cause collapse. In addition, pesticide exposure can interfere with reproduction by reducing queen fertility and the viability of eggs and larvae. Laboratory studies demonstrate that even sublethal doses of neonicotinoids can alter bee behavior, decrease foraging success, and increase mortality rates. Field observations confirm that colonies in regions with heavy pesticide use exhibit higher rates of decline than those in areas with minimal exposure.

Scientist 2

The primary driver of declining bee populations is the loss of habitat and plant diversity. Bees rely on a variety of flowering plants to obtain balanced nutrition, including proteins, lipids, and essential micronutrients. When natural landscapes are replaced with monoculture crops or urban environments, bees experience nutritional deficiencies that can weaken individuals and colonies over time. Areas with high plant diversity support larger and more stable bee populations, whereas areas dominated by a single crop often see reduced colony survival. Nutritional stress can also reduce bees’ ability to maintain brood and forage efficiently, leading to slower colony growth and increased vulnerability to environmental fluctuations. Observational studies indicate that maintaining or restoring diverse habitats correlates with higher colony survival rates.

Scientist 3

The primary cause of bee population decline is the spread of pathogens and parasites, especially the Varroa mite. These parasites feed on bee bodily fluids, directly weakening individuals, and transmit viruses that spread rapidly within colonies. Infestations often lead to colony collapse within a single season. Experimental studies show that colonies with heavy Varroa infestations experience high mortality even when environmental conditions are favorable and food sources are abundant. Parasites also reduce foraging efficiency and lifespan, disrupt brood development, and compromise immune responses. Observations indicate that regions with widespread parasitic infestations have consistently lower bee population densities, regardless of other environmental variables.

Suppose a large plot of land is cleared to make room for a series of new subdivisions to be built in an area that was previously a thriving ecosystem for bees. Which scientists would most likely predict the largest decline in bee population as a result of the construction?

Item 17

Populations of many bee species have declined significantly across multiple regions. Bees play a critical role in pollination, so understanding the causes of these declines has become an important area of research. Three scientists present differing perspectives on the most significant factors affecting bee populations. 

Scientist 1

Exposure to neonicotinoid pesticides is the primary cause of bee population decline. These chemicals affect the nervous system, impairing navigation and memory, which reduces bees’ ability to forage and return to their hives. Chronic, low-level exposure leads to reduced colony efficiency and can eventually cause collapse. In addition, pesticide exposure can interfere with reproduction by reducing queen fertility and the viability of eggs and larvae. Laboratory studies demonstrate that even sublethal doses of neonicotinoids can alter bee behavior, decrease foraging success, and increase mortality rates. Field observations confirm that colonies in regions with heavy pesticide use exhibit higher rates of decline than those in areas with minimal exposure.

Scientist 2

The primary driver of declining bee populations is the loss of habitat and plant diversity. Bees rely on a variety of flowering plants to obtain balanced nutrition, including proteins, lipids, and essential micronutrients. When natural landscapes are replaced with monoculture crops or urban environments, bees experience nutritional deficiencies that can weaken individuals and colonies over time. Areas with high plant diversity support larger and more stable bee populations, whereas areas dominated by a single crop often see reduced colony survival. Nutritional stress can also reduce bees’ ability to maintain brood and forage efficiently, leading to slower colony growth and increased vulnerability to environmental fluctuations. Observational studies indicate that maintaining or restoring diverse habitats correlates with higher colony survival rates.

Scientist 3

The primary cause of bee population decline is the spread of pathogens and parasites, especially the Varroa mite. These parasites feed on bee bodily fluids, directly weakening individuals, and transmit viruses that spread rapidly within colonies. Infestations often lead to colony collapse within a single season. Experimental studies show that colonies with heavy Varroa infestations experience high mortality even when environmental conditions are favorable and food sources are abundant. Parasites also reduce foraging efficiency and lifespan, disrupt brood development, and compromise immune responses. Observations indicate that regions with widespread parasitic infestations have consistently lower bee population densities, regardless of other environmental variables.

According to Scientist 3, which of the following processes most directly contributes to rapid colony collapse?

Item 18

Populations of many bee species have declined significantly across multiple regions. Bees play a critical role in pollination, so understanding the causes of these declines has become an important area of research. Three scientists present differing perspectives on the most significant factors affecting bee populations. 

Scientist 1

Exposure to neonicotinoid pesticides is the primary cause of bee population decline. These chemicals affect the nervous system, impairing navigation and memory, which reduces bees’ ability to forage and return to their hives. Chronic, low-level exposure leads to reduced colony efficiency and can eventually cause collapse. In addition, pesticide exposure can interfere with reproduction by reducing queen fertility and the viability of eggs and larvae. Laboratory studies demonstrate that even sublethal doses of neonicotinoids can alter bee behavior, decrease foraging success, and increase mortality rates. Field observations confirm that colonies in regions with heavy pesticide use exhibit higher rates of decline than those in areas with minimal exposure.

Scientist 2

The primary driver of declining bee populations is the loss of habitat and plant diversity. Bees rely on a variety of flowering plants to obtain balanced nutrition, including proteins, lipids, and essential micronutrients. When natural landscapes are replaced with monoculture crops or urban environments, bees experience nutritional deficiencies that can weaken individuals and colonies over time. Areas with high plant diversity support larger and more stable bee populations, whereas areas dominated by a single crop often see reduced colony survival. Nutritional stress can also reduce bees’ ability to maintain brood and forage efficiently, leading to slower colony growth and increased vulnerability to environmental fluctuations. Observational studies indicate that maintaining or restoring diverse habitats correlates with higher colony survival rates.

Scientist 3

The primary cause of bee population decline is the spread of pathogens and parasites, especially the Varroa mite. These parasites feed on bee bodily fluids, directly weakening individuals, and transmit viruses that spread rapidly within colonies. Infestations often lead to colony collapse within a single season. Experimental studies show that colonies with heavy Varroa infestations experience high mortality even when environmental conditions are favorable and food sources are abundant. Parasites also reduce foraging efficiency and lifespan, disrupt brood development, and compromise immune responses. Observations indicate that regions with widespread parasitic infestations have consistently lower bee population densities, regardless of other environmental variables.

A researcher studies four regions with the following conditions:

• Region 1: High pesticide use, high plant diversity, low parasite presence
• Region 2: Low pesticide use, low plant diversity, low parasite presence
• Region 3: Low pesticide use, high plant diversity, high parasite presence
• Region 4: High pesticide use, high plant diversity, high parasite presence

Which region is most likely to experience the largest decline in bee population according to Scientists 1 and 3?

Item 19

Volcanic eruptions vary depending on magma composition and gas content. Scientists studied 8 volcanoes (Volcanoes A–H) over a 4-month period, measuring silica (SiO₂) content in magma, sulfur dioxide (SO₂) emissions, and eruption frequency.

Table 1 shows the average silica content (% by mass), average daily SO₂ emission (metric tons/day), and total number of eruptions recorded.

Table 1.

Figure 1 shows SO₂ emissions (tons/day) for Volcano D over 8 consecutive days.

Figure 1.

Figure 2  shows the number of eruptions at Volcano D associated with different SO₂ emission ranges during the same 8-day period.

Figure 2. 

Figure 3 shows the relationship between silica content and eruption style. The primary magma type (basaltic, andesitic, and rhyolitic) involved in each eruption style is shown.

Between which 2 consecutive days did Volcano D experience the greatest increase in SO₂ emissions?

Item 20

Volcanic eruptions vary depending on magma composition and gas content. Scientists studied 8 volcanoes (Volcanoes A–H) over a 4-month period, measuring silica (SiO₂) content in magma, sulfur dioxide (SO₂) emissions, and eruption frequency.

Table 1 shows the average silica content (% by mass), average daily SO₂ emission (metric tons/day), and total number of eruptions recorded.

Table 1.

Figure 1 shows SO₂ emissions (tons/day) for Volcano D over 8 consecutive days.

Figure 1.

Figure 2  shows the number of eruptions at Volcano D associated with different SO₂ emission ranges during the same 8-day period.

Figure 2. 

Figure 3 shows the relationship between silica content and eruption style. The primary magma type (basaltic, andesitic, and rhyolitic) involved in each eruption style is shown.

Which volcano has the highest average SO₂ emission per eruption?

Item 21

Volcanic eruptions vary depending on magma composition and gas content. Scientists studied 8 volcanoes (Volcanoes A–H) over a 4-month period, measuring silica (SiO₂) content in magma, sulfur dioxide (SO₂) emissions, and eruption frequency.

Table 1 shows the average silica content (% by mass), average daily SO₂ emission (metric tons/day), and total number of eruptions recorded.

Table 1.

Figure 1 shows SO₂ emissions (tons/day) for Volcano D over 8 consecutive days.

Figure 1.

Figure 2  shows the number of eruptions at Volcano D associated with different SO₂ emission ranges during the same 8-day period.

Figure 2. 

Figure 3 shows the relationship between silica content and eruption style. The primary magma type (basaltic, andesitic, and rhyolitic) involved in each eruption style is shown.

Based on the data shown in Figures 1 and 2 for Volcano D, what can be concluded about the relationship between the number of days in each emission range and the number of eruptions in that range?

Item 22

Volcanic eruptions vary depending on magma composition and gas content. Scientists studied 8 volcanoes (Volcanoes A–H) over a 4-month period, measuring silica (SiO₂) content in magma, sulfur dioxide (SO₂) emissions, and eruption frequency.

Table 1 shows the average silica content (% by mass), average daily SO₂ emission (metric tons/day), and total number of eruptions recorded.

Table 1.

Figure 1 shows SO₂ emissions (tons/day) for Volcano D over 8 consecutive days.

Figure 1.

Figure 2  shows the number of eruptions at Volcano D associated with different SO₂ emission ranges during the same 8-day period.

Figure 2. 

Figure 3 shows the relationship between silica content and eruption style. The primary magma type (basaltic, andesitic, and rhyolitic) involved in each eruption style is shown.

Based on the data, which volcano is most likely to produce primarily effusive eruptions?

Item 23

Volcanic eruptions vary depending on magma composition and gas content. Scientists studied 8 volcanoes (Volcanoes A–H) over a 4-month period, measuring silica (SiO₂) content in magma, sulfur dioxide (SO₂) emissions, and eruption frequency.

Table 1 shows the average silica content (% by mass), average daily SO₂ emission (metric tons/day), and total number of eruptions recorded.

Table 1.

Figure 1 shows SO₂ emissions (tons/day) for Volcano D over 8 consecutive days.

Figure 1.

Figure 2  shows the number of eruptions at Volcano D associated with different SO₂ emission ranges during the same 8-day period.

Figure 2. 

Figure 3 shows the relationship between silica content and eruption style. The primary magma type (basaltic, andesitic, and rhyolitic) involved in each eruption style is shown.

A geologist classifies each volcano observed as basaltic, andesitic, or rhyolitic. Which of the following provides accurate classifications?

Item 24

Researchers investigated how temperature and predator presence affect the physiology, behavior, and survival of the freshwater zooplankton Daphnia magna. Four studies were conducted.

Study 1

Individual Daphnia were placed at four temperatures. Their average heart rate (bpm) was measured (Table 1).

Table 1.

Study 2

Daphnia were placed in vertical water columns. Trials were conducted with (+P) and without (−P) predator chemical cues. The depth to which the zooplankton drifted was measured over 3 trials for each condition (Table 2).

Table 2.

Study 3

Groups of Daphnia were exposed to different combinations of temperature and predator cues. Percent survival after 7 days is shown (Table 3).

Table 3.

Study 4

Researchers measured the average number of offspring per individual after 7 days under the same conditions as Study 3 (Table 4).

Table 4. 

The average depth of Daphnia with predator chemical cues over Trials 1-3 is closest to:

Item 25

Researchers investigated how temperature and predator presence affect the physiology, behavior, and survival of the freshwater zooplankton Daphnia magna. Four studies were conducted.

Study 1

Individual Daphnia were placed at four temperatures. Their average heart rate (bpm) was measured (Table 1).

Table 1.

Study 2

Daphnia were placed in vertical water columns. Trials were conducted with (+P) and without (−P) predator chemical cues. The depth to which the zooplankton drifted was measured over 3 trials for each condition (Table 2).

Table 2.

Study 3

Groups of Daphnia were exposed to different combinations of temperature and predator cues. Percent survival after 7 days is shown (Table 3).

Table 3.

Study 4

Researchers measured the average number of offspring per individual after 7 days under the same conditions as Study 3 (Table 4).

Table 4. 

For which temperature is the percent decrease in survival due to predator cues the greatest? 

Item 26

Researchers investigated how temperature and predator presence affect the physiology, behavior, and survival of the freshwater zooplankton Daphnia magna. Four studies were conducted.

Study 1

Individual Daphnia were placed at four temperatures. Their average heart rate (bpm) was measured (Table 1).

Table 1.

Study 2

Daphnia were placed in vertical water columns. Trials were conducted with (+P) and without (−P) predator chemical cues. The depth to which the zooplankton drifted was measured over 3 trials for each condition (Table 2).

Table 2.

Study 3

Groups of Daphnia were exposed to different combinations of temperature and predator cues. Percent survival after 7 days is shown (Table 3).

Table 3.

Study 4

Researchers measured the average number of offspring per individual after 7 days under the same conditions as Study 3 (Table 4).

Table 4. 

A scientist is studying the ideal temperature to reach a minimum Daphnia survival rate of 80%  and an offspring production of at least 15 without predator cues. How many different temperatures yielded results that meet these requirements?

Item 27

Researchers investigated how temperature and predator presence affect the physiology, behavior, and survival of the freshwater zooplankton Daphnia magna. Four studies were conducted.

Study 1

Individual Daphnia were placed at four temperatures. Their average heart rate (bpm) was measured (Table 1).

Table 1.

Study 2

Daphnia were placed in vertical water columns. Trials were conducted with (+P) and without (−P) predator chemical cues. The depth to which the zooplankton drifted was measured over 3 trials for each condition (Table 2).

Table 2.

Study 3

Groups of Daphnia were exposed to different combinations of temperature and predator cues. Percent survival after 7 days is shown (Table 3).

Table 3.

Study 4

Researchers measured the average number of offspring per individual after 7 days under the same conditions as Study 3 (Table 4).

Table 4. 

A student claims that increasing temperature always increases reproductive output. Which of the following observations best disproves this claim?

Item 28

Researchers investigated how temperature and predator presence affect the physiology, behavior, and survival of the freshwater zooplankton Daphnia magna. Four studies were conducted.

Study 1

Individual Daphnia were placed at four temperatures. Their average heart rate (bpm) was measured (Table 1).

Table 1.

Study 2

Daphnia were placed in vertical water columns. Trials were conducted with (+P) and without (−P) predator chemical cues. The depth to which the zooplankton drifted was measured over 3 trials for each condition (Table 2).

Table 2.

Study 3

Groups of Daphnia were exposed to different combinations of temperature and predator cues. Percent survival after 7 days is shown (Table 3).

Table 3.

Study 4

Researchers measured the average number of offspring per individual after 7 days under the same conditions as Study 3 (Table 4).

Table 4. 

Based on the results of the studies, which of the following statements is best supported?

Item 29

Researchers investigated how temperature and predator presence affect the physiology, behavior, and survival of the freshwater zooplankton Daphnia magna. Four studies were conducted.

Study 1

Individual Daphnia were placed at four temperatures. Their average heart rate (bpm) was measured (Table 1).

Table 1.

Study 2

Daphnia were placed in vertical water columns. Trials were conducted with (+P) and without (−P) predator chemical cues. The depth to which the zooplankton drifted was measured over 3 trials for each condition (Table 2).

Table 2.

Study 3

Groups of Daphnia were exposed to different combinations of temperature and predator cues. Percent survival after 7 days is shown (Table 3).

Table 3.

Study 4

Researchers measured the average number of offspring per individual after 7 days under the same conditions as Study 3 (Table 4).

Table 4. 

Based on the results of the studies, which condition would most likely produce the greatest overall population growth?

Item 30

Students studied the motion of charged particles traveling through a region with a uniform electric field. A particle with charge [latex]q[/latex] and mass [latex]m[/latex] enters the field from rest and accelerates over a horizontal distance [latex]d[/latex]. 

The acceleration is proportional to [latex]\dfrac{qE}{m}[/latex], where [latex]E[/latex] is the electric field strength.

Study 1

The electric field was set to [latex]E=2.0\times 10^3 N/C[/latex], and the distance was [latex]d=0.020 m[/latex]. The time it took for the particle to move through the electric field and its final speed are recorded in Table 1 below.

Table 1.

Study 2

The procedure was repeated with [latex]E=4.0\times 10^3 N/C[/latex], while keeping [latex]d[/latex] constant. The results are shown in Table 2

Table 2.

Study 3

The electric field was reset to [latex]E=2.0\times 10^3 N/C[/latex], but the distance was increased to [latex]d=0.080m[/latex]. The results are shown in Table 3.

Table 3.

The students graphed the final speed [latex]v[/latex] versus [latex]q/m[/latex] for Studies 1 and 2 (see Figure 1).

Figure 1. 

A scientist would like to study whether increasing the distance traveled results in an increase in final speed for the same particle under the same electric field. Which combination of trials would help the scientist figure this out?

Item 31

Students studied the motion of charged particles traveling through a region with a uniform electric field. A particle with charge [latex]q[/latex] and mass [latex]m[/latex] enters the field from rest and accelerates over a horizontal distance [latex]d[/latex]. 

The acceleration is proportional to [latex]\dfrac{qE}{m}[/latex], where [latex]E[/latex] is the electric field strength.

Study 1

The electric field was set to [latex]E=2.0\times 10^3 N/C[/latex], and the distance was [latex]d=0.020 m[/latex]. The time it took for the particle to move through the electric field and its final speed are recorded in Table 1 below.

Table 1.

Study 2

The procedure was repeated with [latex]E=4.0\times 10^3 N/C[/latex], while keeping [latex]d[/latex] constant. The results are shown in Table 2

Table 2.

Study 3

The electric field was reset to [latex]E=2.0\times 10^3 N/C[/latex], but the distance was increased to [latex]d=0.080m[/latex]. The results are shown in Table 3.

Table 3.

The students graphed the final speed [latex]v[/latex] versus [latex]q/m[/latex] for Studies 1 and 2 (see Figure 1).

Figure 1. 

Which of the following statements best explains why the Study 2 curve lies above the Study 1 curve in Figure 1?

Item 32

Students studied the motion of charged particles traveling through a region with a uniform electric field. A particle with charge [latex]q[/latex] and mass [latex]m[/latex] enters the field from rest and accelerates over a horizontal distance [latex]d[/latex]. 

The acceleration is proportional to [latex]\dfrac{qE}{m}[/latex], where [latex]E[/latex] is the electric field strength.

Study 1

The electric field was set to [latex]E=2.0\times 10^3 N/C[/latex], and the distance was [latex]d=0.020 m[/latex]. The time it took for the particle to move through the electric field and its final speed are recorded in Table 1 below.

Table 1.

Study 2

The procedure was repeated with [latex]E=4.0\times 10^3 N/C[/latex], while keeping [latex]d[/latex] constant. The results are shown in Table 2

Table 2.

Study 3

The electric field was reset to [latex]E=2.0\times 10^3 N/C[/latex], but the distance was increased to [latex]d=0.080m[/latex]. The results are shown in Table 3.

Table 3.

The students graphed the final speed [latex]v[/latex] versus [latex]q/m[/latex] for Studies 1 and 2 (see Figure 1).

Figure 1. 

Which of the following best describes the relationship between final speed v and q/m?

Item 33

Students studied the motion of charged particles traveling through a region with a uniform electric field. A particle with charge [latex]q[/latex] and mass [latex]m[/latex] enters the field from rest and accelerates over a horizontal distance [latex]d[/latex]. 

The acceleration is proportional to [latex]\dfrac{qE}{m}[/latex], where [latex]E[/latex] is the electric field strength.

Study 1

The electric field was set to [latex]E=2.0\times 10^3 N/C[/latex], and the distance was [latex]d=0.020 m[/latex]. The time it took for the particle to move through the electric field and its final speed are recorded in Table 1 below.

Table 1.

Study 2

The procedure was repeated with [latex]E=4.0\times 10^3 N/C[/latex], while keeping [latex]d[/latex] constant. The results are shown in Table 2

Table 2.

Study 3

The electric field was reset to [latex]E=2.0\times 10^3 N/C[/latex], but the distance was increased to [latex]d=0.080m[/latex]. The results are shown in Table 3.

Table 3.

The students graphed the final speed [latex]v[/latex] versus [latex]q/m[/latex] for Studies 1 and 2 (see Figure 1).

Figure 1. 

As stated in the passage, the acceleration of a particle in an electric field is calculated using the particle’s charge, mass, and the electric field strength. What is the acceleration of Particle Y in Study 3?

Item 34

Students studied the motion of charged particles traveling through a region with a uniform electric field. A particle with charge [latex]q[/latex] and mass [latex]m[/latex] enters the field from rest and accelerates over a horizontal distance [latex]d[/latex]. 

The acceleration is proportional to [latex]\dfrac{qE}{m}[/latex], where [latex]E[/latex] is the electric field strength.

Study 1

The electric field was set to [latex]E=2.0\times 10^3 N/C[/latex], and the distance was [latex]d=0.020 m[/latex]. The time it took for the particle to move through the electric field and its final speed are recorded in Table 1 below.

Table 1.

Study 2

The procedure was repeated with [latex]E=4.0\times 10^3 N/C[/latex], while keeping [latex]d[/latex] constant. The results are shown in Table 2

Table 2.

Study 3

The electric field was reset to [latex]E=2.0\times 10^3 N/C[/latex], but the distance was increased to [latex]d=0.080m[/latex]. The results are shown in Table 3.

Table 3.

The students graphed the final speed [latex]v[/latex] versus [latex]q/m[/latex] for Studies 1 and 2 (see Figure 1).

Figure 1. 

A new particle W has q/m = 3.0 x 10⁷ C/kg. Under Study 2 conditions, its final speed would most likely be closest to:

Item 35

Students studied the motion of charged particles traveling through a region with a uniform electric field. A particle with charge [latex]q[/latex] and mass [latex]m[/latex] enters the field from rest and accelerates over a horizontal distance [latex]d[/latex]. 

The acceleration is proportional to [latex]\dfrac{qE}{m}[/latex], where [latex]E[/latex] is the electric field strength.

Study 1

The electric field was set to [latex]E=2.0\times 10^3 N/C[/latex], and the distance was [latex]d=0.020 m[/latex]. The time it took for the particle to move through the electric field and its final speed are recorded in Table 1 below.

Table 1.

Study 2

The procedure was repeated with [latex]E=4.0\times 10^3 N/C[/latex], while keeping [latex]d[/latex] constant. The results are shown in Table 2

Table 2.

Study 3

The electric field was reset to [latex]E=2.0\times 10^3 N/C[/latex], but the distance was increased to [latex]d=0.080m[/latex]. The results are shown in Table 3.

Table 3.

The students graphed the final speed [latex]v[/latex] versus [latex]q/m[/latex] for Studies 1 and 2 (see Figure 1).

Figure 1. 

Scientists are studying two new particles, Particle U and Particle V. The mass of Particle U is 2.0 x 10^-26 kg and the mass of particle V is 1.0 x 10^-26 kg. Suppose Particle U is placed under the conditions of Study 2 and Particle V is placed under the conditions of Study 1. Both particles have the same charge q. 

Compared to the acceleration of Particle V, the acceleration of Particle U is: 

Item 36

Students conducted an experiment to see how much of a solid “precipitates” (falls out of solution as a solid) when a hot solution is cooled. They added specific masses of four salts (Potassium Nitrate, KNO₃; Sodium Chloride, NaCl; Lead(II) Nitrate,  Pb(NO₃)₂; and Cerium(III)Sulfate, Ce₂(SO₄)₃) to 100g of H₂O at 100^oC, ensuring they were fully dissolved. Then, they cooled the solutions to the temperatures listed in Table 1 and measured the mass of the solid that precipitated.

Table 1.

Solubility is the maximum amount of a substance (solute) that can dissolve in a specific amount of solvent (usually water) at a given temperature. Figure 1 shows the solubility of the same four salts in water at temperatures ranging from 0^oC to 100^oC.

Figure 1.

At which of the following temperatures is the solubility of KNO₃ closest to the solubility of NaCl?

Item 37

Students conducted an experiment to see how much of a solid “precipitates” (falls out of solution as a solid) when a hot solution is cooled. They added specific masses of four salts (Potassium Nitrate, KNO₃; Sodium Chloride, NaCl; Lead(II) Nitrate,  Pb(NO₃)₂; and Cerium(III)Sulfate, Ce₂(SO₄)₃) to 100g of H₂O at 100^oC, ensuring they were fully dissolved. Then, they cooled the solutions to the temperatures listed in Table 1 and measured the mass of the solid that precipitated.

Table 1.

Solubility is the maximum amount of a substance (solute) that can dissolve in a specific amount of solvent (usually water) at a given temperature. Figure 1 shows the solubility of the same four salts in water at temperatures ranging from 0^oC to 100^oC.

Figure 1.

The maximum amount of KNO₃ in Trial 1 that could remain dissolved in 100g of water at 60^oC was:

Item 38

Students conducted an experiment to see how much of a solid “precipitates” (falls out of solution as a solid) when a hot solution is cooled. They added specific masses of four salts (Potassium Nitrate, KNO₃; Sodium Chloride, NaCl; Lead(II) Nitrate,  Pb(NO₃)₂; and Cerium(III)Sulfate, Ce₂(SO₄)₃) to 100g of H₂O at 100^oC, ensuring they were fully dissolved. Then, they cooled the solutions to the temperatures listed in Table 1 and measured the mass of the solid that precipitated.

Table 1.

Solubility is the maximum amount of a substance (solute) that can dissolve in a specific amount of solvent (usually water) at a given temperature. Figure 1 shows the solubility of the same four salts in water at temperatures ranging from 0^oC to 100^oC.

Figure 1.

If the student had cooled the solution in Trial 4 to 80^oC instead of 60^oC, the mass of the precipitate would have been closest to:

Item 39

Students conducted an experiment to see how much of a solid “precipitates” (falls out of solution as a solid) when a hot solution is cooled. They added specific masses of four salts (Potassium Nitrate, KNO₃; Sodium Chloride, NaCl; Lead(II) Nitrate,  Pb(NO₃)₂; and Cerium(III)Sulfate, Ce₂(SO₄)₃) to 100g of H₂O at 100^oC, ensuring they were fully dissolved. Then, they cooled the solutions to the temperatures listed in Table 1 and measured the mass of the solid that precipitated.

Table 1.

Solubility is the maximum amount of a substance (solute) that can dissolve in a specific amount of solvent (usually water) at a given temperature. Figure 1 shows the solubility of the same four salts in water at temperatures ranging from 0^oC to 100^oC.

Figure 1.

A scientist conducted Trial 7, adding 40g of NaCl at 100^oC and cooling it to 40^oC. The amount dissolved after cooling is expected to be closest to:

Item 40

Students conducted an experiment to see how much of a solid “precipitates” (falls out of solution as a solid) when a hot solution is cooled. They added specific masses of four salts (Potassium Nitrate, KNO₃; Sodium Chloride, NaCl; Lead(II) Nitrate,  Pb(NO₃)₂; and Cerium(III)Sulfate, Ce₂(SO₄)₃) to 100g of H₂O at 100^oC, ensuring they were fully dissolved. Then, they cooled the solutions to the temperatures listed in Table 1 and measured the mass of the solid that precipitated.

Table 1.

Solubility is the maximum amount of a substance (solute) that can dissolve in a specific amount of solvent (usually water) at a given temperature. Figure 1 shows the solubility of the same four salts in water at temperatures ranging from 0^oC to 100^oC.

Figure 1.

In Trial 6, Ce₂(SO₄)₃ was cooled to 80^oC. Suppose a scientist cooled the solution further to 20^oC. Compared to Trial 6, would the scientist expect to see a larger mass of precipitate or a smaller mass and why?