In Faraday’s experiment, moving a magnet into or out of a coil induces an electric current, which is detected by a galvanometer. The faster the magnet moves, the greater the deflection of the needle. The direction of needle deflection depends on whether the magnet is moving toward or away from the coil—reversing as the direction of motion changes. When the magnet is stationary, the needle returns to the center, indicating no induced current.
This simulation allows the user to explore the laws of electromagnetic induction (Faraday and Lenz) by dragging a magnet into and away from a coil.
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This simulation offers a hands-on and dynamic way to explore the physics of projectile motion with and without air resistance. By adjusting parameters such as launch velocity, angle, and air resistance, users can visualize how these factors affect the shape and reach of a projectile’s trajectory. The app provides real-time changes including motion paths, velocity vectors, and a velocity-time graph showing horizontal and vertical components separately. It also calculates and displays key quantities such as maximum height and range under ideal and non-ideal conditions (based on an arbitrary coefficient of drag.
Through interactive experimentation and visual reinforcement, learners gain a deeper understanding of concepts the effect of air resistance, and the difference between theoretical and real-world motion.
This is suitable for JC1’s topic on projectile motion. It can also be used for Upper Sec, if you change the launch angle to 90 degrees.
Click on the canvas to place the body at your desired location.
Label the Body.
Add Forces:
Click the “Add Force” button.
Click on two bodies that exert the force on each other.
Label the Force.
Using System Schema to Understand Newton’s Third Law
Newton’s Third Law states that when Body A exerts a force on Body B, Body B exerts an equal and opposite force on Body A. While this principle is conceptually simple, many students struggle to apply it consistently across different physical scenarios. The System Schema approach provides a powerful way to visualise and analyse these interactions. It is a representation tool developed by The Modeling Instruction program at Arizona State University (Hinrichs, 2004).
A system schema is a diagram that represents objects (as circles) and interactions (as lines) between them. Instead of focusing on individual forces, a system schema helps students see the relationships between objects before applying force diagrams. This method emphasizes Newton’s Third Law by explicitly showing how forces come in pairs between interacting objects.
To correctly identify action-reaction force pairs, consider the following guidelines:
Forces Act on Different Objects: Each force in the pair acts on a different object. For example, if Body A exerts a force on Body B, then Body B simultaneously exerts an equal and opposite force on Body A.
Forces Are Equal in Magnitude and Opposite in Direction: The magnitudes of the two forces are identical, but their directions are opposite.
Forces Are of the Same Type: Both forces in the pair are of the same nature, such as gravitational, electromagnetic, or contact forces.
The steps to applying System Schema to Newton’s Third Law are as follow:
Identify the bodies in the system – Draw each object as a separate circle.
Represent interactions – Draw lines between bodies to indicate forces they exert on each other (e.g., a box on the ground interacts with Earth through gravitational force).
Label force pairs – Each interaction represents an action-reaction force pair (e.g., a hand pushes a wall; the wall pushes back).
By mapping forces this way, students can easily recognize that forces always act between bodies and in pairs, reinforcing the symmetry of Newton’s Third Law.
One of the most common misconceptions of students is that normal contact force and gravitational force acting on a body are action-reaction pairs because they are equal and opposite in a non-accelerating system. By using the system schema, they can see that the two forces involve interaction with different bodies, e.g. the floor of an elevator for normal contact force, and the Earth for gravitational force.
This deck of slides are the ones I will be using for the Symposium on “Leveraging Technology for Engaging and Effective Learning” at the Singapore International Science Teachers’ Conference (SISTC) 2024 on Day 2 of the Conference (20 November). Feel free to download for your reference.
Heating and cooling curves are graphical representations that show how the temperature of a substance changes as heat is added or removed over time. They illustrate the behavior of substances as they go through different states—solid, liquid, and gas.
Heating Curve: This curve shows how the temperature of a substance increases as it absorbs heat. The curve typically rises as the substance heats up, with plateaus indicating phase changes, where the substance absorbs energy but its temperature remains constant. Check out the heating curves for water and nitrogen using the drop-down menu.
Cooling Curve: This curve is the opposite of the heating curve. It shows how the temperature decreases as the substance loses heat. Like the heating curve, it also has plateaus where phase changes occur, but this time, the substance releases energy. In addition to water, you can also see the cooling curve for ethanol.
With these ChatGPT-generated interactive graphs, users can change the rate of heat input or released from the substance. They can also read the descriptions that explain the changes in the average PE and KE of the molecules during each process.
A Geiger-Muller (GM) counter is an instrument for detecting and measuring ionizing radiation. It operates by using a Geiger-Muller tube filled with gas, which becomes ionized when radiation passes through it. This ionization produces an electrical pulse that is counted and displayed, allowing users to determine the presence and intensity of radiation.
This simulation (find it at https://physicstjc.github.io/sls/gm-counter) allows students to explore the random nature of radiation and the significance of accounting for background radiation in experiments. Here’s a guide to help students investigate these concepts using the simulation.
Exploring Background Radiation
Q1: Set the source to “Background” and start the count. Observe the count for a few minutes. What do you notice about the counts recorded?
A1: The counts recorded are relatively low and vary randomly. This reflects the background radiation which is always present.
Q2: Why is it important to measure background radiation before testing other sources?
A2: Measuring background radiation is important to establish a baseline level of radiation. This helps in accurately identifying and quantifying the additional radiation from other sources.
Investigating a Banana as a Radiation Source
Q3: Change the source to “Banana” and reset the data. Start the count and observe the readings. How do the counts from the banana compare to the background radiation?
A3: The counts from the banana are higher than the background radiation. This is because bananas contain a small amount of radioactive potassium-40.
Q4: How do the counts per minute (CPM) for the banana vary over time? Is there a pattern or do the counts appear random?
A4: The counts per minute for the banana vary over time and appear random, reflecting the stochastic nature of radioactive decay.
Exploring a Cesium-137 Source
Q5: Set the source to “Cesium-137” and reset the data. Start the count and observe the readings. How do the counts from Cesium-137 compare to both the background radiation and the banana?
A5: The counts from Cesium-137 are significantly higher than both the background radiation and the banana. This is because Cesium-137 is a much stronger radioactive source.
Q6: What do the counts per minute (CPM) tell you about the intensity of the Cesium-137 source compared to the other sources?
A6: The CPM for Cesium-137 is much higher, indicating a higher intensity of radiation compared to the background and banana sources.
Understanding the Random Nature of Radiation
Q7: By looking at the sample counts, can you predict the next count value? Why or why not?
A7: No, you cannot predict the next count value because radioactive decay is a random process. Each decay event is independent of the previous ones.
Q8: How can you use the background radiation measurement to correct the readings from the banana and Cesium-137 sources?
A8: You can subtract the average background CPM from the CPM of the banana and Cesium-137 sources to get the corrected readings, isolating the radiation from the specific sources.