Learn to operate the Alpha-E system. The user becomes familiar with the functions and operation methods of the Alpha-E benchtop particle accelerator system by setting up the device in different configurations and performing system tests to evaluate performance under standard conditions. The procedures and skills introduced in this lesson are applied in all of the Alpha-E workshop modules. You will prepare the vacuum chamber, ignite an ion beam, and measure fusion products.

Explore how metal deuterides, compounds isotopically-related to hydrides, such as titanium deuteride (TiD₂) and zirconium deuteride (ZrD₂), can serve as fusion targets in the Alpha-E system for deuterium-deuterium (DD) fusion experiments. You will investigate how these deuterides form, how resilient they are to exposure to the ion beam, and other material properties. Through hands-on operation of the Alpha-E and measurements of energetic particles, you will analyze fusion rates to determine how certain material properties affect the fusion yield over time.

Use a PIN-type silicon (Si-PIN) detector to measure energetic charged particles from D-D fusion, such as 3 MeV protons, 1 MeV tritons, and 0.89 MeV helium-3, and a scintillator-based detector with an integrated photomultiplier tube (PMT) to observe 2.45 MeV neutrons. Exercises include acquisition electronics and post-acquisition techniques, such as pulse shape discrimination (PSD) and digital signal processing (DSP). Students may also use the Timepix detector kit, a cutting-edge pixelated particle-imaging sensor developed at CERN.

Record the energy spectrum of charged particles generated by D-D fusion and correlate each event with time-tagged counts from a fast-neutron detector. By analyzing nanosecond-scale coincidences between the neutron and its associated charged particle, you will master the core principle of associated-particle imaging (API)—the technology that underpins neutron generators used in security screening and explosives detection. With the optional FPGA development kit, students will build an FPGA-based data-acquisition chain.

Measure the rate of production of alpha particles from p-B fusion, and use the production rate to determine an estimate of the effective fusion cross section. The cross section is a fictitious area that corresponds to the probability that two specified nuclei will collide in a way that results in fusion. You will predict the output yield of two channels in the DD reaction, and alpha output from p-¹¹B and p-¹⁰B fusion, then correlate theoretical predictions to detector observations.

CR-39 is a tough, clear plastic polymer material useful for observing energetic charged particles. When MeV-scale ions pass through CR-39, they leave behind trails of damaged chemical bonds that can be enlarged into visible track pits by etching in a strong base. You will use the Alpha-E to expose coupons of CR-39 to D-D fusion products, then use YOLOv8, a cutting-edge deep learning tool, to identify, measure and count the tracks. The processed data can train an AI model to quantify fusion yield in a consistent, reproducible way.

Prepare a cloud chamber where invisible protons from D-D fusion leave ghostly trails you can see with your own eyes. The cloud trails are condensation of a supersaturated ethanol vapor forming in the wake of the energetic charged particle. You will capture images of tracks, compare cooling strategies (dry ice vs Peltier thermoelectric modules), and perform statistical comparisons of track density, clarity, and run-time stability.