Detecting Naturally-Occuring Radioactive Isotope Signatures with Scintillation Detectors
Advisor: Dr. Dixit
Internship Location: Berkeley Radwatch, UC Berkeley Nuclear Engineering Dept. 2505 Hearst Ave, Berkeley, CA 94709
Onsite Mentor: Ali Hanks
Radioactive spectra are tools nuclear scientists use to visualize energies in radioactive decay. Energies of radioactive decay are plotted on a histogram which is then analyzed to find the most potent radioactive sources. This project automates the process of identifying radioactive isotopes in the environment via analysis of spectra obtained from Sodium Iodide (NaI) scintillation detectors. Currently, UC Berkeley’s Nuclear Engineering Department conducts environmental surveys around high-risk radiation areas, but manual analysis of spectral data from these surveys is typically employed afterwards, a time-intensive process which this project will streamline. With the new algorithms, radiation monitoring will be easier, and researchers will be able to see at a glance which radioactive isotopes are most likely present in a sample. Outside of the lab, handheld NaI systems running the software from this project can be used to increase education and awareness around radiation and radiation dose.
Project Blog
Week 0: Blog Post
Hi everybody, Welcome to my senior project! Tune in weekly if you’re interested in everything nuclear!
My project revolves around graphing and analyzing radioactive decays in our natural environment that occur in our daily lives. Specifically, I’ll be dealing with radioactive spectra, which are tools nuclear scientists use to visualize energies in radioactive decay. These spectra are displayed as histograms. The x axis of the histogram tracks the energies of decays, while the y axis is the number of decays for a given energy range. Thus, the spectrum is basically a way to keep track of how many radioactive decays occur at what energies during a period of time. Once you have a radioactive spectrum, you can then look at the peaks on the graph to see which radioactive sources are the most potent in the environment.
Data are collected using sensors. For the most accurate results, High Purity Germanium Sensors (HPGe’s) are used, but such sensors are pretty expensive and unwieldy. So, all the analysis done for this project will be with the cheaper, more portable Sodium Iodide (NaI) sensors.
I’ll be working with Radwatch at UC Berkeley’s Nuclear Engineering Department, which will provide all the materials necessary to carry out the project. The department often conducts environmental surveys around high-risk radiation areas, but manual analysis of spectral data from these surveys is typically employed afterwards, a time-intensive process which this project will hopefully streamline. My main goal with this project is to make radiation monitoring easier, and my work will allow researchers to see at a glance which radioactive isotopes are most likely present in a sample when data are obtained from environmental monitoring sensors.
That’s not my only goal, though, and I hope that my project will help increase education around nuclear science! The mission statement of Berkeley Radwatch is “...to provide transparent, relevant measurements of radioactivity in our environment for public education and enable communities to make their own measurements.” I hope that the sensors and code developed from this project will make reading radiation data more intuitive, and will hopefully make education in nuclear science easier. Each week in this blog, I’ll also tell you guys about some interesting things about nuclear science and engineering!
Stay tuned, and see you all soon!
Week 1: Blog Post
Hi everybody!
Welcome to the first official week of senior project! This week, I focused on picking up where I left off last summer, and implementing some of the future improvements I talked about in this article.
Nuclear Physics Weekly
Before anything though, I wanted to start the blog each week with some facts about nuclear physics. This week, let’s talk about what radiation is. The english word “to radiate” comes from the latin verb “radiare,” meaning to shine or beam out. Consequently, radiation in physics is the emission of energy, usually in the form of light or some subatomic particles. When the word “radiation” is mentioned, many tend to think of toxic green waste and a black and yellow trefoil radioactive warning sign. In reality, though, radiation comes in many forms, most of which actually aren’t dangerous at all.
The Electromagnetic Spectrum
As mentioned previously, radiation is usually emitted in the form of light. In the 19th century, work done by Young, Huygens, and Maxwell found that light is a wave that behaves in accordance with Maxwell’s equations of electromagnetism. A couple decades later in the 20th century, physicists Planck and Einstein found that the energy of this electromagnetic wave is proportional to the frequency of the wave (as opposed to the amplitude for sound or water). Subsequently, scientists named the electromagnetic spectrum, which classifies light on a scale of its frequency.
The above diagram also shows the wavelength scale of different light waves, which is simply inversely proportional to the frequency (i.e. longer wavelength → higher frequency → higher energy). Inductiveload, NASA, 26 October 2007, This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license
On the left end of the spectrum lies radio waves, which are the lowest energy waves, used for communication between a smartphone and a cell tower. As we move to the right, the frequency increases, passing microwaves (used in microwaves to heat your food), infrared (used in motion detection systems), to visible light, which is the light we can see. After visible light, the frequency and energy become high enough to be harmful, and can cause cancer if exposed in high enough quantities. Still, in very low quantities, these waves are completely safe! My project focuses on spectra in the gamma regieme—specifically counting the number of different gamma rays emitted by particles in the environment. Next week, I’ll talk about radiation dose and radioactive decay. Stay Tuned!
Project Progress
As mentioned above, project work this week was dedicated to picking up where I left in the summer, getting reaccustomed to coding in python, and implementing future improvements. Specifically, I decided to implement the last improvement “Improving count accuracy by calculating the integral with narrower bounds of integration, possibly using the Gaussian error function, rather than using the area under the Gaussian curve from negative infinity to positive infinity.”
Previously, I was using a gaussian regression to find the shape of a peak, then integrating under the gaussian curve to find the total counts using the formula area = sqrt(2*pi) * A * sigma where A is the height of the gaussian and sigma the standard deviation. However, this formula is for finding the area under the gaussian over the entire number line, even though the actual peak is much smaller. To introduce more appropriate bounds of integration, I used the gaussian error function, which is related to the antiderivative of the gaussian function. With this new implementation, the user can choose a radius (width) around which to integrate the spectrum, or use a generated radius based on the bounds of the peak that the code detects. Ultimately, setting these bounds increases accuracy in counting by decreasing the number of extraneous counts.
That’s it for this week, come back next week to learn about nuclear decay, nuclear safety, and to see where I’m at in my project!
Week 2: Blog Post
Hi Everybody!
Welcome to the second official week of my senior project! This week, I talk about nuclear decay and my progress on the project.
Nuclear Physics Weekly
Last week, we discussed radiation and the electromagnetic spectrum. This week, I want to touch on nuclear decay. In general, nuclear decay is the release of energy (or mass) from an unstable atomic nucleus (i.e. it has too much energy to sustain itself). There are three types of nuclear decay: alpha, beta, and gamma decay.
Types of Nuclear Decay
First, Alpha decay. Alpha decay occurs when an atomic nucleus needs to eject some of its mass, doing so via the emission of two protons and two neutrons (a helium-4 nucleus). Since the nucleus is relatively large, it is unable to penetrate most materials, and is able to be stopped simply with a sheet of paper or human skin; alpha emitters are fairly safe for external contact. However, if ingested, alpha emitters can damage internal organs, which don’t have a layer of skin protecting them.
Next, Beta decay. Beta decay is mediated by one of the fundamental forces of nature: the weak force. There are two types of beta decay, beta plus and beta minus. In beta plus decay, a proton emits a positron and an antineutrino (which is required for certain quantum conservation laws). In beta minus, the reverse happens. A neutron emits an electron and a neutrino. Beta decay is slightly more energetic on average than alpha decay, and is able to penetrate through materials easier.
Last but not least, gamma decay. Gamma decay is the spontaneous emission of a gamma ray from an atomic nucleus, decreasing the energy of the nucleus and thus making it more stable. Since gamma rays are high energy electromagnetic waves, they are able to penetrate through materials much more effectively than alpha or beta decay, with the only real method of stopping them being thick concrete or lead.
Decay Chains
Any element (that we currently know of) with mass greater than that of a lead atom is unstable. Thus, any element heavier than lead will decay until it becomes a lead atom. However, as we just saw, any given nucleus can only release a small amount of energy or mass at a time. Thus, heavier elements release energy in chains of alpha, beta, and gamma decays, called decay chains. We can use characteristic decays in these chains to detect particular elements present in the environment. For example, in the Bismuth-214 chain, there is a certain decay that emits a gamma ray with energy 609 keV (keV is simply a unit of energy). Thus, if many 609keV gamma rays are detected, there is a great chance of finding Bi-214 in the nearby environment.
Next week, I’ll discuss radiation dose, how it’s different from radiation counts, and basic radiation safety.
Project Progress
In terms of project progress, I continued implementing the future improvements listed in this article. Namely, I made two major changes:
To detect local minima in the spectrum, I used a derivative function to take the local slope of the curve. This function worked well for smooth data, but broke for noisier data with more small spikes. To fix the problem, I took an average derivative in the neighborhood around the point instead of just taking a single derivative.
Previously, the minimum finder was not functional for noisy data, because it simply checked for a derivative of zero. I made this criteria stricter by requiring the code to find a point with derivative zero and positive concavity (i.e. it’s U-shaped).
That’s it for this week, see you all next week for more on nuclear safety and project progress!
Week 3: Blog
Hi there! Welcome back to week 3 of my senior project. This week, I discuss radiation dose and noisy data.
Nuclear Physics Weekly
Last week, we discussed radioactive decay. This week, let’s dive into radiation dose, a concept more related to the absorption of radiation and radiation safety. While radiation counts tell us how many particles or photons are detected, dose quantifies how much energy is actually absorbed by a material (like human tissue) and the potential biological effects.
Radiation Dose
Radiation dose refers to the energy deposited by ionizing radiation per unit mass of a material. The more energy absorbed, the higher the dose and the greater the potential for damage. However, not all radiation types affect tissue equally. As mentioned last week, alpha particles don’t penetrate materials easily but can do substantial damage if inhaled. Gamma rays, on the other hand, do penetrate materials easily but generally have less potential to cause harm (per gamma ray). To account for these differences, scientists invented a few units to measure radiation dose.
Units of Dose
Gray (Gy): The SI unit for absorbed dose, defined as one joule of energy deposited per kilogram of material (1 Gy = 1 J/kg). This is a purely physical measurement and doesn’t consider the type of radiation or its biological impact. For instance, a gamma ray and an alpha particle delivering 1 Gy to tissue deposit the same energy, but their health risks differ drastically.
Rad: The older unit of absorbed dose but still used in some contexts. 1 Gray = 100 Rad.
Sievert (Sv): This unit measures equivalent dose, which adjusts absorbed dose for the biological effectiveness of the radiation type. It’s calculated as:
Sievert (Sv) = Gray (Gy) × Radiation Weighting Factor
The radiation weighting factor is a number added to account for the varying ionizing power of different types of radiation. For example, alpha particles have a weighting factor of 20, meaning 1 Gy of alpha radiation equals 20 Sv. Beta and gamma rays have a factor of 1, so 1 Gy equals 1 Sv. Sieverts are used in safety standards to reflect actual health risks. For reference,
Applications
With the above definitions and examples, it’s easy to see why the distinction between radiation counts and radiation dosage is so important. Even though two people might intercept the same number of radioactive particles, one can suffer much stronger effects depending on the radiation type. I like to think of it as wind chill. The colder the temperature (higher the radiation counts), the colder you are going to feel (the higher likelihood that you will sustain some effect of radiation poisoning). However, adding in a wind (the radiation weighting factor), can substantially change the perceived temperature.
Project Progress
This week’s progress was basically increasing noise on the data and adjusting the code to work correctly. A couple of notable changes were:
Tweaking the conditions for isolating a peak. The left side of a peak is simply just a local minimum, but the right side was consistently harder to get correct. This discrepancy is because the radiation curve generally trends downward, meaning that the right side is a lot less steep than the left, causing issues with critical point detection. So, I adopted my old system of stopping when the derivative of a point was 25% of a point 10 spots to the left.
At sufficiently noisy data, the regression stopped working because the peak didn’t resemble a peak enough for the software to regress the data. So, I’ve started working on a simple smoothening function to tame the noise before regression.
That’s it for this week, tune in next week to read about radiation safety and my project!
Week 4: Blog Post
Hello! Welcome to week 4 of my senior project! This week, I discuss radiation safety and progress on my project.
Nuclear Physics Weekly
In the last few weeks, we discussed what radiation is, how it’s created, and what radiation dose is. This week, I want to talk a bit about radiation safety and how to protect yourself from harmful radiation.
First of all, you probably have nothing to worry about. Dangerously radioactive sources are exceedingly rare—especially in everyday life. However, if you do come into contact with a radioactive source, there are three main tenets of radiation safety to keep in mind.
Factors of Radiation Exposure
Time. Time of exposure is very important. For any radioactive source, the amount of dose that you receive is directly proportional to the time of contact you have with the source. Minimizing the time of contact reduces your received dose.
Distance. The radiation dose you receive from a given source varies inversely with the square of the distance. That means, if you’re two inches away from a source, your dose is a quarter of what it is just an inch from the source. Move a whole foot away, and your dose is 144x less than the dose at a distance of one inch.
Shielding. Radiation does not propagate well through dense materials. Thus, putting material between you and the source can minimize radiation. Most of the time, thick slabs of lead or concrete are used, which stop practically all radiation from passing through. This principle is in part why nuclear reactors are kept underwater, and why sunscreen blocks out UV radiation (the thin film of sunblock stops the UV rays).
Beyond those basics, workers in areas of potential hazard wear Personal Protective Equipment (PPE) to properly shield against any radiation, as well as dosimeters to track received dose.
Project Progress
Continuing the theme from last week, this week mainly consisted of increasing the noise in the data until the code stopped functioning, and finding solutions. I’ve also begun the process of getting the code to function on a portable radiation detection system.
The smoothing function that I mentioned last week, which takes a running average of values in the data, has been fully implemented and works well, decreasing the sampling time to less than one hour.
I’ve started to look over UI code to deploy what I’ve been working on onto a portable device, which consists of a Cesium-Iodide detector, a raspberry PI, and a display.
That’s it for this week, see you all next week, where I’ll talk about radiation detection and more progress on the project!
Week 5: Blog Post
Hi everybody!
Welcome to week 5 of my senior project. This week, I talk about radiation detection and more progress on my project.
Nuclear Physics Weekly
Up to now, we’ve discussed what radiation is and basic radiation safety. This week, we’ll discuss how radiation is detected.
Ionizing Radiation
First, we need to make a distinction between ionizing and non ionizing radiation. Ionizing radiation refers to any radiation that can ionize an atom—that is, kick an electron out of reach of the nucleus. All three types of nuclear decay that we covered in week 2 can be forms of ionizing radiation, although today we will focus on electromagnetic radiation. In general, x-rays and gamma rays are considered high enough energy to be ionizing while anything less powerful is classified as non-ionizing.
Geiger Counters
Geiger counters are a tool scientists use to measure the number of ionizing radioactive events that happen in an environment. They’re the tool that you probably see movies, clicking when brought near a radioactive sample.
Geiger counters work by converting the energy in ionizing radiation to a current, then counting the number of current spikes. Inside a Geiger counter, there is a tube of gas with a negatively charged rod in the center (the anode), surrounded by a positively charged shell (the cathode). Between the anode and cathode, a very high voltage difference is maintained, usually on the order of a few hundred volts. Since this voltage is set over just a few centimeters of separation, a strong electric field permeates the gas, drawing negative things to the cathode and positive things to the anode. When ionizing radiation enters the tube, it interacts with a gas particle, knocking an electron off. This electron then goes on to hit other gas atoms, causing a chain reaction and a flood of electrons to be knocked free of their parent atoms. These electrons all rush towards the cathode, creating a current spike that the detector counts as one count. On average, the chain reactions take place over just a few microseconds, allowing the Geiger counter to take precise and accurate measurements for high count rates.
The output of a Geiger counter is a count rate, measured usually in counts per minute (cpm) or counts per second (cps). Note that the Geiger counter doesn’t distinguish between different kinds of radiation, as long as it’s ionizing. Next week, we’ll discuss radiation detectors that do measure the strength of radiation.
Project Progress
This week, I focused on getting my spectral analysis code working on a real-time system. Namely, I inherited user interface code from the nuclear engineering lab and implemented a simulated data collection system, where the code runs a simulation of real-life data collection. This way, I can speed up testing and do tests remotely. I’ve also begun implementing checks to tell the code when enough data has been collected to properly run the spectral analysis code.
That’s it for this week, see you all next week!
Week 6: Blog Post
Hi everybody!
Welcome to week 6 of my senior project. This week, I talk about radiation detectors and progress on my project.
Nuclear Physics Weekly
Last week, we discussed the geiger counter, which provides outputs for radiation counts, not distinguishing between different types of radiation. This week, we discuss the topic of gamma spectrometers—radiation detectors that sort the counts of radiation that they intercept by the energy of the radiation.
HPGe Detectors
High Purity Germanium (HPGe) detectors are the gold standard for high-resolution gamma spectroscopy. Their incredibly high energy resolution is perfect for projects that necessitate precise, accurate measurements. At the center of an HPGe detector is the high purity germanium crystal. When a gamma ray interacts within the crystal, it knocks some electrons out of place, which the detector reads as a current, which is proportional to the energy of the gamma ray. However, this sensitivity comes at a price: HPGe detectors must be cooled to cryogenic temperatures—usually with liquid nitrogen—to suppress thermal noise and maintain resolution. While the cooling requirement makes them somewhat cumbersome compared to room-temperature alternatives, their superior performance in long-term radiation surveys is highly valued.
Scintillation Detectors
Meanwhile, there are also scintillation detectors, which operate on a different principle than HPGe detectors. At the core of a scintillation detector is the scintillation crystal, usually a Sodium Iodide (NaI) crystal, or Cesium Iodide (CsI) crystal. When these specific crystals intercept a gamma ray, they store the energy momentarily, then release a lower-energy visible light ray. The energy of these lower-energy rays are then measured and plotted on a spectrum.
NaI detectors have been a staple of radiation detection for many decades. Their relatively-good output and simple fabrication have made them extremely popular in both research and field applications. However, while NaI detectors are very effective, they do exhibit moderate energy resolution—typically not matching the precision provided by HPGe devices.
Cesium iodide (CsI) detectors, often doped with thallium (CsI(Tl)), offer some advantages as well. CsI detectors are more useful in environments that demand mechanical stability and are less sensitive to moisture or other weather factors compared to their NaI counterparts. This makes CsI detectors fit for portable devices, like the one I am using in my project.
Project Progress
This week, I worked more on getting the code implemented on a real-time device. Specifically, I implemented code that outputs how much error there is in the device. As mentioned in the above section, CsI detectors have quite low resolution, meaning that the error bars on the output are very large. Also, I've started writing code that recognizes the peaks in the spectrum as isotopes, and plotting them as relative counts (percentages), instead of numbers, since that may be more conceptually intuitive to more people.
That’s it for this week! Tune in next week to learn more about radiation spectra and my project!
Week 7: Blog
Hi Everybody!
Welcome to week 7 of my senior project blog! This week, I discuss radioactive spectra and progress on the project.
Nuclear Physics Weekly
Last week, we looked at ways radioactive spectra are generated—with scintillators or HPGe. This week, we’re taking a closer look at the radioactive spectrum itself. Specifically, why does it look the way it does? What determines where the peaks show up?
What Is a Radioactive Spectrum?
By now, you all are probably familiar with the term “radioactive spectrum” and what it dictates. In case you need a quick refresher though, a radioactive spectrum is just a plot showing how many radioactive emissions we detect at different energy levels. The horizontal axis shows energy (usually in units like keV or MeV), and the vertical axis shows how many times radiation of that energy was detected (more on this later)—basically, a count of how often particles with that energy showed up.
These spectra are almost never flat, and today’s article discusses the common shapes you’ll see in a typical radioactive spectrum.
Peaks: The Indicators of Decay
Let’s start with the sharp peaks found im a spectrum. When a gamma ray emitted by a nucleus deposits all (or close to all) of its energy in a detector (like an HPGe or NaI detector), that energy shows up as a spike. These are especially important because each radioactive isotope emits gamma rays of very specific energies. That means every peak is like a fingerprint, helping scientists identify which isotopes are present in a sample.
Notably, a couple of the common isotopes we see in most spectra are Potassium-40 at 1460keV, Bismuth-214 at 609keV, and Thorium-238 at 2300keV. Potassium is found in all plants and animals, while bismuth and thorium are found in dirt and dust. So, these peaks usually stand out in a typical spectrum.
Compton Continuum: The Sloping Background
However, not every gamma ray gives up all its energy in one clean hit. Sometimes it bounces off an electron inside the detector, transferring only part of its energy before exiting the detector. This process is called Compton Scattering, and it creates a sloped, continuous region in the spectrum called the Compton Continuum.
Think of it like this: every scattered gamma ray gives up a random fraction of its energy, depending on the angle it bounces, which is recorded as a smooth downward sloping background. Specifically, the shape of the Compton Continuum is an exponential decay curve.
We can actually derive this shape relatively simply, although it requires some basic calculus. First, let N(E) is the function describing the number of counts for an energy E. Assuming a uniform number of actual radioactive particles at every energy, the number of particles that have an energy greater than a certain value increases as we consider higher energies. Put more simply, let’s say our energy range is 20keV to 2500keV. Any gamma with energy 1000keV-2000keV could contribute to Compton noise at 1000keV. However, any gamma with energy 500keV to 2500keV can contribute to Compton noise at 500keV (considerably more gammas than for 1000keV). Since we assume uniform gammas at all energies, this relation can be described with the equation dN/N = -dE. All this equation is saying is that the probability of there being a Compton gamma decreases as energy increases. Solving for N(E) yields the exponential decay we seek.
Project Progress
This week was again dedicated to making my code work on a real time system. A lot of time was first spent on tweaking the threshold for my code to kick in—that is how smooth the data should be before I start applying the data analysis method. As of now, approximately 30 minutes of data is sufficient, although I want to try and decrease that number even more.
Also, I worked on peak recognition software, which initially stores a list of common radioactive decayed and matches them to peaks found in the spectrum. That way, the user knows what they’re looking at, opposed to just some numbers.
That’s it for this week! Tune in text week to learn more about radiation and my project!
Week 8: Blog Post
Hi everybody!
Welcome to week 6 of my senior project. This week, I talk about radiation spectrum peaks and progress on my project.
Nuclear Physics Weekly
Last week, we discussed the Compton continuum that radiation peaks sit on. Now, let’s shift our focus to the peaks themselves.
Bell-Curve Peaks
When a gamma ray interacts with a CsI crystal, it produces a flash of light whose energy is measured and converted to a voltage reading. Over many events, you’ll see that the pulses corresponding to a particular gamma-ray energy cluster around a mean voltage, but they’re not all identical—instead, they form a symmetric, bell-shaped peak in the spectrum.
This bell shape comes from a combination of small, random fluctuations in the light yield, the photodetector gain, and the electronics. Each individual measurement of a pulse is subject to these errors and the result is a peak that is spread out a bit. Therefore, if we want to find the number of counts at, say, 1000 keV, we need to sum up the counts in a range from, say, 995 to 1005 keV. To model these bell-shaped curves, we use something called a Gaussian Distribution
Gaussian (Normal) Distributions
The Gaussian, or normal distribution, is the go-to model whenever you have many small, independent sources of variation adding together. Usually, it’s seen in the context of probability distribution (PDF) functions, which model the probability of different events occurring. A lot of random events tend to shape themselves in a Gaussian distribution, so using it here is an appropriate choice. Once we get the shape of the gaussian, we simply take the area under the gaussian to determine the number of decays that happened at the peak energy of the gaussian.
Project Progress
The end of the project is nearing, and I’m mostly wrapping up work at this point. I didn’t write much new code this week, but did a lot of cleaning up and organizing existing code into readable chunks. I’m also trying to get my hands on a portable detector, but if that’s not possible, I’ll be running my code on a simulation. Next week, I’ll start writing my presentation and wrapping up!
See you all next week!
Week 9: Blog
Hi everybody!
Welcome to week 9 of my senior project. This week, I talk about radiation spectrum peaks and progress on my project.
Nuclear Physics Weekly
Last week, we covered analyzing gaussian peaks to obtain counts at certain energies. Today, since I’m running code on real sensors now, I wanted to cover some common peaks to look out for in gamma spectra.
Let’s go through a few of the most frequently observed gamma energies and the isotopes that emit them:
1460 keV — Potassium-40
Potassium-40 is a naturally occurring isotope found in soil, bananas, and even the human body. It’s part of the natural background radiation. Its 1460 keV peak is a giveaway—it’s high in energy, relatively isolated, and almost always present if you’re measuring ambient background. Since it’s so prominent and reliable, it’s also typically used to calibrate sensors.
609 keV, 1120 keV, and 1764 keV — Bismuth-214 (²¹⁴Bi)
These peaks often show up together and point to one thing: the uranium-238 decay chain, which usually points to the existence of uranium and/or radon in the environment. Bi-214 is a short-lived daughter product that emits multiple gamma rays. These energies—609, 1120, and 1764 keV—are some of the most intense and are commonly used to track environmental uranium and radon levels.
295 keV and 352 keV — Lead-214 (²¹⁴Pb)
Also part of the U-238 chain, these lower-energy peaks come from Pb-214. When seen alongside the Bi-214 peaks mentioned above, they support the presence of a uranium decay series. These are especially helpful in confirming radon presence when analyzing indoor air or building materials.
511 keV — Positron Annihilation
This peak doesn't come from a specific isotope, but rather from a process. When a positron (the antimatter counterpart of an electron) encounters an electron, they annihilate and produce two 511 keV gamma rays emitted in opposite directions. This can happen in isotopes that decay by beta-plus (positron) emission, such as Fluorine-18 or Sodium-22. So if you see a distinct peak at 511keV, you probably have a positron emitter.
Project Progress
As I near the end of the project, this week was mainly focused on testing with a real sensor and working on my presentation. Luckily, the sensor outputted usable data, and I’ll keep testing and refining next week.
See you all next week!
Week 10: Blog Post
Hi everybody!
Welcome to the last and final week of my senior project blog! It’s been a great journey and I hope you’ve learned something from any of my blogs. Today, I wanted to end off with a summary of my final product, as well as some final remarks.
The Product
Before we get into the product, let’s quickly review the goals of the project:
Program a portable system to identify radioactive isotopes present in the surrounding environment.
Increase nuclear science awareness.
In terms of goal 1, I wrote code (original python files and modifying existing files written by members of the lab) to analyze gamma spectra. I’ve covered the specific details of this process in pretty high detail in the Nuclear Physics Weekly sections of previous blogs so I’ll keep it brief here. In short, the program reads the spectral data from the Cesium Iodide sensor, looks for peaks in the data, then matches those peaks to different isotopes. After the program has a list of isotopes and the number of counts detected per isotope, it calculates the relative abundances of each isotope and plots them on a bar chart.
Here’s an example photo:
On the left is the radioactive spectrum, which is a generally downward sloping curve with a few peaks. On the right is a bar chart showing the relative abundances of each radioactive isotope detected. As expected, Potassium 40 and Bismuth 214 are the most abundant isotopes, with some positrons being detected due to Carbon-14 beta decays.
As for goal 2, I sincerely hope that you all learned something from my blogs. Nuclear physics is an amazing and important field that unfortunately has many misconceptions surrounding it. Through my nuclear physics weekly, I hope that I was able to dispel some myths surrounding the subject, as well as educate with facts about radiation and dose.
I furthermore believe that the portable system running my program can help in nuclear science education. Seeing spectrum and the relative abundances in real time is a great hands-on way to introduce students to radioactive decay and gamma spectroscopy, showing them the relations between features on a graph and radioactive decays.
And that ends my senior project! I hope that everybody reading my blogs learned something! I will be presenting at the BIF senior project symposium on 5/17, if you want to learn more or see the product in person.
Signing out,
Justin