particle

21 Articles

A finger points at a diagram of a battery with two green bars. Above it is another battery with four smaller green bars with a similar area to the first battery's two. The bottom batter is next to a blue box with a blue wave emanating from it and the top battery has a red box with a red wave emanating from it. Below the red wave is written "2x wavelength" and below the top battery is "1/2 energy in a photon."

What Are Photons, Anyway?

August 1, 2024 by 33 Comments

Photons are particles of light, or waves, or something like that, right? [Mithuna Yoganathan] explains this conundrum in more detail than you probably got in your high school physics class.

While quantum physics has been around for over a century, it can still be a bit tricky to wrap one’s head around since some of the behaviors of energy and matter at such a small scale aren’t what we’d expect based on our day-to-day experiences. In classical optics, for instance, a brighter light has more energy, and a greater amplitude of its electromagnetic wave. But, when it comes to ejecting an electron from a material via the photoelectric effect, if your wavelength of light is above a certain threshold (bigger wavelengths are less energetic), then nothing happens no matter how bright the light is.

Scientists pondered this for some time until the early 20th Century when Max Planck and Albert Einstein theorized that electromagnetic waves could only release energy in packets of energy, or photons. These quanta can be approximated as particles, but as [Yoganathan] explains, that’s not exactly what’s happening. Despite taking a few classes in quantum mechanics, I still learned something from this video myself. I definitely appreciate her including a failed experiment as anyone who has worked in a lab knows happens all the time. Science is never as tidy as it’s portrayed on TV.

If you want to do some quantum mechanics experiments at home (hopefully with more luck than [Yoganathan]), then how about trying to measure Planck’s Constant with a multimeter or LEGO? If you’re wondering how you might better explain electromagnetism to others, maybe this museum exhibit will be inspiring.

Continue reading “What Are Photons, Anyway?” →

Google Sheet showing wins and losses of sports team. Data automated by IFTTT, Alexa, and Particle

An Overly Complicated Method Of Tracking Your Favorite Sports Team

December 4, 2021 by 3 Comments

Much of the world appears to revolve around sports, and sports tracking is a pretty big business. So how do people keep up with their favorite team? Well, [Jackson] and [Mourad] decided to devise a custom IoT solution.

Their system is a bit convoluted, so bear with us. First, they tell Alexa whether or not the team won or lost that week. Alexa then sends that information to IFTTT where two different Particle Argon boards are constantly polling the results to decide how to respond next. One Particle responds by lighting up an LED, green for a win and red for a loss. Another Particle board displays the results on an LCD screen. But this is where things get tricky. One of the more confusing aspects of their design is one of the Particle boards then signals back to IFTTT, telling it to tally the number of wins and losses. This seems a bit roundabout since the system started with IFTTT in the first place. Regardless, they seemed to be happy with the result and I’m sure they learned something in the process.

This project might not fulfill any functional need given that Alexa knows everything about all our lives already and you could just ask her how your favorite team is doing whenever you want to. But hey, we’re all about learning by doing here at Hackaday and we’re all guilty of building useless projects here and there just because we can. In any case, their project could serve as a good intro to integrating your Particle with IFTTT or Alexa since there appears to be quite a bit of probably unnecessary handshaking going on here.

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Don’t Tase Me, Keeb!

March 2, 2021 by 12 Comments

Okay, so this doesn’t really use a taser — that’s just click bait and we apologize. An actual taser would be a terrible way to train yourself to be a better typist, because depending on where you choose to deliver the shock, you could damage your typing nerves pretty quickly with a few milliamps at 50,000 volts.

Instead of a taser, [nobody6502] got a pack of prank gum off of Amazon that delivers a much more doable shock that is painful enough to get the user to type more carefully. [nobody6502] set up a simple no-pain, no-train website that presents random English words one at a time and checks for typos against an open-source list of nearly half a million entries. Misspell a word, and a get a relay-driven shock from the gum circuit.

The main brain of this pain trainer is a Particle Argon board which has I/O pins that can be controlled from the web. When the website detects a typo, it sends a signal to the Argon, which turns on a relay that activates the shock mechanism. What’s most impressive is that [nobody6502] doesn’t have a full-blown computer and programmed everything on an iPad. Check out the build video after the break.

Are you a hunt and peck typist? There’s a negative reinforcement keyboard for that.

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Duality Of Light Explored By Revisiting The Double-Slit Experiment

October 18, 2020 by 41 Comments

We’ve all seen recreations of the famous double-slit experiment, which showed that light can behave both as a wave and as a particle. Or rather, it’s likely that what we’ve seen is the results of the double-slit experiment, that barcode-looking pattern of light and dark stripes, accompanied by some handwaving about classical versus quantum mechanics. But if you’ve got 20 minutes to invest, this video of the whole double-slit experiment cuts through the handwaving and opens your eyes to the quantum world.

For anyone unfamiliar with the double-slit experiment,  [Huygens Optics] actually doesn’t spend that much time explaining the background. Our explainer does a great job on the topic, but suffice it to say that when coherent light passes through two closely spaced, extremely fine openings, a characteristic pattern of alternating light and dark bands can be observed. On the one hand, this demonstrates the wave nature of light, just as waves on the ocean or sound waves interfere constructively and destructively. On the other hand, the varying intensity across the interference pattern suggests a particle nature to light.

To resolve this conundrum, [Huygens] jumps right into the experiment, which he claims can be done with simple, easily sourced equipment. This is belied a little by the fact that he used photolithography to create his slits, but it should still be possible to reproduce with slits made in more traditional ways. The most fascinating bit of this for us was the demonstration of single-photon self-interference using nothing but neutral density filters and a CCD camera. The explanation that follows of how it can be that a single photon can pass through both slits at the same time is one of the most approachable expositions on quantum mechanics we’ve ever heard.

[Huygens Optics] has done some really fascinating stuff lately, from variable profile mirrors to precision spirit levels. This one, though, really helped scratch our quantum itch.

The Challenges Of Monitoring Water Streams And Surviving Mother Nature

June 30, 2020 by 23 Comments

Small waterways give life in the form of drinking and irrigation water, but can also be very destructive when flooding occurs. In the US, monitoring of these waterways is done by mainly by the USGS, with accurate but expensive monitoring stations. This means that there is a limit to how many monitoring stations can be deployed. In an effort to come up with a more cost-efficient monitoring solution, [Rohan Menon] and [Ian Vernooy] created Aquametric, a simple water level, temperature and conductivity measuring station.

The device is built around a Particle Electron that features a STM32 microcontroller and a 3G modem. An automotive ultrasonic sensors measures water level, a thermistor measures temperature and a pair of parallel aluminum plates are used to measure conductivity. All the data from the prototype is output to a live dashboard. The biggest challenges for the system came with field deployment.

The great outdoors can be rather merciless with our ideas and electronic devices. [Rohan] and [Ian] did some tests with LoRa, but quickly found that the terrain severely limited the effective range. Power was another challenge, first testing with a solar panel and lithium battery. This proved unreliable especially at temperatures near freezing, so they decided to use 18 AA batteries instead and optimized power usage.

The mounting system is still an ongoing challenge. A metal pole driven into the riverbed at a wider part ended up bent (probably from ice sheets) and covered in debris to the point that it affected water level readings. They then moved to a narrower and shallower section in the hopes of avoiding debris, but the rocky bottom prevented them from effectively driving in a pole. So the mounted the pole on a steel plate which was then packet with rock to keep it in place. This too failed when it tipped over from rising water levels, submerging the entire sensor unit. Surprisingly it survived with only a little moisture getting inside.

For the 2020 Hackaday Prize, Field Ready and Conservation X Labs have issued challenges that need require some careful consideration and testing to build things that can survive the real world. So go forth and hack!

The HackadayPrize2020 is Sponsored by:
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This Is It For The Particle Mesh Network

January 30, 2020 by 24 Comments

The long-held dream of wireless network hackers everywhere is to dispense with centralised network infrastructure, and instead rely on a distributed network in which the clients perform the role of distribution and routing of traffic. These so-called mesh networks promise scalability and simplicity on paper, but are in practice never as easy to implement as the theory might suggest. Much venture capital has been burned over the years by startups chasing that particular dream, yet most of our wireless connectivity still follows a hub topology.

An exciting development in our sphere concerning mesh networking came in early 2018, when Particle, the purveyors of wireless-equipped dev boards, launched their third generation of products. These offered mesh networking alongside their other features, but this week they have announced that they’ll no longer be developing that particular side of their offering. The Wi-Fi-equipped Argon and Cellular-equipped Boron will remain on sale, but they will henceforth discontinue the mesh-only Xenon. Existing owners of the now orphaned board will be compensated with store credit.

Their rationale for discontinuing mesh networking is interesting, and reflects on the sentiment in our first paragraph. Mesh networking is hard, and in particular their attempt to make it work with zero configuration was simply not successful. But then they talk about the realisation that maybe mesh networking was not the right solution for the IoT applications the boards were being used in, and perhaps another technology such as LoRa would be more appropriate.

So the mesh experiment from Particle is over, but the company and its connected dev boards are very much still with us. We salute them for being bold enough to try it, and we wonder when we’ll next find a piece of similar mesh networking hardware.

How To Run ML Applications On Particle Hardware

November 26, 2019 by No comments

With the release of TensorFlow Lite at Google I/O 2019, the accessible machine learning library is no longer limited to applications with access to GPUs. You can now run machine learning algorithms on microcontrollers much more easily, improving on-board inference and computation.

[Brandon Satrom] published a demo on how to run TFLite on Particle devices (tested on Photon, Argon, Boron,  and Xenon) making it possible to make predictions on live data with pre-trained models. While some of the easier computation that occurs on MCUs requires manipulating data with existing equations (mapping analog inputs to a percentage range, for instance), many applications require understanding large, complex sets of sensor data gathered in real time. It’s often more difficult to get accurate results from a simple equation.

The current method is to train ML models on specialty hardware, deploy the models on cloud infrastructure, and backhaul sensor data to the cloud for inference. By running the inference and decision-making on-board, MCUs can simply take action without backhauling any data.

He starts off by constructing a simple TGLite model for MCU execution, using mean squared error for loss and stochastic gradient descent for the optimization. After training the model on sample data, you can save the model and convert it to a C array for the MCU. On the MCU, you can load the model, TFLite libraries, and operations resolver, as well as instantiate an interpreter and tensors. From there you invoke the model on the MCU and see your results!

[Thanks dcschelt for the tip!]