My first job out of grad school was with a biotech company in Cambridge, Massachusetts. It was a small outfit, and everyone had a “lab job” in addition to whatever science they were hired to do — a task to maintain the common areas of the lab. My job was to maintain the water purification systems that made sure everyone had an ample supply of pure, deionized water to work with. The job consisted of mainly changing the filter and ion-exchange cartridges of the final polishing units, which cleaned up the tap water enough for science.
When I changed the filter packs, I was always amazed and revolted by the layers of slime and sediment in them. A glimpse out the window at the banks of the river Charles — love that dirty water — was enough to explain what I was seeing, and it was a lesson in just how much other stuff is mixed in with the water you drink and cook with and bathe in.
While we humans can generally do pretty well with water that rates as only reasonably pure, our industrial processes are quite another thing. Everything from power plants to pharmaceutical manufacturing facilities needs water of much, much higher purity, but nothing requires purer water than the specialized, nanometer-scale operations of a semiconductor fab. But how does ordinary tap water get transformed into a chemical of such purity that contaminants are measured in parts per trillion? And how do fabs produce enough of this ultrapure water to meet their needs? With some big chemistry.
When we were in school, they always told us we can’t see atoms. If you have an electron microscope, then they were wrong. [AlphaPhoenix] has access to a scanning tunneling transmission electron microscope and he shows us some atoms in a very thin slice of a crystal.
Of course, you aren’t directly imaging the atoms. You are looking at the shadows of the atoms, but still. If you’ve never worked with a SEM or STEM before, there are plenty of little details that are interesting like the sample holders and the vacuum system.
Erasto Mpemba’s observations initiated decades of research into the Mpemba effect: whether a liquid (typically water) which is initially hot can freeze faster than the same liquid which begins cold.
There’s a name for the phenomenon of something hot freezing faster than something cold: the Mpemba effect, named for Erasto Mpemba (pictured above) who as a teenager in Tanzania witnessed something strange in high school in the 1960s. His class was making ice cream, and in a rush to secure the last available ice tray, Mpemba skipped waiting for his boiled milk-and-sugar mixture to cool to room temperature first, like everyone else had done. An hour and a half later, his mixture had frozen into ice cream whereas the other students’ samples remained a thick liquid slurry.
Puzzled by this result, Mpemba asked his physics teacher what was going on. He was told “You were confused. That cannot happen.” Mpemba wasn’t convinced by that answer, and his observations ultimately led to decades of research.
What makes this question so hard to nail down? Among many of the issues complicating exactly how to measure such a thing is that water frankly has some odd properties; it is less dense as a solid, and it is also possible for its solid and liquid phases to exist at the same temperature. Also, water in the process of freezing is not in equilibrium, and how exactly things act as they relax into equilibrium is a process for which — physics-wise — we lack a good theory. Practically speaking, it’s also a challenge how to even accurately and meaningfully measure the temperature of a system that is not in equilibrium.
But there is experimental evidence showing that the Mpemba effect can occur, at least in principle. How this can happen seems to come down to the idea that a hot system (having more energy) is able to occupy and explore more configurations, potentially triggering states that act as a kind of shortcut or bypass to a final equilibrium. In this way, something that starts further away from final equilibrium could overtake something starting from closer.
But does the Mpemba effect actually exist — for example, in water — in a meaningful way? Not everyone is convinced, but if nothing else, it has sure driven a lot of research into nonequilibrium systems.
Why not try your own hand at investigating the Mpemba effect? After all, working to prove someone wrong is a time-honored pastime of humanity, surpassed only in popularity by the tradition of dismissing others’ findings, observations, or results without lifting a finger of your own. Just remember to stick to the scientific method. After all, people have already put time and effort into seriously determining whether magnets clean clothes better than soap, so surely the Mpemba effect is worth some attention.
How can a few grams of battery, geared motor, and some nifty materials get a jumping robot over 30 meters into the air? It wasn’t by copying a grasshopper, kangaroo, or an easily scared kitty. How was it done, then?
It’s been observed that of all the things that are possible in nature, out of all the wonderful mechanisms, fluid and aerodynamics, and chemistry, there’s one thing that is so far undiscovered in a living thing: continuous rotation. Yes, that’s right, the simple act of going roundy-round is unique to mechanical devices rather than biological organisms. And when it comes to jumping robots, biomimicry can only go so far.
With this distinct mechanical advantage in mind, [Elliot Hawkes] of the University of California Santa Barbara decided to look beyond biomimicry. As explained in the paper in Nature and demonstrated in the video below the break, the jumping robot being considered uses rubber bands, carbon fiber bows, and commodity items such as a geared motor and LiPo batteries to essentially wind up the spring mechanism and then, like a trap being sprung, release the pent up energy all at once. The result? The little jumper can go almost 100 feet into the air. Be sure to check it out!
When it comes to repairing human bodies, there’s one major difficulty: spare parts are hard to come by. It’s simply not possible to buy a knee joint or a new lung off the shelf.
At best, doctors and surgeons have made do with transplants from donors where possible. However, these are always in short supply, and come with a risk of rejection by the patient’s body.
Researchers at the University of New South Wales and a startup company, Silicon Quantum Computing, published results of their quantum dot experiments. The circuits use up to 10 carbon-based quantum dots on a silicon substrate. Metal gates control the flow of electrons. The paper appears in Nature and you can download the full paper from there.
What’s new about this is that the dots are precisely arranged to simulate an organic compound, polyacetylene. This allowed researchers to model the actual molecule. Simulating molecules is important in the study of exotic matter phases, such as superconductivity. The interaction of particles inside, for example, a crystalline structure is difficult to simulate using conventional methods. By building a model using quantum techniques on the same scale and with the same topology as the molecule in question, simulation is simplified.
The SSH (Su-Schreffer-Heeger) model describes a single electron moving along a one-dimensional lattice with staggered tunnel couplings. At least, that’s what the paper says and we have to believe it. Creating such a model for simple systems has been feasible, but for a “many body” problem, conventional computing just isn’t up to the task. Currently, the 10 dot model is right at the limit of what a conventional computer can simulate reasonably. The team plans to build a 20 dot circuit that would allow for unique simulations not feasible with classic computing tech.
The dots are made with a scanning tunneling microscope and there is a Goldilocks effect regarding the size of the dots. If they are too small, the energy levels are overwhelmed by phosphorous donors. Too large, and capacitive coupling between dots makes the system unstable.
We’ll admit, the science in the paper is pretty dense. But the Methods section outlines what it takes to create something like this. You’ll need silicon, high-temperature ovens, and the ability to handle exotic gasses and perform lithography. Pretty much an IC fab in your basement. However, we did wonder if anyone homebrewing chips had ever tried STM lithography like this as an alternative to optical lithography. Seems like it might be possible.
We can’t help with some of the more exotic gear, but if you want to build an STM, it has been done. While you can make quantum dots in your kitchen, we don’t think they are going to work the same.
Recently, we’ve stumbled upon the extensive effort that is the BirdNET research platform. BirdNET uses a neural network to identify birds by the sounds they make, and is a joint project between the Cornell Lab of Ornithology and the Chemnitz University of Technology. What strikes us is – this project is impressively featureful and accessible for a variety of applications. No doubt, BirdNET is aiming to become a one-stop shop for identifying birds as they sing.
There’s plenty of ways BirdNET can help you. Starting with likely the most popular option among us, there are iOS and Android apps – giving the microphone-enabled “smart” devices in our pockets a feature even the most app-averse hackers can respect. However, the BirdNET team also talks about bringing sound recognition to our browsers, Raspberry Pi and other SBCs, and even microcontrollers. We can’t wait for someone to bring BirdNET to a RP2040! The code’s open-source, the models are freely available – there’s hardly a use case one couldn’t cover with these.
About that Raspberry Pi version! There’s a sister project called BirdNET-Pi – it’s an easy-to-install software package intended for the Raspberry Pi OS. Having equipped your Pi with a USB sound card, you can make it do 24/7 recording and analysis using a “lite” version of BirdNET. Then, you get a web interface you can log into and see bird sounds identified in real-time. Not just that – BirdNET-Pi also processes the sounds and creates spectrograms, keeps the sound in a database, and can even send you notifications.
The BirdNET-Pi project is open, too, of course. Not just that – the BirdNET-Pi team emphasizes everything being fully local, unless you choose otherwise, and perhaps decide to share it with others. Many do make their BirdNET-Pi instances public, and there’s a lovely interactive map that shows bird sounds all across the world!
BirdNET is, undoubtedly, a high-effort project – and a shining example of what a dedicated research team can do with a neural network and an admirable goal in mind. For many of us who feel joy when we hear birds outside, it’s endearing to know that we can plug a USB sound card into our Pi and learn more about them – even if we can’t spot them or recognize them by sight just yet. We’ve covered bird sound recognition on microcontrollers before – also using machine learning.