Chemical Formulas 101

It seems like every other day we hear about some hacker, tinkerer, maker, coder or one of the many other Do-It-Yourself engineer types getting their hands into a complex field once reserved to only a select few. Costs have come down, enabling common everyday folks to equip themselves with 3D printers, laser cutters, CNC mills and a host of other once very expensive pieces of equipment. Getting PCB boards made is literally dirt cheap, and there are more inexpensive Linux single board computers than we can keep track of these days. Combining the lowering hardware costs with the ever increasing wealth of knowledge available on the internet creates a perfect environment for DIYers to push into ever more specific scientific fields.

One of these fields is biomedical research. In labs across the world, you’ll find a host of different machines used to study and create biological and chemical compounds. These machines include DNA and protein synthesizers, mass spectrometers, UV spectrometers, lyophilizers, liquid chromatography machines, fraction collectors… I could go on and on.

These machines are prohibitively expensive to the DIYer. But they don’t have to be. We have the ability to make these machines in our garages if we wanted to. So why aren’t we? One of the reasons we see very few biomedical hacks is because the chemistry knowledge needed to make and operate these machines is generally not in the typical DIYers toolbox. This is something that we believe needs to change, and we start today.

In this article, we’re going to go over how to convert basic chemical formulas, such as C9H804 (aspirin), into its molecular structure, and visa versa. Such knowledge might be elementary, but it is a requirement for anyone who wishes to get started in biomedical hacking, and a great starting point for the curious among us.

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Ask Hackaday: Are Gaming PCs Hard to Build?

No. No they’re not. But let’s talk about it anyway.

The endless trenches of digital worlds are filled with hardcore gamers from all walks of life. They can be found exploring post-apocalyptic Boston in Fallout 4, and commanding Sgt. Recker through a war-torn landscape in Battlefield 4 for hours on end. Their portal into these vast digital worlds come via some sort of computer system.

What type of computer system used is a point of contention between many gamers, and is typically divided between console versus PC. I will not dare to drag you into the captious arguments between the two, but instead we will focus on something that has something in common with our world — how does a previously non-technical console enthusiast cross over and build a gaming PC?

Many hackers have built computers from scratch and [Adam Fabio] just covered a bunch of custom laptop builds this morning. People with such skills can easily build a high-end gaming PC. But what about people without such skills? Can a console gamer with no technical background build a high-end PC gaming system?

Inspiration for this article came after reading something [Emanuel Maiberg] published over the summer on Motherboard. Why someone writing for a publication called Motherboard would have trouble building a gaming rig is beyond me. Certainly I think his starting assumptions are questionable. He asserts that you need an unreasonable amount of time and money to attempt one of these projects. But gaming rigs can be purchased fully-assembled — those that build them are doing it out of passion.

The question is this:  How far should engineers go to make a technical product easier to use for a non-technical person?  If I order an engine for a hot rod, it can be assumed that I know to hook up the gas line without specifically being told to do so. After all, a person who’s going to put an engine in a hot rod probably knows a thing or two about engines.

I think that building a desktop PC has never been easier. We’ve now had 30 years of evolution to help weed out the “slow learners” when it comes to manufacturers. The Internet is a lot easier to use for answers than it used to be, and we have faster means of connecting with communities of experts than ever before.

That said, the neighborhood computer store is beginning to go the way of the dodo. There is an entire generation of “mobile-first” users who will give you a blank stare if you start talking about “desktop computing”. And familiarity with the fact that computer customization is even possible is beginning to fade; if all you’ve ever used are tablets and smartphones “upgrade” and “customization” are software terms, not hardware possibilities.

So we turn it over to you. Are gaming PCs hard to build? Have engineering practices and design choices made it easier than it used to be to get into it? What do you think is happening with the average skill level for working with computers now compared to when you had to open the case to add a modem to your machine? Let us know what you think in the comments below.

The Quantum Eraser

Richard Feynmann noted more than once that complementarity is the central mystery that lies at the heart of quantum theory. Complementarity rules the world of the very small… the quantum world, and surmises that particles and waves are indistinguishable from one other. That they are one and the same. That it is nonsensical to think of something, or even try to visualize that something as an individual “particle” or a “wave.” That the particle/wave/whatever-you-want-to-call-it is in this sort of superposition, where it is neither particle nor wave. It is only the act of trying to measure what it is that disengages the cloaking device and the particle or wave nature is revealed. Look for a particle, and you’ll find a particle. Look for a wave instead, and instead you’ll find a wave.

Complementarity arises from the limits placed on measuring things in the quantum world with classical measuring devices. It turns out that when you try to measure things that are really really really small, some issues come up… some fundamental issues.  For instance, you can’t really know exactly where a sub-atomic particle is located in space. You can only know where it is within a certain probability, and this probability is distributed through space in the form of a wave. Understanding uncertainty in measurement is key to avoiding the disbelief that hits you when thinking about complementarity.

This article is a continuation of the one linked above. I shall pick up where I left off, in that everyone agrees that measurement on the quantum scale presents some big problems. However, not everyone agrees what these problems mean. Some, such as Albert Einstein, say that just because something cannot be measured doesn’t mean it’s not there. Others, including most mainstream physicists, say the opposite — that if something cannot be measured, it for all practical purposes is not there. We shall continue on our journey by using modern technology to peer into the murky world of complementarity. But first, a quick review.

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How the Human Brain Stores Data

Evolution is one clever fellow. Next time you’re strolling about outdoors, pick up a pine cone and take a look at the layout of the bract scales. You’ll find an unmistakable geometric structure. In fact, this same structure can be seen in the petals of a rose, the seeds of a sunflower and even the cochlea bone in your inner ear. Look closely enough, and you’ll find this spiraling structure everywhere. It’s based on a series of integers called the Fibonacci sequence. Leonardo Bonacci discovered the sequence while trying to figure out how many rabbits he could make starting with just two. It’s quite simple — add the right most integer to the previous one to get the next one in the sequence. Starting from zero, this would give you 0-1-1-2-3-5-8-13-21 and so on. If one was to look at this sequence in the form of geometric shapes, they can create square tiles whose sides are the length of the value in the sequence. If you connect the diagonal corners of these tiles with an infinite curve, you end up with the spiral that you saw in the pine cone and other natural objects.

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Source via Geocaching

So how did mother nature discover this geometric structure? Surely it does not know math. How then can it come up with intricate and sophisticated structures? It turns out that this Fibonacci spiral is the most efficient way of squeezing the most amount of stuff in the least amount of space. And if one takes natural selection seriously, this makes perfect sense. Eons of trial and error to make the most copies of itself has stumbled upon a mathematical principle that permeates life on earth.

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Source via John Simmons

The homo sapiens brain is the product of this same evolutionary process, and has been evolving for an estimated 7 million years. It would be foolish to think that this same type of efficiency natural selection has stumbled across would not be present in the current homo sapiens brain. I want to impress upon you this idea of efficiency. Natural selection discovered the Fibonacci sequence solely because it is the most efficient way to do a particular task. If the brain has a task of storing information, it is perfectly reasonable that millions of years of evolution has honed it so that it does this in the most efficient way possible as well. In this article, we shall explore this idea of efficiency in data storage, and leave you to ponder its applications in the computer sciences.

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Hyperuniformity — A Hidden Order Found in the Greatest Set of Eyes

Of all the things evolution has stumbled across, the eye is one of the most remarkable. Acting as sort of a ‘biological electromagnetic transducer’, the eye converts incoming photons into electrical and chemical spikes, known as action potentials. These spikes then drive the brain of the host life form. Billions of years of natural selection has produced several types of eyes, with some better than others. It would be an honest mistake to think that the human eye is at the top of the food chain, as this is not the case. Mammals underwent a long stint scurrying around in dark caves and crevasses, causing our eyes to take a back seat to other more important functions, such as the development of a cortex.

There are color sensitive cones in all eyes. Mammals have three types of cones, which are…wait for it…Red, Blue and Green. Our red and green cones are relatively recent on the evolutionary timescale – appearing about 30 million years ago.

The way these cones are distributed around our eyes is not perfect. They’re scattered around in lumpy, uneven patterns, and thus give us an uneven light sampling of our world. Evolution simply has not had enough time to optimize our eyes.

There is another animal on this planet, however, that never went through “the dark ages” as mammals did. This animal has been soaring high above its predators for over 60 million years, allowing its eyes to reach the pinnacle of the natural selection process. A bald eagle can spot a mouse from over a mile away. Birds eyes have 5 types of light sensitive cones – red, blue and green like our own. But add in violet and a type of cone that can detect no light, or black. But it is the way these cones are distributed around the bird’s eye that is most fascinating, and the subject of today’s article.

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Fractals Among Us

Think not of what you see, but what it took to produce what you see

Benoit Mandelbrot

Randomness is all around you…or so you think. Consider the various shapes of the morning clouds, the jagged points of Colorado’s Rocky Mountains, the twists and turns of England’s coastline and the forks of a lightning bolt streaking through a dark, stormy sky. Such irregularity is commonplace throughout our natural world. One can also find similar irregular structures in biology. The branch-like structures in your lungs called Bronchi, for instance, fork out in irregular patterns that eerily mirror the way rivers bifurcate into smaller streams. It turns out that these irregular structures are not as irregular and random as one might think. They’re self-similar, meaning the overall structure remains the same as you zoom in or out.

The mathematics that describes these irregular shapes and patterns would not be fully understood until the 1970s with the advent of the computer. In 1982, a renegade mathematician by the name of Benoit Mandelbrot published a book entitled “The Fractal Geometry of Nature”.  It was a revision of his previous work, “Fractals: Form, Chance and Dimension” which was published a few years before. Today, they are regarded as one of the ten most influential scientific essays of the 20th century.

Mandelbrot coined the term “Fractal,” which is derived from the Latin word fractus, which means irregular or broken. He called himself a “fractalist,” and often referred to his work as “the study of roughness.” In this article, we’re going to describe what fractals are and explore areas where fractals are used in modern technology, while saving the more technical aspects for a later article.

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Uncertainty – The Key to Quantum Weirdness

All these fifty years of conscious brooding have brought me no nearer to the answer to the question, ‘What are light quanta?’ Nowadays every Tom, Dick and Harry thinks he knows it, but he is mistaken.

                       Albert Einstein, 1954

As 1926 was coming to a close, the physics world lauded Erwin Schrodinger and his wave mechanics. Schrodinger’s purely mathematical tool was being used to probe the internal structure of the atom and to provide predictable experimental outcomes. However, some deep questions still remained – primarily with the idea of discontinuous movements of the electron within a hydrogen atom. Niels Bohr, champion of and chief spokesperson for quantum theory, had developed a model of the atom that explained spectral lines. This model required an electron to move to a higher energy level when absorbing a photon, and releasing a photon when it moved to a lower energy level. The point of contention is how the electron was moving. This quantum jumping, as Bohr called it was said to be instantaneous. And this did not sit well with classical minded physicists, including Schrodinger.

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