Innovation comes in all forms. If you’re looking for inspiration as you develop your innovation management strategy, these five young innovators offer solutions to common innovation struggles.
The human body doesn’t react well to foreign objects implanted in it, even if those objects are helping. Over time, medical implants have to deal with the “foreign body effect,” where the object is contained within a wall of scar tissue to “protect” the body from this “intruder.” Fixing the problem often involves either immune system suppressants or more surgery, both of which can raise the risk of using an implant. Solving this problem long-term will also be crucial to using implants instead of transplants, reducing both the need for organ donation and the cost of long-term critical care.
Dolan’s innovation was to realize that the immune system finds these objects because they’re static, unlike the rest of the body, which has some form of “fluid flow.” Your organs, muscles, and so on are constantly moving, contracting, expanding, even if only by small amounts. The immune system knows something is wrong when it doesn’t find this flow. Just what it thinks is happening is unclear, but what was clear is that where the tide of the body isn’t found, the body’s systems think something is wrong.
Dolan and her team created the dynamic soft reservoir, a material that can oscillate, maintaining the fluidity of an organ and preventing the foreign body effect. The lesson? Don’t try to reinvent the wheel for your innovation. Rather look to how others, including nature, have solved it.
Chemistry, at root, involves combining or separating raw materials through the application of energy. This can be kinetic energy, like a carbonator forcing gas into a liquid, or mechanical energy, like a centrifuge spinning something so fast the bonds of its constituent elements tear apart, and it separates. But for most of the chemical industry, for a long time, the cheapest and most effective way has been heat energy, often by burning enormous amounts of fossil fuels.
Miguel Modestino’s innovation was to go back to basics and ask if this was the most efficient way to achieve these reactions. He found that it was, but only because there hadn’t been the tools to determine precisely the amount of electrical energy needed to start and control the reactions. So Modestino and his team used machine learning to study the pulses needed, at what point in the reaction, and when, to get maximum yield from a reaction with minimal expenditure.
Underneath it all, Modestino was using a radical form of incremental innovation: Revisiting axioms that had been in place for far too long and asking if they still held in the presence of new tools and ideas.
The more efficient a solar panel is, the more energy it can generate. Unfortunately, some space on the front of a panel has to be sacrificed to wiring contacts and other parts in order for the panel to function properly. Wiring on the front of the panel alone is estimated to cost up to 5% in terms of efficiency.
Saive, who’s an assistant professor in the applied physics department at the University of Twente located in the Netherlands, decided to see if the contacts could be integrated into the cell, saving both space and money. Her design uses a 3D printer to embed silver nanoparticles into solar cells with a precise triangle shape, angling the particles so they reflect sun into the cell, ensuring no photon goes to waste.
For innovation strategy, Saive’s approach demonstrates conservation of approach; nothing she used was particularly radical in the field, yet she combined them in a unique and thoughtful way to get amazing results.
Much of what we do online relies on computers using our past behavior to guess what we’re going to do next. For example, when you open your browser every morning, over time, it’ll learn that you visit your email, perhaps news next, then a little social media.
However, when you’re dealing with enormous datasets, this technique, called “pre-fetching,” becomes more complicated. After all, an academic doesn’t know where the data will take them. What Leilani Battle decided to do was change the angle.
Battle studied the patterns researchers followed when looking at data and found they engaged three primary steps: “Foraging,” where they look at the data coarsely; “navigation,” where they’re teasing out a pattern or are shaping what they’re seeking mentally in the data; and “sensemaking,” when they begin delving more deeply to test their hypothesis or see if a pattern holds.
This cut the time spent waiting for data to load by up to 25%, allowing researchers to dig deeper. For innovators, it points out that throwing resources at a problem isn’t the only way to solve it and that innovation comes as much from asking how a process works as it does speeding that process up.
The thermionic converter is probably best known to space fans. The converter heats up one electrode, which sends electrodes across to a cooler one. Think of it like a kettle being heated; as steam comes out of the spout, if you place something cool over it, water condenses. In this analogy, the electrons are the water.
The essential problem is that you need to heat the “kettle” in the first place, so when these were first made practical in the 1950s, they were only useful in extreme environments with lots of heat, such as satellites facing the sun directly and the middle of nuclear reactors. And, over the decades, that’s pretty much only where they were found, especially as they had no moving parts, making risky repairs unnecessary.
Enter Tony Pan. Pan realized that new materials drastically reduced the heating needed to the point where a natural gas source was an ideal source. It’s a reminder that innovation is as much about application and revision as it is new ideas.
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