Results of the team's study show that the greatest electrical conductivity occurs when the carbon nanotubes are not perfectly aligned, but only partially aligned. In addition, a higher conductivity at, or close to, the percolation threshold can be achieved with the presence of agglomerated carbon nanotubes. However, this also limits the amount of increase in the electrical conductivity of the nanocomposite with increased fractions of carbon nanotubes.
Standards for repair and maintenance of commercial aircraft have to date been based on the performance of metal-based planes, where damage is easier to identify. Techniques for repairing the metal portions of aircraft are well known and well established. But that's not at all the case with the composite materials used increasingly for primary structures such as wings and fuselage. Impact damage, for example, can be difficult to detect since it's less visible, and repair practices are not yet established for the multiple materials and repair techniques associated with them.
The self-monitoring aspect of this story is what fascinates me the most. I'd like to read more about this topic, especially what other areas something like this is being used in. Ann, do you happen to know?
Thanks, Rob. Yes, the hope here seems to be that since the use of adhesives is increasing massively along with the use of composites, adhesives can help provide an early-warning system for detecting structural problems in aircraft. Reading about nanotechnology and its possible applications is like reading about science fiction, far more so than most other leading-edge technologies. I covered early carbon tube and carbon wire R&D efforts several years ago, so it was heartening to see that it's advanced to the level of possible real-world applications. Although this, of course, is still in R&D.
The idea that some sort of nanotechnology adhesive can help predict a structural failure in a composite airplane wing is definitely science fiction-like. How far away is this technology from being commercialized given that composites are increasingly being deployed in planes?
This should contribute greatly to our understanding of failure mechanisms of composites in real-world applications. After all, failure mechanisms of steels is well understood, but composites are in still comparatively new in many of these applications. This is an important story.
@Ann: Can you walk me through how a sensor like this would work? I understand that Professor Meguid's group is studying how alignment of the nanotubes or nanowires affects the electrical conductivity of the adhesive. Is the idea that the presence of a crack would alter the alignment of the nanotubes or nanowires, and that this could be measured as a change in conductivity?
The use of the term "percolation threshold" seems to indicate that they are using graph theory, which is a good example of how seemingly abstract branches of mathematics can sometimes have extremely practical uses.
'percolation' is a physical phenomenon, referring to topological arrangements within a multi-component solid. Imagine for instance a matrix of substance A with embedded uniform spheres of conductive substance B. As you increase the concentration of B, at some point they will start touching each other on a macroscopic scale, so that the material would become conductive---that would be an example of percolation.
The concept is used in many contexts, for instance to describe flow of oil through pores in a rock matrix.
@przemek: Thanks for a good explanation of the concept of percolation. The reason I mentioned graph theory is that it looks like work done by Professor Meguid's group (although it's not clear from this article) has been mainly focused on computational modeling of these materials -- not actually making them in the lab. There is an entire branch (no pun intended!) of graph theory which deals with percolation -- called, unsuprisingly, percolation theory.
I'd still like to know what a sensor based on this concept would actually measure, and how it would actually work. If you wanted to locate a crack, it seems like you would need a way to accurately measure local conductivity changes on a fairly small scale. And in order to understand what you were measuring, you would need to have a good understanding of how the presence of a crack changes the alignment of the nanotubes -- which seems like it could be an even tougher compuational challenge.
I can see how this could indeed work to indicate the start of failure. That part does make sense. But the question comes as to how to reset the detection scheme after the repairs are done. In the same way that embedded fiber optics do detect failures, the change is permanent and nonreverseable. Broken fibers and gaps between the microfibers just do not repair. The fix is a replacement. So while the detection system could work, the repairs would equate to replacements.
There is a product that I believe functions by heat reducing the number of particles in contact, and that is the "PTC Fuse", which is a device that looks a lot like a larger disk capacitor.When the current rises above some setpoint the resistance heating separates the particles and the device heats rapidly, moving most of the particles out of contact, which causes a large and nonlinear increase i resistance, which limits the fault current.
What they don't mention in the description is that these devices have a finite life, and after that the trip current becomes lower and lower.
Dave and William, thanks for the percolation discussion. I agree, the research paper did not give details on just how the sensor works, or, for that matter, how it can get reset after detection a fault. I suspect that's because the team may want to commercialize their research, as so often happens these days, and don't want to reveal proprietary information. Just a guess.
According to a study by the National Institute of Standards and Technology, one of the factors in the collapse of the original World Trade Center towers on Sept. 11, 2001, was the reduction in the yield strength of the steel reinforcement as a result of the high temperatures of the fire and the loss of thermal insulation.
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Robots are getting more agile and automation systems are becoming more complex. Yet the most impressive development in robotics and automation is increased intelligence. Machines in automation are increasingly able to analyze huge amounts of data. They are often able to see, speak, even imitate patterns of human thinking. Researchers at European Automation
call this deep learning.
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