Superconductivity Evolves

Light grey background and atomic element symbol "Al" (aluminum/aluminium) surrounded by a back circle which is surrounded by 3 progressively larger gray circles containing 13 solid circles representing the electrons in the atom. The 3 outer rings contain 2, 8, and 3 circles respectively.

Every so often it is reported that a new type of superconductivity or a new material that can super-conduct past a certain energy is discovered. The difference between the previous energy level and the new is typically relatively small — usually within a few single digits of the last (sort by the Tc (K) field). Phys.org reports on a potential breakthrough by researchers at the University of Southern California (USC) who have discovered both a new material type that may super-conduct at a significantly higher energy level (and temperature) than current superconductors. The material? An exotic, rare, and expensive mineral? Nope, plain old aluminum atoms in a cluster.

Light grey background and atomic element symbol "Al" (aluminum/aluminium) surrounded by a back circle which is surrounded by 3 progressively larger gray circles containing 13 solid circles representing the electrons in the atom. The 3 outer rings contain 2, 8, and 3 circles respectively.
Atomic structure of an aluminum/aluminium element.

The aluminum atoms have a potential critical transition (energy/temperature at which pairs of electrons mimic each other’s movements known as Cooper pairs) of 100 K (Kelvin) or about -280 degrees Fahrenheit (F). That sounds pretty cold but not when you compare it to the current high of 39 K (about -389°F). A difference of 61 K or 110°F. The researchers also believe this new material type is only the beginning and could lead to other materials with even higher transition energies and temperatures — potentially even to room temperature superconductors.

Obviously this has yet to be experimentally proven — the researchers did some preliminary experiments that show a very good possibility of superconductivity at those energies but did not actually create an aluminum superconductor. In short — looks good on paper, but may not actually exist. It will still be interesting to watch and see what happens as experiments progress with this new material type.

Efficiency Boost: Laser Ignition of Combustion Engines

Gray box containing a laser warning symbol on it and optical wires protruding from the end of the device going to each of the cylinders of a combustion engine.

Next Big Future reports on an advancement that could boost vehicle mileage by ~10 mpg (miles per gallon). Cars that previously achieved 40 mpg could achieve 50 mpg with a nearly 30% advancement in efficiency. The advancement comes from using a laser to ignite the fuel within the combustion cylinders instead of spark plugs.

Lasers have been a potential replacement for spark plugs for decades but were too big and power-hungry to use within a car. Since then miniaturization of the technology as well as efficiency gains in laser technology are allowing the technology to be used in smaller vehicles without overloading the capacity of the battery. The system is also being considered for larger vehicles such as shipping vessels to improve fuel usage and reduce emissions.

Princeton Optronics, Inc., a New Jersey based producer of lasers and related optical systems has already demonstrated the use of laser ignition in an engine. Ignition using a laser is more efficient than spark plugs since the ignition point can be designed to happen lower in the cylinder allowing for a more thorough burn of the fuel. Lasers could also fire multiple types during a single cycle allowing burn-off of a greater amount of fuel which can reduce emissions further. Even through oil prices have dropped we still continue to look for ways to make engines more and more efficient while simultaneously seeking alternative fuels. Low oil prices are a relief but will not last forever.

Multiprocessing Data Structure

A 4-level skip list where the first column is filled with the number 30, the first row of the second column contains, the first, second, and third rows of the third column are filled with the number 50, the first row of the fourth column is filled with the number 60, the first and second rows of the fifth column is filled with the number 70, and the first row of the sixth column is filled with the number 90. The animation adds two 80s and a 45 by searching through across each row before moving to the row below for the number less than or equal to the number being inserted.

Phys.org reports on an issue with processing priority queues in a world dominated by an ever-increasing number of cores. When processors (CPUs) add, remove, and read through these structures they cache the first item in the list so that it can be easily accessed and processed by a single-core processor. However this cache is the same for all available cores and when something gets changed (added or removed) it means the cache for all the cores needs to be cleared and re-read before it can be read or changed again. As you might imagine when there are 4, 8, or even more (say, 40? 80?) cores all attempting to read through, add, change, or delete items in this structure it can cause a massive slowdown that essentially obliterates the performance gain that should be had from having many cores.

A 4-level skip list where the first column is filled with the number 30, the first row of the second column contains, the first, second, and third rows of the third column are filled with the number 50, the first row of the fourth column is filled with the number 60, the first and second rows of the fifth column is filled with the number 70, and the first row of the sixth column is filled with the number 90. The animation adds two 80s and a 45 by searching through across each row before moving to the row below for the number less than or equal to the number being inserted.
An animation of additions to a skip list by Artyom Kalinin on Wikimedia Commons.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory may have found an answer. First they looked at using a different structure — a linked list. However, this too suffered from a similar issue; You need to access the first item then traverse the sequence to find the memory address needed. Instead they tried skip lists that create rows of linked lists in order to make it more efficient to search through a linked list — a “hierarchical linked list”. They then take it a step further by starting the search lower down the hierarchy depending on how many processing cores are available. The researchers point out that it is not a perfect solution as there can still be a collision — when a data item appears at more than one level of the hierarchy — but the chances of such a collision happening are rare.

WiFi Traffic Management Algorithm

Visual representation of the 2.4 GHz WiFi frequency channels. Each channel is represented by a dotted half-circle representing 22 MHz of bandwidth. The half-circles representing the 3 front channels (1, 6, and 11) have solid outlines. The others overlap behind and between the front 3 channels except for channel 14 which only overlaps the edges of the 12th and 13th channels.

Phys.org reports on a new algorithm developed by a doctoral student at École polytechnique fédérale de Lausanne (EPFL) that changes frequencies and bandwidth usage based on the type of data packets being sent and received. Many routers today are set by default to use channel 6 of the 2.4 GHz frequency which causes a build-up of WiFi traffic on that channel. The problem is that many other channels overlap and use much of the same frequencies. In fact, while there are 14 total channels made available in the 2.4 GHz range, many countries ban the use of some of those frequencies. In the United States (US) channels 12 through 14 are not able to be used yet are the ones with the greatest frequency gap between channels. In effect, because the frequency bands overlap you can argue that there are really only 3 available spaces to transmit data in the 2.4 GHz WiFi band.

Visual representation of the 2.4 GHz WiFi frequency channels. Each channel is represented by a dotted half-circle representing 22 MHz of bandwidth. The half-circles representing the 3 front channels (1, 6, and 11) have solid outlines. The others overlap behind and between the front 3 channels except for channel 14 which only overlaps the edges of the 12th and 13th channels.
Visual representation of the 2.4 GHz WiFi frequency channels and how they overlap (22 MHz channels). Creative commons licensed image by Michael Gauthier on Wikimedia Commons.

The graph above shows the frequency channels for the 2.4 GHz WiFi range and how the channels overlap. Most routers are set to channel 6 by default and while they may change channels depending on availability they generally pick a channel and stick with it. In addition, many routers will use up to 8 of these channels at the same time. The problem is that this rather small range gets filled up in areas where many routers are being run and essentially cause a traffic jam of data. The other problem is that because routers will often stick with a set channel other may actually be open and unused.

The new algorithm would determine the bandwidth requirements of the data being sent and received and would select an appropriate channel and width. It essentially removed the idea of “channels” and instead divvies up the available frequency range into “lanes.” Some of the lanes are specialized similar to having a carpool or bike lane. As an example, if all you did was check your e-mail and browse a few websites you don’t need much bandwidth. The new algorithm would utilize a small amount of bandwidth – say within channels 1 and 2 – for just website browsing and email. Videos such as Vimeo and YouTube, which require much more bandwidth, may get a large chunk of channels 6 through 10 to use, and the remaining could be used for various other purposes such as websites with larger images, chat programs, and cell-phone updates. It spreads out the use over the available bandwidth and specialized certain areas for things like low-bandwidth data such as web and email, cell-phone updates, and high-bandwidth videos. The developer claims that it could increase typical router throughput by up to seven times (7X).