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“Smart skin” can identify weaknesses in bridges and airplanes using laser scanner
Recent research results have demonstrated that two-dimensional, on-demand mapping of the accumulated strain on metal structures will soon be a reality thanks to an engineered “smart skin” that’s only a fraction of the width of a human hair. By utilizing the unique properties of single-walled carbon nanotubes, a two-layer film airbrushed onto surfaces of bridges, pipelines, and airplanes, among others, can be scanned to reveal weaknesses in near real-time. As a bonus, the technology is barely visible even on a transparent surface, making it that much more flexible as an application.
Stress-inducing events, along with regular wear and tear, can deform structures and machines, affecting their safety and operability. Mechanical strain on structural surfaces provides information on the condition of the materials such as damage location and severity. Existing conventional sensors are only able to measure strain in one point along one axis, but with the smart skin technology, strain detection in any direction or location will be possible.
How “Smart Skin” Technology is Used
In 2002, researchers discovered that single-wall carbon nanotubes fluoresce, i.e., glow brightly when stimulated by a light source. Later, the fluorescence was further found to change color when stretched. This optical property was then considered in the context of metal structures that are subject to strain, specifically to apply the property as a diagnostic tool. To obtain the fluorescent data, researchers applied the smart skin to a testing surface, irradiated the area with a small laser scanner, and captured the resulting nanotube color emissions with an infrared spectrometer. Finally, two-dimensional maps of the accumulated strain were generated with the results.
The primary researchers, Professors Satish Nagarajaiah and Bruce Weisman of Rice University in Texas, have published two scientific papers explaining the methods used for achieving this technology and the results of its proof-of-principle application. As described in the papers, aluminum bars with holes or notches in areas of potential stress were tested with the laser technique to demonstrate the full potential of their invention. The points measured were located 1 millimeter apart, but the researchers stated that the points could be located 20 times closer for even more accurate readings. Standard strain sensors have points located several millimeters apart.
What Are Carbon Nanotubes?
Carbon nanotubes (CNTs) are carbon molecules that have been structurally modified into cylinders, or rather, rolled up sheets of carbon atoms. There has been some evidence suggesting that CNTs can be formed via natural processes such as volcanic events. However, to really capitalize on their unique characteristics, production in a laboratory environment is much more efficient.
Several methods can be used for production, but the most widely used method for synthesizing CNTs is chemical vapor deposition (CVD). This process combines a catalyzing metal with a carbon-containing gas which are heated to approximately 1400 degrees Fahrenheit, triggering the carbon molecules to assemble and grow into nanotubes. The resulting formation resembles a forest or lawn grass, each trunk or blade averaging .43 nanometers in diameter. The length is dependent on variables such as the amount of time spent in the high heat environment.
Besides surface analysis, carbon nanotubes have proven invaluable in many research and commercial arenas, their luminescence being only one of many properties that can improve and enable other technologies. Their mechanical tensile strength is 400 times that of steel while only having one sixth the density, making them very lightweight. CNTs also have highly conductive electrical and thermal properties, are extremely resistant to corrosion, and can be filled with other nanomaterials. All of these advantages open up their applications to include solar cells, sensors, drug delivery, electronic devices and shielding, lithium-ion batteries, body armor, and perhaps even a space elevator, assuming significant advances overcome its hurdles.
Next Steps
The nanotube-laced smart skin is ready for scaling up into real-world applications, but its chosen industry may take time to adopt given the general resistance to change in a field with long-standing existing technology. While awaiting embrace in the arena it was primarily designed for, the smart skin has other potential uses in engineering research applications. Bruce Weisman, also the discoverer of CNT fluorescence, anticipates its advantages being used for testing the design of small-scaled structures and engines prior to deployment. Niche applications like these may be the primary entry point into the market for some time to come. In the meantime, the researchers plan to continue developing their strain reader to capture simultaneous readings from large surfaces.
News
Tesla FSD (Supervised) v14.2.2 starts rolling out
The update focuses on smoother real-world performance, better obstacle awareness, and precise end-of-trip routing, among other improvements.
Tesla has started rolling out Full Self-Driving (Supervised) v14.2.2, bringing further refinements to its most advanced driver-assist system. The new FSD update focuses on smoother real-world performance, better obstacle awareness, and precise end-of-trip routing, among other improvements.
Key FSD v14.2.2 improvements
As noted by Not a Tesla App, FSD v14.2.2 upgrades the vision encoder neural network with higher resolution features, enhancing detection of emergency vehicles, road obstacles, and human gestures. New Arrival Options let users select preferred drop-off styles, such as Parking Lot, Street, Driveway, Parking Garage, or Curbside, with the navigation pin automatically adjusting to the user’s ideal spot for precision.
Other additions include pulling over for emergency vehicles, real-time vision-based detours for blocked roads, improved gate and debris handling, and extreme Speed Profiles for customized driving styles. Reliability gains cover fault recovery, residue alerts on the windshield, and automatic narrow-field camera washing for new 2026 Model Y units.
FSD v14.2.2 also boosts unprotected turns, lane changes, cut-ins, and school bus scenarios, among other things. Tesla also noted that users’ FSD statistics will be saved under Controls > Autopilot, which should help drivers easily view how much they are using FSD in their daily drives.
Key FSD v14.2.2 release notes
Full Self-Driving (Supervised) v14.2.2 includes:
- Upgraded the neural network vision encoder, leveraging higher resolution features to further improve scenarios like handling emergency vehicles, obstacles on the road, and human gestures.
- Added Arrival Options for you to select where FSD should park: in a Parking Lot, on the Street, in a Driveway, in a Parking Garage, or at the Curbside.
- Added handling to pull over or yield for emergency vehicles (e.g. police cars, fire trucks, ambulances).
- Added navigation and routing into the vision-based neural network for real-time handling of blocked roads and detours.
- Added additional Speed Profile to further customize driving style preference.
- Improved handling for static and dynamic gates.
- Improved offsetting for road debris (e.g. tires, tree branches, boxes).
- Improve handling of several scenarios, including unprotected turns, lane changes, vehicle cut-ins, and school buses.
- Improved FSD’s ability to manage system faults and recover smoothly from degraded operation for enhanced reliability.
- Added alerting for residue build-up on interior windshield that may impact front camera visibility. If affected, visit Service for cleaning!
- Added automatic narrow field washing to provide rapid and efficient front camera self-cleaning, and optimize aerodynamics wash at higher vehicle speed.
- Camera visibility can lead to increased attention monitoring sensitivity.
Upcoming Improvements:
- Overall smoothness and sentience.
- Parking spot selection and parking quality.
News
Tesla is not sparing any expense in ensuring the Cybercab is safe
Images shared by the longtime watcher showed 16 Cybercab prototypes parked near Giga Texas’ dedicated crash test facility.
The Tesla Cybercab could very well be the safest taxi on the road when it is released and deployed for public use. This was, at least, hinted at by the intensive safety tests that Tesla seems to be putting the autonomous two-seater through at its Giga Texas crash test facility.
Intensive crash tests
As per recent images from longtime Giga Texas watcher and drone operator Joe Tegtmeyer, Tesla seems to be very busy crash testing Cybercab units. Images shared by the longtime watcher showed 16 Cybercab prototypes parked near Giga Texas’ dedicated crash test facility just before the holidays.
Tegtmeyer’s aerial photos showed the prototypes clustered outside the factory’s testing building. Some uncovered Cybercabs showed notable damage and one even had its airbags engaged. With Cybercab production expected to start in about 130 days, it appears that Tesla is very busy ensuring that its autonomous two-seater ends up becoming the safest taxi on public roads.
Prioritizing safety
With no human driver controls, the Cybercab demands exceptional active and passive safety systems to protect occupants in any scenario. Considering Tesla’s reputation, it is then understandable that the company seems to be sparing no expense in ensuring that the Cybercab is as safe as possible.
Tesla’s focus on safety was recently highlighted when the Cybertruck achieved a Top Safety Pick+ rating from the Insurance Institute for Highway Safety (IIHS). This was a notable victory for the Cybertruck as critics have long claimed that the vehicle will be one of, if not the, most unsafe truck on the road due to its appearance. The vehicle’s Top Safety Pick+ rating, if any, simply proved that Tesla never neglects to make its cars as safe as possible, and that definitely includes the Cybercab.
Elon Musk
Tesla’s Elon Musk gives timeframe for FSD’s release in UAE
Provided that Musk’s timeframe proves accurate, FSD would be able to start saturating the Middle East, starting with the UAE, next year.
Tesla CEO Elon Musk stated on Monday that Full Self-Driving (Supervised) could launch in the United Arab Emirates (UAE) as soon as January 2026.
Provided that Musk’s timeframe proves accurate, FSD would be able to start saturating the Middle East, starting with the UAE, next year.
Musk’s estimate
In a post on X, UAE-based political analyst Ahmed Sharif Al Amiri asked Musk when FSD would arrive in the country, quoting an earlier post where the CEO encouraged users to try out FSD for themselves. Musk responded directly to the analyst’s inquiry.
“Hopefully, next month,” Musk wrote. The exchange attracted a lot of attention, with numerous X users sharing their excitement at the idea of FSD being brought to a new country. FSD (Supervised), after all, would likely allow hands-off highway driving, urban navigation, and parking under driver oversight in traffic-heavy cities such as Dubai and Abu Dhabi.
Musk’s comments about FSD’s arrival in the UAE were posted following his visit to the Middle Eastern country. Over the weekend, images were shared online of Musk meeting with UAE Defense Minister, Deputy Prime Minister, and Dubai Crown Prince HH Sheikh Hamdan bin Mohammed. Musk also posted a supportive message about the country, posting “UAE rocks!” on X.
FSD recognition
FSD has been getting quite a lot of support from foreign media outlets. FSD (Supervised) earned high marks from Germany’s largest car magazine, Auto Bild, during a test in Berlin’s challenging urban environment. The demonstration highlighted the system’s ability to handle dense traffic, construction sites, pedestrian crossings, and narrow streets with smooth, confident decision-making.
Journalist Robin Hornig was particularly struck by FSD’s superior perception and tireless attention, stating: “Tesla FSD Supervised sees more than I do. It doesn’t get distracted and never gets tired. I like to think I’m a good driver, but I can’t match this system’s all-around vision. It’s at its best when both work together: my experience and the Tesla’s constant attention.” Only one intervention was needed when the system misread a route, showcasing its maturity while relying on vision-only sensors and over-the-air learning.
Tesla FSD (Supervised) v14.2.2 starts rolling out
Tesla is not sparing any expense in ensuring the Cybercab is safe
