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“Smart skin” can identify weaknesses in bridges and airplanes using laser scanner

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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.

Smart skin technology could be used to monitor the structural integrity in commercial jet engines. | Credit: CC0 via Pixabay, User: blickpixel

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.

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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.

An artistic depiction of a carbon nanotube. | Credit: AJC1 via Flickr, CC BY-SA 2.0

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.

Accidental computer geek, fascinated by most history and the multiplanetary future on its way. Quite keen on the democratization of space. | It's pronounced day-sha, but I answer to almost any variation thereof.

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Tesla Cybercab gets crazy change as mass production begins

Tesla has officially kicked off mass production of its groundbreaking Cybercab robotaxi at Giga Texas, and the first units rolling off the line feature a striking transformation that’s turning heads across the EV community.

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Credit: TechOperator | X

Tesla Cybercab has evidently received a pretty crazy change from an aesthetic standpoint, as the company has made the decision to offer an additional finish on the vehicle as mass production is starting.

Tesla has officially kicked off mass production of its groundbreaking Cybercab robotaxi at Giga Texas, and the first units rolling off the line feature a striking transformation that’s turning heads across the EV community.

VIN Zero—the very first production Cybercab—showcases a vibrant champagne gold exterior with a high-gloss finish, a dramatic departure from the flat, matte-wrapped prototypes that debuted at the 2024 “We, Robot” event.

This glossy sheen is a pretty big pivot from what was initially shown by Tesla. The company has maintained a pretty flat tone in terms of anything related to custom colors or finishes.

A specialized clear coat or process delivers the deep, reflective gloss without conventional painting. The result is a premium, mirror-like shine, and it looks pretty good, and gives the compact two-seater a more luxurious and futuristic presence than the subdued matte prototypes.

Photos shared by Tesla community members reveal VIN Zero in a showroom-like setting at Giga Texas, highlighting refined panel gaps, large aero wheel covers, and the signature no-steering-wheel, no-pedals interior optimized for full autonomy.

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The open frunk in some images offers a glimpse of practical storage, while the overall build quality appears more polished than that of test mules.

This glossy evolution aligns with Tesla’s broader production ramp. After the first unit in February 2026, the company has shifted to volume manufacturing, with dozens of units already spotted in outbound lots. CEO Elon Musk and the team aim for hundreds per week, paving the way for unsupervised FSD robotaxi networks that could slash ride costs to pennies per mile.

The Cybercab holds Tesla’s grand ambitions of operating a full-service ride-hailing service without any drivers in its grasp. Tesla has yet to solve autonomy, but is well on its way, and although its timelines are usually a bit off, improvements often come through the Over-the-Air updates to the Full Self-Driving suite.

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Tesla confirms Cybercab with no steering wheel enters production

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Tesla has confirmed today that its steering wheel-less and pedal-less Cybercab, the vehicle geared toward launching the company’s autonomous ride-hailing hopes, has officially entered production at its Giga Texas production facility outside of Austin.

The Cybercab is a sleek two-door, two-passenger coupe engineered from the ground up as an electric self-driving vehicle. It features no steering wheel or pedals, relying instead on Tesla’s advanced vision-only Full Self-Driving system powered by multiple cameras and artificial intelligence.

The minimalist cabin centers on a large display screen that serves as the primary interface for passengers, creating an open, futuristic space optimized for comfort during unsupervised rides. A compact 35-kilowatt-hour battery pack delivers exceptional efficiency at 5.5 miles per kilowatt-hour, providing an estimated 200-mile range.

Additional innovations include inductive charging compatibility and a lightweight design that enhances aerodynamics and performance.

Production at Giga Texas builds on earlier prototypes and initial units completed earlier in 2026. The facility, already a hub for Model Y and Cybertruck assembly, now ramps up dedicated lines for the Cybercab.

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This shift to volume manufacturing reflects Tesla’s strategy to scale affordable autonomous vehicles rapidly.

By focusing on a dedicated platform rather than adapting existing models, the company aims to keep costs low while prioritizing safety and reliability through continuous AI improvements.

The Cybercab’s debut in production carries broad implications for urban mobility. As the cornerstone of Tesla’s Robotaxi network, it promises on-demand, driverless rides that could slash transportation expenses, reduce traffic accidents caused by human error, and lower emissions through its all-electric powertrain.

Accessibility features, such as space for service animals or assistive devices, further broaden its appeal. Regulators and cities worldwide will soon evaluate its deployment, but the vehicle’s design already addresses key hurdles in scaling unsupervised autonomy.

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Challenges persist, including full regulatory clearance and building charging infrastructure. Yet this production launch signals momentum. With Cybercabs poised to roll out in increasing numbers, Tesla edges closer to a future where personal ownership meets shared fleets of intelligent vehicles.

The start of Cybercab production is more than just a new vehicle entering mass manufacturing for Tesla, as it’s a signal autonomy is near. Being developed without manual controls is such a massive sign by Tesla that it trusts its progress on Full Self-Driving.

While the development of that suite continues, Tesla is making a clear cut statement that it is prepared to get its fully autonomous vehicle out in public roads as it prepares to revolutionize passenger travel once and for all.

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Tesla Summon got insanely good in FSD v14.3.2 — Navigation? Not so much

There were two new lines of improvements in the release notes: one addressing Actually Smart Summon (ASS), and another that now allows drivers to choose a reason for an intervention via a small menu during disengagement.

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(Photo: Hector Perez/YouTube)

Tesla Full Self-Driving v14.3.2 began rolling out to some owners earlier this week, and there are some notable improvements that came with this update.

There were two new lines of improvements in the release notes: one addressing Actually Smart Summon (ASS), and another that now allows drivers to choose a reason for an intervention via a small menu during disengagement.

Overall operation saw a handful of slight improvements, especially with parking performance, which has been the most notable difference with the arrival of FSD v14.3. However, there are still some very notable shortcomings, most notably with region-specific signage and navigation.

Tesla Assisted Smart Summon (ASS) improvements

There are noticeable improvements to ASS operation, which has definitely been inconsistent in terms of performance. Tesla wrote in the release notes for v14.3.2:

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“Unified the model between Actually Smart Summon, FSD, and Robotaxi for more capable and reliable behavior.”

As recently as this month, I used Summon with no success. It had pulled around the parking lot I was in incorrectly, leaving the range at which Summon can be operated and losing a signal while moving in the middle of the lot.

This caused me to sprint across the lot to retrieve the vehicle:

Unfortunately, Summon was not dependable or accurate enough to use regularly. It appears Tesla might have bridged the gap needed to make it an effective feature, as two tests in parking lots proved that Summon was more responsive and faster to navigate to the location chosen.

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It also did so without hesitation, confidently, and at a comfortable speed. I was able to test it twice at different distances:

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I plan to test this more thoroughly and regularly through the next few weeks, and I avoided using it in a congested parking lot initially because I have not had overwhelming success with Summon in the past. I wanted to set a low baseline for it to see if it could simply pull up to the place I pinned in the Tesla app.

It was two for two, which is a big improvement because I don’t think I ever had successful Summon attempts back-to-back. It just seems more confident than ever before.

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New Disengagement Categories

This is a really good idea from Tesla, but there are some issues with it. The categories you can select are Critical, Comfort, Preference, and Other.

I think the reasons why people choose to take over would be a better way to prompt drivers, like, “Traveling Too Fast,” “Incorrect Maneuver,” “Navigation Error,” would be more beneficial.

I say this because it seems that how we each categorize things might be different. For example, I shared a video of an intervention because the car had navigated to an exit to a parking lot and put its left blinker on, despite left turns not being allowed there.

I disengaged and chose Critical as the reason; it’s not a comfort issue, it’s not a preference, it’s quite literally an illegal turn, and it’s also dangerous because it cuts across several lanes of traffic and is 180 degrees.

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Some said I should not have labeled this as Critical, but that’s the description I best characterized the disengagement as.

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Categorizing interventions is a good thing, but it’s kind of hard to determine how to label them correctly.

Inconsistency with Regional Traffic Patterns

Tesla Full Self-Driving is pretty inconsistent with how it handles regional or local traffic patterns and road rules. The most frequent example I like to use is that of the “Except Right Turn” stop sign, which has become a notorious sighting on our social media platforms.

In the initial rollout of v14.3, my Model Y successfully navigated through one of these stop signs with no issues. However, testing at two of these stop signs yesterday proved it is still not sure how to read signs and navigate through them properly.

Off camera, I approached another one of these signs and felt the car coming to a stop, so I nudged it forward with the accelerator pedal pressed.

This helped the car go through the sign without stopping, but I could feel the bucking of the vehicle as the car really wanted to stop.

Musk said on the earnings call earlier this week that unsupervised FSD would probably be available in some regions before others, including a state-to-state basis in the U.S.

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“It’s difficult to release this like to everyone everywhere all at once because we do want to make sure that they’re not unique situations in a city that particularly complex intersection or — actually, they tend to be places where people get into accidents a lot because they’re just — perhaps there’s — and like I said, an unsafe intersection or bad road markings or a lot of weather challenges. So I think we would release unsupervised gradually to the customer fleet as we feel like a particular geography is confirmed to be safe.”

This could be one of those examples that Tesla just has to figure out.

Highway Operation

Full Self-Driving is already pretty good at routine roadway navigation, so I don’t have too much to report here.

However, I was happy with FSD’s decision-making at several points, including its choice not to pass a slightly slower car and remain in the right lane as we approached the off-ramp:

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Better Maneuvering at Stop Signs

Many FSD users report some strange operations at stop signs, especially four-way intersections where there is a stop sign and a line on the road, and they’re not even with one another.

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I experienced this quite frequently and found that FSD would actually double stop: once at the stop sign and again at the line.

This created some interesting scenarios for me and I had many cars honk at me when the second stop would happen. Other vehicles that had waved me on to proceed through the intersection would become frustrated at the second stop.

FSD seems to have worked through this particular maneuver:

FSD should know to go to the more appropriate location (whichever provides better visibility), and proceed when it is the car’s turn to move. The double stop really ruined the flow of traffic at times and generally caused some frustration from other drivers.

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