Superradiant Smith-Purcell radiation (S-SPR) is a kind of free electron radiation with a train of free electron bunches passing over a periodic grating. In theory, the ultra-narrow spectral linewidth of S-SPR could be realized, which would be greatly beneficial to various applications such as imaging, sensing and communication.

However, in the free electron accelerators, customized setups and orotrons, the instability of electron kinetic energy, coulomb effect and the finite number of electron bunches worsened the radiation linewidth, and the large size of equipment limits the application scenarios.

In a new paper published in eLight, a team of scientists, led by Professor Fang Liu and Yidong Huang from the Department of Electronic Engineering, Tsinghua University, China, have developed the first compact S-SPR device with ultra-narrow and continuously tunable spectral linewidth.

! Elon Musk seems to think that the Tesla Bot will take over many of the boring, repetitive, and dangerous jobs that are fundamental to our economy. Elon believes the Tesla Bot will eventually take over the Tesla vehicles as the company’s primary source of revenue…

In a groundbreaking leap toward cleaner, more affordable energy, scientists in France held a fusion reaction steady for over 22 minutes — shattering the previous world record. If that number sounds insignificant, here’s why it’s a big deal: That is 1,337 seconds of controlled, blazing-hot plasma, the critical ingredient needed to power nuclear fusion, a nearly limitless energy source that does not rely on polluting fuels like gas, coal, or oil.

This milestone brings us one step closer to a dream energy future: one where our homes, cities, and electric cars are powered by a technology that mimics the sun — minus the radioactive waste and environmental damage of traditional nuclear power.

Nuclear fusion has the capability to solve a major problem with polluting energy sources. Right now, our power mostly comes from dirty energy that pollutes the air and contributes to extreme weather. While solar and wind energy are gaining momentum, fusion offers something different: the possibility of continuous, around-the-clock clean energy using hydrogen — the most common element in the universe — as fuel.

Questions to inspire discussion.

Q: What are Kirk’s predictions for Tesla’s robotaxi rollout? A: Kirk predicts at least 1 million robotaxis on the road by end of 2026, with potential for 2–5 million by end of 2027.

Q: When does Kirk expect Tesla to sell Optimus robots to third parties? A: Kirk expects Optimus sales to third parties in the second half of 2026.

Q: What is the estimated rental price for Optimus robots? A: Kirk estimates Tesla will rent Optimus robots for $110,000-$120,000 per year.

Market and Economic Predictions.

Q: What economic environment does Kirk predict for late 2024? A: Kirk predicts a booming economic environment by Q4 2024, driven by the AI revolution and productivity increases.

Billions of heat exchangers are in use around the world. These devices, whose purpose is to transfer heat between fluids, are ubiquitous across many commonplace applications: they appear in HVAC systems, refrigerators, cars, ships, aircraft, wastewater treatment facilities, cell phones, data centers, and petroleum refining operations, among many other settings.

Tesla’s Full Self-Driving (FSD) technology is rapidly advancing, impressing users and analysts alike, while navigating challenges in the auto industry and broader economic factors.

Questions to inspire discussion.

Tesla’s FSD Progress.

Q: How many unsupervised miles has Tesla’s FSD driven? A: Tesla’s FSD has driven over 50,000 unsupervised miles, demonstrating significant progress in autonomous driving capabilities.

Q: What indicates Tesla’s transition to software-defined earnings? A: FSD unsupervised miles and operating domain growth are key leading indicators of Tesla’s shift towards software-defined earnings.

Q: How does Tesla’s FSD showcase AI potential in driving? A: Tesla’s FSD unsupervised capabilities, demonstrated in complex driving scenarios, serve as a proof case for artificial intelligence’s potential in autonomous driving.

Elon Musk’s Tesla is on the verge of launching a self-driving platform that could revolutionize transportation with millions of affordable robotaxis, positioning the company to outpace competitors like Uber ## ## Questions to inspire discussion ## Tesla’s Autonomous Driving Revolution.

Q: How is Tesla’s unsupervised FSD already at scale? A: Tesla’s unsupervised FSD is currently deployed in 7 million vehicles, with millions of units of hardware 4 dormant in older vehicles, available at a price point of $30,000 or less.

Q: What makes Tesla’s autonomous driving solution unique? A: Tesla’s solution is vertically integrated with end-to-end ownership of the entire system, including silicon design, software platform, and OEM, allowing them to keep costs low and push down utilization on ride-sharing networks. Impact on Ride-Sharing Industry.

Q: How will Tesla’s autonomous vehicles affect Uber drivers? A: Tesla’s unsupervised self-driving cars will likely replace Uber’s 1.2 million US drivers, being 4x more useful due to no breaks and no human presence, operating at a per-mile cost below 50% of current Uber rates.

Q: What economic pressure will Tesla’s solution put on Uber? A: Tesla’s autonomous driving solution will create tremendous pressure on Uber, with its cost structure acting as a magnet for high utilization, maintaining low pre-pressure costs for Tesla due to their fundamentally different design. Future Implications.

Q: What potential strategy might Uber adopt to compete with Tesla? A: Uber may need to approach Tesla to pre-buy their first 2 million Cyber Caps upfront, including production costs, as potentially the only path to compete with Tesla’s autonomous driving solution.

Purdue University researchers have developed a new type of two-dimensional (2D) nanomaterial called a tungsten carbide MXene. This small but mighty material could be used to produce hydrogen fuel for electric vehicles, possibly becoming the key to a more reliable future.

Jumping is ubiquitous among insects because it offers high locomotion versatility with moderate efficiency. Most jumping insects leverage latch-mediated spring actuation (14) principles to store and impulsively release energy—leading to high jumps that exceed 0.7 m or 115 times their body length (15). Subgram jumping robots (16, 17) adopt a similar design where they can reach up to 0.64-m jump height. However, unlike insects, small robots lack the ability to control attitude and landing position while they are aloft, and they cannot reorient themselves and reload the jumping mechanism after landing. Achieving the ability to perform consecutive jumps can substantially improve microrobot mobility. Hopping, or continuous jumping, has been achieved in mesoscale (35 to 100 g) robots (18–20) to demonstrate efficient and versatile locomotion. Unlike single-jump robots (21, 22) that slowly store energy in a spring-latched system, hopping robots (18, 23) need to quickly store energy in series-elastic mechanisms during the aerial phase and release energy during the stance phase. Most hopping designs are based on the spring-loaded inverted pendulum model (24), and the mechanisms are realized on the basis of a combination of multiple linkages and springs (18–20).

However, these mesoscale designs are difficult to implement in insect-scale robots owing to scaling laws, fabrication, and control challenges. As robot size shrinks, robot dynamics become substantially faster, requiring rapid actuation and control in the range of tens of milliseconds. For a subgram robot to hop, the duration of ground contact becomes shorter than 20 ms, exceeding the control bandwidth of microscale actuators. The short ground impact can also cause large body torques and induce fast rotations, which require another set of actuators and mechanisms to stabilize robot’s attitude in the aerial phase. In addition, it becomes increasingly difficult to construct nonlinear springs, flexures, and linkages at the submillimeter scale. Owing to these challenges, existing insect-scale robots have not achieved hopping despite the potential advantages of versatility and efficiency. At the tens-of-gram scale, our prior work presented a different hopping design by adding a passive telescopic leg to a quadcopter, resulting in a 35-g Hopcopter (20). This Hopcopter demonstrated better controllability and efficiency over other existing designs because it could inject energy and exert stronger attitude control in the aerial phase. Applying this design to insect-scale systems is advantageous because it reduces mechanical complexity. However, new challenges arise because of diminishing robot inertia and substantially faster system dynamics that require fast planning and attitude control. Specifically, high-bandwidth actuators and computationally efficient controllers are needed to replace electromagnetic motors and model-based collision planners (20).

In this work, we demonstrate efficient, versatile, and robust hopping in subgram robots by augmenting a micro–aerial vehicle with a passive elastic leg (Fig. 1, A to C Opens in image viewer, and movie S1). Compared to flight, this hopping design reduces energetic cost by 64% and increases the robot’s payload by over 10 times. The robot can precisely control its jump height, frequency, and landing positions to track set points and leap over large obstacles. This hopping design is also adaptive to a wide range of slippery, uneven, rough, or deformable terrains, including wood, glass, ice, soil, grass, and a floating lotus leaf. Owing to fast robot dynamics and the ability to generate large body torques, the robot further demonstrates unparalleled robustness and agility among microrobots. The robot can hop on dynamically inclined surfaces, recover from strong in-air collisions, and perform acrobatic flips in its aerial phase. In addition, diminishing inertial effects enable challenging locomotive tasks that are infeasible for larger-scale robots. Our robot can jump onto a 29-g quadrotor, which shows that impulsive interaction between two heterogeneous aerial robots can enhance mobility. These demonstrations highlight the unique advantages of insect-scale hopping. Our results also have implications on achieving sensing and power autonomy in payload-constrained microrobots that often confront large obstacles. Compared to insect-scale aerial vehicles, our robot retains the capability of overcoming large obstacles while substantially reducing power consumption and increasing payload. The 10 times payload increase opens opportunities for incorporating onboard sensors, electronics, and power sources.