TU/ecomotive wants to make entire car life cycle CO₂-neutral
Category: transportation
Found in everything from kitchen appliances to sustainable energy infrastructure, stainless steels are used extensively due to their excellent corrosion (rusting) resistance. They’re an important material in many industries, including manufacturing, transportation, oil and gas, nuclear power and chemical processing.
However, stainless steels can undergo a process called sensitization when subjected to a certain range of high temperatures—like during welding—and this substantially deteriorates their corrosion resistance. Left unchecked, corrosion can lead to cracking and structural failure.
“This is a major problem for stainless steels,” says Kumar Sridharan, a professor of nuclear engineering and engineering physics and materials science and engineering at the University of Wisconsin–Madison. “When stainless steel gets corroded, components need to be replaced or remediated. This is an expensive process and causes extended downtime in industry.”
Coordinating complicated interactive systems, whether it’s the different modes of transportation in a city or the various components that must work together to make an effective and efficient robot, is an increasingly important subject for software designers to tackle. Now, researchers at MIT have developed an entirely new way of approaching these complex problems, using simple diagrams as a tool to reveal better approaches to software optimization in deep-learning models.
They say the new method makes addressing these complex tasks so simple that it can be reduced to a drawing that would fit on the back of a napkin.
The new approach is described in the journal Transactions of Machine Learning Research, in a paper by incoming doctoral student Vincent Abbott and Professor Gioele Zardini of MIT’s Laboratory for Information and Decision Systems (LIDS).
Lithium-ion batteries have been a staple in device manufacturing for years, but the liquid electrolytes they rely on to function are quite unstable, leading to fire hazards and safety concerns. Now, researchers at Penn State are pursuing a reliable alternative energy storage solution for use in laptops, phones and electric vehicles: solid-state electrolytes (SSEs).
According to Hongtao Sun, assistant professor of industrial and manufacturing engineering, solid-state batteries—which use SSEs instead of liquid electrolytes—are a leading alternative to traditional lithium-ion batteries. He explained that although there are key differences, the batteries operate similarly at a fundamental level.
“Rechargeable batteries contain two internal electrodes: an anode on one side and a cathode on the other,” Sun said. “Electrolytes serve as a bridge between these two electrodes, providing fast transport for conductivity. Lithium-ion batteries use liquid electrolytes, while solid-state batteries use SSEs.”
Elon Musk says first Tesla robotaxis in Austin will be a fleet of 10 to 20 Model Ys but gives few details: ‘You can just see for yourself in two months’
Posted in Elon Musk, sustainability, transportation | Leave a Comment on Elon Musk says first Tesla robotaxis in Austin will be a fleet of 10 to 20 Model Ys but gives few details: ‘You can just see for yourself in two months’
The Tesla robotaxi service, as stated, would be a significant leap in capability from what is currently available.
In a new Nature Communications study, researchers have developed an in-memory ferroelectric differentiator capable of performing calculations directly in the memory without requiring a separate processor.
The proposed differentiator promises energy efficiency, especially for edge devices like smartphones, autonomous vehicles, and security cameras.
Traditional approaches to tasks like image processing and motion detection involve multi-step energy-intensive processes. This begins with recording data, which is transmitted to a memory unit, which further transmits the data to a microcontroller unit to perform differential operations.
WhatsApp adds chat export block for privacy + Meta fined €200M by EU for illegal ad consent model.
A team of Lehigh University researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time—a development that could lead to the creation of stronger, more reliable materials for high-stress environments, such as combustion engines. A paper describing their novel machine learning method was recently published in Nature Computational Materials.
“Using simulations, we were not only able to predict abnormal grain growth, but we were able to predict it far in advance of when that growth happens,” says Brian Y. Chen, an associate professor of computer science and engineering in Lehigh’s P.C. Rossin College of Engineering and Applied Science and a co-author of the study. “In 86% of the cases we observed, we were able to predict within the first 20% of the lifetime of that material whether a particular grain will become abnormal or not.”
When metals and ceramics are exposed to continuous heat—like the temperatures generated by rocket or airplane engines, for example—they can fail. Such materials are made of crystals, or grains, and when they’re heated, atoms can move, causing the crystals to grow or shrink. When a few grains grow abnormally large relative to their neighbors, the resulting change can alter the material’s properties. A material that previously had some flexibility, for instance, may become brittle.
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…