Work by a team of University of Adelaide scientists to perfect metal and mineral extraction processes is bringing the possibility of mining the wealth contained within asteroids closer to reality. But science fiction won’t become fact until asteroid mining becomes economically as well as technically viable.
“Asteroids such as Bennu are closer to us than Adelaide is to Alice Springs, about 1000 kilometres away in Earth’s near orbit,” says Professor Volker Hessel, Deputy Dean-Research from the University of Adelaide’s Faculty of Engineering, Computer & Mathematical Sciences (ECMS) and Professor in the School of Chemical Engineering.
“Advances in space exploration mean that these bodies which contain nickel, cobalt, and platinum as well as water and organic matter, are now within reach.”
In “Avengers: Endgame,” Tony Stark warned Scott Lang that sending him into the quantum realm and bringing him back would be a “billion-to-one cosmic fluke.”
In reality, shrinking a light beam to a nanometer-sized point to spy on quantum-scale light-matter interactions and retrieving the information is not any easier. Now, engineers at the University of California, Riverside, have developed a new technology to tunnel light into the quantum realm at an unprecedented efficiency.
In a Nature Photonics paper, a team led by Ruoxue Yan, an assistant professor of chemical and environmental engineering, and Ming Liu, an assistant professor of electrical and computer engineering, describe the world’s first portable, inexpensive, optical nanoscopy tool that integrates a glass optical fiber with a silver nanowire condenser. The device is a high-efficiency round-trip light tunnel that squeezes visible light to the very tip of the condenser to interact with molecules locally and send back information that can decipher and visualize the elusive nanoworld.
As anyone who has purchased jewelry can attest, platinum is expensive. That’s tough for consumers but also a serious hurdle for a promising source of electricity for vehicles: the hydrogen fuel cell, which relies on platinum.
Now a research team led by Bruce E. Koel, a professor of biological and chemical engineering at Princeton University, has opened a door to finding far cheaper alternatives. In a paper published April 4 in the journal Nature Communications, the researchers reported that a chemical compound based on hafnium worked about 60 percent as effectively as platinum-related materials but at about one-fifth the cost.
“We hope to find something that is more abundant and cheaper to catalyze reactions,” said Xiaofang Yang, principal scientist at HiT Nano Inc. and visiting collaborator at Princeton who is working with Koel on the project.
Neuromorphic systems carry out robust and efficient neural computation using hardware implementations that operate in physical time. Typically they are event- or data-driven, they employ low-power, massively parallel hybrid analog/digital VLSI circuits, and they operate using the same physics of computation used by the nervous system. Although there are several forums for presenting research achievements in neuromorphic engineering, none are exclusively dedicated to this increasingly large research community. Either because they are dedicated to single disciplines, such as electrical engineering or computer science, or because they serve research communities which focus on analogous areas (such as biomedical engineering or computational neuroscience), but with fundamentally different goals and objectives. The mission of Neuromorphic Engineering is to provide a publication medium dedicated exclusively and specifically to this field. Topics covered by this publication include: Analog and hybrid analog/digital electronic circuits for implementing neural processes, such as conductances, neurons, synapses, plasticity mechanisms, photoreceptors, cochleae, etc. Neuromorphic circuits and systems for implementing real-time event-based neural processing architectures. Hardware models of neural and sensorimotor processing systems, such as selective attention systems, coordinate transformation systems, auditory and/or visual processing systems, sensory fusion systems, etc. Implementations of neural computational systems found in insects, birds, mammals, etc. Embedded neuromorphic systems, including actuated or robotic platforms which process sensory signals and interact with the environment using event-based sensors and circuits. To ensure high quality and state-of-the-art material, publications should demonstrate experimental results, using physical implementations of neuromorphic systems, and possibly show the links between the artificial system and the neural/biological one they model.
Maybe we can’t keep packing transistors onto substrates the way Gordon Moore showed us how to do. So how about if we replaced those millions of transistors with components “inspired by the true story” of the brain?
The launch was expected to encounter many technical and engineering challenges, including simplified procedures for pre-launch testing, the rocking motion of the ship and heat dissipation in a confined space.
China has become the first nation to fully own and operate a floating launch platform for its space missions.
Atomic BECs were first achieved in 1995. Although it has become easier to realize atomic BECs since their discovery, they still require very low temperatures for operation. For most purposes, this is too expensive and impractical. Alternatively, negatively charged quatrons are quasi-particles composed of a hole and three electrons which form a stable BEC when coupled to light in triple quantum layer structures in semiconductor microcavities. This allows for both the greater experimental control found in quantum optics, and the benefits of matter wave systems, such as superconductivity and coherence. Moreover, due to the extremely small effective mass of the quasi-particles, quatrons can be used to achieve superconducting BECs at room temperature.
The Create the Future Design Contest was launched in 2002 by the publishers of NASA Tech Briefs magazine to help stimulate and reward engineering innovation. The annual event has attracted more than 8,000 product design ideas from engineers, entrepreneurs, and students worldwide.
Researchers, from biochemists to material scientists, have long relied on the rich variety of organic molecules to solve pressing challenges. Some molecules may be useful in treating diseases, others for lighting our digital displays, still others for pigments, paints, and plastics. The unique properties of each molecule are determined by its structure—that is, by the connectivity of its constituent atoms. Once a promising structure is identified, there remains the difficult task of making the targeted molecule through a sequence of chemical reactions. But which ones?
Organic chemists generally work backwards from the target molecule to the starting materials using a process called retrosynthetic analysis. During this process, the chemist faces a series of complex and inter-related decisions. For instance, of the tens of thousands of different chemical reactions, which one should you choose to create the target molecule? Once that decision is made, you may find yourself with multiple reactant molecules needed for the reaction. If these molecules are not available to purchase, then how do you select the appropriate reactions to produce them? Intelligently choosing what to do at each step of this process is critical in navigating the huge number of possible paths.
Researchers at Columbia Engineering have developed a new technique based on reinforcement learning that trains a neural network model to correctly select the “best” reaction at each step of the retrosynthetic process. This form of AI provides a framework for researchers to design chemical syntheses that optimize user specified objectives such synthesis cost, safety, and sustainability. The new approach, published May 31 by ACS Central Science, is more successful (by ~60%) than existing strategies for solving this challenging search problem.
