Scientists have unlocked a new understanding of mesoporous silicon, a nanostructured version of the well-known semiconductor. Unlike standard silicon, its countless tiny pores give it unique electrical and thermal properties, opening up potential applications in biosensors, thermal insulation, photovoltaics, and even quantum computing.
Performing computation using quantum-mechanical phenomena such as superposition and entanglement.
A research team led by Professor Takayuki Hoshino of Nagoya University’s Graduate School of Engineering in Japan has demonstrated the world’s smallest shooting game by manipulating nanoparticles in real time, resulting in a game that is played with particles approximately 1 billionth of a meter in size.
This research is a significant step toward developing a computer interface system that seamlessly integrates virtual objects with real nanomaterials. They published their study in the Japanese Journal of Applied Physics.
The game demonstrates what the researchers call “nano-mixed reality (MR),” which integrates digital technology with the physical nanoworld in real time using high-speed electron beams. These beams generate dynamic patterns of electric fields and optical images on a display surface, allowing researchers to control the force field acting on the nanoparticles in real time to move and manipulate them.
Laying the groundwork for quantum communication systems of the future, engineers at Caltech have demonstrated the successful operation of a quantum network of two nodes, each containing multiple quantum bits, or qubits—the fundamental information-storing building blocks of quantum computers.
To achieve this, the researchers developed a new protocol for distributing quantum information in a parallel manner, effectively creating multiple channels for sending data, or multiplexing. The work was accomplished by embedding ytterbium atoms inside crystals and coupling them to optical cavities—nanoscale structures that capture and guide light. This platform has unique properties that make it ideal for using multiple qubits to transmit quantum information-carrying photons in parallel.
“This is the first-ever demonstration of entanglement multiplexing in a quantum network of individual spin qubits,” says Andrei Faraon (BS ‘04), the William L. Valentine Professor of Applied Physics and Electrical Engineering at Caltech. “This method significantly boosts quantum communication rates between nodes, representing a major leap in the field.”
Researchers at the National Graphene Institute at the University of Manchester have achieved a significant milestone in the field of quantum electronics with their latest study on spin injection in graphene. The paper, published recently in Communications Materials, outlines advancements in spintronics and quantum transport.
Spin transport electronics, or spintronics, represents a revolutionary alternative to traditional electronics by utilizing the spin of electrons rather than their charge to transfer and store information. This method promises energy-efficient and high-speed solutions that exceed the limitations of classical computation, for next generation classical and quantum computation.
The Manchester team, led by Dr. Ivan Vera-Marun, has fully encapsulated monolayer graphene in hexagonal boron nitride, an insulating and atomically flat 2D material, to protect its high quality. By engineering the 2D material stack to expose only the edges of graphene, and laying magnetic nanowire electrodes over the stack, they successfully form one-dimensional (1D) contacts.
Deep Nanometry (DNM) is an innovative technique combining high-speed optical detection with AI-driven noise reduction, allowing researchers to find rare nanoparticles like extracellular vesicles (EVs).
Since EVs play a role in disease detection, DNM could revolutionize early cancer diagnosis. Its applications stretch beyond healthcare, promising advances in vaccine research, and environmental science.
A Breakthrough in Nanoparticle Detection.
In an international collaboration, researchers have made an important breakthrough in the therapeutic delivery of microRNAs against Duchenne muscular dystrophy, a disease with no cure, to date.
Duchenne muscular dystrophy is a genetic disorder characterized by the progressive loss of muscle mass, due to mutations in the dystrophin gene. Without the corresponding functional protein, muscles cannot function or repair themselves properly, resulting in the deterioration of skeletal, heart, and lung muscles. Because the dystrophin gene is located on the X chromosome, it mainly affects males, while females are usually carriers.
Researchers have developed a strategy to treat muscular dystrophy, which uses nanoparticles as vehicles to transport therapeutical microRNAs to muscle stem cells. Once inside the muscle stem cells, the nanoparticles release the microRNA to stimulate the production of muscle fibers.
Designing high performance, scalable, and energy efficient spiking neural networks remains a challenge. Here, the authors utilize mixed-dimensional dual-gated Gaussian heterojunction transistors from single-walled carbon nanotubes and monolayer MoS2 to realize simplified spiking neuron circuits.
Apical periodontitis, a chronic and hard-to-treat dental infection, affects more than half of the population worldwide and is the leading cause of tooth loss. Root canal is the standard treatment, but existing approaches to treat the infection have many limitations that can cause complications, leading to treatment failure.
Now, researchers at the School of Dental Medicine, Perelman School of Medicine, and School of Engineering and Applied Sciences have identified a promising new therapeutic option that could potentially disrupt current treatments. The team of researchers is part of the Center for Innovation & Precision Dentistry, a joint research center between Penn Dental Medicine and Penn Engineering that leverages engineering and computational approaches to advance oral and craniofacial health care innovation.
In a paper published in the Journal of Clinical Investigation, they show that ferumoxytol, an FDA-approved iron oxide nanoparticle formulation, greatly reduces infection in patients diagnosed with apical periodontitis.
Using machine learning, a team of researchers in Canada has created ultrahigh-strength carbon nanolattices, resulting in a material that’s as strong as carbon steel, but only as dense as Styrofoam.
The team noted last month that it was the first time this branch of AI had been used to optimize nano-architected materials. University of Toronto’s Peter Serles, one of the authors of the paper describing this work in Advanced Materials, praised the approach, saying, “It didn’t just replicate successful geometries from the training data; it learned from what changes to the shapes worked and what didn’t, enabling it to predict entirely new lattice geometries.”
To quickly recap, nanomaterials are engineered by arranging atoms or molecules in precise patterns, much like constructing structures with extremely tiny LEGO blocks. These materials often exhibit unique properties due to their nanoscale dimensions.
