Lifeboat Foundation

Cutting intricate patterns as small as several billionths of a meter deep and wide, the focused ion beam (FIB) is an essential tool for deconstructing and imaging tiny industrial parts to ensure they were fabricated correctly. When a beam of ions, typically of the heavy metal gallium, bombards the material to be machined, the ions eject atoms from the surface—a process known as milling—to sculpt the workpiece.

Beyond its traditional uses in the semiconductor industry, the FIB has also become a critical tool for fabricating prototypes of complex three-dimensional devices, ranging from lenses that focus light to conduits that channel fluid. Researchers also use the FIB to dissect biological and material samples to image their internal structure.

However, the FIB process has been limited by a trade-off between high speed and fine resolution. On the one hand, increasing the ion current allows a FIB to cut into the workpiece deeper and faster. On the other hand, the increased current carries a larger number of positively charged ions, which electrically repel each other and defocus the beam. A larger, diffuse beam, which can be about 100 nanometers in diameter or 10 times wider than a typical narrow beam, not only limits the ability to fabricate fine patterns but can also damage the workpiece at the perimeter of the milled region. As a result, the FIB has not been the process of choice for those trying to machine many tiny parts in a hurry.

As the size of modern technology shrinks down to the nanoscale, weird quantum effects—such as quantum tunneling, superposition, and entanglement—become prominent. This opens the door to a new era of quantum technologies, where quantum effects can be exploited. Many everyday technologies make use of feedback control routinely; an important example is the pacemaker, which must monitor the user’s heartbeat and apply electrical signals to control it, only when needed. But physicists do not yet have an equivalent understanding of feedback control at the quantum level. Now, physicists have developed a “master equation” that will help engineers understand feedback at the quantum scale. Their results are published in the journal Physical Review Letters.

“It is vital to investigate how feedback control can be used in quantum technologies in order to develop efficient and fast methods for controlling quantum systems, so that they can be steered in real time and with high precision,” says co-author Björn Annby-Andersson, a quantum physicist at Lund University, in Sweden.

An example of a crucial feedback-control process in quantum computing is quantum error correction. A quantum computer encodes information on physical qubits, which could be photons of light, or atoms, for instance. But the quantum properties of the qubits are fragile, so it is likely that the encoded information will be lost if the qubits are disturbed by vibrations or fluctuating electromagnetic fields. That means that physicists need to be able to detect and correct such errors, for instance by using feedback control. This error correction can be implemented by measuring the state of the qubits and, if a deviation from what is expected is detected, applying feedback to correct it.

Interaction between glass spheres suspended in a vacuum might one day lead to advances in quantum computing.

The success of COVID-19 vaccines is a great example of gene medicine’s tremendous potential to prevent viral infections. One reason for the vaccines’ success is their use of lipid nanoparticles, or LNPs, to carry delicate messenger RNA to cells to generate and boost immunity. LNPs—tiny fat particles—have become increasingly popular as a carrier to deliver various gene-based medicines to cells, but their use is complicated because each LNP must be tailored specifically for the therapeutic payload it carries.

A team led by Hai-Quan Mao, a Johns Hopkins materials scientist, has created a platform that shows promise to speed up the LNP design process and make it more affordable. The new approach also can be adapted to other gene therapies.

“In a nutshell, what we have done is creating a method that screens lipid nanoparticle components and their proportions to quickly help identify and create the optimal design for use with various therapeutic genes,” said Mao, director of the Institute for NanoBioTechnology at Johns Hopkins Whiting School of Engineering and professor in the departments of Materials Science and Engineering and Biomedical Engineering.

Researchers from RIKEN in Japan have achieved a major step toward large-scale quantum computing by demonstrating error correction in a three-qubit silicon-based quantum computing system. This work, published in Nature, could pave the way toward the achievement of practical quantum computers.

Quantum computers are a hot area of research today, as they promise to make it possible to solve certain important problems that are intractable using conventional computers. They use a completely different architecture, using superimposition states found in quantum physics rather than the simple 1 or 0 binary bits used in conventional computers. However, because they are designed in a completely different way, they are very sensitive to environmental noise and other issues, such as decoherence, and require error correction to allow them to do precise calculations.

One important challenge today is choosing what systems can best act as “qubits”—the basic units used to make quantum calculations. Different candidate systems have their own strengths and weaknesses. Some of the popular systems today include superconducting circuits and ions, which have the advantage that some form of error correction has been demonstrated, allowing them to be put into actual use albeit on a small scale. Silicon-based quantum technology, which has only begun to be developed over the past decade, is known to have an advantage in that it utilizes a semiconductor nanostructure similar to what is commonly used to integrate billions of transistors in a small chip, and therefore could take advantage of current production technology.

A nano-scale sensor technology records precise signatures of light striking a surface.

In recent years, roboticists have developed a wide variety of robotic systems with different body structures and capabilities. Most of these robots are either made of hard materials, such as metals, or soft materials, such as silicon and rubbery materials.

Researchers at Hong Kong University (HKU) and Lawrence Berkeley National Laboratory have recently created Aquabots, a new class of soft robots that are predominantly made of liquids. As most biological systems are predominantly made up of water or other aqueous solutions, the new robots, introduced in a paper published in ACS Nano, could have highly valuable biomedical and environmental applications.

“We have been engaged in the development of adaptive interfacial assemblies of materials at the oil-water and water-water interface using nanoparticles and polyelectrolytes,” Ho Cheung (Anderson) Shum, Thomas P. Russell, and Shipei Zhu told TechXplore via email. “Our idea was to assemble the materials that the interface and the assemblies lock in the shapes of the liquids. The shapes are dictated using external forces to generate arbitrary shapes or to use all-liquid 3D printing to be able to spatially organize the assemblies.”

Constructing a tiny robot out of DNA

DNA, or deoxyribonucleic acid, is a molecule composed of two long strands of nucleotides that coil around each other to form a double helix. It is the hereditary material in humans and almost all other organisms that carries genetic instructions for development, functioning, growth, and reproduction. Nearly every cell in a person’s body has the same DNA. Most DNA is located in the cell nucleus (where it is called nuclear DNA), but a small amount of DNA can also be found in the mitochondria (where it is called mitochondrial DNA or mtDNA).

The device uses magnetic fields to carry out computation rather than shuttling electricity around, so it consumes far less power.