Aurelia Butler

Due to their very high efficiency in transporting electric charges from light, perovskites are known as the next generation material for solar panels and LED displays. A team led by Dr Lei Su at Queen Mary University of London now have invented a brand-new application of perovskites as optical fibres.
Optical fibres are tiny wires as thin as a human hair, in which light travels at a superfast speed — 100 times faster than electrons in cables. These tiny optical fibres transmit the majority of our internet data. At present, most optical fibres are made of glass. The perovskite optical fibre made by Dr Su’s team consists of just one piece of a perovskite crystal. The optical fibres have a core width as low as 50 μm (the width of a human hair) and are very flexible — they can be bent to a radius of 3.5mm
Compared to their polycrystal counterparts, single-crystal organometallic perovskites are more stable, more efficient, more durable and have fewer defects. Scientists have therefore been seeking to make single-crystal perovskite optical fibres that can bring this high efficiency to fibre optics.
Dr Su, Reader in Photonics at Queen Mary University of London, said: ‘Single-crystal perovskite fibres could be integrated into current fibre-optical networks, to substitute key components in this system — for example in more efficient lasing and energy conversions, improving the speed and quality of our broadband networks.’
Dr Su’s team were able to grow and precisely control the length and diameter of single-crystal organometallic perovskite fibres in liquid solution (which is very cheap to run) by using a new temperature growth method. They gradually changed the heating position, line contact and temperature during the process to ensure continuous growth in the length while preventing random growth in the width. With their method, the length of the fibre can be controlled, and the cross section of the perovskite fibre core can be varied.
In line with their predictions, due to the single-crystal quality, their fibres proved to have good stability over several months, and a small transmission loss — lower than 0.7dB/cm sufficient for making optical devices. They have great flexibility (can be bent to a radius as small as 3.5mm), and larger photocurrent values than those of a polycrystalline counterpart (the polycrystalline MAPbBr3 milliwire photodetector with similar length).
Dr Su said, ‘This technology could also be used in medical imaging as high-resolution detectors. The small diameter of the fibre can be used to capture a much smaller pixel compared to the state of the art. So that means by using our fibre so we can have the pixel in micrometer scales, giving a much, much higher resolution image for doctors to make better and more accurate diagnosis. We could also use these fibres in textiles that absorb the light. Then when we’re wearing for example clothes or a device with these kinds of fibre woven into the textile, they could convert the solar energy into the electrical power. So we could have solar powered clothing.’
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Materials provided by Queen Mary University of London. Note: Content may be edited for style and length. More

From a box of Cracker Jack to The Da Vinci Code, everybody enjoys deciphering secret messages. But biomedical engineers at Duke University have taken the decoder ring to place it’s never been before — the patterns created by bacterial colonies.
Depending on the initial conditions used, such as nutrient levels and space constraints, bacteria tend to grow in specific ways. The researchers created a virtual bacterial colony and then controlled growth conditions and the numbers and sizes of simulated bacterial dots to create an entire alphabet based on how the colonies would look after they fill a virtual Petri dish. They call this encoding scheme emorfi.
The encoding is not one-to-one, as the final simulated pattern corresponding to each letter is not exactly the same every time. However, the researchers discovered that a machine learning program could learn to distinguish between them to recognize the letter intended.
“A friend may see many images of me over the course of time, but none of them will be exactly the same,” explained Lingchong You, professor of biomedical engineering at Duke. “But if the images are all consistently reinforcing what I generally look like, the friend will be able to recognize me even if they’re shown a picture of me they’ve never seen before.”
To encrypt real messages, the encoder ends up creating a movie of a series of patterns, each correlating to a different letter. While they may look similar to the untrained eye, the computer algorithm can distinguish between them. So long as the receiver knows the set of initial conditions that led to their creation, an interloper should not be able to crack the code without a powerful AI of their own.
Give the cypher a try yourself. You can type in anything from your name to the Gettysburg Address, or even the Christmas classic, “Be sure to drink your Ovaltine”:

This research was supported by the National Science Foundation (MCB-1937259), the Office of Naval Research (N00014-20-1-2121), the David and Lucile Packard Foundation and the Google Cloud Research Credits program.
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Materials provided by Duke University. Original written by Ken Kingery. Note: Content may be edited for style and length. More

Developing life-saving medicines can take billions of dollars and decades of time, but University of Central Florida researchers are aiming to speed up this process with a new artificial intelligence-based drug screening process they’ve developed.
Using a method that models drug and target protein interactions using natural language processing techniques, the researchers achieved up to 97% accuracy in identifying promising drug candidates. The results were published recently in the journal Briefings in Bioinformatics.
The technique represents drug-protein interactions through words for each protein binding site and uses deep learning to extract the features that govern the complex interactions between the two.
“With AI becoming more available, this has become something that AI can tackle,” says study co-author Ozlem Garibay, an assistant professor in UCF’s Department of Industrial Engineering and Management Systems. “You can try out so many variations of proteins and drug interactions and find out which are more likely to bind or not.”
The model they’ve developed, known as AttentionSiteDTI, is the first to be interpretable using the language of protein binding sites.
The work is important because it will help drug designers identify critical protein binding sites along with their functional properties, which is key to determining if a drug will be effective. More

There has been a lot of buzz about quantum computers and for good reason. The futuristic computers are designed to mimic what happens in nature at microscopic scales, which means they have the power to better understand the quantum realm and speed up the discovery of new materials, including pharmaceuticals, environmentally friendly chemicals, and more. However, experts say viable quantum computers are still a decade away or more. What are researchers to do in the meantime?
A new Caltech-led study in the journal Science describes how machine learning tools, run on classical computers, can be used to make predictions about quantum systems and thus help researchers solve some of the trickiest physics and chemistry problems. While this notion has been shown experimentally before, the new report is the first to mathematically prove that the method works.
“Quantum computers are ideal for many types of physics and materials science problems,” says lead author Hsin-Yuan (Robert) Huang, a graduate student working with John Preskill, the Richard P. Feynman Professor of Theoretical Physics and the Allen V. C. Davis and Lenabelle Davis Leadership Chair of the Institute for Quantum Science and Technology (IQIM). “But we aren’t quite there yet and have been surprised to learn that classical machine learning methods can be used in the meantime. Ultimately, this paper is about showing what humans can learn about the physical world.”
At microscopic levels, the physical world becomes an incredibly complex place ruled by the laws of quantum physics. In this realm, particles can exist in a superposition of states, or in two states at once. And a superposition of states can lead to entanglement, a phenomenon in which particles are linked, or correlated, without even being in contact with each other. These strange states and connections, which are widespread within natural and human-made materials, are very hard to describe mathematically.
“Predicting the low-energy state of a material is very hard,” says Huang. “There are huge numbers of atoms, and they are superimposed and entangled. You can’t write down an equation to describe it all.”
The new study is the first mathematical demonstration that classical machine learning can be used to bridge the gap between us and the quantum world. Machine learning is a type of computer application that mimics the human brain to learn from data. More

Quantum computers and communication devices work by encoding information into individual or entangled photons, enabling data to be quantum securely transmitted and manipulated exponentially faster than is possible with conventional electronics. Now, quantum researchers at Stevens Institute of Technology have demonstrated a method for encoding vastly more information into a single photon, opening the door to even faster and more powerful quantum communication tools.
Typically, quantum communication systems “write” information onto a photon’s spin angular momentum. In this case, photons carry out either a right or left circular rotation, or form a quantum superposition of the two known as a two-dimensional qubit. It’s also possible to encode information onto a photon’s orbital angular momentum — the corkscrew path that light follows as it twists and torques forward, with each photon circling around the center of the beam. When the spin and angular momentum interlock, it forms a high-dimensional qudit — enabling any of a theoretically infinite range of values to be encoded into and propagated by a single photon.
Qubits and qudits, also known as flying qubits and flying qudits, are used to propagate information stored in photons from one point to another. The main difference is that qudits can carry much more information over the same distance than qubits, providing the foundation for turbocharging next generation quantum communication.
In a cover story in the August 2022 issue of Optica, researchers led by Stefan Strauf, head of the NanoPhotonics Lab at Stevens, show that they can create and control individual flying qudits, or “twisty” photons, on demand — a breakthrough that could dramatically expand the capabilities of quantum communication tools. The work builds upon the team’s 2018 paper in Nature Nanotechnology.
“Normally the spin angular momentum and the orbital angular momentum are independent properties of a photon. Our device is the first to demonstrate simultaneous control of both properties via the controlled coupling between the two,” explained Yichen Ma, a graduate student in Strauf’s NanoPhotonics Lab, who led the research in collaboration with Liang Feng at the University of Pennsylvania, and Jim Hone at Columbia University.
“What makes it a big deal is that we’ve shown we can do this with single photons rather than classical light beams, which is the basic requirement for any kind of quantum communication application,” Ma said. More

Nanoengineers at the University of California San Diego have developed microscopic robots, called microrobots, that can swim around in the lungs, deliver medication and be used to clear up life-threatening cases of bacterial pneumonia.
In mice, the microrobots safely eliminated pneumonia-causing bacteria in the lungs and resulted in 100% survival. By contrast, untreated mice all died within three days after infection.
The results are published Sept. 22 in Nature Materials.
The microrobots are made of algae cells whose surfaces are speckled with antibiotic-filled nanoparticles. The algae provide movement, which allows the microrobots to swim around and deliver antibiotics directly to more bacteria in the lungs. The nanoparticles containing the antibiotics are made of tiny biodegradable polymer spheres that are coated with the cell membranes of neutrophils, which are a type of white blood cell. What’s special about these cell membranes is that they absorb and neutralize inflammatory molecules produced by bacteria and the body’s immune system. This gives the microrobots the ability to reduce harmful inflammation, which in turn makes them more effective at fighting lung infection.
The work is a joint effort between the labs of nanoengineering professors Joseph Wang and Liangfang Zhang, both at the UC San Diego Jacobs School of Engineering. Wang is a world leader in the field of micro- and nanorobotics research, while Zhang is a world leader in developing cell-mimicking nanoparticles for treating infections and diseases. Together, they have pioneered the development of tiny drug-delivering robots that can be safely used in live animals to treat bacterial infections in the stomach and blood. Treating bacterial lung infections is the latest in their line of work.
“Our goal is to do targeted drug delivery into more challenging parts of the body, like the lungs. And we want to do it in a way that is safe, easy, biocompatible and long lasting,” said Zhang. “That is what we’ve demonstrated in this work.”
The team used the microrobots to treat mice with an acute and potentially fatal form of pneumonia caused by the bacteria Pseudomonas aeruginosa. This form of pneumonia commonly affects patients who receive mechanical ventilation in the intensive care unit. The researchers administered the microrobots to the lungs of the mice through a tube inserted in the windpipe. The infections fully cleared up after one week. All mice treated with the microrobots survived past 30 days, while untreated mice died within three days. More

Using the wrong mathematical formula to assess heartbeat rhythms may lead oncologists to inappropriately stop life-saving chemotherapy, according to research findings from UNC Lineberger Comprehensive Cancer Center scientists. Standardizing the mathematical formulas for measuring heartbeat rhythms with electrocardiograms, and avoiding one commonly used formula, could reduce this unintended outcome, the researchers reported.
The study findings were published in JAMA Oncology.
The formulas in this study are based on how the cardiac system recharges itself after each heartbeat. In reading an electrocardiogram (ECG), heartbeat spikes and bumps, called P through U waves, indicate when the heart is contracting and relaxing. The interval between the start of the Q wave and end of the T wave, when prolonged, is of most concern for people receiving chemotherapy. When the heart muscle takes a comparatively longer time to contract and relax than usual, which is known as QT prolongation, it may increase the risk of developing abnormal heart rhythms that can lead to sudden cardiac arrest.
Because QT prolongation is a potentially serious side effect, every chemotherapy drug goes through rigorous testing for QT prolongation in its approval process. Many chemotherapy agents that prolong the QT interval today fall into a class known as targeted therapies. As the use of targeted therapies expands, monitoring QT prolongation becomes even more important, especially for many blood cancers that are often treated with targeted drugs, such as those that were part of this study.
In their study of different formulas, the researchers discovered that one formula, the Bazett formula, was associated with a three-fold increase in the corrected QT interval compared to other formulas used with oncology patients. The overestimation of the QT interval by the Bazett formula can potentially lead to misguided chemotherapy modification that can impact clinical care.
“The mathematics that determine a QT formula matters because if an inappropriate formula is used, it could lead oncologists to reduce chemotherapy unnecessarily and possibly affect the potential for cure,” said Daniel R. Richardson, MD, MSc, assistant professor of medicine at UNC Lineberger and corresponding author of the article. “The differences we found between QT formula were pretty striking and we did not anticipate the magnitude of difference when we started this project. It certainly has changed how I treat patients.”
The researchers looked at medical records of 6,881 adult cancer patients who received 24 different types of chemotherapy between 2010 and 2020. The patients were seen at the North Carolina Basnight Cancer Hospital and received nearly 20,000 ECGs.
The investigators found that the Bazett formula resulted in longer QT prolongation periods than two other formulas (Framingham and Fridericia) in 40.9% of ECGs examined; this was concerning as Bazett is the default formula used with many ECG devices.
“We initially discovered this problem while treating a patient with acute promyelocytic leukemia with arsenic trioxide, a drug known to cause QT prolongation. We realized that there was inconsistent guidance about how to assess the QT interval with this drug and what values should lead to dose reductions,” said senior author Joshua F. Zeidner, MD, an associate professor of medicine and chief of leukemia research at UNC Lineberger. “The clinical protocol that ultimately led to the approval of this drug used a very specific QT formula — Framingham — and we were using a different formula — Bazett — to guide our treatment decisions. Prior to this discovery, most of us were not aware that there were multiple formulas available for corrected QT intervals. The findings from this study have been practice changing as we no longer recommend the Bazett formula for clinical guidance.”
For their next steps, the researchers are considering conducting a study evaluating oncologists’ and pharmacists’ awareness of the different QT prolongation formulas and their impact as this would help researchers better grasp the magnitude of the issue. Primarily, though, the researchers want to advocate for an understanding of the effect of formula choice on outcomes and to advocate for standardization when assessing oncology patients. More