Aurelia Butler

National University of Singapore (NUS) scientists demonstrated a conceptual breakthrough by fabricating atomically precise quantum antidots (QAD) using self-assembled single vacancies (SVs) in a two-dimensional (2D) transition metal dichalcogenide (TMD).
Quantum dot confines electrons on a nanoscale level. In contrast, an antidot refers to a region characterised by a potential hill that repels electrons. By strategically introducing antidot patterns (“voids”) into carefully designed antidot lattices, intriguing artificial structures emerge. These structures exhibit periodic potential modulation to change 2D electron behaviour, leading to novel transport properties and unique quantum phenomena. As the trend towards miniaturised devices continue, it is important to accurately control the size and spacing of each antidot at the atomic level. This control together with resilience to environmental perturbations is crucial to address technological challenges in nanoelectronics.
A research team led by Associate Professor Jiong LU from the NUS Department of Chemistry and the NUS Institute for Functional Intelligent Materials introduced a method to fabricate a series of atomic-scale QADs with elegantly engineered quantum hole states in a 2D three-atom-layer TMD. QADs can serve as a promising new-generation candidate that can be used for applications such as quantum information technologies. This was achieved through the self-assembly of the SVs into a regular pattern. The atomic and electronic structure of the QADs is analysed using both scanning tunnelling microscopy and non-contact atomic force microscopy . This work is performed in collaboration with Assistant Professor Aleksandr RODIN’s research group from the Yale-NUS College.
The study was published in the journal Nature Nanotechnology on 31 August 2023.
A defective platinum ditelluride (PtTe2) sample containing numerous tellurium (Te) SVs was intentionally grown for this study. After thermal annealing, the Te SVs behave as “atomic Lego,” self-assembling into highly ordered vacancy-based QADs. These SVs inside QADs are spaced by a single Te atom, representing the minimal distance possible in conventional antidot lattices. When the number of SVs in QADs increases, it strengthens the cumulative repulsive potential. This leads to enhanced interference of the quasiparticles within the QADs. This, in turn, results in the creation of multi-level quantum hole states, featuring an adjustable energy gap spanning from the telecommunication to far-infrared ranges.
Due to their geometry-protected characteristics, these precisely engineered quantum hole states survived in the structure even when vacancies in QADs are occupied by oxygen after exposure to air. This exceptional robustness against environmental influences is an added advantage of this method.
Assoc Prof Lu said, “The conceptual demonstration of the fabrication of these QADs opens the door for the creation of a new class of artificial nanostructures in 2D materials with discrete quantum hole states. These structures provide an excellent platform to enable the exploration of novel quantum phenomena and the dynamics of hot electron in previously inaccessible regimes.”
“Further refinement of these QADs by introducing spin-polarised atoms to fabricate magnetic QADs and antiferromagnetic Ising systems on a triangular lattice could provide valuable atomic insights into exotic quantum phases. These insights hold potential for advancing a wide variety of material technologies,” added Assoc Prof Lu. More

Over the past few years, research has been conducted on quantum technologies that exploit the quantum mechanical properties of microscopic entities. Quantum thermodynamics is a notable field in this domain. Within this field, quantum heat engines and quantum batteries, leveraging quantum characteristics, have been theoretically studied and practically tested. A critical indicator of the performance of such devices is the magnitude of heat current (heat transferred per unit time) flowing from the ambient environment to the quantum system as the system’s size increases. However, the fundamental limit of the heat current flowing into such an ensemble of quantum systems remains undefined.
In this study, the researchers mathematically derived a novel inequality that defines the limit of the heat current flowing into a quantum system. Based on this inequality, they demonstrated that as a quantum system incorporates increasing number of particles, the heat current flowing into the system does not rise faster than a cubic function of the particle count.
Furthermore, they derived an inequality applicable under more realistic conditions wherein the heat current does not rise faster than a square function of the particle count. Interestingly, the phenomenon related to energy radiation termed as “super-radiance” was identified as the most efficient mechanism for achieving the fundamental heat current limit derived in this study.
While earlier research has hinted at nonlinear heat current surge with respect to the particle count in various specific scenarios, this study is pioneering in pinpointing a fundamental limit that is universally applicable. Notably, these findings could be instrumental for cooling engines associated with quantum devices and other similar applications.
This work was supported from JSPS KAKENHI (No. 20H01827), JST’s Moonshot R&D (No. JPMJMS2061) and JST PRESTO (No. JP-MJPR1919), Japan. More

Industrial Internet of Things (IIoTs) refers to a technology that combines wireless sensors, controllers, and mobile communication technologies to make every aspect of industrial production processes intelligent and efficient. Since IIoTs can involve several small battery-driven devices and sensors, there is a growing need to develop a robust network for data transmission and power transfer to monitor the IIoT environment.
In this regard, wireless power transfer is a promising technology. It utilizes radio frequency signals to power small devices that consume minimal power. Recently, simultaneous wireless information and power transfer (SWIPT), which utilizes a single radio frequency signal to simultaneously perform energy harvesting and information decoding, has attracted significant interest for IIoTs. Additionally, with smart devices rapidly growing in number, SWIPT has been combined with nonorthogonal multiple access (NOMA) system, which is a promising candidate for IIoTs due to their ability to extend the battery life of sensors and other devices. However, the energy efficiency of this system falls significantly with transmission distance from the central controller.
To overcome this limitation, a team of researchers from South Korea, led by Associate Professor Dong-Wook Seo from the Division of Electronics and Electrical Information Engineering at Korea Maritime and Ocean University, has developed a new framework by applying SWIPT-aided NOMA to a distributed antenna system (DAS), significantly improving the energy and spectral efficiencies of IIoTs. “By applying a DAS with supporting antennas relatively close to edge users alongside a central base station, SWIPT-NOMA’s loss with growing distance can be reduced efficiently. This improves information decoding and energy harvesting performance,” explains Dr. Seo.
Their study was made available online on 27 October 2022 and published in Volume 19, Issue 7 of the journal IEEE Transactions on Industrial Informatics in 01 July 2023.
The researchers formulated a three-step iterative algorithm to maximize the energy efficiency of the SWIPT-NOMA-DAS system. They first optimized the power allocation for the central IoT controller. After that, the power allocation for NOMA signaling and power splitting (PS) assignment for SWIPT were optimized jointly, while minimizing the data rates and harvested energy requirements. Finally, the team analyzed an outage event in which the system cannot provide sufficient energy and data rates, thereby extending the joint power allocation and PS assignment optimization method to the multi-cluster scenario.
They validated their algorithm through extensive numerical simulations, finding that the proposed SWIPT-NOMA-DAS system is five times more energy efficient than SWIPT-NOMA without DAS. Also, it shows a more than 10% improvement in performance over SWIPT-OMA-DAS.
Highlighting the significance of their study, Dr. Seo says: “This technology ensures very efficient energy consumption and offers various advantages such as convenience, low power, and battery life extension. Thus, it can be applied to smartphones, laptops, wearable devices, and electric vehicles. Most importantly, the SWIPT-NOMA-DAS system can optimize resource allocation and efficiently perform wireless charging and information transmission for users in an IoT environment.” More

Using a standardized assessment, researchers in the UK compared the performance of a commercially available artificial intelligence (AI) algorithm with human readers of screening mammograms. Results of their findings were published in Radiology, a journal of the Radiological Society of North America (RSNA).
Mammographic screening does not detect every breast cancer. False-positive interpretations can result in women without cancer undergoing unnecessary imaging and biopsy. To improve the sensitivity and specificity of screening mammography, one solution is to have two readers interpret every mammogram.
According to the researchers, double reading increases cancer detection rates by 6 to 15% and keeps recall rates low. However, this strategy is labor-intensive and difficult to achieve during reader shortages.
“There is a lot of pressure to deploy AI quickly to solve these problems, but we need to get it right to protect women’s health,” said Yan Chen, Ph.D., professor of digital screening at the University of Nottingham, United Kingdom.
Prof. Chen and her research team used test sets from the Personal Performance in Mammographic Screening, or PERFORMS, quality assurance assessment utilized by the UK’s National Health Service Breast Screening Program (NHSBSP), to compare the performance of human readers with AI. A single PERFORMS test consists of 60 challenging exams from the NHSBSP with abnormal, benign and normal findings. For each test mammogram, the reader’s score is compared to the ground truth of the AI results.
“It’s really important that human readers working in breast cancer screening demonstrate satisfactory performance,” she said. “The same will be true for AI once it enters clinical practice.”
The research team used data from two consecutive PERFORMS test sets, or 120 screening mammograms, and the same two sets to evaluate the performance of the AI algorithm. The researchers compared the AI test scores with the scores of the 552 human readers, including 315 (57%) board-certified radiologists and 237 non-radiologist readers consisting of 206 radiographers and 31 breast clinicians. More

ChatGPT could help to increase vaccine uptake by debunking myths around jab safety, say the authors of a study published in the peer-reviewed journal Human Vaccines and Immunotherapeutics.
The researchers asked the artificial intelligence (AI) chatbot the top 50 most frequently-asked Covid-19 vaccine questions. They included queries based on myths and fake stories such as the vaccine causing Long Covid.
Results show that ChatGPT scored nine out of 10 on average for accuracy. The rest of the time it was correct but left some gaps in the information provided, according to the study.
Based on these findings, experts who led the study from the GenPoB research group based at the Instituto de Investigación Sanitaria (IDIS) — Hospital Clinico Universitario of Santiago de Compostela, say the AI tool is a “reliable source of non-technical information to the public,” especially for people without specialist scientific knowledge.
However, the findings do highlight some concerns about the technology such as ChatGPT changing its answers in certain situations.
“Overall, ChatGPT constructs a narrative in line with the available scientific evidence, debunking myths circulating on social media,” says lead author Antonio Salas, who as well as leading the GenPoB research group, is also a Professor at the Faculty of Medicine at the University of Santiago de Compostela, in Spain.
“Thereby it potentially facilitates an increase in vaccine uptake. ChatGPT can detect counterfeit questions related to vaccines and vaccination. The language this AI uses is not too technical and therefore easily understandable to the public but without losing scientific rigor. More

Digital information exchange can be safer, cheaper and more environmentally friendly with the help of a new type of random number generator for encryption developed at Linköping University, Sweden. The researchers behind the study believe that the new technology paves the way for a new type of quantum communication.
In an increasingly connected world, cybersecurity is becoming increasingly important to protect not just the individual, but also, for example, national infrastructure and banking systems. And there is an ongoing race between hackers and those trying to protect information. The most common way to protect information is through encryption. So when we send emails, pay bills and shop online, the information is digitally encrypted.
To encrypt information, a random number generator is used, which can either be a computer programme or the hardware itself. The random number generator provides keys that are used to both encrypt and unlock the information at the receiving end.
Different types of random number generators provide different levels of randomness and thus security. Hardware is the much safer option as randomness is controlled by physical processes. And the hardware method that provides the best randomness is based on quantum phenomena — what researchers call the Quantum Random Number Generator, QRNG.
“In cryptography, it’s not only important that the numbers are random, but that you’re the only one who knows about them. With QRNG’s, we can certify that a large amount of the generated bits is private and thus completely secure. And if the laws of quantum physics are true, it should be impossible to eavesdrop without the recipient finding out,” says Guilherme B Xavier, researcher at the Department of Electrical Engineering at Linköping University.
His research group, together with researchers at the Department of Physics, Chemistry and Biology (IFM), has developed a new type of QRNG, that can be used for encryption, but also for betting and computer simulations. The new feature of the Linköping researchers’ QRNG is the use of light emitting diodes made from the crystal-like material perovskite.
Their random number generator is among the best produced and compares well with similar products. Thanks to the properties of perovskites, it has the potential to be cheaper and more environmentally friendly. More

A team of UC Davis and University of Oxford researchers have developed an innovative tool: SparkMaster 2. The open-source software allows scientists to analyze normal and abnormal calcium signals in cells automatically.
Calcium is a key signaling molecule in all cells, including muscles like the heart. The new software enables the automatic analysis of distinct patterns of calcium release in cells. This includes calcium “sparks,” microscopic releases of calcium within cardiac cells associated with irregular heartbeats, also known as arrhythmia.
A research article demonstrating the capabilities of SparkMaster 2 was published in Circulation Research.
Jakub Tomek, the first author of the research article, is a Sir Henry Wellcome Fellow in the Department of Physiology, Anatomy and Genetics at the University of Oxford. He spent his fellowship year at UC Davis, working with Distinguished Professor Donald M. Bers.
“It was great to present SparkMaster 2 at recent conferences and see the enthusiastic response. I felt it would be an outlier and that few people would care. But many people were excited about having a new analysis tool that overcomes many of the limitations they have experienced with prior tools,” Tomek said.
Fellowship at UC Davis leads to updated tool
Problems with how and when calcium is released by cells can have an impact on a range of diseases, including arrhythmia and hypertension. To understand the mechanisms behind these diseases, researchers use fluorescent calcium indicators and microscopic imaging that can measure the calcium changes at the cellular level. More

Researchers have developed an automated version of the viral plaque assay, the gold-standard method for detecting and quantifying viruses. The new method uses time-lapse holographic imaging and deep learning to greatly reduce detection time and eliminate staining and manual counting. This advance could help streamline the development of new vaccines and antiviral drugs.
Yuzhu Li from Ozcan Lab at the University of California, Los Angeles (UCLA), will present this research at Frontiers in Optics + Laser Science (FiO LS), which will be held 9 — 12 October 2023 at the Greater Tacoma Convention Center in Tacoma (Greater Seattle Area), Washington.
“By cutting down the detection time compared to traditional viral plaque assays, this technique might help expedite vaccine and drug development research by significantly reducing the detection time needed and eliminating chemical staining and manual counting entirely, explains Li. “In the event of a new virus outbreak, vaccines or antiviral treatments could be developed, tested, and made available to the public at a significantly accelerated rate, resulting in a faster response time to virus-induced health emergencies.”
Although the viral plaque assay is a cost-effective way to assess virus infectivity and quantify the amount of a virus in a sample, it is time consuming to perform. Samples are first diluted and then added to cultured cells. If the virus kills the infected cells, a region free of cells — a plaque — develops. Experts then manually count the stained plaque-forming units (PFUs), a process that is susceptible to staining irregularities and human counting errors.
The new stain-free automated viral plaque assay system replaces manual plaque counting with a lens-free holographic imaging system that images the spatiotemporal features of PFUs during incubation. A deep learning algorithm is then used to detect, classify and locate PFUs based on changes observed.
To show the efficacy of their system, the researchers infected cultured cells with the Vesicular stomatitis virus. After just 20 hours of incubation, the automated system detected more than 90% of the viral PFUs without any false positives. This was much faster than the traditional plaque assay, which requires 48 hours of incubation for this virus. They also applied the automated approach to herpes simplex virus type-1 and encephalomyocarditis virus. They demonstrated even shorter incubation times for these viruses, saving an average of around 48 and 20 hours, respectively.
The researchers report that no false positives were detected across all time points. In addition, because the system can identify individual PFUs during their early growth, before the formation of PFU clusters, it can be used to analyze viral samples with about 10 times higher virus concentrations than traditional approaches.
“As for the next steps, UCLA researchers are improving their system design to further increase its sensitivity and specificity for various types of viruses, paving the way for broad adoption in laboratory and industrial settings, said Li. “They are also exploring other potential applications of this technique in virology research for high-throughput and cost-effective screening of antiviral drugs.” More