Aurelia Butler

Researchers have experimentally demonstrated, for the first time, a mechanically flexible silver mesh that is visibly transparent, allows high-quality infrared wireless optical communication and efficiently shields electromagnetic interference in the X band portion of the microwave radio region. Optical communication channels are important to the operation of many devices and are often used for remote sensing and detection.
Electronic devices are now found throughout our homes, on factory floors and in medical facilities. Electromagnetic interference shielding is often used to prevent electromagnetic radiation from these devices from interfering with each other and affecting device performance.
Electromagnetic shielding, which is also used in the military to keep equipment and vehicles hidden from the enemy, can also block the optical communication channels needed for remote sensing, detection or operation of the devices. A shield that can block interference but allow for optical communication channels could help to optimize device performance in a variety of civilian and military settings.
“Many conventional transparent electromagnetic interference shields allow only visible light signals through,” said research team leader Liu Yang from Zhejiang University in China. “However, visible wavelengths are not well suited for optical communication, especially free-space — or wireless — optical communication, because of the huge amount of background noise.”
In the journal Optical Materials Express, the researchers describe their new mesh. They show that when combined with transparent silicone and polyethylene, it can achieve a high average electromagnetic shielding effectiveness of 26.2 dB in the X band with good optical transmittance at a wide range of wavelengths, including those in the infrared.
“We take the advantage of the ultrabroad transparency and low haze of a metallic micromesh to demonstrate efficient electromagnetic shielding, visible transparency and high-quality free-space optical communication,” said Yang. “Sandwiching the mesh between transparent materials improves the chemical stability and mechanical flexibility of the silver mesh while also imparting a self-cleaning quality. These properties will enable our silver mesh to be applied widely both indoors and outdoors, even on corrosive and free-form surfaces.”
A flexible and transparent mesh
The researchers designed the new silver mesh with a very simple structure — a repeating square grid pattern applied to a transparent and flexible polyethylene substrate. The continuous grid structure makes the silver mesh very flexible by releasing stress during bending. Because the transparency of the silver mesh is primarily determined by the opening ratio, a measure of the size of the holes in the mesh, it is independent of the incident light wavelength.
“A large opening ratio, for example, is beneficial for a high, broadband transparency and low haze but is detrimental to high conductivity and thus electromagnetic shielding performance,” said Yang. “Because the physical parameters for our mesh can be easily optimized by changing the grid period, line width and thickness, it is easier to achieve well-balanced optical, electrical and electromagnetic properties compared with what is possible with other kinds of transparent conductive films such as silver nanowire networks, ultrathin metallic films and carbon-based materials.”
To demonstrate their new technology, the researchers fabricated a silver mesh onto a polyethylene substrate. The mesh had a grid period of approximately 150 μm, a grid line width of approximately 6 μm and a thickness that ranged from 59 to 220 nm. This was then covered with a layer of 60-μm thick polydimethylsiloxane. The resulting film showed high transmission for a broad wavelength range from 400 nm to 2000 nm and sheet resistance as low as 7.12 Ω/sq, allowing a high electromagnetic shield effectiveness up to 26.2 dB in the X band. The researchers also showed that the film could shield low-frequency mobile phone signals.
The researchers caution that this work is only a prototype demonstration, so there is much room for improvement. For example, using more conductive materials would improve the electromagnetic shielding effectiveness, and materials that are more transparent and have a lower haze could improve not only the visible transparency but also the free-space optical communication quality.
They are also exploring mid-infrared transparent conductive materials, which would extend the FSO communication to longer wavelengths where atmospheric interference is reduced and higher communication quality can be achieved. For commercialization, the mesh would also have to be more practical to install and less expensive. More

Inspired by sea cucumbers, engineers have designed miniature robots that rapidly and reversibly shift between liquid and solid states. On top of being able to shape-shift, the robots are magnetic and can conduct electricity. The researchers put the robots through an obstacle course of mobility and shape-morphing tests in a study publishing January 25 in the journal Matter.
Where traditional robots are hard-bodied and stiff, “soft” robots have the opposite problem; they are flexible but weak, and their movements are difficult to control. “Giving robots the ability to switch between liquid and solid states endows them with more functionality,” says Chengfeng Pan, an engineer at The Chinese University of Hong Kong who led the study.
The team created the new phase-shifting material — dubbed a “magnetoactive solid-liquid phase transitional machine” — by embedding magnetic particles in gallium, a metal with a very low melting point (29.8 °C).
“The magnetic particles here have two roles,” says senior author and mechanical engineer Carmel Majidi of Carnegie Mellon University. “One is that they make the material responsive to an alternating magnetic field, so you can, through induction, heat up the material and cause the phase change. But the magnetic particles also give the robots mobility and the ability to move in response to the magnetic field.”
This is in contrast to existing phase-shifting materials that rely on heat guns, electrical currents, or other external heat sources to induce solid-to-liquid transformation. The new material also boasts an extremely fluid liquid phase compared to other phase-changing materials, whose “liquid” phases are considerably more viscous.
Before exploring potential applications, the team tested the material’s mobility and strength in a variety of contexts. With the aid of a magnetic field, the robots jumped over moats, climbed walls, and even split in half to cooperatively move other objects around before coalescing back together. In one video, a robot shaped like a person liquifies to ooze through a grid after which it is extracted and remolded back into its original shape.
“Now, we’re pushing this material system in more practical ways to solve some very specific medical and engineering problems,” says Pan.
On the biomedical side, the team used the robots to remove a foreign object from a model stomach and to deliver drugs on-demand into the same stomach. They also demonstrate how the material could work as smart soldering robots for wireless circuit assembly and repair (by oozing into hard-to-reach circuits and acting as both solder and conductor) and as a universal mechanical “screw” for assembling parts in hard-to-reach spaces (by melting into the threaded screw socket and then solidifying; no actual screwing required.)
“Future work should further explore how these robots could be used within a biomedical context,” says Majidi. “What we’re showing are just one-off demonstrations, proofs of concept, but much more study will be required to delve into how this could actually be used for drug delivery or for removing foreign objects.” More

Engineers and physicians have developed a wearable ultrasound device that can assess both the structure and function of the human heart. The portable device, which is roughly the size of a postage stamp, can be worn for up to 24 hours and works even during strenuous exercise.
The goal is to make ultrasound more accessible to a larger population, said Sheng Xu, a professor of nanoengineering at the University of California San Diego, who is leading the project. Currently, echocardiograms- ultrasound examinations for the heart- require highly trained technicians and bulky devices.
“The technology enables anybody to use ultrasound imaging on the go,” Xu said.
Thanks to custom AI algorithms, the device is capable of measuring how much blood the heart is pumping. This is important because the heart not pumping enough blood is at the root of most cardiovascular diseases. And issues with heart function often manifest only when the body is in motion.
The work is described in the Jan. 25 issue of the journal Nature.
Cardiac imaging is an essential clinical tool to assess long-term heart health, detect problems as they arise and care for critically ill patients. This new wearable, non-invasive heart monitor for humans provides real-time, automated insights on the difficult-to-capture pumping activity of the heart, even when a person is exercising.

The wearable heart monitoring system uses ultrasound to continuously capture images of the four chambers of the heart in different angles, and analyze a clinically relevant subset of the images in real time using a custom-built AI technology. The project builds on the team’s previous advances in wearable imaging technologies for deep tissues.
“The increasing risk of heart diseases calls for more advanced and inclusive monitoring procedures,” Xu said. “By providing patients and doctors with more thorough details, continuous and real-time cardiac image monitoring is poised to fundamentally optimize and reshape the paradigm of cardiac diagnoses.”
In comparison, existing non-invasive methods have limited sampling capabilities and provide limited data. The wearable technology developed by Xu’s team enables safe, non-invasive and high-quality cardiac imaging, resulting in images with high spatial resolution, temporal resolution and contrast. “It also minimizes patient discomfort and overcomes some limitations of noninvasive technologies such as CT and PET, which could expose patients to radiation,” said Hao Huang, a PhD student in the Xu group at UC San Diego.
The unique design of the sensor makes it ideal for bodies in motion. “The device can be attached to the chest with minimal constraint to the subjects’ movement, even providing a continuous recording of cardiac activities before, during and after exercise,” said Xiaoxiang Gao, a postdoctoral researcher in the Xu group at UC San Diego.
The importance of cardiac imaging
Cardiac diseases are the leading cause of death among the elderly, and are also becoming more prevalent among the young due to lifestyle factors. The signs of cardiac diseases are transient and unpredictable, making them hard to spot. This has upped demand for more advanced, inclusive, non-invasive and cost-effective monitoring technologies such as long-term cardiac imaging, which this wearable device facilitates.

Cardiac imaging is one of the most powerful tools for screening and diagnosing cardiac issues before they become problems. “The heart undergoes all kinds of different pathologies,” said Hongjie Hu, a postdoctoral researcher in the Xu lab at UC San Diego. “Cardiac imaging will disclose the true story underneath. Whether it be that a strong but normal contraction of heart chambers leads to the fluctuation of volumes, or that a cardiac morphological problem has occurred as an emergency, real-time image monitoring on the heart tells the whole picture in vivid detail and visual effect.”
How it works in detail
The new system gathers information through a wearable patch as soft as human skin, designed for optimal adherence. The patch measures 1.9 cm (L) x 2.2 cm (W) x 0.09 cm (T) , about the size of a postage stamp. It sends and receives the ultrasound waves which are used to generate a constant stream of images of the structure of the heart in real time. This ultrasound patch is soft and stretchable, and it adheres well to human skin, even during exercise.
The system can examine the left ventricle of the heart in separate bi-plane views using ultrasound, generating more clinically useful images than were previously available. As a use case, the team demonstrated imaging of the heart during exercise, which is not possible with the rigid, cumbersome equipment used in clinical settings.
The performance of the heart is characterized by three factors: stroke volume (the volume of blood the heart pumps out each beat), ejection fraction (the percentage of blood pumped out of the left ventricle of the heart every beat) and cardiac output (the volume of blood the heart pumps out every minute).
Xu’s team developed an algorithm to facilitate continuous, AI-assisted automatic processing.
“A deep learning model automatically segments the shape of the left ventricle from the continuous image recording, extracting its volume frame-by-frame and yielding waveforms to measure stroke volume, cardiac output and ejection fraction,” said Mohan Li, a master’s student in the Xu group at UC San Diego.
“Specifically, the AI component involves a deep learning model for image segmentation, an algorithm for heart volume calculation, and a data imputation algorithm,” said Ruixiang Qi, a master’s student in the Xu group at UC San Diego. “We use this machine learning model to calculate the heart volume based on the shape and area of the left ventricle segmentation. The imaging-segmentation deep learning model is the first to be functionalized in wearable ultrasound devices. It enables the device to provide accurate and continuous waveforms of key cardiac indices in different physical states, including static and after exercise, which has never been achieved before.”
Thus, this technology can generate curves of these three indices continuously and noninvasively, as the AI component processes the continuous stream of images to generate numbers and curves.
To create the platform, the team faced some technical challenges that required careful decision-making. To produce the wearable device itself, the researchers used a piezoelectric 1-3 composite bonded with Ag-epoxy backing as the material for transducers in the ultrasound imager, reducing risk and improving efficiency over previous methods. When choosing the transmission configuration of the transducer array, they achieved superior results through wide-beam compounding transmission. They also selected from nine popular models for machine-learning-based image segmentation, landing on FCN-32, which achieved the highest possible accuracy.
In the current iteration, the patch is connected through cables to a computer, which can download the data automatically while the patch is still on. The team has developed a wireless circuit for the patch, which will be covered in a forthcoming publication.
Next steps
Xu plans to commercialize this technology through Softsonics, a company spun off from UC San Diego that he cofounded with engineer Shu Xiang. He also encourages others in his scientific community to follow his lead and work on areas of this research that warrant further exploration.
To follow up on these results, Xu recommends four immediate next steps: B-mode imaging, which allows more diagnostic capabilities involving different organs The design of the soft imager, which allows researchers to fabricate large transducer probes that cover multiple positions simultaneously Miniaturization of the back-end system that powers the soft imager Working toward a general machine learning model that fits more subjectsThis work was supported by the National Institutes of Health (1R21EB025521-01, 1R21EB027303-01A1, 3R21EB027303-02S1, and 1R01EB033464-01). More

Engineers at Tokyo Institute of Technology (Tokyo Tech) have demonstrated a simple computational approach for supporting the classification performance of neural networks operating on sensor time series. The proposed technique involves feeding the recorded signal as an external forcing into an elementary non-linear dynamical system, and providing its temporal responses to this disturbance to the neural network alongside the original data.
In the world around us, a proliferation of sensors is taking place, promising to support the efficiency and sustainability of practically all aspects of human activity. One challenge that engineers involved in delivering the internet-of-things to society have to face, is how to handle the flood of data resulting from such sensors. Especially, there is a need to reduce the data as much as possible at the edge, close to the sensors themselves, because streaming all data to the cloud would have an unacceptable technical, economic and environmental footprint. As a response to this, much research is being conducted worldwide towards small-sized, highly efficient classifiers suitable for detecting particular behaviors and situations of interest while running on limited computational resources. An example application scenario is the real-time monitoring of the behavior of livestock, having the purpose of detecting subtle changes that are indicative of prodromal disease.
“An emerging approach to support the development of time series classifiers suitable for edge artificial intelligence is that of data augmentation. Basically, it is about finding creative and innovative ways of generating additional data to help get the very best performance out of neural networks that necessarily have to be quite small to meet power and size requirements. While the theory of classifiers is well established, it can be said that data augmentation is still almost in its infancy for time series. In our laboratory, for example, we have been working on a variety of techniques based on empirical considerations as well as mathematical principles,” explains Ms. Chao Li, doctoral student at the Nano Sensing Unit where the study was conducted, and joint-lead author of the study.
Usually, data augmentation is performed just before or during classifier training, and runs on powerful workstations or cloud computers. The result is that the amount of data available to train a classifier is extended along the time dimension, as would be the case if longer recordings had been made available. This is important because high-quality data of the type necessary for classifier training is precious and expensive to prepare. However, this is not the only form of data augmentation possible. “We came up with the idea of extending the data along the other dimension, that is, the number of time series, meaning the number of input dimensions. Usually, edge applications may operate on one, or at most a few sensor time series. One possibility is performing computational operations to generate more of them, which try to make as much as possible of the initial information available to the classifier in a form suitable for it to learn it efficiently. While many signal processing operations could be implemented, a particularly disruptive computation is to simulate a dynamical system, endowed with its own intrinsic activity, and try to disturb it by externally forcing it with a signal recorded from the environment,” explains Dr. Ludovico Minati, lead author of the study.
Starting from a concept previously developed and patented in the Biointerfaces unit for improving the performance of brain-interface systems, the researchers carefully considered many practical aspects of how to realize it. Targeting the classification of the basic cattle behaviors using a collar-mounted accelerometer, they developed ways to filter and preprocess the kinematic signals and of injecting them so that the simulated dynamical system would accept and respond to them without diverging. Then, they explored how to extract the most relevant time series from its activity, in order to supply it either to a predetermined feature extractor and multi-layer perceptron or to a convolutional neural network. “Many low-dimensional systems such as the Rössler and Lorenz systems, which have been studied for decades by physicists and control engineers, actually have a remarkable computational potential that remains largely unexplored. This study takes an unusual step towards deploying it in a concrete application scenario,” explains Prof. Mattia Frasca from the University of Catania (Italy), who provided several theoretical contributions to the Tokyo Tech researchers on the behaviors of these kinds of systems and their implementations as analog circuits.
By augmenting the data through the additional time series derived from the dynamical systems, namely one separate Rössler system per accelerometer axis, the researchers were able to increase the classification performance by an appreciable amount. “While this is truly just an initial study to propose a provocative idea and substantial future work is needed, we were also able to realize the dynamical system using a very simple analog hardware circuit and still observe an improvement thanks to exploiting its responses,” adds Dr. Ludovico Minati. “Our approach reminds of reservoir computing, on which we recently conducted research using elementary transistor circuits known as the Minati-Frasca oscillators. However, it is actually different, because the dynamics are low dimensional, and a single oscillator is used instead of a network. In this sense, it may be even more suitable for low-power implementation” adds Mr. Jim Bartels, also a doctoral student at the unit.
After the interview, the team explained that this type of exploratory research will need to be extended and developed on other datasets and settings to ascertain its general applicability to concrete cases, though these initial results are promising. “One take-home point is that this approach can be implemented with quite limited resources, either digitally or in an analog way. Our past work in fact has shown CMOS chaotic systems operating with as low power as 1 μW, which could be suitable for this usage. As optimizations of process technologies and conventional designs approach their limits, the confident exploration of radically new ideas such as this one seems necessary for continued innovation,” concludes Dr. Hiroyuki Ito, head of the unit. The methodology, results and related considerations are reported in a recent article published in the journal Chaos, Solitons and Fractals, and all of the experimental recordings have been made freely available for others to use in future work. More