by Blogger | Jul 15, 2023 | Technology
Chinese scientists have devised a technique to coat everyday materials like paper and plastic with liquid metal, potentially creating “smart devices.” The method, which involves adjusting pressure rather than using a binding material, successfully enables the liquid metal to adhere to surfaces, a previously challenging task due to high surface tension.
Everyday materials such as paper and plastic could be transformed into electronic “smart devices” by using a simple new method to apply liquid metal to surfaces, according to scientists in Beijing, China. It demonstrates a technique for applying a liquid metal coating to surfaces that do not easily bond with liquid metal. The approach is designed to work at a large scale and may have applications in wearable testing platforms, flexible devices, and soft robotics.
“Before, we thought that it was impossible for liquid metal to adhere to non-wetting surfaces so easily, but here it can adhere to various surfaces only by adjusting the pressure, which is very interesting,” said Bo Yuan, a scientist at Tsinghua University and the first author of the study.
Scientists seeking to combine liquid metal with traditional materials have been impeded by liquid metal’s extremely high surface tension, which prevents it from binding with most materials, including paper. To overcome this issue, previous research has mainly focused on a technique called “transfer printing,” which involves using a third material to bind the liquid metal to the surface. But this strategy comes with drawbacks—adding more materials can complicate the process and may weaken the end product’s electrical, thermal, or mechanical performance.
To explore an alternative approach that would allow them to directly print liquid metal on substrates without sacrificing the metal’s properties, Yuan and colleagues applied two different liquid metals (eGaln and BilnSn) to various silicone and silicone polymer stamps, then applied different forces as they rubbed the stamps onto paper surfaces.
“At first, it was hard to realize stable adhesion of the liquid metal coating on the substrate,” said Yuan. “However, after a lot of trial and error, we finally had the right parameters to achieve stable, repeatable adhesion.”
The researchers found that rubbing the liquid metal-covered stamp against the paper with a small amount of force enabled the metal droplets to bind effectively to the surface, while applying larger amounts of force prevented the droplets from staying in place.
Next, the team folded the metal-coated paper into a paper crane, demonstrating that the surface can still be folded as usual after the process is completed. And after doing so, the modified paper still maintains its usual properties.
While the technique appears promising, Yuan noted that the researchers are still figuring out how to guarantee that the liquid metal coating stays in place after it has been applied. For now, a packaging material can be added to the paper’s surface, but the team hopes to figure out a solution that won’t require it.
“Just like wet ink on paper can be wiped off by hand, the liquid metal coating without packaging here also can be wiped off by the object it touches as it is applied,” said Yuan. “The properties of the coating itself will not be greatly affected, but objects in contact may be soiled.”
In the future, the team also plans to build on the method so that it can be used to apply liquid metal to a greater variety of surfaces, including metal and ceramic.
by Blogger | Jul 15, 2023 | Technology
Now that the emergency phase of the COVID-19 pandemic has ended, scientists are looking at ways to surveil indoor environments in real-time for viruses. By combining recent advances in aerosol sampling technology and an ultrasensitive biosensing technique, researchers at Washington University in St. Louis have created a real-time monitor that can detect any of the SARS-CoV-2 virus variants in a room in about 5 minutes.
The inexpensive, proof-of-concept device could be used in hospitals and health care facilities, schools, and public places to help detect CoV-2 and potentially monitor for other respiratory virus aerosols, such as influenza and respiratory syncytial virus (RSV).
Cirrito and Yuede had previously developed a micro-immunoelectrode (MIE) biosensor that detects amyloid beta as a biomarker for Alzheimer’s disease and wondered if it could be converted into a detector for SARS-CoV-2. They reached out to Chakrabarty, who assembled a team that included Puthussery, who had expertise in building real-time instruments to measure the toxicity of air.
To convert the biosensor from detecting amyloid beta to coronavirus, the researchers exchanged the antibody that recognizes amyloid beta for a nanobody from llamas that recognizes the spike protein from the SARS-CoV-2 virus. David Brody, MD, PhD, a former faculty member in the Department of Neurology at the School of Medicine and an author on the paper, developed the nanobody in his lab at the National Institutes of Health (NIH). The nanobody is small, easy to reproduce and modify and inexpensive to make, the researchers said.
“The nanobody-based electrochemical approach is faster at detecting the virus because it doesn’t need a reagent or a lot of processing steps,” Yuede said. “SARS-CoV-2 binds to the nanobodies on the surface, and we can induce oxidation of tyrosines on the surface of the virus using a technique called square wave voltammetry to get a measurement of the amount of virus in the sample.”
Chakrabarty and Puthussery integrated the biosensor into an air sampler that operates based on the wet cyclone technology. Air enters the sampler at very high velocities and gets mixed centrifugally with the fluid that lines the walls of the sampler to create a surface vortex, thereby trapping the virus aerosols. The wet cyclone sampler has an automated pump that collects the fluid and sends it to the biosensor for seamless detection of the virus using electrochemistry.
“The challenge with airborne aerosol detectors is that the level of virus in the indoor air is so diluted that it even pushes toward the limit of detection of polymerase chain reaction (PCR) and is like finding a needle in a haystack,” Chakrabarty said. “The high virus recovery by the wet cyclone can be attributed to its extremely high flow rate, which allows it to sample a larger volume of air over a 5-minute sample collection compared with commercially available samplers.”
Most commercial bioaerosol samplers operate at relatively low flow rates, Puthussery said, while the team’s monitor has a flow rate of about 1,000 liters per minute, making it one of the highest flow-rate devices available. It is also compact at about 1 foot wide and 10 inches tall and lights up when a virus is detected, alerting administrators to increase airflow or circulation in the room.
The team tested the monitor in the apartments of two COVID-positive patients. The real-time PCR results of air samples from the bedrooms were compared with air samples collected from a virus-free control room. The devices detected RNA of the virus in the air samples from the bedrooms but did not detect any in the control air samples.
In laboratory experiments that aerosolized SARS-CoV-2 into a room-sized chamber, the wet cyclone and biosensor were able to detect varying levels of airborne virus concentrations after only a few minutes of sampling.
“We are starting with SARS-CoV-2, but there are plans to also measure influenza, RSV, rhinovirus and other top pathogens that routinely infect people,” Cirrito said. “In a hospital setting, the monitor could be used to measure for staph or strep, which cause all kinds of complications for patients. This could really have a major impact on people’s health.”
The team is working to commercialize the air quality monitor.
by Blogger | Jul 15, 2023 | Technology
This AI system only needs a small amount of data to predict molecular properties, which could speed up drug discovery and material development.
Discovering new materials and drugs typically involves a manual, trial-and-error process that can take decades and cost millions of dollars. To streamline this process, scientists often use machine learning to predict molecular properties and narrow down the molecules they need to synthesize and test in the lab.
Researchers from MIT and the MIT-Watson AI Lab have developed a new, unified framework that can simultaneously predict molecular properties and generate new molecules much more efficiently than these popular deep-learning approaches.
To teach a machine-learning model to predict a molecule’s biological or mechanical properties, researchers must show it millions of labeled molecular structures — a process known as training. Due to the expense of discovering molecules and the challenges of hand-labeling millions of structures, large training datasets are often hard to come by, which limits the effectiveness of machine-learning approaches.
By contrast, the system created by the MIT researchers can effectively predict molecular properties using only a small amount of data. Their system has an underlying understanding of the rules that dictate how building blocks combine to produce valid molecules. These rules capture the similarities between molecular structures, which helps the system generate new molecules and predict their properties in a data-efficient manner.
This method outperformed other machine-learning approaches on both small and large datasets, and was able to accurately predict molecular properties and generate viable molecules when given a dataset with fewer than 100 samples.
To achieve the best results with machine-learning models, scientists need training datasets with millions of molecules that have similar properties to those they hope to discover. In reality, these domain-specific datasets are usually very small. So, researchers use models that have been pretrained on large datasets of general molecules, which they apply to a much smaller, targeted dataset. However, because these models haven’t acquired much domain-specific knowledge, they tend to perform poorly.
The MIT team took a different approach. They created a machine-learning system that automatically learns the “language” of molecules — what is known as a molecular grammar — using only a small, domain-specific dataset. It uses this grammar to construct viable molecules and predict their properties.
In language theory, one generates words, sentences, or paragraphs based on a set of grammar rules. You can think of a molecular grammar the same way. It is a set of production rules that dictate how to generate molecules or polymers by combining atoms and substructures.
Just like a language grammar, which can generate a plethora of sentences using the same rules, one molecular grammar can represent a vast number of molecules. Molecules with similar structures use the same grammar production rules, and the system learns to understand these similarities.
Since structurally similar molecules often have similar properties, the system uses its underlying knowledge of molecular similarity to predict properties of new molecules more efficiently.
“Once we have this grammar as a representation for all the different molecules, we can use it to boost the process of property prediction,” Guo says.
The system learns the production rules for a molecular grammar using reinforcement learning — a trial-and-error process where the model is rewarded for behavior that gets it closer to achieving a goal.
But because there could be billions of ways to combine atoms and substructures, the process to learn grammar production rules would be too computationally expensive for anything but the tiniest dataset.
The researchers decoupled the molecular grammar into two parts. The first part, called a metagrammar, is a general, widely applicable grammar they design manually and give the system at the outset. Then it only needs to learn a much smaller, molecule-specific grammar from the domain dataset. This hierarchical approach speeds up the learning process.
In experiments, the researchers’ new system simultaneously generated viable molecules and polymers, and predicted their properties more accurately than several popular machine-learning approaches, even when the domain-specific datasets had only a few hundred samples. Some other methods also required a costly pretraining step that the new system avoids.
The technique was especially effective at predicting physical properties of polymers, such as the glass transition temperature, which is the temperature required for a material to transition from solid to liquid. Obtaining this information manually is often extremely costly because the experiments require extremely high temperatures and pressures.
To push their approach further, the researchers cut one training set down by more than half — to just 94 samples. Their model still achieved results that were on par with methods trained using the entire dataset.
by Blogger | Jul 14, 2023 | Technology
Are you having trouble opening websites quickly on your iPhone? Fortunately, you don’t need to download and install a new app, remove a virus, or replace your device. You probably only need to clear your mobile gadget’s cache, which is its website data storage. Clearing that takes a few steps, and you will likely see noticeable differences.
Some people panic when their phones don’t load quickly, prompting them to download sketchy apps. Worse, they might follow prank tutorials that could trick them into breaking their device. Clearing your iPhone cache is a safe troubleshooting solution that usually works, preventing you from wasting time and money on potentially dangerous methods.
This article will explain how to clear your iPhone cache. I will cover Safari, Google Chrome, and Firefox methods, the most commonly used web browsers.
Safari is the default browser on iPhones and iPhones. It follows Apple’s well-known commitment to simplicity and convenience, so clearing your cache takes a few steps:
Open the book icon on your Safari app.
Then, tap the clock icon.
Tap the Clear option.
Choose how much of your browsing history you want to erase.
The official Apple website says clearing your browsing history doesn’t remove records from websites you visited. Also, it doesn’t affect the browsing history in other apps.
Google Chrome is Android’s counterpart to Safari. If you’re used to Chrome, you might have installed it on your Apple device. Clear the cache with these steps:
Tap the triple dot icon on your Chrome app.
Next, tap the History option.
Select the Clear browsing data option.
Check the Cookies, Site Data, and Cached Images and Files options.
Afterward, tap the Clear browsing data option.
by Blogger | Jul 14, 2023 | Technology
The United States Space Force’s Space Development Agency (SDA) has published a draft solicitation for a “FOO Fighter” satellite constellation. The Fire-control On Orbit-support-to-the-war Fighter program gives it its full name, F2; the constellation is intended to detect, track, and coordinate the interception of hypersonic missiles.
Published on July 7, 2023, the program asks for eight satellites fitted with infrared and optical sensors. These satellites will aid in detecting, warning, and precisely tracking advanced missile threats, including hypersonic missile systems. Their deployment is intended to enhance fire-control capabilities on a global scale. Given the relatively low altitudes hypersonic missiles travel at (compared to intercontinental ballistic missiles), these satellites will extend the warning time the US (and its allies) can achieve to detect and respond to hypersonic threats.
“Fire control incorporates various technologies such as radar or other sensors, targeting computers and ranged weapons together into a cohesive system that can detect threats or targets and then direct weapons or other countermeasures at them.” While few other details are available, the SDA plans to launch the prototype FOO Fighter constellation in 2026, as stated in the contract opportunity. However, further information about the program is classified as “Top Secret.”
“The Space Development Agency (SDA) is issuing this DRAFT solicitation for the Proliferated Warfighter Space Architecture’s (PWSA) Fire-control On Orbit-support-to-the-war Fighter (FOO Fighter) Program. The draft solicitation provides an opportunity for industry to review and offer feedback [before] final solicitation posting,” the solicitation says.
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