Author: chemistadmin

  • AI-enhanced brain sensor tracks poorly understood chemistry

    AI-enhanced brain sensor tracks poorly understood chemistry

    [ad_1]

    At left, an orange catheter with a glowing tip and periodic black lines running down its length sits on a lab bench. At right, a three-dimensional rendering of a skull shows how the catheter would be inserted through the skull and into brain tissue.

    Credit: C&EN/Shutterstock/Imperial College London

    A catheter containing a fiber bundle (left), shown fluorescing under laser excitation, is inserted through the skull into the brain (right) to monitor six biomarkers. 


    Researchers have developed a device that can simultaneously measure six markers of brain health. The sensor, which is inserted through the skull into the brain, can pull off this feat thanks to an artificial intelligence (AI) system that pieces apart the six signals in real time (ACS Sens. 2024, DOI: 10.1021/acssensors.4c02126).

    Being able to continuously monitor biomarkers in patients with traumatic brain injury could improve outcomes by catching swelling or bleeding early enough for doctors to intervene. But most existing devices measure just one marker at a time. They also tend to be made with metal, so they can’t easily be used in combination with magnetic resonance imaging.

    The new device relies on metal-free fiber optics to measure physical and chemical properties of the brain: temperature, pH, and the concentrations of sodium ions, calcium ions, glucose, and dissolved oxygen. Those biomarkers were chosen based on previous studies of how they change in the cerebrospinal fluid (CSF) in the spinal cord, says Ali Yetisen, a chemical engineer at Imperial College London, who led the study along with Nan Jiang of Sichuan University. While researchers believe the measurements should give them insight into the energy metabolism of the brain, these markers have not been extensively studied, because previous equipment has not been able to measure them in real time and simultaneously, Yetisen says.

    The sensor consists of seven optical fibers—one for each biomarker plus a spare—each coated with a commercially available small molecule or enzyme that fluoresces when it interacts with the marker it’s looking for. The fibers are inserted into the brain using a soft, flexible catheter designed to minimize damage to brain tissue.

    Each fluorescing molecule is encapsulated in a polymer chosen to work with the property it measures. For instance, the dissolved oxygen probe is coated in polydimethylsiloxane, which is permeable by oxygen but not water. For the ion probes, the team used a poly(ethylene glycol) diacrylate–acrylamide hydrogel, which admits water but keeps the sensor’s fluorophore stable, says Yubing Hu, a research associate at Imperial College London who worked on the device.

    A laser that can emit three different wavelengths is shot through the fiber bundle to trigger the fluorescence. And finally the fluorescent signal bounces back up the fibers into a light sensor.

    An issue with measuring so many signals simultaneously is that some of them overlap in the researchers’ spectra or get lost in background noise. To get around that, the researchers trained an AI system called a neural network. The system learned from data the device gathered from three sources: a lamb brain immersed in artificial CSF, a laboratory solution, and CSF taken from patients at a local hospital. The AI essentially makes the sensor more powerful because it can pick up signals that are otherwise hard to spot.

    The AI can also identify characteristics of the signals, such as their intensity. It can predict changes in the markers in real time, so if it finds, for instance, an increase in sodium levels that might indicate a problem, it can alert doctors.

    The device will need to be tested in live animals and then in clinical trials in humans before it can be cleared for clinical use, a process that Yetisen says could take 5–10 years.

    “This looks like a clever strategy for multimodal sensing,” says John Rogers, a chemist at Northwestern University who was not involved in the research. “It will be interesting to determine in future work whether this approach can be used for long-term monitoring.”

    [ad_2]

    Source link

  • The ancient board games we finally know how to play – thanks to AI

    The ancient board games we finally know how to play – thanks to AI

    [ad_1]

    New Scientist. Science news and long reads from expert journalists, covering developments in science, technology, health and the environment on the website and the magazine.

    In the 1970s, in a grave in a Bronze Age cemetery in Shahr-i Sokhta, Iran, an incredible object was unearthed next to a human skull: the oldest complete board game ever discovered. Around 4500 years old, it consists of a board with 20 circular spaces created from the coils of a carved snake, four dice and 27 geometric pieces.

    The Shahr-i Sokhta game is one of many ancient board games discovered around the world, such as the Roman game Ludus Latrunculorum and the Egyptian game Senet, found in Tutankhamun’s tomb. But we have only been able to guess how to play these games. There are no preserved rulebooks – with the notable exception of the Royal Game of Ur from ancient Mesopotamia, whose long-lost rules were deciphered in 2007 from a cuneiform tablet in the British Museum.

    Now, though, another tool is helping to bring these games back to life. In recent years, researchers have been harnessing artificial intelligence to assist in the hunt for likely rules. The goal is to make these forgotten games realistically playable again, while also gaining insights into the evolution of game types. “These games act as a window into the past, offering glimpses into the social and cultural dynamics of the people who played them,” says Eric Piette at the Catholic University of…

    [ad_2]

    Source link

  • New strategy for designing pure red OLED materials shows potential for ultrahigh-definition displays

    New strategy for designing pure red OLED materials shows potential for ultrahigh-definition displays

    [ad_1]

    New strategy for designing pure red OLED materials
    Molecular design concept and chemical structures of the emitters. Credit: USTC

    A research team has proposed a new strategy for designing pure-red organic light emitting diodes (OLED) materials. These materials have achieved a milestone with electroluminescence efficiencies exceeding 43%, marking a significant step toward high-performance ultrahigh-definition OLED displays. The study was published online in the Journal of the American Chemical Society.

    OLEDs have emerged as a leading technology due to their unique features such as flexibility and bright self-emission. However, the performance of red OLEDs, especially in the saturated red region, has lagged behind that of their blue and green counterparts. The development of efficient red emitters with high color purity has been a major challenge in the field.

    Focusing on overcoming the challenge of red light emitters, the research team proposed a new strategy for the design of pure-red OLED materials with high luminous efficiency, excellent color purity, and long-term stability. The key innovation lies in the molecule BNTPA, which was designed to incorporate secondary electron-donating units and extend the π-skeleton within multiresonance cores. This structural modification significantly enhances intramolecular charge transfer, enabling the molecule to more efficiently handle the excitation energy.

    As a result, light emission is effectively shifted into the red spectrum, while still maintaining narrowband characteristics for ensuring high color fidelity, which is a key requirement for high-definition displays. To further improve the molecular design, the team optimized the reverse inter-system crossing (RISC) process. BNTPA’s refined structure not only accelerates the RISC rate but also ensures a balanced combination of short-range and long-range charge transfer characteristics.

    This balance is particularly important for improving the overall photophysical performance of the emitter, as it minimizes energy loss and improves both the luminous efficiency and stability of the OLEDs. Additionally, the integration of secondary electron-donating units stabilizes the excited states of BNTPA, reducing non-radiative decay and preventing energy loss that often occurs in red-emitting materials.

    This enhancement of the molecular architecture ensures that BNTPA-based OLEDs achieve greater operational stability and longer lifetimes, making them suitable for practical, long-term use in real-world applications.

    OLEDs based on BNTPA achieved a record-breaking external quantum efficiency exceeding 43%. Its CIE value is (0.657, 0.343), and aligns closely with NTSC standards (0.67, 0.33), achieving excellent color purity. These advancements are attributed to the molecule’s optimized design, which enhances energy efficiency and operational stability. This establishes BNTPA as a benchmark for next-generation high-performance red MR-TADF emitters.

    This research sets a precedent for future research and practical deployment in high-definition displays and next-generation electronic devices. It also contributes to the development of energy-efficient and durable lighting systems, enabling OLED displays to meet stringent color standards.

    The team was led by Prof. Cui Songlin at University of Science and Technology of China (USTC), in collaboration with Prof. Zhou Meng’s team from Beijing Information Science and Technology University (BISTU).

    More information:
    Lishuang Ge et al, Efficient and Stable Narrowband Pure-Red Light-Emitting Diodes with Electroluminescence Efficiencies Exceeding 43%, Journal of the American Chemical Society (2024). DOI: 10.1021/jacs.4c13375

    Provided by
    University of Science and Technology of China


    Citation:
    New strategy for designing pure red OLED materials shows potential for ultrahigh-definition displays (2024, December 9)
    retrieved 9 December 2024
    from https://phys.org/news/2024-12-strategy-pure-red-oled-materials.html

    This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
    part may be reproduced without the written permission. The content is provided for information purposes only.



    [ad_2]

    Source link

  • Best Gifts for Hikers, Backpackers, Outdoorsy People (2024)

    Best Gifts for Hikers, Backpackers, Outdoorsy People (2024)

    [ad_1]

    Buying gifts for the outdoor enthusiast is damn near impossible. Your hiking/camping/cycling outdoorsy friends are often serious gear heads, meticulously poring over reviews, guides, and the perennial wisdom of ounce-counting, basement-dwelling Reddit users to find the exact right thing.

    Don’t stress over trying to pick that exact right thing yourself. The chances of you figuring it out and getting it for them as a gift is exactly zero. That doesn’t mean you should punt and buy an REI gift card, though (although they might like that). Instead, get them something they totally didn’t expect—one of these fun, but useful, sometimes whimsical, things that are almost guaranteed to not only delight them but actually get used outdoors.

    And what about you? While you’re here, don’t you need to replace your sleeping pad? Don’t forget to check out the rest of our buying guides, including our Best Sleeping Bags guide, our Best Barefoot Shoes guide, and our Best Merino Wool guide.

    Updated December 2024: We’ve added Campfire coffee, a Motor City ax, and a Butcher Box subscription.

    Power up with unlimited access to WIRED. Get best-in-class reporting that’s too important to ignore for just $2.50 $1 per month for 1 year. Includes unlimited digital access and exclusive subscriber-only content. Subscribe Today.

    [ad_2]

    Source link

  • Scientists achieve low-temperature, efficient degradation of ‘forever chemicals’

    Scientists achieve low-temperature, efficient degradation of ‘forever chemicals’

    [ad_1]

    Scientists achieve low-temperature, efficient degradation of 'forever chemicals'
    Credit: USTC

    A research team led by Prof. Kang Yanbiao from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences has made a significant discovery in the field of environmental chemistry. They developed a novel photocatalyst named KQGZ, which can photocatalytically defluorinate polyfluoroalkyl and perfluoroalkyl substances (PFAS) at a low temperature range of 40°C–60°C. This finding has been published in Nature.

    PFAS, referred to as “forever chemicals,” possess high thermal and chemical stabilities as well as hydrophobic and oleophobic properties because of the inert carbon-fluorine (C–F) bonds. As a result, they are widely used in various fields such as chemicals, electronics and medical devices.

    However, the inertness of the C–F bonds also makes it difficult for PFAS to decompose through defluorination under natural environment or mild conditions. For example, pyrolysis of Teflon usually proceeds at over 500°C and toxic gases are released. The disposal of PFAS into the natural environment has led to a series of environmental and health issues.

    To address the challenges, the team designed and created an organic super-photoreductant called KQGZ based on the characteristics of photoreductants’ strong reducibility under specific light conditions. As a type of photoreductant, KQGZ can be excited by absorbing light and transfer an electron from its excited state to other organic molecules.

    By adding KQGZ to the reaction system and experimenting with various reaction conditions, the team achieved complete defluorination and mineralization of Teflon and small molecule PFAS at low temperatures for the first time, efficiently recycling them into inorganic fluoride salts and carbon resources.

    More specifically, the study’s core experiments involved the application of KQGZ as a photocatalyst under visible light to defluorinate a range of PFASs, including polytetrafluoroethylene (PTFE), perfluorocarbons (PFCs), perfluorooctane sulfonic acid (PFOS), polyfluorooctanoic acid (PFOA), and their derivatives.

    The process resulted in the formation of amorphous carbon and fluoride salts from PTFE, while oligomeric PFASs yielded a variety of carbonate, formate, oxalate, and trifluoroacetate products. This not only addresses the degradation of PFASs but also enables the recycling of fluorine in the form of inorganic fluoride salts.

    A detailed mechanistic investigation was also conducted to understand the reaction behavior and product composition differences between PTFE and oligomeric PFAS. The researchers discovered that the photocatalytic reduction ability is not directly correlated with the excited oxidation potential of the photocatalyst, challenging the traditional paradigm in the field.

    This insight suggests that the electron transfer ability of the photocatalyst may be related to the torsion of the carbazole ring, a finding that could guide the design of more effective photocatalysts in the future.

    Finally, the study also meticulously investigated the effects of various reducing reagents, finding that most demonstrated good reactivity, with γ-terpinene and cesium formate yielding the highest results. Control experiments confirmed the indispensable role of light, photocatalyst, and reducing reagent in the defluorination process.

    This study not only reports for the first time the promoting effect of highly twisted carbazole-cores on the electron transfer of super-photoreductants, but also shows that the excited oxidative potential of photoreductants is not directly related to their reduction ability, and therefore should not be the only standard for the photoreduction ability. In addition, the ability to completely defluorinate Teflon and other PFAS can serve as a standard for the reduction ability of organic reductants.

    More information:
    Hao Zhang et al, Photocatalytic low-temperature defluorination of PFASs, Nature (2024). DOI: 10.1038/s41586-024-08179-1

    Provided by
    University of Science and Technology of China


    Citation:
    Scientists achieve low-temperature, efficient degradation of ‘forever chemicals’ (2024, December 9)
    retrieved 9 December 2024
    from https://phys.org/news/2024-12-scientists-temperature-efficient-degradation-chemicals.html

    This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
    part may be reproduced without the written permission. The content is provided for information purposes only.



    [ad_2]

    Source link

  • Thirteen proteins in your blood could reveal the age of your brain

    Thirteen proteins in your blood could reveal the age of your brain

    [ad_1]

    Researchers trained an artificial intelligence model to gauge people’s ages from their brain scans

    laboratory/Alamy

    The abundance of 13 proteins in your blood seems to be a strong indicator of how rapidly your brain is ageing. This suggests that blood tests could one day help people track and even boost their brain health.

    Most previous studies that have looked at protein markers of brain ageing in the blood have involved fewer than 1000 people, says Nicholas Seyfried at Emory University in Atlanta, Georgia, who wasn’t involved in the new research.

    To get a broader idea of the impact of these proteins, Wei-Shi Liu at Fudan University in China and his colleagues analysed MRI brain scan data from nearly 11,000 adults from the UK Biobank project, whose ages ranged from around 50 to 80 at the time of imaging.

    Using data from 70 per cent of the participants, Liu’s team trained an artificial intelligence model to predict how old the participants were based on features of the brain images, such as the size of different brain regions and how distinct parts connected to each other. When the model was applied to the remaining 30 per cent of participants, its predictions were accurate to within 2.7 years of their actual ages.

    Next, the researchers used the model to predict the age of a separate group of nearly 4700 people, aged 63 on average, who also had their brains imaged for the UK Biobank. The team calculated the difference between these participants’ actual ages and the ones predicted by AI, called the brain age gap. “The higher the AI-predicted age is relative to their actual age, the faster their brain is ageing,” says Liu.

    This group also gave blood samples at around the same time that their brains were imaged. From this, the team pinpointed eight proteins that seemed to strongly increase, and five that became less abundant, with a larger brain age gap.

    In an analysis of data from previous studies, the researchers confirmed that the proteins are produced by brain cells and that their levels may influence the risk of dementia and stroke.

    This suggests that blood tests for these proteins could indicate how quickly someone’s brain is ageing. “These markers could be the canary in the coal mine to tell you, ‘hey, look, let’s start intervening to slow your brain ageing now while you’ve got enough time’,” says Seyfried.

    But for this to be useful, we need to know that these proteins can be altered by lifestyle changes. “You want to be able to say, ‘if you run this much, you lose this much weight, you change your diet, [then] you can modify those levels to bring them back into the normal range’,” says Seyfried.

    The research was mostly carried out on white, wealthy people, so further research is needed to see whether the results apply to other populations of more diverse ethnicities and income levels, says Seyfried.

    The team now hopes to carry out research in animals to pinpoint how the 13 proteins affect the brain. For instance, the researchers may test whether disrupting levels of these proteins affects cognition or even the development of neurodegenerative conditions, says Liu. “In the next couple decades, this could open up ways to target the proteins to slow ageing and disease.”

    Topics:

    [ad_2]

    Source link

  • Is Google’s new Willow quantum computer really such a big deal?

    Is Google’s new Willow quantum computer really such a big deal?

    [ad_1]

    New Scientist. Science news and long reads from expert journalists, covering developments in science, technology, health and the environment on the website and the magazine.

    Google says its new quantum chip is its most powerful yet

    Google Quantum AI

    Google has unveiled a new quantum computer and is once more claiming to have pulled ahead in the race to show that these exotic machines can beat even the world’s best conventional supercomputers – so does that mean useful quantum computers are finally here?

    Researchers at the tech giant were the first in the world to demonstrate this feat, known as quantum supremacy, with the announcement of the Sycamore quantum computing chip in 2019. But since then, supercomputers have caught up, leaving Sycamore behind. Now, Google has produced a new quantum chip, called Willow, which Julian Kelly at Google Quantum AI says is the firm’s best yet.

    “You can think of this as having all the advantages of Sycamore, but if you were to look under the hood, we changed the geometry… we reimagined the processor,” he says.

    While the most advanced version of Sycamore boasted 67 qubits, or quantum bits, to process information, Willow has been upgraded to 105. Ideally, larger quantum computers should also be more powerful, but researchers have found that the qubits in larger devices struggle to remain coherent, losing their quantumness. This has also been seen by competitors IBM and California-based start-up Atom Computing, which both recently debuted quantum computers with more than 1000 qubits.

    Kelly says that because of this, qubit quality has been a big focus for the team, and that Willow’s qubits can preserve their intricate quantum states – and therefore reliably encode information – more than five times longer than Sycamore’s can.

    Google uses a specific benchmarking task called RCS to assess its quantum computers’ performance, which Willow excelled at, says Hartmut Neven, also at Google Quantum AI. The task involves verifying that a sample of numbers output by a program run on the chip have as random a distribution as possible. For several years, Sycamore could do this faster than the world’s best supercomputers, but in 2022, and then again in 2024, new records were set by conventional computers.

    Google says Willow has again widened the gap between quantum and traditional machines, as the task took 5 minutes on the chip, while the firm estimates that it would take 10 septillion years, or much more than the age of the universe squared, on a leading supercomputer.

    In this comparison, the researchers modelled a version of the Frontier supercomputer (which was recently downgraded to only the second-most powerful supercomputer in the world) with more memory than it is currently able to use, which only underscores the computational power of Willow, says Neven. While Sycamore’s records were broken, he is confident that Willow will maintain its champion status for much longer as conventional computing methods reach their limits.

    What still isn’t clear is whether Willow can actually do anything useful, given the RCS benchmarking test has no practical application. Kelly says succeeding at the benchmark is a “necessary but not sufficient” condition for the usefulness of a quantum computer, though any chip that fails to be great at RCS doesn’t stand a chance of being practical later.

    But the Google team has another reason to believe in Willow’s bright future – it is very good at correcting its own errors. The propensity of quantum computers to make errors is one of the biggest issues currently preventing them from delivering on the promise of being more powerful than any other type of computer. To improve this, researchers, including Google’s team, group physical qubits together to form “logical qubits”, which are much more resilient to errors.

    With Willow, the team showed that as the logical qubits were made larger, they got better at preventing errors, making around half as many errors as the physical qubits that comprised them. What’s more, that error rate further halved when the logical qubits were roughly doubled in size. In this way, the Google researchers reached a threshold where they believe they can keep increasing the number of qubits – making larger and larger quantum computers – and have them get better and better at running calculations, which hasn’t been a trend so far.

    “This is, in my opinion, a signature result, and while we are still a long way from demonstrating a practical quantum computer, it is an important and necessary step towards that goal,” says Andrew Cleland at the University of Chicago.

    Martin Weides at the University of Glasgow, UK, says that the new work sets out a route towards building “fault-tolerant” quantum computers – those that could catch and correct all of their errors. Challenges remain, but these advancements pave the way for transformative applications in quantum chemistry, such as drug discovery and materials design, he says, as well as in cryptography and machine learning.

    The focus on error correction across academic labs and the burgeoning quantum computing industry has made advances in logical qubits an important point of comparison between today’s best quantum computers. In 2023, a team of researchers at Harvard University and start-up QuEra used qubits made from extremely cold rubidium atoms to set the record for the most logical qubits ever created. Earlier this year, researchers at Microsoft and Atom Computing linked a record-breaking number of logical qubits through quantum entanglement.

    Google’s approach is different because it focuses on making single logical qubits larger and larger, as well as better and better, instead of maximising their number. “We could divide our chip into smaller and smaller logical qubits and run algorithms, but we really wanted to get to this threshold. This is where all the underlying challenges with science and engineering [of quantum computing] are,” says Kelly.

    Ultimately, however, the biggest test of Willow’s impact will be whether it can meet the goal that all other quantum computers are also chasing – to reliably compute something that is useful but not possible on any conventional computer. Neven says Sycamore had already been used to make scientific discoveries, such as in quantum physics, but the team is setting its sights on more real-world applications with Willow. “We are moving towards new calculations and simulations that classical computers could not do.”

    Topics:

    [ad_2]

    Source link

  • Is Google’s new quantum computer a big deal?

    Is Google’s new quantum computer a big deal?

    [ad_1]

    New Scientist. Science news and long reads from expert journalists, covering developments in science, technology, health and the environment on the website and the magazine.

    Google says its new quantum chip is its most powerful yet

    Google Quantum AI

    Google has unveiled a new quantum computer and is once more claiming to have pulled ahead in the race to show that these exotic machines can beat even the world’s best conventional supercomputers – so does that mean useful quantum computers are finally here?

    Researchers at the tech giant were the first in the world to demonstrate this feat, known as quantum supremacy, with the announcement of the Sycamore quantum computing chip in 2019. But since then, supercomputers have caught up, leaving Sycamore behind. Now, Google…

    [ad_2]

    Source link

  • Tailoring all-metal-made aerogels as self-supported electrocatalysts

    Tailoring all-metal-made aerogels as self-supported electrocatalysts

    [ad_1]

    Unveiling multimetallic effects: tailoring all-metal-made aerogels as self-supported electrocatalysts
    The proposed mechanism of the atomic radius-induced ligament size control. Credit: Matter (2024). DOI: 10.1016/j.matt.2024.10.023

    Have you ever imagined that high-density metals could be converted into an ultralight aerogel? This counterintuitive idea was presented in 2009 by Eychmüller’s group, where all-metal-made aerogels, i.e., metal aerogels (MAs), were produced by assembling metal nanoparticles in a controlled manner. Since then, these special and promising materials have been explored by global scientists, gradually forming a new field in materials science.

    MAs composed of more than one metal, namely multimetallic aerogels (MMAs), have received particular attention, because MMAs feature widely tunable properties stimulated by the synergy of multiple metals. For a material structured from multiple components, the first thought might be whether this material will have attributes stemming from each constituent, or if it will feature enhanced performance because of the synergy of different constituents.

    Indeed, many research articles demonstrate that MMAs are often better than single-component MAs in, for example, electrocatalysis. Better performance was primarily achieved by tuning the difference in electrical conductivity, lattice parameters and electronic structure of dissimilar metals.

    I am interested in controlled synthesis because I believe the synthesis ability dictates how far a material goes. Therefore, instead of the application aspect, I am concentrating on the synthesis aspect incurred by multimetallic effects. This is the motivation of our paper published in Matter titled “Manipulating multimetallic effects: Programming size-tailored metal aerogels as self-standing electrocatalysts.”

    In our study, we found that multimetallic effects concurrently impacted the sol-gel process of metals and the ligament size of the resulting MMAs.

    We discovered an unconventional, gravity-driven gelation behavior of metal systems in a Science Advances paper five years ago. We found that the gelation process of metal systems is similar to a precipitation process. Driven by the high density of metals (e.g., the density of gold is ~19.3 g cm-3), the as-formed metal aggregates eventually settle down with the lapse of time and form a monolithic gel at the vessel bottom.

    If the metal aggregate is not solely made up of gold, for example, what will happen for a gold-silver bimetallic system? The incorporation of relatively low-density silver (~10.5 g cm-3) will reduce the average density of metals and thus slow down the sedimentation process, leading to a prolonged gelation time.

    This was proven by our experiments and characterizations using a variety of metal combinations (single, binary and triple metals). It not only offers a way to tune the sol-gel process but also confirms the generality of our proposed gravity-driven gelation mechanism.

    The most exciting and important part is the ligament size control via multimetallic effects. The ligament size is a critical parameter for MAs, for it dictates the nano effects and thus many physicochemical properties of materials.

    Historically, the ligament size is tuned by modulating the initiators or introducing ligands, which may contaminate the resulting MAs. Taking a glance at all reported MAs since 2009, one will recognize that some MAs (e.g., Au, Ag) often feature large ligament sizes while others (e.g., Pd, Pt, Ru, Rh) often feature small ligament sizes. However, almost all alloy aerogels feature small ligament sizes. Then the question arises: What happens when two metals come together?

    Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights.
    Sign up for our free newsletter and get updates on breakthroughs,
    innovations, and research that matter—daily or weekly.

    We thoroughly studied the ligament size change by controllably introducing different types and amounts of auxiliary metals into the main metal systems (e.g., introducing nickel sources to gold sources before conducting the gelation process). We found that 1% auxiliary metals drastically reduced the ligament size by ~ 30% to 78%, which worked for Au, Ag and Cu-based aerogels.

    This impressive phenomenon was rationalized by the atomic radius mismatch between the main metal and the auxiliary metal. The mismatch will retard the layer-type deposition of metal atoms. Instead, the ligament growth will follow an island-type deposition style, thus producing more branches and thinning the ligament size (see image above). Depending on the mismatch degree and the proportion of the auxiliary metal atoms, the ligament size can be well adjusted.

    Finally, using the gravity-driven gelation behavior, we developed a sedimentation-based, non-destructive strategy to boost the electrocatalytic performance of MMAs. This technique avoids the sonication-led structure destruction that was suffered by previously reported MA-based electrocatalysts.

    Briefly, several pieces of carbon paper were placed at the bottom of the reaction vessel, accepting the settled metal aggregates. The in-situ-generated metal aggregates will gradually sediment and enrich on the carbon paper, thus forming a CP-supported intact gel film (the Au-Pt system was used as an example).

    This CP-supported intact Au-Pt gel film was directly used as the working electrode to catalyze the alcohol oxidation reaction. Because of its well-retained network, this intact metal gel manifested record-high performance for both methanol- and ethanol- oxidation reactions.

    In summary, our study not only provides a fresh viewpoint on using multimetallic effects for tuning the preparation and structure of MMAs but also solves the long-lasting challenge of preparing intact metal gel-based electrocatalysts for high-performance catalysis.

    This story is part of Science X Dialog, where researchers can report findings from their published research articles. Visit this page for information about Science X Dialog and how to participate.

    More information:
    Qian Cui et al, Manipulating multimetallic effects: Programming size-tailored metal aerogels as self-standing electrocatalysts, Matter (2024). DOI: 10.1016/j.matt.2024.10.023

    Ran Du received his B.E. in 2011 from Beijing Institute of Technology and PhD degree in 2016 from Peking University. After successive research stays at Nanyang Technological University (2016–2017), TU Dresden (2017–2019, sponsored by Humboldt fellowship), and Hong Kong University (2020–2021), he joined the Beijing Institute of Technology as a professor in 2021. His research interest lies in the creative synthesis of advanced aerogels (e.g., metal aerogels, nanocarbon aerogels, etc.) and exploring their smart applications in catalysis, environment remediation, and smart materials.

    Citation:
    Unveiling multimetallic effects: Tailoring all-metal-made aerogels as self-supported electrocatalysts (2024, December 9)
    retrieved 9 December 2024
    from https://phys.org/news/2024-12-unveiling-multimetallic-effects-tailoring-metal.html

    This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
    part may be reproduced without the written permission. The content is provided for information purposes only.



    [ad_2]

    Source link

  • Scientists solve one of the hardest problems in the computational atomic-scale mechanics of materials

    Scientists solve one of the hardest problems in the computational atomic-scale mechanics of materials

    [ad_1]

    Scientists solve one of the hardest problems in the computational atomic-scale mechanics of materials
    Graphical abstract. Credit: Macromolecules (2024). DOI: 10.1021/acs.macromol.4c01360

    Currently employed computational methods to simulate materials and their mechanical behavior are based on molecular dynamics (MD) with atomistic force-fields. These methods provide an excellent description of the thermodynamically stable phases of materials with arbitrary chemical and microstructural complexity.

    However, simulating the mechanical deformation behavior of materials at the atomistic level, or, in general, the response of a material to an external time-dependent stimulus, has been an open challenge for a long time. The main bottleneck is represented by the inevitably short time scale of integration of the equations of motion (just a few femtoseconds) that atomistic MD methods rely on. This is a necessary step in order to discretize the equations of motion that govern atomic motions and collisions, in order to solve them on a computer.

    This limitation makes it impossible to simulate the dynamical deformation of materials on long time scales encountered in experiments, i.e., for deformation rates lower than ~10 to 100 gigahertz. This fundamental time-scale bridging problem is currently unsolved, and prevents the computational prediction of material mechanics in the regimes that are experimentally accessible in standard mechanical tests and rheology.

    With my post-doc, Dr. Vinay Vaibhav, and with my long-time collaborator at the US Army Research Lab, Dr. Tim Sirk, I have now developed a computational framework that provides a working solution to this problem, arguably one of the biggest problems in molecular simulations of materials under deformations and external stimuli.

    The key idea of our approach is that the mechanical response at the low frequencies (e.g., around the Hertz) is dominated by atomic displacements known as nonaffine displacements. A nonaffine displacement is a swerve in the trajectory of an atom, which thus deviates from the trajectory prescribed by the externally imposed deformation (akin to Epicurus’s “clinamen,” if you are familiar with Greek philosophy).

    The origin of this swerve is the necessity to enforce mechanical equilibrium at every step in the deformation. In other words, at each step, the atom receives forces from its neighbor atoms, which need to be relaxed via an extra motion, the nonaffine swerve.

    As my collaborators and I have come to realize over the years, implementing this description of atomic trajectories implies computing the vibrational normal modes of the system, which can be done with modern computational techniques.

    This has now allowed us, in a paper published in the journal Macromolecules, to achieve a parameter-free agreement with the viscoelastic moduli of a real complex material, a crosslinked epoxy polymer glass in its amorphous solid state, at frequencies that are about 10 orders of magnitude lower than those that can be achieved by simulating the deformation process in standard molecular dynamic simulations.

    The agreement with experimental data from mechanical tests is striking, considering that no adjustable parameters are involved in the comparison.

    Our approach can still be refined in future work, e.g., by taking larger snapshots of the material configuration, with an increasing number of atoms, which will improve our predictions and reduce the noise from numerical fluctuations.

    An exciting prospect offered by this method is that of being able to single out the atomic and molecular vibrations, and motions, that are mostly responsible for the stiffness and hardness of a given material (or, conversely, for its softness), with plenty of opportunities for the development of new materials with high-performance properties for many technological and engineering applications.

    This story is part of Science X Dialog, where researchers can report findings from their published research articles. Visit this page for information about Science X Dialog and how to participate.

    More information:
    Vinay Vaibhav et al, Time-Scale Bridging in Atomistic Simulations of Epoxy Polymer Mechanics Using Nonaffine Deformation Theory, Macromolecules (2024). DOI: 10.1021/acs.macromol.4c01360

    Bio:
    Alessio Zaccone received his Ph.D. from the Department of Chemistry of ETH Zurich in 2010. From 2010 till 2014 he was an Oppenheimer Research Fellow at the Cavendish Laboratory, University of Cambridge.
    After being on the faculty of Technical University Munich (2014–2015) and of University of Cambridge (2015–2018), he has been a full professor and chair of theoretical physics in the Department of Physics at the University of Milano since 2022. Awards include the ETH Silver Medal, the 2020 Gauss Professorship of the Göttingen Academy of Sciences, the Fellowship of Queens’ College Cambridge, and an ERC Consolidator grant (“Multimech”).
    Research contributions include the analytical solution to the jamming transition problem (Zaccone & Scossa-Romano PRB 2011), the analytical solution to the random close packing problem in 2d and 3d (Zaccone PRL 2022), the theory of thermally-activated reaction rate processes in shear flows (Zaccone et al PRE 2009), the theory of crystal nucleation under shear flow (Mura & Zaccone PRE 2016), the theoretical prediction of boson-like peaks in the vibrational spectra of crystals (Milkus & Zaccone PRB 2016; Baggioli & Zaccone PRL 2019), the theory of the glass transition in polymers (Zaccone & Terentjev PRL 2013), the theoretical and computational discovery of topological defects in glasses (Baggioli, Kriuchevskyi, Sirk, Zaccone PRL 2021), and the theoretical prediction of superconductivity enhancement effects due to phonon damping (Setty, Baggioli, Zaccone PRB 2020).
    Research interests range from the statistical physics of disordered systems (random packings, jamming, glasses and the glass transition, colloids, nonequilibrium thermodynamics) to solid-state physics and superconductivity.

    Citation:
    Scientists solve one of the hardest problems in the computational atomic-scale mechanics of materials (2024, December 9)
    retrieved 9 December 2024
    from https://phys.org/news/2024-12-scientists-hardest-problems-atomic-scale.html

    This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
    part may be reproduced without the written permission. The content is provided for information purposes only.



    [ad_2]

    Source link