Tag Archives: University of Michigan

How memristors retain information without a power source? A mystery solved

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A September 10, 2024 news item on ScienceDaily provides a technical explanation of how memristors, without a power source, can retain information,

Phase separation, when molecules part like oil and water, works alongside oxygen diffusion to help memristors — electrical components that store information using electrical resistance — retain information even after the power is shut off, according to a University of Michigan led study recently published in Matter.

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A September 11, 2024 University of Michigan press release (also on EurekAltert but published September 10, 2024), which originated the news item, delves further into the research,

Up to this point, explanations have not fully grasped how memristors retain information without a power source, known as nonvolatile memory, because models and experiments do not match up.

“While experiments have shown devices can retain information for over 10 years, the models used in the community show that information can only be retained for a few hours,” said Jingxian Li, U-M doctoral graduate of materials science and engineering and first author of the study.

To better understand the underlying phenomenon driving nonvolatile memristor memory, the researchers focused on a device known as resistive random access memory or RRAM, an alternative to the volatile RAM used in classical computing, and are particularly promising for energy-efficient artificial intelligence applications. 

The specific RRAM studied, a filament-type valence change memory (VCM), sandwiches an insulating tantalum oxide layer between two platinum electrodes. When a certain voltage is applied to the platinum electrodes, a conductive filament forms a tantalum ion bridge passing through the insulator to the electrodes, which allows electricity to flow, putting the cell in a low resistance state representing a “1” in binary code. If a different voltage is applied, the filament is dissolved as returning oxygen atoms react with the tantalum ions, “rusting” the conductive bridge and returning to a high resistance state, representing a binary code of “0”. 

It was once thought that RRAM retains information over time because oxygen is too slow to diffuse back. However, a series of experiments revealed that previous models have neglected the role of phase separation. 

“In these devices, oxygen ions prefer to be away from the filament and will never diffuse back, even after an indefinite period of time. This process is analogous to how a mixture of water and oil will not mix, no matter how much time we wait, because they have lower energy in a de-mixed state,” said Yiyang Li, U-M assistant professor of materials science and engineering and senior author of the study.

To test retention time, the researchers sped up experiments by increasing the temperature. One hour at 250°C is equivalent to about 100 years at 85°C—the typical temperature of a computer chip.

Using the extremely high-resolution imaging of atomic force microscopy, the researchers imaged filaments, which measure only about five nanometers or 20 atoms wide, forming within the one micron wide RRAM device.  

“We were surprised that we could find the filament in the device. It’s like finding a needle in a haystack,” Li said. 

The research team found that different sized filaments yielded different retention behavior. Filaments smaller than about 5 nanometers dissolved over time, whereas filaments larger than 5 nanometers strengthened over time. The size-based difference cannot be explained by diffusion alone.

Together, experimental results and models incorporating thermodynamic principles showed the formation and stability of conductive filaments depend on phase separation. 

The research team leveraged phase separation to extend memory retention from one day to well over 10 years in a rad-hard memory chip—a memory device built to withstand radiation exposure for use in space exploration. 

Other applications include in-memory computing for more energy efficient AI applications or memory devices for electronic skin—a stretchable electronic interface designed to mimic the sensory capabilities of human skin. Also known as e-skin, this material could be used to provide sensory feedback to prosthetic limbs, create new wearable fitness trackers or help robots develop tactile sensing for delicate tasks.

“We hope that our findings can inspire new ways to use phase separation to create information storage devices,” Li said.

Researchers at Ford Research, Dearborn; Oak Ridge National Laboratory; University at Albany; NY CREATES; Sandia National Laboratories; and Arizona State University, Tempe contributed to this study.

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Here’s a link to and a citation for the paper,

Thermodynamic origin of nonvolatility in resistive memory by Jingxian Li, Anirudh Appachar, Sabrina L. Peczonczyk, Elisa T. Harrison, Anton V. Ievlev, Ryan Hood, Dongjae Shin, Sangmin Yoo, Brianna Roest, Kai Sun, Karsten Beckmann, Olya Popova, Tony Chiang, William S. Wahby, Robin B. Jacobs-Godrim, Matthew J. Marinella, Petro Maksymovych, John T. Heron, Nathaniel Cady, Wei D. Lu, Suhas Kumar, A. Alec Talin, Wenhao Sun, Yiyang Li. Matter DOI: https://doi.org/10.1016/j.matt.2024.07.018 Published online: August 26, 2024

This paper is behind a paywall.

Wearable air curtain (an invisible mask) kills viruses and blocks 99.8% of aerosols

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If you are vegetarian or vegan or have some objections to animal processing plants, this video is most likely not for you,

A July 8, 2024 University of Michigan news release (also on EurekAlert) describe a technology primarily designed for use by agricultural and industrial workers,

An air curtain shooting down from the brim of a hard hat can prevent 99.8% of aerosols from reaching a worker’s face. The technology, created by University of Michigan startup Taza Aya, potentially offers a new protection option for workers in industries where respiratory disease transmission is a concern.

Independent, third-party testing of Taza Aya’s device showed the effectiveness of the air curtain, curved to encircle the face, coming from nozzles at the hat’s brim. But for the air curtain to effectively protect against pathogens in the room, it must first be cleansed of pathogens itself. Previous research by the group of Taza Aya co-founder Herek Clack, U-M associate professor of civil and environmental engineering, showed that their method can remove and kill 99% of airborne viruses in farm and laboratory settings. 

“Our air curtain technology is precisely designed to protect wearers from airborne infectious pathogens, using treated air as a barrier in which any pathogens present have been inactivated so that they are no longer able to infect you if you breathe them in,” Clack said. “It’s virtually unheard of—our level of protection against airborne germs, especially when combined with the improved ergonomics it also provides.”

Fire has been used throughout history for sterilization, and while we might not usually think of it this way, it’s what’s known as a thermal plasma. Nonthermal, or cold, plasmas are made of highly energetic, electrically charged molecules and molecular fragments that achieve a similar effect without the heat. Those ions and molecules stabilize quickly, becoming ordinary air before reaching the curtain nozzles.

Taza Aya’s prototype features a backpack, weighing roughly 10 pounds, that houses the nonthermal plasma module, air handler, electronics and the unit’s battery pack. The handler draws air into the module, where it’s treated before flowing to the air curtain’s nozzle array.

Taza Aya’s progress comes in the wake of the COVID-19 pandemic and in the midst of a summer when the U.S. Centers for Disease Control and Prevention have reported four cases of humans testing positive for bird flu. During the pandemic, agriculture suffered disruptions in meat production due to shortages in labor, which had a direct impact on prices, the availability of some products and the extended supply chain.

In recent months, Taza Aya has conducted user experience testing with workers at Michigan Turkey Producers in Wyoming, Michigan, a processing plant that practices the humane handling of birds. The plant is home to hundreds of workers, many of them coming into direct contact with turkeys during their work day.

To date, paper masks have been the main strategy for protecting employees in such large-scale agriculture productions. But on a noisy production line, where many workers speak English as a second language, masks further reduce the ability of workers to communicate by muffling voices and hiding facial clues.

“During COVID, it was a problem for many plants—the masks were needed, but they prevented good communication with our associates,” said Tina Conklin, Michigan Turkey’s vice president of technical services.  

In addition, the effectiveness of masks is reliant on a tight seal over the mouth and noise to ensure proper filtration, which can change minute to minute during a workday. Masks can also fog up safety goggles, and they have to be removed for workers to eat. Taza Aya’s technology avoids all of those problems.

As a researcher at U-M, Clack spent years exploring the use of nonthermal plasma to protect livestock. With the arrival of COVID-19 in early 2020, he quickly pivoted to how the technology might be used for personal protection from airborne pathogens.

In October of that year, Taza Aya was named an awardee in the Invisible Shield QuickFire Challenge—a competition created by Johnson & Johnson Innovation in cooperation with the U.S. Department of Health and Human Services. The program sought to encourage the development of technologies that could protect people from airborne viruses while having a minimal impact on daily life.

“We are pleased with the study results as we embark on this journey,” said Alberto Elli, Taza Aya’s CEO. “This real-world product and user testing experience will help us successfully launch the Worker Wearable [Protection] in 2025.”

There’s a bit more information about the 3rd party testing mentioned at the start of the news release in a June 26, 2024 posting by Herek Clack on the Taza Aya company blog. You find out more about Worker and Individual Wearable Protection on Taza Aya’s The Solution webpage, scroll down abut 55% of the way.

Nanobiotics and artificial intelligence (AI)

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Antibiotics at the nanoscale = nanobiotics. For a more complete explanation, there’s this (Note: the video runs a little longer than most of the others embedded on this blog),

Before pushing further into this research, a note about antibiotic resistance. In a sense, we’ve created the problem we (those scientists in particular) are trying to solve.

Antibiotics and cleaning products kill 99.9% of the bacteria, leaving 0.1% that are immune. As so many living things on earth do, bacteria reproduce. Now, a new antibiotic is needed and discovered; it too kills 99.9% of the bacteria. The 0.1% left are immune to two antibiotics. And,so it goes.

As the scientists have made clear, we’re running out of options using standard methods and they’re hoping this ‘nanoparticle approach’ as described in a June 5, 2023 news item on Nanowerk will work, Note: A link has been removed,

Identifying whether and how a nanoparticle and protein will bind with one another is an important step toward being able to design antibiotics and antivirals on demand, and a computer model developed at the University of Michigan can do it.

The new tool could help find ways to stop antibiotic-resistant infections and new viruses—and aid in the design of nanoparticles for different purposes.

“Just in 2019, the number of people who died of antimicrobial resistance was 4.95 million. Even before COVID, which worsened the problem, studies showed that by 2050, the number of deaths by antibiotic resistance will be 10 million,” said Angela Violi, an Arthur F. Thurnau Professor of mechanical engineering, and corresponding author of the study that made the cover of Nature Computational Science (“Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles”).

In my ideal scenario, 20 or 30 years from now, I would like—given any superbug—to be able to quickly produce the best nanoparticles that can treat it.”

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A June 5, 2023 University of Michigan news release (also on EurekAlert), which originated the news item, provides more technical details, Note: A link has been removed,

Much of the work within cells is done by proteins. Interaction sites on their surfaces can stitch molecules together, break them apart and perform other modifications—opening doorways into cells, breaking sugars down to release energy, building structures to support groups of cells and more. If we could design medicines that target crucial proteins in bacteria and viruses without harming our own cells, that would enable humans to fight new and changing diseases quickly.

The new [computer] model, named NeCLAS [NeCLAS (Nanoparticle-Computed Ligand Affinity Scoring)], uses machine learning—the AI technique that powers the virtual assistant on your smartphone and ChatGPT. But instead of learning to process language, it absorbs structural models of proteins and their known interaction sites. From this information, it learns to extrapolate how proteins and nanoparticles might interact, predict binding sites and the likelihood of binding between them—as well as predicting interactions between two proteins or two nanoparticles.

“Other models exist, but ours is the best for predicting interactions between proteins and nanoparticles,” said Paolo Elvati, U-M associate research scientist in mechanical engineering.

AlphaFold, for example, is a widely used tool for predicting the 3D structure of a protein based on its building blocks, called amino acids. While this capacity is crucial, this is only the beginning: Discovering how these proteins assemble into larger structures and designing practical nanoscale systems are the next steps.

“That’s where NeCLAS comes in,” said Jacob Saldinger, U-M doctoral student in chemical engineering and first author of the study. “It goes beyond AlphaFold by showing how nanostructures will interact with one another, and it’s not limited to proteins. This enables researchers to understand the potential applications of nanoparticles and optimize their designs.”

The team tested three case studies for which they had additional data: 

  • Molecular tweezers, in which a molecule binds to a particular site on another molecule. This approach can stop harmful biological processes, such as the aggregation of protein plaques in diseases of the brain like Alzheimer’s.
  • How graphene quantum dots break up the biofilm produced by staph bacteria. These nanoparticles are flakes of carbon, no more than a few atomic layers thick and 0.0001 millimeters to a side. Breaking up biofilms is likely a crucial tool in fighting antibiotic-resistant infections—including the superbug methicillin-resistant Staphylococcus aureus (MRSA), commonly acquired at hospitals.
  • Whether graphene quantum dots would disperse in water, demonstrating the model’s ability to predict nanoparticle-nanoparticle binding even though it had been trained exclusively on protein-protein data.

While many protein-protein models set amino acids as the smallest unit that the model must consider, this doesn’t work for nanoparticles. Instead, the team set the size of that smallest feature to be roughly the size of the amino acid but then let the computer model decide where the boundaries between these minimum features were. The result is representations of proteins and nanoparticles that look a bit like collections of interconnected beads, providing more flexibility in exploring small scale interactions.

“Besides being more general, NeCLAS also uses way less training data than AlphaFold. We only have 21 nanoparticles to look at, so we have to use protein data in a clever way,” said Matt Raymond, U-M doctoral student in electrical and computer engineering and study co-author.  

Next, the team intends to explore other biofilms and microorganisms, including viruses.

The Nature Computational Science study was funded by the University of Michigan Blue Sky Initiative, the Army Research Office and the National Science Foundation. 

Here’s a link to and a citation for the paper,

Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles by Jacob Charles Saldinger, Matt Raymond, Paolo Elvati & Angela Violi. Nature Computational Science volume 3, pages 393–402 (2023) DOI: https://doi.org/10.1038/s43588-023-00438-x Published: 01 May 2023 Issue Date: May 2023

This paper is behind a paywall.

Artificially-grown mini-brains (organoids)—without animal components— offer opportunities for neuroscience

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There’s a good (brief) description of how these fibres become organoids in the photo caption,

Engineered extracellular matrices composed of fibrillar fibronectin are suspended over a porous polymer framework and provide the niche for stem cells to attach, differentiate, and mature into organoids. Credit: Ayse Muñiz Courtesy: Michigan Medicine – University of Michigan

A July 13 ,2023 University of Michigan (Michigan Medicine) news release by Noah Fromson (also on EurekAlert) announces ‘kinder, gentler’ brain organoids. Coincidentally, these organoids more closely resemble human brains, Note: Links have been removed,

Researchers at University of Michigan developed a method to produce artificially grown miniature brains — called human brain organoids — free of animal cells that could greatly improve the way neurodegenerative conditions are studied and, eventually, treated.

Over the last decade of researching neurologic diseases, scientists have explored the use of human brain organoids as an alternative to mouse models. These self-assembled, 3D tissues derived from embryonic or pluripotent stem cells more closely model the complex brain structure compared to conventional two-dimensional cultures.

Until now, the engineered network of proteins and molecules that give structure to the cells in brain organoids, known as extracellular matrices, often used a substance derived from mouse sarcomas called Matrigel. That method suffers significant disadvantages, with a relatively undefined composition and batch-to-batch variability.

The latest U-M research, published in Annals of Clinical and Translational Neurology, offers a solution to overcome Matrigel’s weaknesses. Investigators created a novel culture method that uses an engineered extracellular matrix for human brain organoids — without the presence of animal components – and enhanced the neurogenesis of brain organoids compared to previous studies.

“This advancement in the development of human brain organoids free of animal components will allow for significant strides in the understanding of neurodevelopmental biology,” said senior author Joerg Lahann, Ph.D., director of the U-M Biointerfaces Institute and Wolfgang Pauli Collegiate Professor of Chemical Engineering at U-M.

“Scientists have long struggled to translate animal research into the clinical world, and this novel method will make it easier for translational research to make its way from the lab to the clinic.”

The foundational extracellular matrices of the research team’s brain organoids were comprised of human fibronectin, a protein that serves as a native structure for stem cells to adhere, differentiate and mature. They were supported by a highly porous polymer scaffold.

The organoids were cultured for months, while lab staff was unable to enter the building due to the COVID 19-pandemic.

Using proteomics, researchers found their brain organoids developed cerebral spinal fluid, a clear liquid that flows around healthy brain and spinal cords. This fluid more closely matched human adult CSF compared to a landmark study of human brain organoids developed in Matrigel.

“When our brains are naturally developing in utero, they are of course not growing on a bed of extracellular matrix produced by mouse cancer cells,” said first author Ayşe Muñiz, Ph.D., who was a graduate student in the U-M Macromolecular Science and Engineering Program at the time of the work.  

“By putting cells in an engineered niche that more closely resembles their natural environment, we predicted we would observe differences in organoid development that more faithfully mimics what we see in nature.”

The success of these xenogeneic-free human brain organoids opens the door for reprogramming with cells from patients with neurodegenerative diseases, says co-author Eva Feldman, M.D., Ph.D., director of the ALS Center of Excellence at U-M and James W. Albers Distinguished Professor of Neurology at U-M Medical School.

“There is a possibility to take the stem cells from a patient with a condition such as ALS or Alzheimer’s and, essentially, build an avatar mini brain of that patients to investigate possible treatments or model how their disease will progress,” Feldman said. “These models would create another avenue to predict disease and study treatment on a personalized level for conditions that often vary greatly from person to person.”

Here’s a link to and a citation for the paper,

Engineered extracellular matrices facilitate brain organoids from human pluripotent stem cells by Ayşe J. Muñiz, Tuğba Topal, Michael D. Brooks, Angela Sze, Do Hoon Kim, Jacob Jordahl, Joe Nguyen, Paul H. Krebsbach, Masha G. Savelieff, Eva L. Feldman, Joerg Lahann. Annals of Clinical and Translational Neurology DOI: https://doi.org/10.1002/acn3.51820 First published: 07 June 2023

This paper is open access.

Nanobiotics and a new machine learning model

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A May 16, 2022 news item on phys.org announces work on a new machine learning model that could be useful in the research into engineered nanoparticles for medical purposes (Note: Links have been removed),

With antibiotic-resistant infections on the rise and a continually morphing pandemic virus, it’s easy to see why researchers want to be able to design engineered nanoparticles that can shut down these infections.

A new machine learning model that predicts interactions between nanoparticles and proteins, developed at the University of Michigan, brings us a step closer to that reality.

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A May 16, 2022 University of Michigan news release by Kate McAlpine, which originated the news item, delves further into the work (Note: Links have been removed),

“We have reimagined nanoparticles to be more than mere drug delivery vehicles. We consider them to be active drugs in and of themselves,” said J. Scott VanEpps, an assistant professor of emergency medicine and an author of the study in Nature Computational Science.

Discovering drugs is a slow and unpredictable process, which is why so many antibiotics are variations on a previous drug. Drug developers would like to design medicines that can attack bacteria and viruses in ways that they choose, taking advantage of the “lock-and-key” mechanisms that dominate interactions between biological molecules. But it was unclear how to transition from the abstract idea of using nanoparticles to disrupt infections to practical implementation of the concept. 

“By applying mathematical methods to protein-protein interactions, we have streamlined the design of nanoparticles that mimic one of the proteins in these pairs,” said Nicholas Kotov, the Irving Langmuir Distinguished University Professor of Chemical Sciences and Engineering and corresponding author of the study. 

“Nanoparticles are more stable than biomolecules and can lead to entirely new classes of antibacterial and antiviral agents.”

The new machine learning algorithm compares nanoparticles to proteins using three different ways to describe them. While the first was a conventional chemical description, the two that concerned structure turned out to be most important for making predictions about whether a nanoparticle would be a lock-and-key match with a specific protein.

Between them, these two structural descriptions captured the protein’s complex surface and how it might reconfigure itself to enable lock-and-key fits. This includes pockets that a nanoparticle could fit into, along with the size such a nanoparticle would need to be. The descriptions also included chirality, a clockwise or counterclockwise twist that is important for predicting how a protein and nanoparticle will lock in.

“There are many proteins outside and inside bacteria that we can target. We can use this model as a first screening to discover which nanoparticles will bind with which proteins,” said Emine Sumeyra Turali Emre, a postdoctoral researcher in chemical engineering and co-first author of the paper, along with Minjeong Cha, a PhD student in materials science and engineering.

Emre and Cha explained that researchers could follow up on matches identified by their algorithm with more detailed simulations and experiments. One such match could stop the spread of MRSA, a common antibiotic-resistant strain, using zinc oxide nanopyramids that block metabolic enzymes in the bacteria.  

“Machine learning algorithms like ours will provide a design tool for nanoparticles that can be used in many biological processes. Inhibition of the virus that causes COVID-19 is one good example,” said Cha. “We can use this algorithm to efficiently design nanoparticles that have broad-spectrum antiviral activity against all variants.”

This breakthrough was enabled by the Blue Sky Initiative at the University of Michigan College of Engineering. It provided $1.5 million to support the interdisciplinary team carrying out the fundamental exploration of whether a machine learning approach could be effective when data on the biological activity of nanoparticles is so sparse.

“The core of the Blue Sky idea is exactly what this work covers: finding a way to represent proteins and nanoparticles in a unified approach to understand and design new classes of drugs that have multiple ways of working against bacteria,” said Angela Violi, an Arthur F. Thurnau Professor, a professor of mechanical engineering and leader of the nanobiotics Blue Sky project.

Emre led the building of a database of interactions between proteins that could help to predict nanoparticle and protein interaction. Cha then identified structural descriptors that would serve equally well for nanoparticles and proteins, working with collaborators at the University of Southern California, Los Angeles to develop a machine learning algorithm that combed through the database and used the patterns it found to predict how proteins and nanoparticles would interact with one another. Finally, the team compared these predictions for lock-and-key matches with the results from experiments and detailed simulations, finding that they closely matched.

Additional collaborators on the project include Ji-Young Kim, a postdoctoral researcher in chemical engineering at U-M, who helped calculate chirality in the proteins and nanoparticles. Paul Bogdan and Xiongye Xiao, a professor and PhD student, respectively, in electrical and computer engineering at USC [University of Southern California] contributed to the graph theory descriptors. Cha then worked with them to design and train the neural network, comparing different machine learning models. All authors helped analyze the data.

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Here are links to and a citation for the research briefing and paper, respectively,

Universal descriptors to predict interactions of inorganic nanoparticles with proteins. Nature Computational Science (2022) [Research briefing] DOI: https://doi.org/10.1038/s43588-022-00230-3 Published: 28 April 2022

This paper is behind a paywall.

Unifying structural descriptors for biological and bioinspired nanoscale complexes by Minjeong Cha, Emine Sumeyra Turali Emre, Xiongye Xiao, Ji-Young Kim, Paul Bogdan, J. Scott VanEpps, Angela Violi & Nicholas A. Kotov. Nature Computational Science volume 2, pages 243–252 (2022) Issue Date: April 2022 DOI: https://doi.org/10.1038/s43588-022-00229-w Published: 28 April 2022

This paper appears to be open access.

Entropic bonding for nanoparticle crystals

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A January 19, 2022 University of Michigan news release (also on EurekAlert) is written in a Q&A (question and answer style) not usually seen on news releases, Note: Links have been removed),

Turns out entropy binds nanoparticles a lot like electrons bind chemical crystals

ANN ARBOR—Entropy, a physical property often explained as “disorder,” is revealed as a creator of order with a new bonding theory developed at the University of Michigan and published in the Proceedings of the National Academy of Sciences [PNAS]. 

Engineers dream of using nanoparticles to build designer materials, and the new theory can help guide efforts to make nanoparticles assemble into useful structures. The theory explains earlier results exploring the formation of crystal structures by space-restricted nanoparticles, enabling entropy to be quantified and harnessed in future efforts. 

And curiously, the set of equations that govern nanoparticle interactions due to entropy mirror those that describe chemical bonding. Sharon Glotzer, the Anthony C. Lembke Department Chair of Chemical Engineering, and Thi Vo, a postdoctoral researcher in chemical engineering, answered some questions about their new theory.

What is entropic bonding?

Glotzer: Entropic bonding is a way of explaining how nanoparticles interact to form crystal structures. It’s analogous to the chemical bonds formed by atoms. But unlike atoms, there aren’t electron interactions holding these nanoparticles together. Instead, the attraction arises because of entropy. 

Oftentimes, entropy is associated with disorder, but it’s really about options. When nanoparticles are crowded together and options are limited, it turns out that the most likely arrangement of nanoparticles can be a particular crystal structure. That structure gives the system the most options, and thus the highest entropy. Large entropic forces arise when the particles become close to one another. 

By doing the most extensive studies of particle shapes and the crystals they form, my group found that as you change the shape, you change the directionality of those entropic forces that guide the formation of these crystal structures. That directionality simulates a bond, and since it’s driven by entropy, we call it entropic bonding.

Why is this important?

Glotzer: Entropy’s contribution to creating order is often overlooked when designing nanoparticles for self-assembly, but that’s a mistake. If entropy is helping your system organize itself, you may not need to engineer explicit attraction between particles—for example, using DNA or other sticky molecules—with as strong an interaction as you thought. With our new theory, we can calculate the strength of those entropic bonds.

While we’ve known that entropic interactions can be directional like bonds, our breakthrough is that we can describe those bonds with a theory that line-for-line matches the theory that you would write down for electron interactions in actual chemical bonds. That’s profound. I’m amazed that it’s even possible to do that. Mathematically speaking, it puts chemical bonds and entropic bonds on the same footing. This is both fundamentally important for our understanding of matter and practically important for making new materials.

Electrons are the key to those chemical equations though. How did you do this when no particles mediate the interactions between your nanoparticles?

Glotzer: Entropy is related to the free space in the system, but for years I didn’t know how to count that space. Thi’s big insight was that we could count that space using fictitious point particles. And that gave us the mathematical analogue of the electrons.

Vo: The pseudoparticles move around the system and fill in the spaces that are hard for another nanoparticle to fill—we call this the excluded volume around each nanoparticle. As the nanoparticles become more ordered, the excluded volume around them becomes smaller, and the concentration of pseudoparticles in those regions increases. The entropic bonds are where that concentration is highest. 

In crowded conditions, the entropy lost by increasing the order is outweighed by the entropy gained by shrinking the excluded volume. As a result, the configuration with the highest entropy will be the one where pseudoparticles occupy the least space.

The research is funded by the Simons Foundation, Office of Naval Research, and the Office of the Undersecretary of Defense for Research and Engineering. It relied on the computing resources of the National Science Foundation’s Extreme Science and Engineering Discovery Environment. Glotzer is also the John Werner Cahn Distinguished University Professor of Engineering, the Stuart W. Churchill Collegiate Professor of Chemical Engineering, and a professor of material science and engineering, macromolecular science and engineering, and physics at U-M.

Here’s a link to and a citation for the paper,

A theory of entropic bonding by Thi Vo and Sharon C. Glotzer. PNAS January 25, 2022 119 (4) e2116414119 DOI: https://doi.org/10.1073/pnas.2116414119

This paper is behind a paywall.

The nanoscale precision of pearls

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An October 21, 2021 news item on phys.org features a quote about nothingness and symmetry (Note: A link has been removed),

In research that could inform future high-performance nanomaterials, a University of Michigan-led team has uncovered for the first time how mollusks build ultradurable structures with a level of symmetry that outstrips everything else in the natural world, with the exception of individual atoms.

“We humans, with all our access to technology, can’t make something with a nanoscale architecture as intricate as a pearl,” said Robert Hovden, U-M assistant professor of materials science and engineering and an author on the paper. “So we can learn a lot by studying how pearls go from disordered nothingness to this remarkably symmetrical structure.” [emphasis mine]

The analysis was done in collaboration with researchers at the Australian National University, Lawrence Berkeley National Laboratory, Western Norway University [of Applied Sciences] and Cornell University.

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a. A Keshi pearl that has been sliced into pieces for study. b. A magnified cross-section of the pearl shows its transition from its disorderly center to thousands of layers of finely matched nacre. c. A magnification of the nacre layers shows their self-correction—when one layer is thicker, the next is thinner to compensate, and vice-versa. d, e: Atomic scale images of the nacre layers. f, g, h, i: Microscopy images detail the transitions between the pearl’s layers. Credit: University of Michigan

An October 21, 2021 University of Michigan news release (also on EurekAlert), which originated the news item, reveals a surprise,

Published in the Proceedings of the National Academy of Sciences [PNAS], the study found that a pearl’s symmetry becomes more and more precise as it builds, answering centuries-old questions about how the disorder at its center becomes a sort of perfection. 

Layers of nacre, the iridescent and extremely durable organic-inorganic composite that also makes up the shells of oysters and other mollusks, build on a shard of aragonite that surrounds an organic center. The layers, which make up more than 90% of a pearl’s volume, become progressively thinner and more closely matched as they build outward from the center.

Perhaps the most surprising finding is that mollusks maintain the symmetry of their pearls by adjusting the thickness of each layer of nacre. If one layer is thicker, the next tends to be thinner, and vice versa. The pearl pictured in the study contains 2,615 finely matched layers of nacre, deposited over 548 days.

“These thin, smooth layers of nacre look a little like bed sheets, with organic matter in between,” Hovden said. “There’s interaction between each layer, and we hypothesize that that interaction is what enables the system to correct as it goes along.”

The team also uncovered details about how the interaction between layers works. A mathematical analysis of the pearl’s layers show that they follow a phenomenon known as “1/f noise,” where a series of events that seem to be random are connected, with each new event influenced by the one before it. 1/f noise has been shown to govern a wide variety of natural and human-made processes including seismic activity, economic markets, electricity, physics and even classical music.

“When you roll dice, for example, every roll is completely independent and disconnected from every other roll. But 1/f noise is different in that each event is linked,” Hovden said. “We can’t predict it, but we can see a structure in the chaos. And within that structure are complex mechanisms that enable a pearl’s thousands of layers of nacre to coalesce toward order and precision.”

The team found that pearls lack true long-range order—the kind of carefully planned symmetry that keeps the hundreds of layers in brick buildings consistent. Instead, pearls exhibit medium-range order, maintaining symmetry for around 20 layers at a time. This is enough to maintain consistency and durability over the thousands of layers that make up a pearl.

The team gathered their observations by studying Akoya “keshi” pearls, produced by the Pinctada imbricata fucata oyster near the Eastern shoreline of Australia. They selected these particular pearls, which measure around 50 millimeters in diameter, because they form naturally, as opposed to bead-cultured pearls, which have an artificial center. Each pearl was cut with a diamond wire saw into sections measuring three to five millimeters in diameter, then polished and examined under an electron microscope.

Hovden says the study’s findings could help inform next-generation materials with precisely layered nanoscale architecture.

“When we build something like a brick building, we can build in periodicity through careful planning and measuring and templating,” he said. “Mollusks can achieve similar results on the nanoscale by using a different strategy. So we have a lot to learn from them, and that knowledge could help us make stronger, lighter materials in the future.”

Here’s a link to and a citation for the paper,

The mesoscale order of nacreous pearls by Jiseok Gim, Alden Koch, Laura M. Otter, Benjamin H. Savitzky, Sveinung Erland, Lara A. Estroff, Dorrit E. Jacob, and Robert Hovden. PNAS vol. 118 no. 42 e2107477118 DOI: https://doi.org/10.1073/pnas.2107477118 Published in issue October 19, 2021 Published online October 18, 2021

This paper appears to be open access.

Memristors with better mimicry of synapses

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It seems to me it’s been quite a while since I’ve stumbled across a memristor story from the University of Micihigan but it was worth waiting for. (Much of the research around memristors has to do with their potential application in neuromorphic (brainlike) computers.) From a December 17, 2018 news item on ScienceDaily,

A new electronic device developed at the University of Michigan can directly model the behaviors of a synapse, which is a connection between two neurons.

For the first time, the way that neurons share or compete for resources can be explored in hardware without the need for complicated circuits.

“Neuroscientists have argued that competition and cooperation behaviors among synapses are very important. Our new memristive devices allow us to implement a faithful model of these behaviors in a solid-state system,” said Wei Lu, U-M professor of electrical and computer engineering and senior author of the study in Nature Materials.

A December 17, 2018 University of Michigan news release (also on EurekAlert), which originated the news item, provides an explanation of memristors and their ‘similarity’ to synapses while providing more details about this latest research,

Memristors are electrical resistors with memory–advanced electronic devices that regulate current based on the history of the voltages applied to them. They can store and process data simultaneously, which makes them a lot more efficient than traditional systems. They could enable new platforms that process a vast number of signals in parallel and are capable of advanced machine learning.

The memristor is a good model for a synapse. It mimics the way that the connections between neurons strengthen or weaken when signals pass through them. But the changes in conductance typically come from changes in the shape of the channels of conductive material within the memristor. These channels–and the memristor’s ability to conduct electricity–could not be precisely controlled in previous devices.

Now, the U-M team has made a memristor in which they have better command of the conducting pathways.They developed a new material out of the semiconductor molybdenum disulfide–a “two-dimensional” material that can be peeled into layers just a few atoms thick. Lu’s team injected lithium ions into the gaps between molybdenum disulfide layers.
They found that if there are enough lithium ions present, the molybdenum sulfide transforms its lattice structure, enabling electrons to run through the film easily as if it were a metal. But in areas with too few lithium ions, the molybdenum sulfide restores its original lattice structure and becomes a semiconductor, and electrical signals have a hard time getting through.

The lithium ions are easy to rearrange within the layer by sliding them with an electric field. This changes the size of the regions that conduct electricity little by little and thereby controls the device’s conductance.

“Because we change the ‘bulk’ properties of the film, the conductance change is much more gradual and much more controllable,” Lu said.

In addition to making the devices behave better, the layered structure enabled Lu’s team to link multiple memristors together through shared lithium ions–creating a kind of connection that is also found in brains. A single neuron’s dendrite, or its signal-receiving end, may have several synapses connecting it to the signaling arms of other neurons. Lu compares the availability of lithium ions to that of a protein that enables synapses to grow.

If the growth of one synapse releases these proteins, called plasticity-related proteins, other synapses nearby can also grow–this is cooperation. Neuroscientists have argued that cooperation between synapses helps to rapidly form vivid memories that last for decades and create associative memories, like a scent that reminds you of your grandmother’s house, for example. If the protein is scarce, one synapse will grow at the expense of the other–and this competition pares down our brains’ connections and keeps them from exploding with signals.
Lu’s team was able to show these phenomena directly using their memristor devices. In the competition scenario, lithium ions were drained away from one side of the device. The side with the lithium ions increased its conductance, emulating the growth, and the conductance of the device with little lithium was stunted.

In a cooperation scenario, they made a memristor network with four devices that can exchange lithium ions, and then siphoned some lithium ions from one device out to the others. In this case, not only could the lithium donor increase its conductance–the other three devices could too, although their signals weren’t as strong.

Lu’s team is currently building networks of memristors like these to explore their potential for neuromorphic computing, which mimics the circuitry of the brain.

Here’s a link to and a citation for the paper,

Ionic modulation and ionic coupling effects in MoS2 devices for neuromorphic computing by Xiaojian Zhu, Da Li, Xiaogan Liang, & Wei D. Lu. Nature Materials (2018) DOI: https://doi.org/10.1038/s41563-018-0248-5 Published 17 December 2018

This paper is behind a paywall.

The researchers have made images illustrating their work available,

A schematic of the molybdenum disulfide layers with lithium ions between them. On the right, the simplified inset shows how the molybdenum disulfide changes its atom arrangements in the presence and absence of the lithium atoms, between a metal (1T’ phase) and semiconductor (2H phase), respectively. Image credit: Xiaojian Zhu, Nanoelectronics Group, University of Michigan.

A diagram of a synapse receiving a signal from one of the connecting neurons. This signal activates the generation of plasticity-related proteins (PRPs), which help a synapse to grow. They can migrate to other synapses, which enables multiple synapses to grow at once. The new device is the first to mimic this process directly, without the need for software or complicated circuits. Image credit: Xiaojian Zhu, Nanoelectronics Group, University of Michigan.
An electron microscope image showing the rectangular gold (Au) electrodes representing signalling neurons and the rounded electrode representing the receiving neuron. The material of molybdenum disulfide layered with lithium connects the electrodes, enabling the simulation of cooperative growth among synapses. Image credit: Xiaojian Zhu, Nanoelectronics Group, University of Michigan.

That’s all folks.

Bringing memristors to the masses and cutting down on energy use

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One of my earliest posts featuring memristors (May 9, 2008) focused on their potential for energy savings but since then most of my postings feature research into their application in the field of neuromorphic (brainlike) computing. (For a description and abbreviated history of the memristor go to this page on my Nanotech Mysteries Wiki.)

In a sense this July 30, 2018 news item on Nanowerk is a return to the beginning,

A new way of arranging advanced computer components called memristors on a chip could enable them to be used for general computing, which could cut energy consumption by a factor of 100.

This would improve performance in low power environments such as smartphones or make for more efficient supercomputers, says a University of Michigan researcher.

“Historically, the semiconductor industry has improved performance by making devices faster. But although the processors and memories are very fast, they can’t be efficient because they have to wait for data to come in and out,” said Wei Lu, U-M professor of electrical and computer engineering and co-founder of memristor startup Crossbar Inc.

Memristors might be the answer. Named as a portmanteau of memory and resistor, they can be programmed to have different resistance states–meaning they store information as resistance levels. These circuit elements enable memory and processing in the same device, cutting out the data transfer bottleneck experienced by conventional computers in which the memory is separate from the processor.

A July 30, 2018 University of Michigan news release (also on EurekAlert), which originated the news item, expands on the theme,

… unlike ordinary bits, which are 1 or 0, memristors can have resistances that are on a continuum. Some applications, such as computing that mimics the brain (neuromorphic), take advantage of the analog nature of memristors. But for ordinary computing, trying to differentiate among small variations in the current passing through a memristor device is not precise enough for numerical calculations.

Lu and his colleagues got around this problem by digitizing the current outputs—defining current ranges as specific bit values (i.e., 0 or 1). The team was also able to map large mathematical problems into smaller blocks within the array, improving the efficiency and flexibility of the system.

Computers with these new blocks, which the researchers call “memory-processing units,” could be particularly useful for implementing machine learning and artificial intelligence algorithms. They are also well suited to tasks that are based on matrix operations, such as simulations used for weather prediction. The simplest mathematical matrices, akin to tables with rows and columns of numbers, can map directly onto the grid of memristors.

The memristor array situated on a circuit board.

The memristor array situated on a circuit board. Credit: Mohammed Zidan, Nanoelectronics group, University of Michigan.

Once the memristors are set to represent the numbers, operations that multiply and sum the rows and columns can be taken care of simultaneously, with a set of voltage pulses along the rows. The current measured at the end of each column contains the answers. A typical processor, in contrast, would have to read the value from each cell of the matrix, perform multiplication, and then sum up each column in series.

“We get the multiplication and addition in one step. It’s taken care of through physical laws. We don’t need to manually multiply and sum in a processor,” Lu said.

His team chose to solve partial differential equations as a test for a 32×32 memristor array—which Lu imagines as just one block of a future system. These equations, including those behind weather forecasting, underpin many problems science and engineering but are very challenging to solve. The difficulty comes from the complicated forms and multiple variables needed to model physical phenomena.

When solving partial differential equations exactly is impossible, solving them approximately can require supercomputers. These problems often involve very large matrices of data, so the memory-processor communication bottleneck is neatly solved with a memristor array. The equations Lu’s team used in their demonstration simulated a plasma reactor, such as those used for integrated circuit fabrication.

This work is described in a study, “A general memristor-based partial differential equation solver,” published in the journal Nature Electronics.

It was supported by the Defense Advanced Research Projects Agency (DARPA) (grant no. HR0011-17-2-0018) and by the National Science Foundation (NSF) (grant no. CCF-1617315).

Here’s a link and a citation for the paper,

A general memristor-based partial differential equation solver by Mohammed A. Zidan, YeonJoo Jeong, Jihang Lee, Bing Chen, Shuo Huang, Mark J. Kushner & Wei D. Lu. Nature Electronicsvolume 1, pages411–420 (2018) DOI: https://doi.org/10.1038/s41928-018-0100-6 Published: 13 July 2018

This paper is behind a paywall.

For the curious, Dr. Lu’s startup company, Crossbar can be found here.