Category: Chemistry

Neurodegenerative diseases like Alzheimer’s, Parkinson’s or Huntington’s disease are characterized by the deposition of protein clumps, so-called protein aggregates, in the brains of patients. Even though disease-relevant proteins—such as the huntingtin protein in Huntington’s disease—are present in all cells of the human brain, aggregates of huntingtin form in a specific region of the brain during the initial stage of the disease.

A recent study by the group of Ulrich Hartl from the Max Planck Institute of Biochemistry investigates the influence, that the cell type has on this preference of aggregate formation in a distinct brain region. The study has been published in the scientific journal Molecular Cell. To address this phenomenon, the researchers performed experiments in a yeast model system.

Artificial protein aggregation through blue light illumination

Similar to the human brain, the formation of huntingtin aggregates in yeast also depend on the cell type, the so-called yeast strain. While the huntingtin protein forms aggregates in some yeast strains, it remains soluble in others. Why this is the case hasn’t been understood so far.

To investigate the distinction between different yeast strains and how they contribute to the formation of huntingtin aggregates, the researchers utilized recent advances in the field of optogenetics. They biotechnologically manipulated yeast strains that normally do not allow the aggregation of huntingtin and integrated a molecular switch that could be activated with blue light. This way, huntingtin aggregates could be formed simply by illuminating the cells with blue light.

Comparing the yeast cells that naturally form huntingtin aggregates with those, that only do so after their activation with blue light, caught the researchers by surprise. Only in cells where huntingtin aggregates already form naturally, but not in those where the aggregation of huntingtin was artificially induced with blue light, were toxic effects observed.

The first author of the study, Michael Gropp, reasoned that this phenomenon came about because smaller intermediates, rather than large aggregates, are the actual toxic version of the protein. Only in yeast cells that form huntingtin aggregates naturally, do these smaller toxic intermediates, the oligomers, exist. Here, large aggregates arise slowly, through the accumulation of proteins around the smaller intermediates.

These small intermediates are bypassed when the aggregation of huntingtin is induced artificially with blue light. Large aggregates then appear much more rapidly, avoiding toxic effects.

The role of prions during aggregate formation

But why do some yeast strains form huntingtin aggregates, while other genetically identical strains do not? Further assays in yeast and experiments with purified proteins—proteins that were artificially enriched in a test tube—helped the researchers to understand this phenomenon. Some yeast strains naturally contain protein aggregates of certain proteins, the prions.

These prion aggregates are not harmful for the cells. However, due to their specific structure, these prion aggregates can influence soluble huntingtin proteins and impose their structure on them. As a result, soluble huntingtin proteins convert into an aggregated state. A side effect of this process is the appearance of toxic intermediates. Yeast strains that naturally do not form huntingtin aggregates also do not possess prions and are therefore unable to generate toxic intermediates, despite the artificial induction of large huntingtin aggregates with blue light.

Possible implications for human disease

In recent years, many human proteins have been characterized that share similarities with the prions in yeast. A bioinformatic analysis of previously published data sets from mouse models and human cell cultures showed that mammalian proteins with such prion-like characteristics preferentially accumulate in neurons.

With the increasing age of an individual, they tend to form aggregates. The authors of the study suspect that the aggregates of these prion-like proteins can in turn force the aggregation of disease-relevant proteins, such as huntingtin, in certain brain areas and thus contribute to the disease progression in neurodegenerative disorders. Further investigation of this hypothesis is still ongoing.

A new type of material can learn and improve its ability to deal with unexpected forces thanks to a unique lattice structure with connections of variable stiffness, as described in a new paper by my colleagues and me.

The new material is a type of architected material, which gets its properties mainly from the geometry and specific traits of its design rather than what it is made out of. Take hook-and-loop fabric closures like Velcro, for example. It doesn’t matter whether it is made from cotton, plastic or any other substance. As long as one side is a fabric with stiff hooks and the other side has fluffy loops, the material will have the sticky properties of Velcro.

My colleagues and I based our new material’s architecture on that of an artificial neural network—layers of interconnected nodes that can learn to do tasks by changing how much importance, or weight, they place on each connection. We hypothesized that a mechanical lattice with physical nodes could be trained to take on certain mechanical properties by adjusting each connection’s rigidity.

To find out if a mechanical lattice would be able to adopt and maintain new properties—like taking on a new shape or changing directional strength—we started off by building a computer model. We then selected a desired shape for the material as well as input forces and had a computer algorithm tune the tensions of the connections so that the input forces would produce the desired shape. We did this training on 200 different lattice structures and found that a triangular lattice was best at achieving all of the shapes we tested.

Once the many connections are tuned to achieve a set of tasks, the material will continue to react in the desired way. The training is—in a sense—remembered in the structure of the material itself.

We then built a physical prototype lattice with adjustable electromechanical springs arranged in a triangular lattice. The prototype is made of 6-inch connections and is about 2 feet long by 1½ feet wide. And it worked. When the lattice and algorithm worked together, the material was able to learn and change shape in particular ways when subjected to different forces. We call this new material a mechanical neural network.

Besides some living tissues, very few materials can learn to be better at dealing with unanticipated loads. Imagine a plane wing that suddenly catches a gust of wind and is forced in an unanticipated direction. The wing can’t change its design to be stronger in that direction.

The prototype lattice material we designed can adapt to changing or unknown conditions. In a wing, for example, these changes could be the accumulation of internal damage, changes in how the wing is attached to a craft or fluctuating external loads. Every time a wing made out of a mechanical neural network experienced one of these scenarios, it could strengthen and soften its connections to maintain desired attributes like directional strength. Over time, through successive adjustments made by the algorithm, the wing adopts and maintains new properties, adding each behavior to the rest as a sort of muscle memory.

This type of material could have far reaching applications for the longevity and efficiency of built structures. Not only could a wing made of a mechanical neural network material be stronger, it could also be trained to morph into shapes that maximize fuel efficiency in response to changing conditions around it.

So far, our team has worked only with 2D lattices. But using computer modeling, we predict that 3D lattices would have a much larger capacity for learning and adaptation. This increase is due to the fact that a 3D structure could have tens of times more connections, or springs, that don’t intersect with one another. However, the mechanisms we used in our first model are far too complex to support in a large 3D structure.

The material my colleagues and I created is a proof of concept and shows the potential of mechanical neural networks. But to bring this idea into the real world will require figuring out how to make the individual pieces smaller and with precise properties of flex and tension.

We hope new research in the manufacturing of materials at the micron scale, as well as work on new materials with adjustable stiffness, will lead to advances that make powerful smart mechanical neural networks with micron-scale elements and dense 3D connections a ubiquitous reality in the near future.

A joint research team led by Prof. Pan Xiulian, Prof. Bao Xinhe and Prof. Hou Guangjin from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS) revealed the reaction mechanism of ketene transformation to gasoline on the zeotype H-SAPO-11.

The study was published in Journal of the American Chemical Society on Oct. 3.

In 2016, the team proposed a new catalyst concept based on metal oxide-zeolite bifunctional catalysts and ketene intermediate was detected by highly sensitive synchrotron-based vacuum ultraviolet photoionization mass spectrometry during syngas conversion.

Further studies demonstrated that ketene can be converted to mixed light olefins over molecular zeotype SAPO-34 and transformed to ethene over the eight-membered ring side pocket acid sites of mordenite. Although ketene has also been identified as an important intermediate in other zeolite-catalyzed C1 chemistry, its transformation mechanism is not well understood.

In this study, the researchers studied ketene conversion over H-SAPO-11 employing kinetic analyses, in situ infrared spectroscopy, and solid-state nuclear magnetic resonance spectroscopy.

They found that ketene transformed to butene on the acid sites via either acetyl species following an acetic acid ketonization pathway or acetoacetyl species with keto-enol tautomerism following an acetoacetic acid decarboxylation pathway in the presence of water.

“Our study revealed experimentally for the first time on the reaction network of catalytic ketene conversion over zeotypes and may benefit the further understanding of C1 chemistry,” said Prof. Pan.

Creating a hydrogen economy is no small task, but Rice University engineers have discovered a method that could make oxygen evolution catalysis in acids, one of the most challenging topics in water electrolysis for producing clean hydrogen fuels, more economical and practical.

The lab of chemical and biomolecular engineer Haotian Wang at Rice’s George R. Brown School of Engineering has replaced rare and expensive iridium with ruthenium, a far more abundant precious metal, as the positive-electrode catalyst in a reactor that splits water into hydrogen and oxygen.

The lab’s successful addition of nickel to ruthenium dioxide (RuO₂) resulted in a robust anode catalyst that produced hydrogen from water electrolysis for thousands of hours under ambient conditions.

“There’s huge industry interest in clean hydrogen,” Wang said. “It’s an important energy carrier and also important for chemical fabrication, but its current production contributes a significant portion of carbon emissions in the chemical manufacturing sector globally. We want to produce it in a more sustainable way, and water-splitting using clean electricity is widely recognized as the most promising option.”

Iridium costs roughly eight times more than ruthenium, he said, and it could account for 20% to 40% of the expense in commercial device manufacturing, especially in future large-scale deployments.

The process developed by Wang, Rice postdoctoral associate Zhen-Yu Wu and graduate student Feng-Yang Chen, and colleagues at the University of Pittsburgh and the University of Virginia is detailed in Nature Materials.

Water splitting involves the oxygen and hydrogen evolution reactions by which polarized catalysts rearrange water molecules to release oxygen and hydrogen. “Hydrogen is produced by the cathode, which is a negative electrode,” Wu said. “At the same time, it has to balance the charge by oxidizing water to generate oxygen on the anode side.”

“The cathode is very stable and not a big problem, but the anode is more prone to corrosion when using an acidic electrolyte,” Chen said. “Commonly used transition metals like manganese, iron, nickel and cobalt get oxidized and dissolve into the electrolyte.

“That’s why the only practical material used in commercial proton exchange membrane water electrolyzers is iridium,” he said. “It’s stable for tens of thousands of hours, but it’s very expensive.”

Setting out to find a replacement, Wang’s lab settled on ruthenium dioxide for its known activity, doping it with nickel, one of several metals tried.

The researchers demonstrated that ultrasmall and highly crystalline RuO2 nanoparticles with nickel dopants, used at the anode, facilitated water-splitting for more than 1,000 hours at a current density of 200 milliamps per square centimeter with negligible degradation.

They tested their anodes against others of pure ruthenium dioxide that catalyzed water electrolysis for a few hundred hours before beginning to decay.

The lab is working to improve its ruthenium catalyst to slot into current industrial processes. “Now that we’ve reached this stability milestone, our challenge is to increase the current density by at least five to 10 times while still maintaining this kind of stability,” Wang said. “This is very challenging, but still possible.”

He sees the need as urgent. “The annual production of iridium won’t help us to produce the amount of hydrogen we need today,” Wang said. “Even using all the iridium globally produced will simply not generate the amount of hydrogen we will need if we want it to be produced via water electrolysis.

“That means we can’t fully rely on iridium,” he said. “We have to develop new catalysts to either reduce its use or eliminate it from the process entirely.”