Tag: Tokyo University of Science

With a focus on the Meiji Restoration, researchers delve into the connections between political regime changes and social mobility.

Japan’s regime change in the 19th century from feudal to modern rule offers a classic example of elite mobility. In this regard, a new study of the Meiji Restoration by researchers from Japan provides novel insights on elite mobility during this transition. It tells us how this change allowed commoners to join the elite ranks. However, while the political changes facilitated social mobility, the extent of upward mobility differed at various stages of the change.

Social mobility refers to the movement of individuals from one socio-economic strata to another, followed by a change in their social status. In today’s world, social mobility is largely driven by personal motivation, education, skills and migration. But an analysis of historical data tells us that social mobility is primarily caused by changes in political rule. Political upheavals that caused the downfall of established regimes were followed by massive changes in the composition of elite mobility.

A new study by Junior Associate Professor Tomoko Matsumoto of the Tokyo University of Science and Professor Tetsuji Okazaki from the University of Tokyo provides new evidence on the correlation between elite mobility at various stages of the Meiji Restoration. The results of their study were published online in the British Journal of Sociology on 31st January, 2023.

Under the Tokugawa regime, Japan had a rigid class structure. The Shogun, Daimyos (feudal lords), and Samurai (noble class) constituted the upper class that ruled Japan. They possessed political privileges and were prohibited from interacting with the commoners (peasants, craftspeople, merchants). This restricted their social mobility as they were not allowed to change occupations, travel, or marry into a different class. In 1868, the Tokugawa Shogunate was defeated in a civil war and a new political regime came to power. This event marks the Meiji Restoration, that culminated in the formation of the ‘Imperial Diet’, which later became the highest framework of state power in Japan, called the National Diet.

“If we look back in history, we find that the Meiji Restoration was the first time that people were able to choose their future regardless of the environment in which they were born. During this historical period of transition, when equality of all natives and freedom of choice of occupation were recognized, how much social mobility actually occurred? This study was initiated in the hope of adding new insights during this period of transition,” say the researchers, explaining the rationale behind their study.

The study used statistical tools like hypothesis testing and data sampling to test the Meiji Restoration. “The Meiji Restoration has three advantageous aspects for our study besides data availability. Firstly, before the regime change, social mobility was extremely low. Secondly, the Meiji Restoration reformed the educational system drastically, and lastly, the regime change produced a new system of elite hierarchy,” explain the researchers about why they chose to focus on this period in history. The researchers divided the collected data into two cohorts to analyze the pre and post stages in regime change, and their varying effects on social mobility.

Regime changes lay the groundwork for non-elites to elevate themselves to the elite class, regardless of their social origin. During the various stages of a successful regime change, the new elites are not necessarily opposed to the old elites. The change is a gradual process which starts with a hostile relationship between the old and incumbent elites, but slowly evolves into a compromising one, after the political transfer of power is done. The researchers found that with the initial overthrowing of the old regime, the commoners had the biggest opportunity to join the elite classes. Meritocracy played a huge role at this stage. However, after the consolidation of the new regime, the opportunities for elite mobility gradually declined, as a stable structure, based on elite compromise, was created.

The results supported the hypotheses that social mobility usually occurred before the consolidation of a new political power. At that time, the commoners had the greatest chance to join the elite group and attain a high rank within the group. Meritocracy, based on education, also plays a vital role. After the new regime fortifies its position, the chances for merit-based elite mobility diminishes. This stage also signaled a decline in educational meritocracy.

“How can we realize a society where people can have the future they want if they make an effort, regardless of the environment in which they are born?” ponders Dr. Matsumoto. She hopes that with the findings of their study, this issue will be discussed objectively, looking back on short-term as well as long-term historical data.

Today, when we are aspiring for a society based on meritocracy and not on nepotism, these findings continue to enlighten us

 

Reference

Title of original paper: Elite Mobility and Continuity during a Regime Change

Journal: British Journal of Sociology

DOI: https://doi.org/10.1111/1468-4446.13000

Magnesium is a promising candidate as an energy carrier for next-generation batteries. However, the cycling performance and capacity of magnesium batteries need to improve if they are to replace lithium-ion batteries. To this end, a research team focused on a novel cathode material with a spinel structure, Mg1.33V1.67−xMnxO4. Following extensive characterization and electrochemical performance experiments, they have found a specific composition that could open doors to high-performance magnesium rechargeable batteries.

Lithium-ion batteries have remained unrivaled in terms of overall performance for several applications, as evidenced by their widespread use in everything from portable electronics to cellular base stations. However, they suffer from few important disadvantages that are difficult to ignore. For one, lithium is rather expensive, and the fact that it is being mined at an extreme pace does not help. Moreover, the energy density of lithium-ion batteries is not enough to grant autonomy to electric vehicles and heavy machinery. These concerns, coupled with the fact that the batteries are highly unsafe when punctured or at high temperatures, have caused scientists to look for alternative technologies.

Among the various elements being tested as efficient energy carriers for rechargeable batteries, magnesium (Mg) is a promising candidate. Apart from its safety and abundance, Mg has the potential to realize higher battery capacities. However, some problems need to be solved first. These include the low voltage window that Mg ions provide, as well as the unreliable cycling performance observed in Mg battery materials.

To tackle these issues, a research team led by Vice President and Professor Yasushi Idemoto from Tokyo University of Science, Japan has been on the lookout for new cathode materials for Mg batteries. In particular, they have been searching for ways to improve the performance of cathode materials based on the MgV (V: vanadium) system. Fortunately, as reported in a recent study made available online on 8 December 2022 and published in Volume 928 of the Journal of Electroanalytical Chemistry on 1 January 2023,  they have now found the right track to success.

The researchers focused on the Mg1.33V1.67O4 system but substituted some amount of vanadium with manganese (Mn), obtaining materials with the formula Mg1.33V1.67−xMnxO4, where x goes from 0.1 to 0.4. While this system offered high theoretical capacity, more details about its structure, cyclability, and cathode performance needed to be analyzed to understand its practical utility. Accordingly, the researchers characterized the synthesized cathode materials using a wide variety of standard techniques.

First, they studied the composition, crystal structure, electron distribution, and particle morphologies of Mg1.33V1.67−xMnxO4 compounds using  X-ray diffraction and absorption, as well as transmission electron microscopy. The analyses showed that Mg1.33V1.67−xMnxO4 has a spinel structure with a remarkably uniform composition. Next, the researchers conducted a series of electrochemical measurements to evaluate the battery performance of Mg1.33V1.67−xMnxO4, using different electrolytes and testing the resulting charge/discharge properties at various temperatures.

The team observed a high discharge capacity for these cathode materials—especially Mg1.33V1.57Mn0.1O4—but it also varied significantly depending on the cycle number. To understand why, they analyzed the local structure near the vanadium atoms in the material. “It appears that the particularly stable crystal structure along with a large amount of charge compensation by vanadium leads to the superior charge–discharge properties we observed for Mg1.33V1.57Mn0.1O4,” remarks Prof. Idemoto. “Taken together, our results indicate that Mg1.33V1.57Mn0.1O4 could be a good candidate cathode material for magnesium rechargeable batteries.”

Satisfied with the present findings and hopeful about what is to come, Prof. Idemoto concludes: “Through future research and development, magnesium batteries could surpass lithium-ion batteries thanks to the former’s higher energy density.”

Indeed, substituted MgV systems could eventually lead to the much awaited next-generation batteries. Let us hope the highly anticipated alternative to lithium for our rechargeable battery needs will be realized soon!

Reference                        

Title of original paper: Electrochemical properties and crystal and electronic structure changes during charge/discharge of spinel type cathode-materials Mg1.33V1.67-xMnxO4 for magnesium secondary batteries

Journal:  Journal of Electroanalytical Chemistry

DOI: https://doi.org/10.1016/j.jelechem.2022.117064

Authors: Yasushi Idemoto1,2,3, Mina Takamatsu1, Chiaki Ishibashi1,2, Naoya Ishida1, Toshihiko Mandai3, Naoto Kitamura1,2

From financial transactions to the use of communication applications with artificial intelligence, our data is frequently transmitted from personal devices to the cloud. Handling this encrypted data in a secure but computationally efficient manner is becoming increasingly important in our data-driven society. Now, researchers from Tokyo University of Science develop a method that can perform computations with encrypted data faster and at a lower cost than conventional methods, while also improving security. 

Society 5.0 envisions a connected society driven by data shared between people and artificial intelligence devices connected via the Internet of Things (IoT). While this can be beneficial, it is also essential to protect the privacy of data for secure processing, transmission, and storage. Currently, homomorphic encryption and secret sharing are two methods used to compute sensitive data while preserving its privacy. 

Homomorphic encryption involves performing computations on encrypted data on a single server. While being a straightforward method, it is computationally intensive. On the other hand, secret sharing is a fast and computationally efficient way to handle encrypted data. In this method, the encrypted data or secret input is divided and distributed among multiple servers, each of which performs a computation such as multiplication with its piece of data. The results of these computations are then used to reconstruct the original data. In such a system, the secret can only be reconstructed if a certain number of pieces, known as the threshold, are available. Therefore, if the servers are managed by a single organization, there is a higher risk that the data could be compromised if the required number of pieces falls into the hands of an attacker. 

To improve data security, it is ideal for multiple companies to manage computing servers in a decentralized manner such that each server is operated independently. This approach reduces the likelihood of an attacker gaining access to the threshold number of pieces required to reconstruct a secret. However, implementing this system can be challenging in practice due to the need for a fast communication network to allow geographically separated servers to communicate with each other.

This leads to an important question: is there a way to maintain data integrity without having to rely on independent servers, and without incurring a high computational cost?

In a study published on November 14, 2022, in Volume 10 of IEEE Access, Professor Keiichi Iwamura and Assistant Professor Ahmad A. Aminuddin of Tokyo University of Science, Japan, introduced a new secure computation method where all the computations are performed on a single server without a significant computational cost. 

The system consists of a trusted third party (TTP), one computing server, four players who provide secret inputs to the server, and one player who restores the computation result. The TTP is a neutral organization that generates random numbers which are provided to the server (these are known as shares) and the players in certain combinations. These random numbers are used to encrypt the data. Each player then performs a computation with the random numbers and generates secret inputs which are sent to a server. The server then uses the shares and secret inputs, along with new values computed by the TTP, to perform a series of computations, the results of which are sent to a final player who reconstructs the computation result (Figure 1). This method allows for the decentralized computation of encrypted data while still performing the computation on a single server.

“In our proposed method, we realize the advantage of homomorphic encryption without the significant computational cost incurred by homomorphic encryption, thereby devising a way to securely handle data,” says Prof. Iwamura, who led the study and is the paper’s first author. Moreover, the method can also be modified such that the random numbers generated by the TTP can be stored securely by a Trusted Execution Environment (TEE), which is a secure area in a device’s hardware (processor). As the TEE takes over the role of the TPP during the subsequent computational process, it reduces the communication time and improves the speed at which the encrypted data is handled.

As our society becomes more reliant on the internet, we are moving towards storing data on the cloud rather than locally. To securely manage the growing amount of data, it is important to have a reliable and efficient method of handling it. “We realize a method that addresses all the drawbacks of the aforementioned methods, and it is possible to realize faster and more secure computations than conventional methods using secret sharing,” says Assistant Prof. Aminuddin. Here’s to better data privacy in the future, thanks to research like this!

 

Reference                     

DOI: https://doi.org/10.1109/ACCESS.2022.3222312

 

Researchers develop a super-hierarchical and explanatory analysis of magnetization reversal that could improve the reliability of spintronics devices.

The reliability of data storage and writing speed in advanced magnetic devices depend on drastic, complex changes in microscopic magnetic domain structures. However, it is extremely challenging to quantify these changes, limiting our understanding of magnetic phenomena. To tackle this, researchers from Japan developed, using machine learning and topology, an analysis method that quantifies the complexity of the magnetic domain structures, revealing hidden features of magnetization reversal that are hardly seen by human eyes. Spintronic devices and their operation are governed by the microstructures of magnetic domains. These magnetic domain structures undergo complex, drastic changes when an external magnetic field is applied to the system. The resulting fine structures are not reproducible, and it is challenging to quantify the complexity of magnetic domain structures. Our understanding of the magnetization reversal phenomenon is, thus, limited to crude visual inspections and qualitative methods, representing a severe bottleneck in material design. It has been difficult to even predict the stability and shape of the magnetic domain structures in Permalloy, which is a well-known material studied over a century.

Addressing this issue, a team of researchers headed by Professor Masato Kotsugi from Tokyo University of Science, Japan, recently developed an AI-based method for analyzing material functions in a more quantitative manner. In their work published in Science and Technology of Advanced Materials: Methods, the team used topological data analysis and developed a super-hierarchical and explanatory analysis method for magnetic reversal processes. In simple words, “super-hierarchical” means, according to the research team, the connection between micro and macro properties, which are usually treated as isolated but, in the big scheme, contribute jointly to the physical explanation.

The team quantified the complexity of the magnetic domain structures using persistent homology, a mathematical tool used in computational topology that measures topological features of data persisting across multiple scales. The team further visualized the magnetization reversal process in two-dimensional space using principal component analysis, a data analysis procedure that summarizes large datasets by smaller “summary indices,” facilitating better visualization and analysis. As Prof. Kotsugi explains, “The topological data analysis can be used for explaining the complex magnetization reversal process and evaluating the stability of the magnetic domain structure quantitatively.” The team discovered that slight changes in the structure invisible to the human eye that indicated a hidden feature dominating the metastable/stable reversal processes can be detected by this analysis. They also successfully determined the cause of the branching of the macroscopic reversal process in the original microscopic magnetic domain structure.

 

The novelty of this research lies in its ability to connect magnetic domain microstructures and macroscopic magnetic functions freely across hierarchies by applying the latest mathematical advances in topology and machine learning. This enables the detection of subtle microscopic changes and subsequent prediction of stable/metastable states in advance that was hitherto impossible.  “This super-hierarchical and explanatory analysis would improve the reliability of spintronics devices and our understanding of stochastic/deterministic magnetization reversal phenomena,” says Prof. Kotsugi.

Interestingly, the new algorithm, with its superior explanatory capability, can also be applied to study chaotic phenomenon as the butterfly effect. On the technological front, it could potentially improve the reliability of next-generation magnetic memory writing, and aid the development of new hardware for the next generation of devices.

Reference

DOI: https://doi.org/10.1080/27660400.2022.2149037

Title of original paper: Super-hierarchical and explanatory analysis of magnetization reversal process using topological data analysis

Journal: Science and Technology of Advanced Materials: Methods

Coercivity is a physical property of magnetic materials that has much importance in the optimization of energy efficiency in various applications, such as electric motors. However, it is difficult to analyze using the currently available theories, since they cannot account for the material’s defects and other types of inhomogeneities. To tackle this, scientists combined data science, materials informatics, and an extension of the Ginzburg–Landau model to explain how coercivity arises from microstructures in magnetic materials.

Soft magnetic materials, i.e., materials that can be easily magnetized and demagnetized, play an essential role in transformers, generators, and motors. The ability of a magnetic material to resist an external magnetic field without changing its magnetization is known as “coercivity,” a property closely linked to the energy loss. In applications such as electric cars, low-coercivity materials are highly desirable to achieve higher energy efficiency.

However, coercivity and other magnetic phenomena associated with energy losses in soft magnetic materials originate from very complex interactions. The usual macroscale analysis suffer from oversimplification of the material’s structure and they often need additional parameters to adjust the theory to the experiment. Thus far, although the tools and frameworks to analyze coercivity are widely available, they mostly do not consider directly the defects and boundaries in the material, which is fundamental to develop new applications.

Against this backdrop, a research team including Prof. Masato Kotsugi from Tokyo University of Science (TUS), Japan, recently developed a new approach to connect the microscale characteristics to a macroscopic physical property, coercivity, using a combination of data science, machine learning, and an extension of the GL model. This study, led by Dr. Alexandre Lira Foggiatto from TUS, was published in Communications Physics on 8 November 2022.

The team aimed to find a way to automate the coercivity analysis of magnetic materials while accounting for their microstructural characteristics. To this end, they first gathered data for both simulated and real magnetic materials in the form of microscopic images of their magnetic domains. The images, after preprocessing, were used as input for a machine learning technique called principal component analysis (PCA), which is commonly used to analyze large datasets. Through PCA, the team condensed the most relevant information (features) in these preprocessed images into a two-dimensional “feature space.”

This approach, combined with others machine learning techniques, such as artificial neural networks, allowed the researchers to visualize a realistic energy landscape of magnetization reversal in the material within the feature space. A careful comparison of the results for experimental and simulated images demonstrated the proposed methodology to be a convenient strategy for mapping the most important features of the material in a meaningful way. “Describing the energy landscape using machine learning showed good results for both experimental and simulated data. Both shared similar shapes as well as similar explanatory variables and correlations between them,” remarks Dr. Foggiatto.

Overall, this study showcases how materials informatics can be cleverly leveraged to not only automate but also clarify the physical origin of coercivity in soft magnetic materials. With any luck, it will help materials scientists and physicists derive new physical laws and models to go beyond the state-of-the-art models and frameworks. Moreover, the applications of this strategy go well beyond coercivity, as Dr. Foggiatto highlights: “Our method can be extended to other systems for analyzing properties such as temperature and strain/stress, as well as the dynamics of high-speed magnetization reversal processes.”

Interestingly, this is the second study Prof. Masato Kotsugi and his colleagues have published in relation to the extended Landau free-energy model they are developing. They hope that, in the near future, their functional analysis models will help achieve high efficiency in electric car motors, paving the way to more sustainable transportation.

A study of trigeminal nerves reveals how the intranasal administration of the novel glucagon-like peptide-2 can produce anti-depressant effects. 

In a recent study, Japanese scientists have developed a novel concept of a nose-to-brain system for the clinical application of neuropeptides. They developed a derivative of glucagon-like peptide-2 and found that when administered intranasally, it is efficiently delivered through the trigeminal nerve to the site of action and exhibits antidepressant-like effects. This is the first demonstration in the world that intranasally administered neuropeptides reach the brain (hippocampus and hypothalamus) via neurons.

Intranasal (in.) administration has been garnering increasing popularity as a non-invasive approach to deliver drugs directly to the brain. This approach involves the respiratory or olfactory epithelia of the nasal mucosa through which the drugs reach the central nervous system (CNS). Transport from the respiratory epithelium via the trigeminal nerve is considerably slower than transport from the olfactory epithelium route via the olfactory bulb (OB) or cerebrospinal fluid (CSF). However, only a small portion of the nasal mucosa in humans is made up of olfactory epithelium, propelling researchers to focus on improving in. drug delivery time through the predominant respiratory epithelium.

To facilitate this, a team of researchers including Professor Chikamasa Yamashita from Tokyo University of Science, Japan, developed a novel drug to test its uptake efficacy by the CNS.

To offer more insight, Prof. Yamashita states: “In a previous study, we combined functional sequences (namely, a membrane permeability-promoting sequence [CPP] and an endosomal escape-promoting sequence [PAS]) to glucagon-like peptide-2 (GLP-2), which is effective against treatment-resistant depression, so that it can be efficiently taken up by neurons. Using this, we aimed to construct a nose-to-brain system mediated by the trigeminal nerve in the respiratory epithelium”.

 

 

While studying the uptake of this novel PAS-CPP-GLP-2 by the CNS, the team noted that its anti-depressant effects via in. administration remained on par with intracerebroventricular (icv.) administration at identical doses. Therefore, Prof. Yamashita and his colleagues elucidated a nose-to-brain transfer mechanism to explain why intranasally administered GLP-2 derivatives show drug effects at the same dose as intracerebroventricularly administered GLP-2 derivatives. The team’s findings have been documented in a study made available online on 30 September 2022 in Volume 351 of the Journal of Controlled Release.

The team performed icv. and in. administration of PAS-CPP-GLP-2 into mice. The amount of drug transferred to the whole brain was quantified by enzyme-linked immunosorbent assay (ELISA). Surprisingly, the ELISA revealed that a much smaller amount of intranasally administered PAS-CPP-GLP-2 reached the brain than intracerebroventricularly administered PAS-CPP-GLP-2. However, both icv. and in. administration showed efficacy at the same dose. This is attributed to the fact that icv. administration introduces drugs to the place of origin of CSF (ventricle), causing them to diffuse into the CSF and spread through the brain. Since the CSF is present in the spaces outside the capillaries of the brain, the team saw that a large portion of PAS-CPP-GLP-2 was likely to stay here without being transported to its working sites of action. On the other hand, nasally administered GLP-2 derivatives were rapidly taken up by the trigeminal nerve of the respiratory epithelium, and efficiently reached the site of action while transiting neurons.

Prof. Yamashita explains: “This suggests that the peptide delivered to the site of action by icv. administration is present in large amounts in the brain but only in very small amounts, as it remains in the perivascular space. On the other hand, intranasally administered PAS-CPP-GLP-2, unlike icv. administration, may be transferred to the site of action without passing through the CSF or perivascular space”.

These results prompted the team to identify the central transfer drug delivery route following in. administration. This route involved the principal sensory trigeminal nucleus, followed by the trigeminal lemniscus of the trigeminal nerve, and led to the drug’s working sites. Finally, it was discovered that the migration of PAS-CPP-GLP-2 via nerve transit was the reason behind its pharmacological activity despite its low levels in the brain upon in. administration.

Prof. Yamashita explains, “This is the world’s first drug delivery system that allows intranasally administered peptides to be delivered to the central nervous system via nerve cells, delivering peptides to the site of action with the same efficiency as icv. administration.”

Speaking about the future applications of the team’s findings, Prof. Yamashita concludes: “Current data suggests the possibility of extending the use of this system from treating depression to delivering drugs in patients with Alzheimer’s disease. It is therefore expected to be applied to neurodegenerative diseases with high, unmet medical demand.”

Reference:

DOI: https://doi.org/10.1016/j.jconrel.2022.09.047

Title of original paper: Involvement of trigeminal axons in nose-to-brain delivery of glucagon-like peptide-2 derivative

Journal: Journal of Controlled Release

Researchers use an arc-wind tunnel to test the heat resistance of carbon fiber-reinforced ultra-high-temperature ceramic matrix composites. 

Carbon fiber-reinforced ultra-high-temperature ceramic (UHTC) matrix composites are extensively used in space shuttles and high-speed vehicles. However, these composites suffer from a lack of oxidation resistance. Recently, researchers from Japan tested the heat resistance of these composites at very high temperatures, providing insight into the modifications needed to prevent UHTC degradation. Their findings could have huge implications for the manufacture of space shuttle orbiters.

Carbon fiber-reinforced carbon (C/C) is a composite material made of carbon fiber reinforced in a matrix of glassy carbon or graphite. It is best known as the material used in hypersonic vehicles and space shuttle orbiters, which cruise at speeds greater than Mach 5. Since the 1970s, it has also been used in the brake system in Formula One racing cars. Even though C/C has excellent mechanical properties at high temperatures and inert atmospheres, it lacks oxidation resistance in these conditions, making its widespread use limited.

Researchers have found that ultra-high-temperature ceramics (UHTCs), which include transition metal carbides and diborides, show good oxidation resistance. In previous studies, zirconium-titanium (Zr-Ti) alloy infiltration has shown promising results for improving the heat resistance of carbon fiber-reinforced UHTC matrix composites (C/UHTCMCs). However, their use at high temperatures (>2000 °C) is not known.

Set against this backdrop, a group of researchers from Japan have evaluated the potential utility of Zr-Ti alloy-infiltrated C/UHTCMCs at temperatures above 2000 °C. Their study, led by Junior Associate Professor Ryo Inoue from Tokyo University of Science (TUS), was published in the Journal of Materials Science and made available online on October 27, 2022. The research team consisted of Mr. Noriatsu Koide and Assistant Professor Yutaro Arai from TUS, Professor Makoto Hasegawa from Yokohama National University, and Dr. Toshiyuki Nishimura from the National Institute for Materials Science.

Speaking of the motivation behind their study, “The research is an extension of the research and development of ceramics and ceramics-based composite materials. In recent years, we have received inquiries from several manufacturers of heavy industries regarding materials that can be used at temperatures above 2000 °C. We have also started to work with these manufacturers to develop new materials,” says Prof. Inoue.

The C/UHTCMC was manufactured using melt infiltration, which is the most cost-effective way to fabricate these materials. To study the applicability of this material, three types of C/UHTCMCs were fabricated with three different alloy compositions. The three alloy compositions used had varying atomic ratios of Zr:Ti. To characterize the heat resistance, the team used a method called arc-wind tunnel testing. This method involves exposing the material to extremely high enthalpy airflow inside a tunnel, similar to conditions that spacecrafts experience while re-entering the atmosphere.

The team found that the amount of Zr in the alloy had a strong effect on the degradation of the composite for all temperatures. This is owing to the thermodynamic preference for the oxidation of Zr-rich carbides compared to Ti-rich carbides. Further, the Zr and Ti oxides formed on the composite surface prevented further oxidation, and the oxide composition depended on the composition of the infiltrated alloys. Thermodynamic analysis revealed that the oxides formed on the composite surface were composed of ZrO2, ZrTiO4, and TiO2 solid solutions.

At temperatures above 2000 °C, the thickness and weight of the samples increased with the Zr content of the composites after the arc-wind tunnel tests. The team also observed that the melting point of the surface oxides increased as the Zr content increased. For temperatures above 2600 °C, the only oxides formed were liquid-phase, requiring a thermodynamic design of the matrix composition to prevent the recession of UHTC composites.

“We have successfully studied the degradation of C/UHTCMC at temperatures above 2000 °C using thermodynamic analysis. We have also shown that the matrix design needs modification to prevent the degradation of the composites. Our research has the potential to contribute to the realization of ultra-high-speed passenger aircraft, re-entry vehicle, and other hypersonic vehicles,” concludes Prof. Inoue.

These results could have important consequences in the production of advanced space shuttle orbiters and high-speed vehicles.

Reference:

DOI: https://doi.org/10.1007/s10853-022-07861-x

Title of original paper: Degradation of carbon fiber-reinforced ultra-high-temperature ceramic matrix composites at extremely high temperature using arc-wind tunnel tests

Journal: Journal of Materials Science

Alzheimer’s disease (AD), characterized by an accumulation of β-amyloid protein (Aβ) in brain tissue, is a leading cause of dementia. Researchers at Tokyo University of Science have previously reported on the oxytocin-induced reversal of impaired synaptic plasticity triggered by amyloid β peptide (25-35) (Aβ25-35). They now show that an oxytocin derivative with modifications to enhance brain perfusion can reverse Aβ25-35-induced cognitive impairment in mice.

The cognitive decline and memory loss observed in Alzheimer’s disease (AD) is attributed to the accumulation of β-amyloid protein (Aβ), which impairs neural function in the brain. Experimentation has shown that oxytocin, a peptide hormone primarily responsible for parturition, bonding, and lactation, also regulates cognitive behavior in the rodent central nervous system (CNS). This finding, along with the identification of oxytocin receptors in CNS neurons, has spurred interest in the potential role of oxytocin in reversing memory loss tied to cognitive disorders like AD.

However, peptides like oxytocin are characterized by weak blood-brain barrier permeability, and so can only by efficiently delivered to the brain via intracerebroventricular (ICV) administration. ICV, however, is an invasive technique which is impractical to implement clinically.

Delivering peptides to the CNS via intranasal (IN) administration is a viable clinical option. Prof. Chikamasa Yamashita at Tokyo University of Science recently patented a method to increase the efficiency of peptide delivery to the brain, by introducing cell-penetrating peptides (CPPs) and a penetration accelerating sequence (PAS) through structural modifications. Previous work had confirmed that both CPPs and the PAS benefit the nose-to-brain delivery pathway. Now, a group of researchers, led by Prof. Akiyoshi Saitoh and Prof. Jun-Ichiro Oka, leveraged this approach to prepare an oxytocin derivative: PAS-CPPs-oxytocin. Their findings were published online in Neuropsychopharmacology Reports on 19 September 2022.

“We have previously shown that oxytocin reverses amyloid β peptide (25-35) (Aβ25-35)-induced impairment of synaptic plasticity in rodents. We wanted to see if PAS-CPPs-oxytocin could be delivered more efficiently to the mouse brain for clinical application, and if it improved cognitive functional behavior in mice,” states Prof. Oka.

 

The group first developed an Aβ25-35 peptide-induced amnesia model by supplying Aβ25-35 to the mouse brain using ICV delivery. During the course of the study, the spatial working and spatial reference memories of these mice were evaluated using the Y-maze and Morris water maze (MWM) tests. After confirming that memory was affected in Aβ25-35-impaired mice, PAS-CPPs-oxytocin and native oxytocin were administered using the IN and ICV routes respectively, to see if learning and memory improved in the treated mice. Finally, the distribution of the IN-administered oxytocin derivative in brain tissue was profiled by imaging of a fluorescent-tagged oxytocin derivative.

The results of this study were quite promising! The tagged PAS-CPPs-oxytocin showed distribution throughout the mouse brain following its IN administration. While the ICV administration of native oxytocin improved test outcomes in both the Y-maze and MWM tests, the IN administered PAS-CPPs-oxytocin yielded memory improving effects in the Y-maze test. Hailing the team’s discovery, Prof. Oka says, “My team is the first to show that the oxytocin derivative can improve the Aβ25-35-induced memory impairment in mice. This suggests that oxytocin may help reduce the cognitive decline we see in Alzheimer’s disease.”

Why are these findings clinically useful? Prof. Oka explains the broader implications of their work, “The oxytocin derivative enters the brain more efficiently. Furthermore, since IN delivery is a non-invasive procedure, this modified version of the hormone could potentially be a clinically viable treatment for Alzheimer’s disease.”

Researchers effectively tune the parameters of a perturbation method to preserve chaos in the Bernoulli shift map output

The Bernoulli shift map is a well-known chaotic map in chaos theory. For a binary system, however, the output is not chaotic and converges to zero instead. One way to prevent this is by perturbing the state space of the map. In a new study, researchers explore one such perturbation method to obtain non-converging outputs with long periods and analyze these periods using modular arithmetic, obtaining a complete list of parameter values for optimal perturbations.

Is it possible for a deterministic system to be unpredictable? Although counter-intuitive, the answer is yes. Such systems are called “chaotic systems,” which are characterized by sensitive dependence on initial conditions and long-term unpredictability. The behavior of such systems is often described using what is known as a “chaotic map.” Chaotic maps finds applications in areas such as algorithm design, data analysis, and numerical simulations.

One well-known example of a chaotic map is the Bernoulli shift map. In practical applications of the Bernoulli shift map, the outputs are often required to have long periods. Strangely enough, however, when the Bernoulli shift map is implemented in a binary system, such as a digital computer, the output sequence is no longer chaotic and instead converges to zero!

To this end, perturbation methods are an effective strategy where a disturbance is applied to the state of the Bernoulli shift map to prevent its output from converging. However, the choice of parameters for obtaining suitable perturbations lacks a theoretical underpinning.

In a recent study made available online on October 21, 2022 and published in Volume 165, Part 1 of the journal Chaos, Solitons & Fractals on December 2022, Professor Tohru Ikeguchi from the Tokyo University of Science in association with Dr. Noriyoshi Sukegawa from University of Tsukuba, both in Japan, have now addressed this issue, laying the theoretical foundations for effective parameter tuning. “While numerical simulations can tell us which values of the parameters can prevent convergence, there is no theoretical background for choosing these values. In this paper, we aimed to investigate the theoretical support behind this choice,” explains Prof. Ikeguchi.

Accordingly, the researchers made use of modular arithmetic to tune a dominant parameter in the perturbation method. In particular, they defined the best value for the parameter, which depended on the bit length specified in implementations. The team further analyzed the output period for which the parameter had the best value. Their findings showed that the resulting periods came close to the trivial theoretical upper bounds. Based on this, the researchers obtained a complete list of the best parameter values for a successful implementation of the Bernoulli shift map.

Additionally, an interesting consequence of their investigation was its relation to Artin’s conjecture on primitive roots, an open question in number theory. The researchers suggested that, provided Artin’s conjecture were true, their approach would be theoretically guaranteed to be effective for any bit length.

Overall, the theoretical foundations put forth in this research are of paramount importance in the practical applications of chaotic maps in general. “A notable advantage of our approach is that it provides a theoretical support to the choice of best parameters. In addition, our analysis can also be partially applied to other chaotic maps, such as the tent map and the logistic map,” highlights Dr. Sukegawa.

With distinct advantages, such as simplicity and ease of implementation, the Bernoulli shift maps is highly desirable in several practical applications. And, as this study shows, sometimes chaos is preferable to order!

 

 

Inefficient fluid machinery used in the energy and transportation sector are responsible for greenhouse gas emissions and the resulting global warming. To improve efficiency, it is necessary to characterize and reduce flow separation on curved surfaces. To this end, researchers from Japan have now developed a flexible, thin film microelectromechanical system-based airflow sensor that can be utilized to measure complex, three-dimensional flow separation in curved walls for high-speed airflows.

The energy and transportation sector often make use of different kinds of fluid machinery, including pumps, turbines, and aircraft engines, all of which entail a high carbon footprint. This result mainly from inefficiencies in the fluid machinery caused by flow separation around curved surfaces, which are typically quite complex in nature.

To improve the efficiency of fluid machinery, one, therefore, needs to characterize near-wall flow on the curved surface to suppress this flow separation. The challenge in accomplishing this is multifold. First, conventional flow sensors are not flexible enough to fit into the curved walls of fluid machinery. Second, existing flexible sensors suitable for curved surfaces cannot detect the fluid angle (direction of flow). Moreover, these sensors are limited to only detecting flow separation at speeds less than 30 m/s.

In a new study, Prof. Masahiro Motosuke from the Tokyo University of Science (TUS) in Japan and his colleagues, Mr. Koichi Murakami, Mr. Daiki Shiraishi and Dr. Yoshiyasu Ichikawa from TUS, in collaboration with Mitsubishi Heavy Industries, Japan, and Iwate University, Japan, took on this challenge. As Prof. Motosuke states, “Sensing the shear stress and its direction on curved surfaces, where flow separation easily occurs, has been difficult to achieve in particular without using a novel technique.” Their work was published in Volume 13 Issue 8 of Micromachines on 12 August 2022.

The team, in their study, developed a polyimide thin film-based flexible flow sensor that can be easily installed on curved surfaces without disturbing the surrounding airflow, a key requirement for efficient measurement. To enable this, the sensor was based on microelectromechanical system (MEMS) technology. Moreover, the novel design allowed multiple sensors to be integrated for simultaneous measurement of the wall shear stress and flow angle on the surface of the wall.

To measure the shear stress on the walls, the sensor measured the heat loss from a micro-heater, while the flow angle was estimated using an array of six temperature sensors around the heater that facilitated multidirectional measurement. The team conducted numerical simulations of the air flow to optimize the geometry of the heaters and sensor arrays. Using a high-speed airflow tunnel as the testing environment, the team achieved effective flow measurements with wide range of airflow speeds from (30 – 170) m/s. The developed sensor demonstrated both high flexibility and scalability. “The circuits around the sensor can be pulled out using a flexible printed circuit board and installed in a different location, so that only a thin sheet is attached to the measurement target, minimizing the effect on the surrounding flow,” elaborates Prof. Motosuke.

The team estimated the heater output to vary as the one-third power of the wall shear stress, while the sensor output comparing the temperature difference between two oppositely placed sensors demonstrated a peculiar sinusoidal oscillation as the flow angle was changed.

The developed sensor has the potential for a wide range of applications in industrial-scale fluid machinery that often involve complex flow separation around three-dimensional surfaces. Moreover, the working principle used to develop this sensor can be extended beyond high-speed subsonic airflows.

“Although this sensor is designed for fast airflows, we are currently developing sensors that measure liquid flow and can be attached to humans based on the same principle. Such thin and flexible flow sensors can open up many possibilities,” highlights Prof. Motosuke.

Taken together, the novel MEMS sensor could be a game-changer in the development of efficient fluid machineries with reduced detrimental effects on our environment.

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Reference

DOI: https://doi.org/10.3390/mi13081299

Title of original paper: Development of a Flexible MEMS Sensor for Subsonic Flow

Journal: Micromachines

About The Tokyo University of Science

Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan’s development in science through inculcating the love for science in researchers, technicians, and educators.

With a mission of “Creating science and technology for the harmonious development of nature, human beings, and society”, TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today’s most vital fields. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel Prize winner and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.

Website: https://www.tus.ac.jp/en/mediarelations/

About Professor Masahiro Motosuke from Tokyo University of Science

Masahiro Motosuke is a Professor in the Department of Mechanical Engineering at the Tokyo University of Science (TUS), Japan. He earned his PhD in Engineering from Keio University, Japan, and has held positions at the Japan Society for the Promotion of Science and the Technical University of Denmark. His research into thermofluidics and thermofluidics-based sensors has resulted in multiple journal articles, conference papers and book chapters. Prof. Motsuke has received multiple awards for his research from professional organizations such as the Heat Transfer Society of Japan. For more information, visit: https://www.rs.tus.ac.jp/motlab/en/index.html