Science & Nanotech Archives

Progress towards potassium-ion batteries

Potassium-ion batteries could have a higher energy density than sodium-ion batteries. This is important for large-scale energy storage such as for renewable energy.

In a review published in Science and Technology of Advanced Materials, researchers have surveyed the battery technologies that will be vital for a sustainable green transition. Eunho Lim and colleagues at Korea’s Dongguk University discuss recent advances and challenges, and point towards the research needed to develop an alternative to lithium-ion batteries.

Although lithium-ion batteries have been invaluable in the electronics revolution—powering laptops, smartphones, electronic vehicles, and much more—their expanding use faces a critical challenge. Lithium is not a common resource. Increasing demand has turned it into a high-value, strategic resource, and the green transition is expected to increase demand further still.

One alternative is to develop battery technologies based around a more common material. Sodium-ion batteries are an option, and the technology is nearly ready for commercialization. But potassium-ion batteries would be even better, since they could have a higher energy density, which is especially important for large-scale energy storage, such as for renewable energy.

“Potassium-ion batteries are emerging as a viable alternative due to the abundance and cost-effectiveness of potassium, but realizing their potential requires the development of advanced anode materials tailored to the unique properties of potassium ions,” explains Lim.

Professor Lim’s review addresses the research needed to realize the potential of potassium-ion batteries. The paper systematically examines the strengths and weaknesses of different anode materials and the electrochemical mechanisms each would rely on. The paper also outlines strategies that could overcome the weaknesses of each approach, as well as the trade-offs between performance and stability. One important point that emerges is the interaction of electrochemical parameters and physical structures in determining the potassium-ion batteries’ capacity and longevity. Based on this overview, the team highlights paths for future research to advance potassium-ion battery technology.

Lim plans to build on this groundwork, aiming to design new materials that can deliver the promise of potassium-ion batteries while working around their limitations. “My research will focus on the development of cost-effective, high-performance, and safe anode materials for potassium-ion batteries,” he says. He also plans to use advanced characterization techniques to investigate some of the fundamental phenomena that happen in the battery materials. “Understanding these mechanisms will be crucial for optimizing material design and electrode architecture.”

“Ultimately,” he says, “my goal is to contribute to the commercialization of potassium-ion batteries by developing materials that can rival or exceed the performance of current lithium-ion battery anodes.”

Further information
Eunho Lim
Dongguk University, Republic of Korea
eunholim@dgu.ac.kr

Paper: https://doi.org/10.1080/14686996.2025.2518746

About Science and Technology of Advanced Materials (STAM)

Open access journal STAM publishes outstanding research articles across all aspects of materials science, including functional and structural materials, theoretical analyses, and properties of materials. https://www.tandfonline.com/STAM

Dr. Kazuya Saito
STAM Publishing Director
Email: SAITO.Kazuya@nims.go.jp

Press release distributed by Asia Research News for Science and Technology of Advanced Materials.

New method to blend functions for soft electronics

Soft electronics are an exciting and innovative class of technology that brings together bendable, stretchable semiconducting materials for applications in areas ranging from fashion to healthcare. Researchers have recently developed a new technique to adjust the properties of liquids that could be used to create soft electronics.

Researchers successfully blended various combinations and proportions of three solvent-free alkyl-π room-temperature liquids that fluoresced red, green, or blue light, with no color variation within the material showing that the alkyl-π liquids had merged evenly. Credit: Image is reproducible by CC-BY license. Please credit the STAM Journal.

Room-temperature alkylated-π molecular liquids (known as alkyl-π liquids) are an exciting new material that holds great promise for soft electronics. However, one challenge with these fascinating liquids lies in fine-tuning their physical, chemical, and electronic properties – including their ability to interact with light – to achieve the desired functionality.

A new study, led by researchers from the National Institute of Materials Science (NIMS) in Tsukuba, Japan, has explored a strategy for blending together alkyl-π liquids to merge their functions homogeneously. The researchers used photoluminescent color tuning to demonstrate how well the process has worked. Their findings have been published in the journal Science and Technology of Advanced Materials.

Previous efforts to control the properties of alkyl-π liquids have taken one of two approaches. The first involves incorporating small amounts of other molecules, such as dyes, into the liquid. “When modulating function by adding solid dopants, the dopant molecules have poor solubility, leading to insoluble aggregates and inconsistencies in properties such as luminescent color,” says Dr. Takashi Nakanishi of the Research Center for Materials Nanoarchitectonics at NIMS.

The second approach involves chemically modifying the alkyl-π liquids. While this can achieve a uniform result, designing and synthesising entirely new molecules is difficult and less time- and cost-effective.

In the new study, researchers synthesised three solvent-free alkyl-π room-temperature liquids that fluoresced red, green, or blue light, and then they blended the liquids together in varying proportions. They successfully created a range of homogeneous liquid blends of colors with no color variation within the material, showing that the alkyl-π liquids had merged evenly.

The team also assessed how well the two liquids had mixed by changing the temperature and studying how the flow of the mixed liquids changed over time at different temperatures. This approach further confirmed that the liquids were successfully blended together.

“The liquid–liquid blending method implemented in this study for alkyl-π liquids facilitates the production of low-volatility, ink-like materials that exhibit a diverse spectrum of uniform luminescent colors, devoid of any color unevenness,” Dr. Nakanishi says. “This means it will be possible to apply or coat the desired function with simple operations such as painting, sandwiching, or soaking the liquid materials wherever needed.”

The research opens the path to blending alkyl-π liquids to vary other functions, such as photoconductivity, charge retention, or gas sensing.

Further information
Takashi Nakanishi
National Institute for Materials Science (NIMS)
nakanishi.takashi@nims.go.jp

Paper: https://doi.org/10.1080/14686996.2025.2515007

About Science and Technology of Advanced Materials (STAM)

Dr. Kazuya Saito
STAM Publishing Director
Email: SAITO.Kazuya@nims.go.jp

Press release distributed by Asia Research News for Science and Technology of Advanced Materials.

New Database of Materials Accelerates Electronics Innovation

In a collaboration between Murata Manufacturing Co., Ltd., and the National Institute for Materials Science (NIMS), researchers have built a comprehensive new database of dielectric material properties curated from thousands of scientific papers. The study, published in Science and Technology of Advanced Materials: Methods, also offers insights that could accelerate the development of next-generation electronic materials and energy storage technologies.

Large-scale dielectric materials database built with the open database project “Starrydata” and generation of a materials map using machine learning-based data visualization.

AI-driven materials discovery has great potential to accelerate innovation, but it relies on large and diverse datasets. The lack of such data remains a major bottleneck in the field.ã€€To address this challenge, researchers used the Starrydata2 web system to collect experimental data on over 20,000 material samples from more than 5,000 publications. The NIMS team has developed a standardized approach to extract data from graphs, including temperature-dependent properties, which are often omitted in other databases. “What makes our work unique is the meticulous process of manually tracing graphs and correcting inconsistencies in original research papers to create a clean, high-quality dataset,” the researchers said.

The database focuses on a specific class of materials necessary for electronics and is the largest ever reported, significantly surpassing previous collections. With this wealth of information, the team used machine learning (ML) to predict the properties of materials and how they would behave electronically.

Although the ML models were effective, they initially worked as “black boxes” — the researchers couldn’t see why the models made their predictions. To understand the context for predictions, the team created visual maps of the data, making complex information easier to interpret. They used clustering algorithms to automatically group similar materials. This analysis helped them spot patterns in how a material’s composition affects its properties. The team was also able to categorize the materials into distinct groups, including seven important ferroelectric families, providing a global landscape of the entire compositional space.

The team took a closer look at ABO3 Perovskites, a family of materials which are essential components in everyday electronic devices and energy storage technologies, such as smartphones, computers, and solar cells. Their visualizations showed a simple link between the basic structure of the material and its dielectric permittivity, which coincides with previous academic knowledge.

This work advances our understanding of dielectric materials and moves research beyond traditional trial-and-error approaches. “By curating the largest dataset as ever and combining various machine-learning methods, we succeeded in visualizing the landscape of the entire compositional space in unprecedented detail,” the team explained.

The NIMS team plans to make the dataset publicly available next year, allowing scientists worldwide to leverage it for new discoveries. Future work may involve expanding data collection to include manufacturing methods and processing conditions, allowing for more comprehensive predictions that would link production processes to material properties.

“We hope that this foundational work will inspire similar data collection initiatives and new approaches to materials discovery, ultimately leading to smarter materials development pathways that benefit society through improved electronic technologies,” the researchers concluded.

Further information
Tomoki Murata
Murata Manufacturing Co., Ltd.
Email: tomoki.murata258@murata.com

Yukari Katsura
National Institute for Materials Science
Email: KATSURA.Yukari@nims.go.jp

Paper: https://doi.org/10.1080/27660400.2025.2485018

About Science and Technology of Advanced Materials: Methods (STAM-M)

STAM Methods is an open access sister journal of Science and Technology of Advanced Materials (STAM), and focuses on emergent methods and tools for improving and/or accelerating materials developments, such as methodology, apparatus, instrumentation, modeling, high-through put data collection, materials/process informatics, databases, and programming. https://www.tandfonline.com/STAM-M

Dr Kazuya Saito
STAM Methods Publishing Director
Email: SAITO.Kazuya@nims.go.jp

Press release distributed by Asia Research News for Science and Technology of Advanced Materials.

High-brilliance radiation quickly finds the best composition for half-metal alloys

Half-metals are unique magnetic compounds that have been attracting interest in the developments of mass-storage technologies. Some of the materials in the family of Heusler alloys were predicted to have a half-metallic nature, but their half-metallic electronic structure varies with their composition ratio and atomic ordered structure.

The composition-spread thin film of Co75–xMnxSi25 on substrates from the side (left) and top (right). The composition x changes continuously along the film. Copyright: Yuya Sakuraba

One property, spin polarization, is fundamental to the material’s half-metallic properties. Spin polarization ratio is a physical property that indicates how polarized the number of electrons with spin in the up and down directions is. Because spin polarization is influenced by the elemental composition of the Heusler alloy, it’s important to characterise and optimise the atomic composition of Heusler alloys to achieve the highest spin polarization. But current methods for determining the spin polarization of half-metals are either time-consuming or only provide an indirect measure.

Now, a team of researchers from Japan, led by Professor Yuya Sakuraba of the National Institute for Materials Science in Tsukuba, have developed a method to quickly determine the spin polarization of Heusler alloys, using high-brilliance synchrotron radiation. With this approach, they were able to rapidly identify the ideal composition of elements to achieve the highest spin polarisation. Their findings have been published in the journal Science and Technology of Advanced Materials.

The Heusler alloy in question was a mix of cobalt, manganese and silicon. The research team created composition-spread thin films of the alloy, varying the proportion of manganese from 10 to 40 percent along the length of the sample.

They then bombarded the film with extremely bright radiation at the NanoTerasu synchrotron facility which opened in April 2024. This bombardment revealed that the ideal composition to maximise spin polarization in the alloy was for manganese to make up 27 percent of the alloy’s atomic weight.

This experiment marked the first successful application of photoelectron spectroscopy at NanoTerasu facility. The experiment was done in a single day – much faster than existing methods of determining the spin polarization.

“The findings address a critical challenge in materials science by drastically reducing the time required to evaluate and optimize spin polarization in half-metallic materials,” Professor Sakuraba says.

This study paves the way for wider application of the technique not just to half-metallic materials but a variety of other magnetic and spintronic materials. “The rapid, efficient method presented in this study could significantly impact the development of next-generation technologies, such as high-capacity hard disk drives and advanced spintronic devices,” says Professor Sakuraba.

Further information
Yuya Sakuraba
National Institute for Materials Science (NIMS)
SAKURABA.Yuya@nims.go.jp

Paper: https://doi.org/10.1080/14686996.2024.2439781

About Science and Technology of Advanced Materials (STAM)

Dr Yasufumi Nakamichi
STAM Publishing Director
Email: NAKAMICHI.Yasufumi@nims.go.jp

Press release distributed by Asia Research News for Science and Technology of Advanced Materials.

Machine learning used to optimise polymer production

By identifying the ideal manufacturing conditions, machine learning reduces the need for expensive and time-consuming experimentation.

Polymers, such as plastics, are essential in many aspects of life and industry, from packaging and cars to medical devices and optic fibres. Their value comes from diverse properties that are largely determined by their monomers – the single chemical units – that make up a polymer. Unfortunately, it can be challenging to control the chemical behaviour of monomers during manufacture to achieve a desired outcome.

The flow synthesis reactor with two bottles containing a monomer, initiator and solvent mixed using a micromixer. The synthesis is controlled with AI-based design of experimental conditions such as the temperature and a flow rate.

Now, a team of researchers led by Professor Mikiya Fujii of the Nara Institute of Science and Technology in Japan have used machine learning to mathematically model the polymerization process and reduce the need for time-consuming and expensive experimentation. Their results have been published in the journal Science and Technology of Advanced Materials: Methods.

Machine learning algorithms need data, so the researchers designed a polymerization process that would quickly and efficiently generate experimental data to feed into the mathematical model. The target molecule was a styrene-methyl methacrylate co-polymer, which was made by mixing styrene and methyl methacrylate monomers, both already dissolved in a solvent with an added initiator substance, then heating them in a water bath.

The team also used a method called flow synthesis, in which the two monomer solutions are mixed and heated in a constant flow. This allows for better mixing, more efficient heating, and more precise control of heating time and flow rate, which makes it ideal for use with machine learning.

The modelling evaluated the effect of five key variables in the polymerization process: the concentration of the initiator, the ratio of solvent to monomer, the proportion of styrene, the temperature of the reaction, and the time spent in the water bath. The goal was to have an end product with as close to 50% styrene as possible.

Once enough experimental data was available, the machine learning process took only five cycles of calculation to achieve the ideal proportion of styrene to methyl methacrylate. The results showed that the key was a lower temperature and longer time in the water bath, as well as lowering the relative concentration of the monomer in the solvent. The researchers were surprised to discover that the solvent concentration was just as important as the proportion of monomers going into the mix.

“Our results demonstrate that machine learning not only can explicitly reveal what humans may have implicitly taken for granted but can also provide new insights that weren’t recognized before,” Professor Mikiya Fujii says. “The use of machine learning in chemistry could open the door for smarter, greener manufacturing processes with reduced waste and energy consumption.”

Further information
Mikiya Fujii
Nara Institute of Science and Technology
Email: fujii.mikiya@ms.naist.jp

Paper: https://doi.org/10.1080/27660400.2024.2425178

About Science and Technology of Advanced Materials: Methods (STAM-M)

Dr Yasufumi Nakamichi
STAM Publishing Director
Email: NAKAMICHI.Yasufumi@nims.go.jp

Press release distributed by Asia Research News for Science and Technology of Advanced Materials.

Machine learning can predict the mechanical properties of polymers

Polymers such as polypropylene are fundamental materials in the modern world, found in everything from computers to cars. Because of their ubiquity, it’s vital that materials scientists know exactly how each newly developed polymer will perform under different preparation conditions. Thanks to a new study, which was published in Science and Technology of Advanced Materials, scientists can now use machine learning to determine what to expect from a new polymer.

Machine learning predicts the material properties of new polymers with high accuracy, providing a nondestructive alternative to conventional polymer testing methods.

Predicting the mechanical properties of new polymers, such as their tensile strength or flexibility, usually involves putting them through destructive and costly physical tests. However, a team of researchers from Japan, led by Dr. Ryo Tamura, Dr. Kenji Nagata, and Dr. Takashi Nakanishi from the National Institute for Materials Science in Tsukuba, showed that machine learning can predict the material properties of polymers. They developed the method on a group of polymers called homo-polypropylenes, using X-ray diffraction patterns of the polymers under different preparation conditions to provide detailed information about their complex structure and features.

“Machine learning can be applied to data from existing materials to predict the properties of unknown materials,” Drs. Tamura, Nagata, and Nakanishi explain. “However, to achieve accurate predictions, it’s essential to use descriptors that correctly represent the features of these materials.”

Thermoplastic crystalline polymers, such as polypropylene, have a particularly complex structure that is further altered during the process of molding them into the shape of the end product. It was, therefore, important for the team to adequately capture the details of the polymers’ structure with X-ray diffraction and to ensure that the machine learning algorithm could identify the most important descriptors in that data.

The new method accurately captured the structural changes of commonly used plastic Polypropylene during the molding process into the end product.

To that end, they analysed two datasets using a tool called Bayesian spectral deconvolution, which can extract patterns from complex data. The first dataset was X-ray diffraction data from 15 types of homo-polypropylenes subjected to a range of temperatures, and the second was data from four types of homo-polypropylenes that underwent injection molding. The mechanical properties analysed included stiffness, elasticity, the temperature at which the material starts to deform, and how much it would stretch before breaking.

The team found that the machine learning analysis accurately linked features in the X-ray diffraction imagery with specific material properties of the polymers. Some of the mechanical properties were easier to predict from the X-ray diffraction data, while others, such as the stretching break point, were more challenging.

“We believe our study, which describes the procedure used to provide a highly accurate machine learning prediction model using only the X-ray diffraction results of polymer materials, will offer a nondestructive alternative to conventional polymer testing methods,” the NIMS researchers say.

The team also suggested that their Bayesian spectral deconvolution approach could be applied to other data, such as X-ray photoelectron spectroscopy, and used to understand the properties of other materials, both inorganic and organic.

“It could become a test case for future data-driven approaches to polymer design and science,” the NIMS team says.

Further information
Ryo Tamura
National Institute for Materials Science (NIMS)
tamura.ryo@nims.go.jp

Kenji Nagata
National Institute for Materials Science (NIMS)
nagata.kenji@nims.go.jp

Takashi Nakanishi
National Institute for Materials Science (NIMS)
nakanishi.takashi@nims.go.jp

Paper: https://doi.org/10.1080/14686996.2024.2388016

About Science and Technology of Advanced Materials (STAM)

Dr Yasufumi Nakamichi
STAM Publishing Director
Email: NAKAMICHI.Yasufumi@nims.go.jp

Press release distributed by Asia Research News for Science and Technology of Advanced Materials.

New Book Reveals First Ever Neuroscience-Powered GenAI Tools Global Brands Are Using to Win Consumers

Written by Sensori.Ai CEO Dr. A.K. Pradeep, “Neuro AI: How to Win the Minds of Consumers Using Neuroscience-Powered Gen AI” leverages his success working with the biggest global brands to capture the nonconscious mind of the consumer, where 95% of purchase decisions are made

Sensori.Ai, the only company blending neuroscience with generative AI (GenAI) to transform product innovation, consumer engagement and experiences, today announced the launch of the latest book from CEO and founder Dr. A.K. Pradeep and experts at Sensori AI. “Neuro AI: How to Win the Minds of Consumers Using Neuroscience-Powered Gen AI” (Neuro AI) is prescriptive for practitioners of innovation, messaging and marketing and explores how to gain a competitive advantage in the marketplace by transforming GenAI from ‘generic AI’ to truly ‘generative AI.’ Through the use of real world case studies and use cases, “NeuroAI” reveals practical applications for appealing to the nonconscious mind, where 95% of decisions are made. Published by Wiley and co-authored by Dr. Anirudh Acharya, Dr. Rajat Chakravarty and Ratnakar Dev, the book is now available via Amazon and all major booksellers.

Since its founding in late 2022, Sensori.Ai has developed a number of solutions designed to embed neuroscience principles and breakthrough algorithms into GenAI to create unique consumer understanding, product innovation and sensory design platforms. The company pioneers the use of unique nonconscious data (data consumed by the decision making nonconscious mind of the consumer) to create its breakthroughs. For example, Sensori.Ai created the first neuroAI-based algorithm to aid in the creation of fragrance, flavor and music. To date, the company has helped develop over 20 new products, over 100 campaigns and packaging designs and repositioning for most of the recognized global brands in the CPG, FMCG, and retail spaces. “Neuro AI” provides a primer on the neuroscientific principles and research backing these solutions with a transparent look at how these algorithms are created and the rules they follow.

“Make no mistake: GenAI is revolutionizing every part of our lives today. Apple Intelligence is just the beginning of this revolution. ‘Neuro AI’ shows how memory structures and translational neuroscience puts humanity back at the center of GenAI-based design,” said Dr. Pradeep. “For instance, every product maker wants their consumer to desire their product. Algorithmically embedding the neuroscience of desire in GenAI creates exciting product designs and market messaging that speak deeply to the nonconscious mind of the consumer, which is the powerful, driving force behind purchasing decisions.”

95% of desires and decisions are rooted in the unconscious mind. By leveraging its architecture and selectively utilizing the data streams that feed it, GenAI algorithms can be trained to operate in vastly more efficient and creatively effective ways. This return to an anthropocentric GenAI rooted in neuroscience-hence the term neuroAI-is both the thesis of the book and the focus for Sensori.Ai. GenAI trained on key neuroscientific principles and using proprietary algorithms can quickly develop and refine product concepts, packaging, pricing, messaging and other key features to appeal to consumers in a deep, personal and brand-guided manner.

Sensori.Ai’s solutions focus on using neuroAI for four specific business needs: consumer understanding (including neurographic profiles, archetypes, price sensitivity, personality and proclivities); product and brand innovation (including product features, product patent and IP protection, product pricing, product promotion and product volumetrics prediction); sensorial innovation (including fragrance design, flavor design, music design and packaging visual design); and desire creation (including advertising, messaging, desire scoring, desire-based message redesign and desire-based PDP redesign). “NeuroAI” details all of the science and methods in these four areas, which are also contained in the Sensori.Ai platform and solution suite.

“AI is everywhere now, but there’s precious little practical information about it that’s available to people,” said Priya Nair, Business Group President, Beauty & Wellbeing at Unilever. “NeuroAI” changes that. The big add is that the book also covers consumer neuroscience. It’s a book that everyone who wants and needs to get a good grasp of this stunning technology, and how the mind works, should have.”

“Neuro AI” builds on Dr. Pradeep’s decades-long experience in the field of consumer neuroscience, detailed in the titles “AI for Marketing and Product Innovation” (2019) and “The Buying Brain” (2010). Dr. Pradeep, a twice successful Silicon Valley entrepreneur, holds over 90 patents and previously founded NeuroFocus (acquired by Nielsen) to provide neurological testing for consumer research. He also founded BoardVantage (acquired by Nasdaq), to provide SaaS board governance software for boards of directors and executives.

To learn more about Sensori.Ai and “Neuro AI: How to Win the Minds of Consumers Using Neuroscience-Powered Gen AI,” please visit sensori.ai. The book can be purchased through Amazon in the following regions:

About Sensori.Ai
Sensori.Ai is the only company in the world blending generative AI with mined data from the human nonconscious to develop new ways for brands to reach consumers and appeal to their deepest desires. Founded in 2022 by serial entrepreneur and consumer neuroscience expert Dr. A.K. Pradeep, the company works with most of the recognized global brands in the CPG, FMCG, and retail spaces. For more information, follow Sensori.Ai on LinkedIn, X and YouTube.

Related Video: https://youtu.be/3kU4ZiPD2TY

PR Contact
Songue PR for Sensori.Ai
sensoriai@songuepr.com

SOURCE: Sensori.Ai

Dual-action therapy shows promise against aggressive oral cancer

A new treatment combining tiny iron particles and nitric oxide gas shows promise in targeting oral cancer cells for destruction.

In a new study, scientists at Shanghai Jiaotong University School of Medicine found a promising new way to treat a type of oral cancer known as oral squamous cell carcinoma. The method specifically targets the cancer cells through a combination of nitric oxide gas therapy and nanocatalytic therapy, killing them more effectively and with fewer side effects. The study was published in Science and Technology of Advanced Materials.

Illustration of nitric oxide-releasing iron atoms being created and used to treat oral squamous cell carcinoma.

Oral squamous cell carcinoma is a very aggressive cancer that tends to spread quickly and to reappear after treatment. Traditional treatments, such as surgery, chemotherapy, and radiation, often have serious side effects, including trouble speaking or eating and painful conditions like mouth sores and dry mouth. Researchers are trying to develop better treatments that wouldn’t have such harsh side effects. One approach relies partly on nanocatalysts, tiny particles measuring 1 to 100 nanometers in size which are used to accelerate chemical reactions.

“We created tiny iron particles composed of individual iron atoms, designed to interact with hydrogen peroxide—a substance found in elevated levels inside tumor cells,” explains Professor Ping Xiong, who led the study. “These atoms use a chemical process called the Fenton reaction, where the iron atoms act as catalysts to convert hydrogen peroxide into highly toxic hydroxyl radicals.”

Hydroxyl radicals are extremely reactive and cause intense oxidative stress by damaging cellular components such as DNA and proteins. The iron particles also carried molecules that released nitric oxide gas when activated by near-infrared laser light. The nitric oxide gas amplified the effect of the hydroxyls by triggering apoptosis, a controlled form of cell death crucial for removing damaged cells.

In animal model experiments, a single dose of treatment combined with a laser pulse was found to suppress tumors by around 85.5%, suggesting that the treatment is very effective. “This treatment is highly specific to cancer cells, reducing damage to healthy tissues and minimizing side effects, which makes it both more efficient and better tolerated by the body,” says Yuting Xie, one of the study’s authors.

One major difficulty was ensuring that the infrared laser targeted only the tumor, especially in hard-to-reach areas like the sides and bottom of the tongue. The team is exploring ways to improve the precision of the laser treatment to avoid inadvertent damage to the surrounding healthy tissues, which could result in unwanted side effects. One approach being investigated involves developing nanocatalysts that would be administered through an intravenous injection, which could enhance the targeting by interacting with the laser in a more controlled manner.

The researchers are also working on strategies to prevent the cancer from spreading or returning after treatment. By further refining these technologies, they hope to create a more effective and targeted treatment option for this invasive cancer.

Further information
Ping Xiong
Shanghai Jiaotong University School of Medicine
Email: xiongp@shsmu.edu.cn

Paper: https://doi.org/10.1080/14686996.2024.2368452

About Science and Technology of Advanced Materials (STAM)
Open access journal STAM publishes outstanding research articles across all aspects of materials science, including functional and structural materials, theoretical analyses, and properties of materials. https://www.tandfonline.com/STAM

Dr Yasufumi Nakamichi
STAM Publishing Director
Email: NAKAMICHI.Yasufumi@nims.go.jp

Press release distributed by Asia Research News for Science and Technology of Advanced Materials.

Brawijaya University researcher develops honey-processing tech

A researcher from the Faculty of Agricultural Technology of Brawijaya University, Anang Lastriyanto, has developed a technology for processing honey that is expected to provide added value to the product.

A researcher from the Faculty of Agricultural Technology of Brawijaya University, Anang Lastriyanto, with honey and powdered honey produced with the technology he developed, in Malang city, East Java, on Wednesday (April 3, 2024). — Anang Lastriyanto, a researcher from the Faculty of Agricultural Technology, Brawijaya University, produces honey and powdered honey with technology he has developed. (Image: ANTARA FOTO, 16/Apr/24)

According to Lastriyanto, his research took 3.5 years to produce powdered honey through an integrated process. “Not many people can create this technology on how to process honey into powder,” he says.

The first stage of the research, which was funded by the Indonesia Endowment Fund for Education Agency, involved developing the initial steps for the honey-processing process and producing a prototype of the tools used.

In the first year, honey was processed using pasteurization and rapid cooling or vacuum cooler methods, he informed. The development of the honey-processing process continued in the second year with the aim of increasing production to an industrial scale.

To increase production, pasteurization was used in processing, but the end product contained foam, indicating that the honey was not of good quality. “Honey becomes foamy when heated, so quality assurance and processing time are not necessarily guaranteed,” he explained.

However, Lastriyanto said, through rapid cooling after pasteurization, the problem of foam production during heating was resolved. In addition, the water content in the processed honey was reduced.

Thus, in the two years of research a number of processes were introduced, starting with pasteurization, rapid cooling, foam removal, and water content reduction. The four processes were integrated into a honey-processing technology, or “4 in 1” process.

“4 in 1 is a process of heating, cooling quickly, removing foam, and reducing water,” he said. He explained that in the third year of the technology’s development, the focus was on producing powdered honey, of which the most important process is formulation.

“In the process of (making) powdered honey, the most important thing is the formulation. We are targeting this formulation for acacia honey. Because breeders of acacia forest honey are facing hardship to market their products since prices have fallen,” he said.

The formulation process was carried out through a gradual process of research and evaluation of results. The formulation, which is currently being patented, was then continued with the heating process of the formulated honey.

Once heated, the mixture expands and then dries into lumps. The chunks are cooled, and then ground into powdered honey. “When exposed to heat, the mixture expands. The honey is protected by the (formulated) ingredients and becomes encapsulated,” he said.

Ultimately, in the course of his 3.5-year research, Lastriyanto managed to produce integrated processed honey, powdered honey, as well as a machine to process honey.

In the long term, powdered honey is expected to become a raw material for the industrial sector, both for domestic and international markets. The final product can also be used to supply needs in countries in Africa and Southeast Asia.

A new spin on materials analysis

Researchers Koichiro Yaji and Shunsuke Tsuda at the National Institute for Materials Science in Japan have developed an improved type of microscope that can visualize key aspects of electron spin states in materials. The quantum mechanical property of electrons called spin is more complex than the spin of objects in our everyday world but is related to it as a measure of an electron’s angular momentum. The spin states of electrons can have a significant impact on the electronic and magnetic behavior of the materials they are part of.

Schematic diagram of the iSPEM and the images it can obtain

The technology developed by Yaji and Tsuda is known as imaging-type spin-resolved photoemission microscopy (iSPEM). It uses the interaction of light with the electrons in a material to detect the relative alignment of the electron spins. It is particularly focused on electron spin polarization – the extent to which electron spins are collectively aligned in a specific direction.

The team’s iSPEM machine consists of three interconnected ultra-high vacuum chambers for preparing and analyzing the sample. Electrons are emitted from the sample by absorbing light energy, accelerated through the apparatus, and then analyzed by interaction with a spin filter crystal. The results are displayed as images which experts can use to glean the necessary information about the electron spin states in the sample.

“Compared to conventional machines, our iSPEM machine drastically improves the data acquisition efficiency by ten-thousand times, with a more than ten-times improvement in spatial resolution, ” says Yaji. “This offers tremendous opportunities for characterizing the electronic structure of microscopic materials and devices at previously inaccessible levels in the sub-micrometer region.”

This advance could promote improvements in using electron spin states in information processing and other electronic devices, as part of the fast-developing field know as spintronics. In spintronics applications, the spin state of electrons is utilized to store and process information, in addition to the traditional use of electric charge.

“This could lead to more energy-efficient and faster electronic devices, including quantum computers” says Yaji. Applying the subtleties of quantum mechanical behavior to computing is at the forefront of efforts to take computing powers to another level, but until now most advances have been restricted to arcane demonstrations rather than practical applications. Mastering the understanding, control and visualization of electron spin could be a significant step forward.

“We now plan to use our machine to investigate the possibilities for developing a new generation of electron spin-based devices, because it will let us look into the properties of tiny and structurally complex samples previously hidden from view,” Yaji concludes.

Further information
Name: Koichiro Yaji
National Institute for Materials Science
Email: yaji.koichiro@nims.go.jp

Paper: https://doi.org/10.1080/27660400.2024.2328206

About Science and Technology of Advanced Materials: Methods (STAM-M)

Dr Yasufumi Nakamichi
STAM Publishing Director
Email: NAKAMICHI.Yasufumi@nims.go.jp

Press release distributed by Asia Research News for Science and Technology of Advanced Materials.