Tag Archives: Tsinghua University

Excellent electrochromic smart window performance with yolk-shell NiO (nitrogen oxide) nanospheres

Electrochromic windows hold great promise where energy savings are concerned. So far, it’s still just a promise but perhaps the research in this April 17, 2023 news item on phys.org will help realize it, Note: Links have been removed,

Researchers from Tsinghua University synthesized porous yolk-shell NiO nanospheres (PYS-NiO NSs) via a solvothermal and subsequent calcination process of Ni-MOF. As the large specific surface areas and hollow porous nanostructures were conducive to ionic transport, PYS-NiO NSs exhibited a fast coloring/bleaching speed (3.6/3.9 s per one coloring/bleaching cycle) and excellent cycling stability (82% of capacity retention after 3000 cycles). These superior electrochromic (EC) properties indicated that the PYS-NiO NSs was a promising candidate for high performance EC devices.

Electrochromic (EC) materials (ECMs) are defined as the materials which have reversible changes in their colors and optical properties (transmittance, reflectance, and absorption) under different external voltages. Over the past decades, ECMs show promising advantages and application prospects in many fields such as smart windows, adaptive camouflage, electronic displays, and energy storage, etc., because of their excellent optical modulation abilities.

This image doesn’t seem all that helpful (to me) in understanding the research,

Caption: Porous yolk-shell nanospheres exhibit a fast coloring/bleaching speed. Credit: Baoshun Wang, Tsinghua University

An April 17, 2023 Particuology (journal) news release on EurekAlert, which originated the news item, does provide more detail, Note: Links have been removed,

Transition metal oxides (TMOs) are one of the most important ECMs which have been widely studied. They have many advantages such as rich nanostructure design, simple synthesis process, high security, etc. Among them, nickel oxide (NiO) is an attractive anode ECM and has attracted extensive research interest due to its high optical contrast, high coloring efficiency, low cost, etc. However, NiO-based ECMs still face the challenges of long EC switching times and poor cycling life which are caused by their poor ionic/electronic diffusion kinetics and low electrical conductivity.

Metal-organic frameworks (MOFs) have attracted enormous attention, because of their high porosity and large surface areas, and could be adjusted to achieve different properties by selecting different metal ions and organic bridging ligands. Due to the porosity and long-range orderliness, MOFs can provide fast and convenient channels for small molecules and ions to insert and extract during the transformation process. Therefore, MOFs can be used as effective templates for the preparation of hollow and porous TMOs with high ion transport efficiency, excellent specific capacitance, and electrochemical activities.

So the authors proposed a new strategy to design a kind of NiO with hollow and porous structure to obtain excellent EC performance and cyclic stability. As a proof-of-concept demonstration, the authors successfully synthesized MOFs-derived porous yolk-shell NiO nanospheres (PYS-NiO NSs) which exhibited excellent EC performance. Ni-organic framework spheres were prepared by a simple solvothermal method and then converted to PYS-NiO NSs by thermal decomposition. The PYS-NiO NSs exhibited relatively high specific surface areas and stable hollow nanostructures, which not only provided a large contact area between active sites and electrolyte ions in the EC process but also helped the NiO to accommodate large volume changes without breaking. Besides, the PYS-NiO NSs also shortened the ionic diffusion length and provided efficient channels for transferring electronics and ions. In addition, the coupling with carbon also rendered the PYS-NiO NSs with improved electronic conductivity and obtained better EC performance. The PYS-NiO NSs exhibited a fast coloring/bleaching speed (3.6/3.9 s). Besides, PYS-NiO NSs also exhibited excellent cycling stability (82% of capacity retention after 3000 cycles). These superior EC properties indicate that the PYS-NiO NSs is a promising candidate for high-performance EC devices. The as-prepared PYS-NiO NSs are believed to be a promising candidate for smart windows, displays, antiglare rearview mirrors, etc. More importantly, this work provides a new and feasible strategy for the efficient preparation of ECMs with fast response speed and high cyclic stability.

Particuology (IF=3.251) is an interdisciplinary journal that publishes frontier research articles and critical reviews on the discovery, formulation and engineering of particulate materials, processes and systems. Topics are broadly relevant to the production of materials, pharmaceuticals and food, the conversion of energy resources, and protection of the environment. For more information, please visit: https://www.journals.elsevier.com/particuology.

Here’s a link to and a citation for the paper, Note: There is an unusually long lead time between online access and print access,

Novel self-assembled porous yolk-shell NiO nanospheres with excellent electrochromic performance for smart windows by Baoshun Wang, Ya Huang, Siming Zhao, Run Li, Di Gao, Hairong Jiang, Rufan Zhang. Particuology Volume 84, January 2024, Pages 72-80 DOI: https://doi.org/10.1016/j.partic.2023.03.007 Available online: April 17, 2023

This paper is open access.

Future firefighters and wearable technology

I imagine this wearable technology would also be useful for the military too. However, the focus for these researchers from China is firefighting. (Given the situation with the Canadian wildfires in June 2023, we have 10x more than the average at this time in the season over the last 10 years, it’s good to see some work focused on safety for firefighters.) From a January 17, 2023 news item on phys.org,

Firefighting may look vastly different in the future thanks to intelligent fire suits and masks developed by multiple research institutions in China.

Researchers published results showing breathable electrodes woven into fabric used in fire suits have proven to be stable at temperatures over 520ºC. At these temperatures, the fabric is found to be essentially non-combustible with high rates of thermal protection time.

Caption: Scientists from multiple institutions address the challenges and limitations of current fire-fighting gear by introducing wearable, breathable sensors and electrodes to better serve firefighters. Credit: Nano Research, Tsinghua University Press

A January 17, 2023 Tsinghua University Press press release on EurekAlert, which originated the news item, provides more technical details,

The results show the efficacy and practicality of Janus graphene/poly(p-phenylene benzobisoxazole), or PBO, woven fabric in making firefighting “smarter” with the main goal being to manufacture products on an industrial scale that are flame-retardant but also intelligent enough to warn the firefighter of increased risks while traversing the flames.

“Conventional firefighting clothing and fire masks can ensure firemen’s safety to a certain extent,” said Wei Fan, professor at the School of Textile Science and Engineering at Xi’an Polytechnic University. “However, the fire scene often changes quickly, sometimes making firefighters trapped in the fire for failing to judge the risks in time. At this situation, firefighters also need to be rescued.”

The key here is the use of Janus graphene/PBO, woven fabrics. While not the first of its kind, the introduction of PBO fibers offers better strength and fire protection than other similar fibers, such as Kevlar. The PBO fibers are first woven into a fabric that is then irradiated using a CO2 infrared laser. From here, the fabric becomes the Janus graphene/PBO hybrid that is the focus of the study.   

The mask also utilizes a top and bottom layer of Janus graphene/PBO with a piezoelectric layer in between that acts as a way to convert mechanical pressures to electricity.

“The mask has a good smoke particle filtration effect, and the filtration efficiency of PM2.5 and PM3.0 reaches 95% and 100%, respectively. Meanwhile, the mask has good wearing comfort as its respiratory resistance (46.8 Pa) is lower than 49 Pa of commercial masks. Besides, the mask is sensitive to the speed and intensity of human breathing, which can dynamically monitor the health of the firemen” said Fan.

Flame-retardant electronics featured in these fire suits are flexible, heat resistant, quick to make and low-cost which makes scaling for industrial production a tangible achievement. This makes it more likely that the future of firefighting suits and masks will be able to effectively use this technology. Quick, effective responses can also reduce economic losses attributed to fires.

“The graphene/PBO woven fabrics-based sensors exhibit good repeatability and stability in human motion monitoring and NO2 gas detection, the main toxic gas in fires, which can be applied to firefighting suits to help firefighters effectively avoiding danger” Fan said. Being able to detect sharp increases in NO2 gas can help firefighters change course in an instant if needed and could be a lifesaving addition to firefighter gear.

Major improvements can be made in the firefighting field to better protect the firefighters by taking advantage of graphene/PBO woven and nonwoven fabrics. Widescale use of this technology can help the researchers reach their ultimate goal of reducing mortality and injury to those who risk their lives fighting fires.

Yu Luo and Yaping Miao of the School of Textile Science and Engineering at Xi’an Polytechnic University contributed equally to this work. Professor Wei Fan is the corresponding author. Yingying Zhang and Huimin Wang of the Department of Chemistry at Tsinghua University, Kai Dong of the Beijing Institute of Nanoenergy and Nanosystems at the Chinese Academy of Sciences, and Lin Hou and Yanyan Xu of Shaanxi Textile Research Institute Co., LTD, Weichun Chen and Yao Zhang of the School of Textile Science and Engineering at Xi’an Polytechnic University contributed to this research. 

This work was supported by the National Natural Science Foundation of China, Textile Vision Basic Research Program of China, Key Research and Development Program of Xianyang Science and Technology Bureau, Key Research and Development Program of Shaanxi Province, Natural Science Foundation of Shaanxi Province, and Scientific Research Project of Shaanxi Provincial Education Department.

Here are two links and a citation for the same paper,

Laser-induced Janus graphene/poly(p-phenylene benzobisoxazole) fabrics with intrinsic flame retardancy as flexible sensors and breathable electrodes for fire-fighting field by Yu Luo, Yaping Miao, Huimin Wang, Kai Dong, Lin Hou, Yanyan Xu, Weichun Chen, Yao Zhang, Yingying Zhang & Wei Fan. Nano Research (2023) DOI: https://doi.org/10.1007/s12274-023-5382-y Published12 January 2023

This link leads to a paywall.

Here’s the second link (to SciOpen)

Laser-induced Janus graphene/poly(p-phenylene benzobisoxazole) fabrics with intrinsic flame retardancy as flexible sensors and breathable electrodes for fire-fighting field. SciOpen Published January 12, 2023

This link leads to an open access journal published by Tsinghua University Press.

New chip for neuromorphic computing runs at a fraction of the energy of today’s systems

An August 17, 2022 news item on Nanowerk announces big (so to speak) claims from a team researching neuromorphic (brainlike) computer chips,

An international team of researchers has designed and built a chip that runs computations directly in memory and can run a wide variety of artificial intelligence (AI) applications–all at a fraction of the energy consumed by computing platforms for general-purpose AI computing.

The NeuRRAM neuromorphic chip brings AI a step closer to running on a broad range of edge devices, disconnected from the cloud, where they can perform sophisticated cognitive tasks anywhere and anytime without relying on a network connection to a centralized server. Applications abound in every corner of the world and every facet of our lives, and range from smart watches, to VR headsets, smart earbuds, smart sensors in factories and rovers for space exploration.

The NeuRRAM chip is not only twice as energy efficient as the state-of-the-art “compute-in-memory” chips, an innovative class of hybrid chips that runs computations in memory, it also delivers results that are just as accurate as conventional digital chips. Conventional AI platforms are a lot bulkier and typically are constrained to using large data servers operating in the cloud.

In addition, the NeuRRAM chip is highly versatile and supports many different neural network models and architectures. As a result, the chip can be used for many different applications, including image recognition and reconstruction as well as voice recognition.

..

An August 17, 2022 University of California at San Diego (UCSD) news release (also on EurekAlert), which originated the news item, provides more detail than usually found in a news release,

“The conventional wisdom is that the higher efficiency of compute-in-memory is at the cost of versatility, but our NeuRRAM chip obtains efficiency while not sacrificing versatility,” said Weier Wan, the paper’s first corresponding author and a recent Ph.D. graduate of Stanford University who worked on the chip while at UC San Diego, where he was co-advised by Gert Cauwenberghs in the Department of Bioengineering. 

The research team, co-led by bioengineers at the University of California San Diego, presents their results in the Aug. 17 [2022] issue of Nature.

Currently, AI computing is both power hungry and computationally expensive. Most AI applications on edge devices involve moving data from the devices to the cloud, where the AI processes and analyzes it. Then the results are moved back to the device. That’s because most edge devices are battery-powered and as a result only have a limited amount of power that can be dedicated to computing. 

By reducing power consumption needed for AI inference at the edge, this NeuRRAM chip could lead to more robust, smarter and accessible edge devices and smarter manufacturing. It could also lead to better data privacy as the transfer of data from devices to the cloud comes with increased security risks. 

On AI chips, moving data from memory to computing units is one major bottleneck. 

“It’s the equivalent of doing an eight-hour commute for a two-hour work day,” Wan said. 

To solve this data transfer issue, researchers used what is known as resistive random-access memory, a type of non-volatile memory that allows for computation directly within memory rather than in separate computing units. RRAM and other emerging memory technologies used as synapse arrays for neuromorphic computing were pioneered in the lab of Philip Wong, Wan’s advisor at Stanford and a main contributor to this work. Computation with RRAM chips is not necessarily new, but generally it leads to a decrease in the accuracy of the computations performed on the chip and a lack of flexibility in the chip’s architecture. 

“Compute-in-memory has been common practice in neuromorphic engineering since it was introduced more than 30 years ago,” Cauwenberghs said.  “What is new with NeuRRAM is that the extreme efficiency now goes together with great flexibility for diverse AI applications with almost no loss in accuracy over standard digital general-purpose compute platforms.”

A carefully crafted methodology was key to the work with multiple levels of “co-optimization” across the abstraction layers of hardware and software, from the design of the chip to its configuration to run various AI tasks. In addition, the team made sure to account for various constraints that span from memory device physics to circuits and network architecture. 

“This chip now provides us with a platform to address these problems across the stack from devices and circuits to algorithms,” said Siddharth Joshi, an assistant professor of computer science and engineering at the University of Notre Dame , who started working on the project as a Ph.D. student and postdoctoral researcher in Cauwenberghs lab at UC San Diego. 

Chip performance

Researchers measured the chip’s energy efficiency by a measure known as energy-delay product, or EDP. EDP combines both the amount of energy consumed for every operation and the amount of times it takes to complete the operation. By this measure, the NeuRRAM chip achieves 1.6 to 2.3 times lower EDP (lower is better) and 7 to 13 times higher computational density than state-of-the-art chips. 

Researchers ran various AI tasks on the chip. It achieved 99% accuracy on a handwritten digit recognition task; 85.7% on an image classification task; and 84.7% on a Google speech command recognition task. In addition, the chip also achieved a 70% reduction in image-reconstruction error on an image-recovery task. These results are comparable to existing digital chips that perform computation under the same bit-precision, but with drastic savings in energy. 

Researchers point out that one key contribution of the paper is that all the results featured are obtained directly on the hardware. In many previous works of compute-in-memory chips, AI benchmark results were often obtained partially by software simulation. 

Next steps include improving architectures and circuits and scaling the design to more advanced technology nodes. Researchers also plan to tackle other applications, such as spiking neural networks.

“We can do better at the device level, improve circuit design to implement additional features and address diverse applications with our dynamic NeuRRAM platform,” said Rajkumar Kubendran, an assistant professor for the University of Pittsburgh, who started work on the project while a Ph.D. student in Cauwenberghs’ research group at UC San Diego.

In addition, Wan is a founding member of a startup that works on productizing the compute-in-memory technology. “As a researcher and  an engineer, my ambition is to bring research innovations from labs into practical use,” Wan said. 

New architecture 

The key to NeuRRAM’s energy efficiency is an innovative method to sense output in memory. Conventional approaches use voltage as input and measure current as the result. But this leads to the need for more complex and more power hungry circuits. In NeuRRAM, the team engineered a neuron circuit that senses voltage and performs analog-to-digital conversion in an energy efficient manner. This voltage-mode sensing can activate all the rows and all the columns of an RRAM array in a single computing cycle, allowing higher parallelism. 

In the NeuRRAM architecture, CMOS neuron circuits are physically interleaved with RRAM weights. It differs from conventional designs where CMOS circuits are typically on the peripheral of RRAM weights.The neuron’s connections with the RRAM array can be configured to serve as either input or output of the neuron. This allows neural network inference in various data flow directions without incurring overheads in area or power consumption. This in turn makes the architecture easier to reconfigure. 

To make sure that accuracy of the AI computations can be preserved across various neural network architectures, researchers developed a set of hardware algorithm co-optimization techniques. The techniques were verified on various neural networks including convolutional neural networks, long short-term memory, and restricted Boltzmann machines. 

As a neuromorphic AI chip, NeuroRRAM performs parallel distributed processing across 48 neurosynaptic cores. To simultaneously achieve high versatility and high efficiency, NeuRRAM supports data-parallelism by mapping a layer in the neural network model onto multiple cores for parallel inference on multiple data. Also, NeuRRAM offers model-parallelism by mapping different layers of a model onto different cores and performing inference in a pipelined fashion.

An international research team

The work is the result of an international team of researchers. 

The UC San Diego team designed the CMOS circuits that implement the neural functions interfacing with the RRAM arrays to support the synaptic functions in the chip’s architecture, for high efficiency and versatility. Wan, working closely with the entire team, implemented the design; characterized the chip; trained the AI models; and executed the experiments. Wan also developed a software toolchain that maps AI applications onto the chip. 

The RRAM synapse array and its operating conditions were extensively characterized and optimized at Stanford University. 

The RRAM array was fabricated and integrated onto CMOS at Tsinghua University. 

The Team at Notre Dame contributed to both the design and architecture of the chip and the subsequent machine learning model design and training.

The research started as part of the National Science Foundation funded Expeditions in Computing project on Visual Cortex on Silicon at Penn State University, with continued funding support from the Office of Naval Research Science of AI program, the Semiconductor Research Corporation and DARPA [{US} Defense Advanced Research Projects Agency] JUMP program, and Western Digital Corporation. 

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

A compute-in-memory chip based on resistive random-access memory by Weier Wan, Rajkumar Kubendran, Clemens Schaefer, Sukru Burc Eryilmaz, Wenqiang Zhang, Dabin Wu, Stephen Deiss, Priyanka Raina, He Qian, Bin Gao, Siddharth Joshi, Huaqiang Wu, H.-S. Philip Wong & Gert Cauwenberghs. Nature volume 608, pages 504–512 (2022) DOI: https://doi.org/10.1038/s41586-022-04992-8 Published: 17 August 2022 Issue Date: 18 August 2022

This paper is open access.

Reconfiguring a LEGO-like AI chip with light

MIT engineers have created a reconfigurable AI chip that comprises alternating layers of sensing and processing elements that can communicate with each other. Credit: Figure courtesy of the researchers and edited by MIT News

This image certainly challenges any ideas I have about what Lego looks like. It seems they see things differently at the Massachusetts Institute of Technology (MIT). From a June 13, 2022 MIT news release (also on EurekAlert),

Imagine a more sustainable future, where cellphones, smartwatches, and other wearable devices don’t have to be shelved or discarded for a newer model. Instead, they could be upgraded with the latest sensors and processors that would snap onto a device’s internal chip — like LEGO bricks incorporated into an existing build. Such reconfigurable chipware could keep devices up to date while reducing our electronic waste. 

Now MIT engineers have taken a step toward that modular vision with a LEGO-like design for a stackable, reconfigurable artificial intelligence chip.

The design comprises alternating layers of sensing and processing elements, along with light-emitting diodes (LED) that allow for the chip’s layers to communicate optically. Other modular chip designs employ conventional wiring to relay signals between layers. Such intricate connections are difficult if not impossible to sever and rewire, making such stackable designs not reconfigurable.

The MIT design uses light, rather than physical wires, to transmit information through the chip. The chip can therefore be reconfigured, with layers that can be swapped out or stacked on, for instance to add new sensors or updated processors.

“You can add as many computing layers and sensors as you want, such as for light, pressure, and even smell,” says MIT postdoc Jihoon Kang. “We call this a LEGO-like reconfigurable AI chip because it has unlimited expandability depending on the combination of layers.”

The researchers are eager to apply the design to edge computing devices — self-sufficient sensors and other electronics that work independently from any central or distributed resources such as supercomputers or cloud-based computing.

“As we enter the era of the internet of things based on sensor networks, demand for multifunctioning edge-computing devices will expand dramatically,” says Jeehwan Kim, associate professor of mechanical engineering at MIT. “Our proposed hardware architecture will provide high versatility of edge computing in the future.”

The team’s results are published today in Nature Electronics. In addition to Kim and Kang, MIT authors include co-first authors Chanyeol Choi, Hyunseok Kim, and Min-Kyu Song, and contributing authors Hanwool Yeon, Celesta Chang, Jun Min Suh, Jiho Shin, Kuangye Lu, Bo-In Park, Yeongin Kim, Han Eol Lee, Doyoon Lee, Subeen Pang, Sang-Hoon Bae, Hun S. Kum, and Peng Lin, along with collaborators from Harvard University, Tsinghua University, Zhejiang University, and elsewhere.

Lighting the way

The team’s design is currently configured to carry out basic image-recognition tasks. It does so via a layering of image sensors, LEDs, and processors made from artificial synapses — arrays of memory resistors, or “memristors,” that the team previously developed, which together function as a physical neural network, or “brain-on-a-chip.” Each array can be trained to process and classify signals directly on a chip, without the need for external software or an Internet connection.

In their new chip design, the researchers paired image sensors with artificial synapse arrays, each of which they trained to recognize certain letters — in this case, M, I, and T. While a conventional approach would be to relay a sensor’s signals to a processor via physical wires, the team instead fabricated an optical system between each sensor and artificial synapse array to enable communication between the layers, without requiring a physical connection. 

“Other chips are physically wired through metal, which makes them hard to rewire and redesign, so you’d need to make a new chip if you wanted to add any new function,” says MIT postdoc Hyunseok Kim. “We replaced that physical wire connection with an optical communication system, which gives us the freedom to stack and add chips the way we want.”

The team’s optical communication system consists of paired photodetectors and LEDs, each patterned with tiny pixels. Photodetectors constitute an image sensor for receiving data, and LEDs to transmit data to the next layer. As a signal (for instance an image of a letter) reaches the image sensor, the image’s light pattern encodes a certain configuration of LED pixels, which in turn stimulates another layer of photodetectors, along with an artificial synapse array, which classifies the signal based on the pattern and strength of the incoming LED light.

Stacking up

The team fabricated a single chip, with a computing core measuring about 4 square millimeters, or about the size of a piece of confetti. The chip is stacked with three image recognition “blocks,” each comprising an image sensor, optical communication layer, and artificial synapse array for classifying one of three letters, M, I, or T. They then shone a pixellated image of random letters onto the chip and measured the electrical current that each neural network array produced in response. (The larger the current, the larger the chance that the image is indeed the letter that the particular array is trained to recognize.)

The team found that the chip correctly classified clear images of each letter, but it was less able to distinguish between blurry images, for instance between I and T. However, the researchers were able to quickly swap out the chip’s processing layer for a better “denoising” processor, and found the chip then accurately identified the images.

“We showed stackability, replaceability, and the ability to insert a new function into the chip,” notes MIT postdoc Min-Kyu Song.

The researchers plan to add more sensing and processing capabilities to the chip, and they envision the applications to be boundless.

“We can add layers to a cellphone’s camera so it could recognize more complex images, or makes these into healthcare monitors that can be embedded in wearable electronic skin,” offers Choi, who along with Kim previously developed a “smart” skin for monitoring vital signs.

Another idea, he adds, is for modular chips, built into electronics, that consumers can choose to build up with the latest sensor and processor “bricks.”

“We can make a general chip platform, and each layer could be sold separately like a video game,” Jeehwan Kim says. “We could make different types of neural networks, like for image or voice recognition, and let the customer choose what they want, and add to an existing chip like a LEGO.”

This research was supported, in part, by the Ministry of Trade, Industry, and Energy (MOTIE) from South Korea; the Korea Institute of Science and Technology (KIST); and the Samsung Global Research Outreach Program.

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

Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence by Chanyeol Choi, Hyunseok Kim, Ji-Hoon Kang, Min-Kyu Song, Hanwool Yeon, Celesta S. Chang, Jun Min Suh, Jiho Shin, Kuangye Lu, Bo-In Park, Yeongin Kim, Han Eol Lee, Doyoon Lee, Jaeyong Lee, Ikbeom Jang, Subeen Pang, Kanghyun Ryu, Sang-Hoon Bae, Yifan Nie, Hyun S. Kum, Min-Chul Park, Suyoun Lee, Hyung-Jun Kim, Huaqiang Wu, Peng Lin & Jeehwan Kim. Nature Electronics volume 5, pages 386–393 (2022) 05 May 2022 Issue Date: June 2022 Published: 13 June 2022 DOI: https://doi.org/10.1038/s41928-022-00778-y

This paper is behind a paywall.

Bruno Latour, science, and the 2021 Kyoto Prize in Arts and Philosophy: Commemorative Lecture

The Kyoto Prize (Wikipedia entry) was first given out in 1985. These days (I checked out a currency converter today, November 15, 2021), the Inamori Foundation, which administers the prize, gives out $100M yen per prize, worth about $1,098,000 CAD or $876,800 USD.

Here’s more about the prize from the November 9, 2021 Inamori Foundation press release on EurekAlert,

The Kyoto Prize is an international award of Japanese origin, presented to individuals who have made significant contributions to the progress of science, the advancement of civilization, and the enrichment and elevation of the human spirit. The Prize is granted in the three categories of Advanced Technology, Basic Sciences; Arts and Philosophy, each of which comprises four fields, making a total of 12 fields. Every year, one Prize is awarded in each of the three categories with prize money of 100 million yen per category.

One of the distinctive features of the Kyoto Prize is that it recognizes both “science” and “arts and philosophy” fields. This is because of its founder Kazuo Inamori’s conviction that the future of humanity can be assured only when there is a balance between scientific development and the enrichment of the human spirit.

The recipient for arts and philosophy, Bruno Latour has been mentioned here before (from a July 15, 2020 posting titled, ‘Architecture, the practice of science, and meaning’),

The 1979 book, Laboratory Life: the Social Construction of Scientific Facts by Bruno Latour and Steve Woolgar immediately came to mind on reading about a new book (The New Architecture of Science: Learning from Graphene) linking architecture to the practice of science (research on graphene). It turns out that one of the authors studied with Latour. (For more about Laboratory Life see: Bruno Latour’s Wikipedia entry; scroll down to Main Works)

Back to Latour and his prize from the November 9, 2021 Inamori Foundation press release,

Bruno Latour, Professor Emeritus at Paris Institute of Political Studies (Sciences Po), received the 2021 Kyoto Prize in Arts and Philosophy for his radically re-examining “modernity” by developing a philosophy that focuses on interactions between technoscience and social structure. Latour’s Commemorative Lecture “How to React to a Change in Cosmology” will be released on November 10, 2021, 10:00 AM JST at the 2021 Kyoto Prize Special Website.

“Viruses–we don’t even know if viruses are our enemies or our friends!” says Latour in his lecture. By using the ongoing Covid epidemic as a sort of lead, Latour discusses the shift in cosmology, a structure that distributes agencies around. He then suggests a “new project” we have to work on now, which he assumes is very different from the modernist project.

Bruno Latour has revolutionized the conventional view of science by treating nature, humans, laboratory equipment, and other entities as equal actors, and describing technoscience as the hybrid network of these actors. His philosophy re-examines “modernity” based on the dualism of nature and society. He has a large influence across disciplines, with his multifaceted activities that include proposals regarding global environmental issues.

Latour and the other two 2021 Kyoto Prize laureates are introduced on the 2021 Kyoto Prize Special Website with information about their work, profiles, and three-minute introduction videos. The Kyoto Prize in Advanced Technology for this year went to Andrew Chi-Chih Yao, Professor of Institute for Interdisciplinary Information Sciences at Tsinghua University, and Basic Sciences to Robert G. Roeder, Arnold and Mabel Beckman Professor of Biochemistry and Molecular Biology at The Rockefeller University. 

The folks at the Kyoto Prize have made a three-minute video introduction to Bruno Latour available,

For more information you can check out the Inamori Foundation website. There are two Kyoto Prize websites, the 2021 Kyoto Prize Special Website and the Kyoto Prize website. These are all English language websites and, if you have the language skills and the interest, it is possible to toggle (upper right hand side) and get the Japanese language version.

Finally, there’s a dedicated Bruno Latour webpage on the 2021 Kyoto Prize Special Website and Bruno Latour has his own website where French and English are items are mixed together but it seems the majority of the content is in English.

Memristor artificial neural network learning based on phase-change memory (PCM)

Caption: Professor Hongsik Jeong and his research team in the Department of Materials Science and Engineering at UNIST. Credit: UNIST

I’m pretty sure that Professor Hongsik Jeong is the one on the right. He seems more relaxed, like he’s accustomed to posing for pictures highlighting his work.

Now on to the latest memristor news, which features the number 8.

For anyone unfamiliar with the term memristor, it’s a device (of sorts) which scientists, involved in neuromorphic computing (computers that operate like human brains), are researching as they attempt to replicate brainlike processes for computers.

From a January 22, 2021 Ulsan National Institute of Science and Technology (UNIST) press release (also on EurekAlert but published March 15, 2021),

An international team of researchers, affiliated with UNIST has unveiled a novel technology that could improve the learning ability of artificial neural networks (ANNs).

Professor Hongsik Jeong and his research team in the Department of Materials Science and Engineering at UNIST, in collaboration with researchers from Tsinghua University in China, proposed a new learning method to improve the learning ability of ANN chips by challenging its instability.

Artificial neural network chips are capable of mimicking the structural, functional and biological features of human neural networks, and thus have been considered the technology of the future. In this study, the research team demonstrated the effectiveness of the proposed learning method by building phase change memory (PCM) memristor arrays that operate like ANNs. This learning method is also advantageous in that its learning ability can be improved without additional power consumption, since PCM undergoes a spontaneous resistance increase due to the structural relaxation after amorphization.

ANNs, like human brains, use less energy even when performing computation and memory tasks, simultaneously. However, the artificial neural network chip in which a large number of physical devices are integrated has a disadvantage that there is an error. The existing artificial neural network learning method assumes a perfect artificial neural network chip with no errors, so the learning ability of the artificial neural network is poor.

The research team developed a memristor artificial neural network learning method based on a phase-change memory, conceiving that the real human brain does not require near-perfect motion. This learning method reflects the “resistance drift” (increased electrical resistance) of the phase change material in the memory semiconductor in learning. During the learning process, since the information update pattern is recorded in the form of increasing electrical resistance in the memristor, which serves as a synapse, the synapse additionally learns the association between the pattern it changes and the data it is learning.

The research team showed that the learning method developed through an experiment to classify handwriting composed of numbers 0-9 has an effect of improving learning ability by about 3%. In particular, the accuracy of the number 8, which is difficult to classify handwriting, has improved significantly. [emphasis mine] The learning ability improved thanks to the synaptic update pattern that changes differently according to the difficulty of handwriting classification.

Researchers expect that their findings are expected to promote the learning algorithms with the intrinsic properties of memristor devices, opening a new direction for development of neuromorphic computing chips.

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

Spontaneous sparse learning for PCM-based memristor neural networks by Dong-Hyeok Lim, Shuang Wu, Rong Zhao, Jung-Hoon Lee, Hongsik Jeong & Luping Shi. Nature Communications volume 12, Article number: 319 (2021) DOI: https://doi.org/10.1038/s41467-020-20519-z Published 12 January 2021

This paper is open access.

New boron nanostructure—carbon, watch out!

Carbon nanotubes, buckminsterfullerenes (also known as, buckyballs), and/or graphene are names for different carbon nanoscale structures and, as far as I’m aware,carbon is the only element that merits some distinct names at the nanoscale. By comparison, gold can be gold nanorods, gold nanostars, gold nanoparticles, and so on. In short, nanostructures made of gold (and most other elements) are always prefaced with the word ‘gold’ followed by a word with ‘nano’ in it.

Scientists naming a new boron nanoscale structure seem to have adopted both strategies for a hybrid name. Here’s more from a June 25, 2020 news item on phys.org,

The discovery of carbon nanostructures like two-dimensional graphene and soccer ball-shaped buckyballs helped to launch a nanotechnology revolution. In recent years, researchers from Brown University [located in Rhode Island, US] and elsewhere have shown that boron, carbon’s neighbor on the periodic table, can make interesting nanostructures too, including two-dimensional borophene and a buckyball-like hollow cage structure called borospherene.

Caption: The family of boron-based nanostructures has a new member: metallo-borospherenes, hollow cages made from 18 boron atoms and three atoms of lanthanide elements. Credit: Wang Lab / Brown University

A June 25, 2020 Brown University news release (also on EurekAlert), wbich originated the news item, describes these new structures in detail,

Now, researchers from Brown and Tsinghua University have added another boron nanostructure to the list. In a paper published in Nature Communications, they show that clusters of 18 boron atoms and three atoms of lanthanide elements form a bizarre cage-like structure unlike anything they’ve ever seen.

“This is just not a type of structure you expect to see in chemistry,” said Lai-Sheng Wang, a professor of chemistry at Brown and the study’s senior author. “When we wrote the paper we really struggled to describe it. It’s basically a spherical trihedron. Normally you can’t have a closed three-dimensional structure with only three sides, but since it’s spherical, it works.”

The researchers are hopeful that the nanostructure may shed light on the bulk structure and chemical bonding behavior of boron lanthanides, an important class of materials widely used in electronics and other applications. The nanostructure by itself may have interesting properties as well, the researchers say.

“Lanthanide elements are important magnetic materials, each with very different magnetic moments,” Wang said. “We think any of the lanthanides will make this structure, so they could have very interesting magnetic properties.”

Wang and his students created the lanthanide-boron clusters by focusing a powerful laser onto a solid target made of a mixture of boron and a lanthanide element. The clusters are formed upon cooling of the vaporized atoms. Then they used a technique called photoelectron spectroscopy to study the electronic properties of the clusters. The technique involves zapping clusters of atoms with another high-powered laser. Each zap knocks an electron out of the cluster. By measuring the kinetic energies of those freed electrons, researchers can create a spectrum of binding energies for the electrons that bond the cluster together.

“When we see a simple, beautiful spectrum, we know there’s a beautiful structure behind it,” Wang said.

To figure out what that structure looks like, Wang compared the photoelectron spectra with theoretical calculations done by Professor Jun Li and his students from Tsinghua. Once they find a theoretical structure with a binding spectrum that matches the experiment, they know they’ve found the right structure.

“This structure was something we never would have predicted,” Wang said. “That’s the value of combining theoretical calculation with experimental data.”

Wang and his colleagues have dubbed the new structures metallo-borospherenes, and they’re hopeful that further research will reveal their properties.

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

Spherical trihedral metallo-borospherenes by Teng-Teng Chen, Wan-Lu Li, Wei-Jia Chen, Xiao-Hu Yu, Xin-Ran Dong, Jun Li & Lai-Sheng Wang. Nature Communications volume 11, Article number: 2766 (2020) DOI: https://doi.org/10.1038/s41467-020-16532-x Published: 02 June 2020

This paper is open access.

China is world leader in nanotechnology and in other fields too?

State of Chinese nanoscience/nanotechnology

China claims to be the world leader in the field in a white paper announced in an August 29, 2017 Springer Nature press release,

Springer Nature, the National Center for Nanoscience and Technology, China and the National Science Library of the Chinese Academy of Sciences (CAS) released in both Chinese and English a white paper entitled “Small Science in Big China: An overview of the state of Chinese nanoscience and technology” at NanoChina 2017, an international conference on nanoscience and technology held August 28 and 29 in Beijing. The white paper looks at the rapid growth of China’s nanoscience research into its current role as the world’s leader [emphasis mine], examines China’s strengths and challenges, and makes some suggestions for how its contribution to the field can continue to thrive.

The white paper points out that China has become a strong contributor to nanoscience research in the world, and is a powerhouse of nanotechnology R&D. Some of China’s basic research is leading the world. China’s applied nanoscience research and the industrialization of nanotechnologies have also begun to take shape. These achievements are largely due to China’s strong investment in nanoscience and technology. China’s nanoscience research is also moving from quantitative increase to quality improvement and innovation, with greater emphasis on the applications of nanotechnologies.

“China took an initial step into nanoscience research some twenty years ago, and has since grown its commitment at an unprecedented rate, as it has for scientific research as a whole. Such a growth is reflected both in research quantity and, importantly, in quality. Therefore, I regard nanoscience as a window through which to observe the development of Chinese science, and through which we could analyze how that rapid growth has happened. Further, the experience China has gained in developing nanoscience and related technologies is a valuable resource for the other countries and other fields of research to dig deep into and draw on,” said Arnout Jacobs, President, Greater China, Springer Nature.

The white paper explores at China’s research output relative to the rest of the world in terms of research paper output, research contribution contained in the Nano database, and finally patents, providing insight into China’s strengths and expertise in nano research. The white paper also presents the results of a survey of experts from the community discussing the outlook for and challenges to the future of China’s nanoscience research.

China nano research output: strong rise in quantity and quality

In 1997, around 13,000 nanoscience-related papers were published globally. By 2016, this number had risen to more than 154,000 nano-related research papers. This corresponds to a compound annual growth rate of 14% per annum, almost four times the growth in publications across all areas of research of 3.7%. Over the same period of time, the nano-related output from China grew from 820 papers in 1997 to over 52,000 papers in 2016, a compound annual growth rate of 24%.

China’s contribution to the global total has been growing steadily. In 1997, Chinese researchers co-authored just 6% of the nano-related papers contained in the Science Citation Index (SCI). By 2010, this grew to match the output of the United States. They now contribute over a third of the world’s total nanoscience output — almost twice that of the United States.

Additionally, China’s share of the most cited nanoscience papers has kept increasing year on year, with a compound annual growth rate of 22% — more than three times the global rate. It overtook the United States in 2014 and its contribution is now many times greater than that of any other country in the world, manifesting an impressive progression in both quantity and quality.

The rapid growth of nanoscience in China has been enabled by consistent and strong financial support from the Chinese government. As early as 1990, the State Science and Technology Committee, the predecessor of the Ministry of Science and Technology (MOST), launched the Climbing Up project on nanomaterial science. During the 1990s, the National Natural Science Foundation of China (NSFC) also funded nearly 1,000 small-scale projects in nanoscience. In the National Guideline on Medium- and Long-Term Program for Science and Technology Development (for 2006−2020) issued in early 2006 by the Chinese central government, nanoscience was identified as one of four areas of basic research and received the largest proportion of research budget out of the four areas. The brain boomerang, with more and more foreign-trained Chinese researchers returning from overseas, is another contributor to China’s rapid rise in nanoscience.

The white paper clarifies the role of Chinese institutions, including CAS, in driving China’s rise to become the world’s leader in nanoscience. Currently, CAS is the world’s largest producer of high impact nano research, contributing more than twice as many papers in the 1% most-cited nanoscience literature than its closest competitors. In addition to CAS, five other Chinese institutions are ranked among the global top 20 in terms of output of top cited 1% nanoscience papers — Tsinghua University, Fudan University, Zhejiang University, University of Science and Technology of China and Peking University.

Nano database reveals advantages and focus of China’s nano research

The Nano database (http://nano.nature.com) is a comprehensive platform that has been recently developed by Nature Research – part of Springer Nature – which contains nanoscience-related papers published in 167 peer-reviewed journals including Advanced Materials, Nano Letters, Nature, Science and more. Analysis of the Nano database of nanomaterial-containing articles published in top 30 journals during 2014–2016 shows that Chinese scientists explore a wide range of nanomaterials, the five most common of which are nanostructured materials, nanoparticles, nanosheets, nanodevices and nanoporous materials.

In terms of the research of applications, China has a clear leading edge in catalysis research, which is the most popular area of the country’s quality nanoscience papers. Chinese nano researchers also contributed significantly to nanomedicine and energy-related applications. China is relatively weaker in nanomaterials for electronics applications, compared to other research powerhouses, but robotics and lasers are emerging applications areas of nanoscience in China, and nanoscience papers addressing photonics and data storage applications also see strong growth in China. Over 80% of research from China listed in the database explicitly mentions applications of the nanostructures and nanomaterials described, notably higher than from most other leading nations such as the United States, Germany, the UK, Japan and France.

Nano also reveals the extent of China’s international collaborations in nano research. China has seen the percentage of its internationally collaborated papers increasing from 36% in 2014 to 44% in 2016. This level of international collaboration, similar to that of South Korea, is still much lower than that of the western countries, and the rate of growth is also not as fast as those in the United States, France and Germany.

The United States is China’s biggest international collaborator, contributing to 55% of China’s internationally collaborated papers on nanoscience that are included in the top 30 journals in the Nano database. Germany, Australia and Japan follow in a descending order as China’s collaborators on nano-related quality papers.

China’s patent output: topping the world, mostly applied domestically

Analysis of the Derwent Innovation Index (DII) database of Clarivate Analytics shows that China’s accumulative total number of patent applications for the past 20 years, amounting to 209,344 applications, or 45% of the global total, is more than twice as many as that of the United States, the second largest contributor to nano-related patents. China surpassed the United States and ranked the top in the world since 2008.

Five Chinese institutions, including the CAS, Zhejiang University, Tsinghua University, Hon Hai Precision Industry Co., Ltd. and Tianjin University can be found among the global top 10 institutional contributors to nano-related patent applications. CAS has been at the top of the global rankings since 2008, with a total of 11,218 patent applications for the past 20 years. Interestingly, outside of China, most of the other big institutional contributors among the top 10 are commercial enterprises, while in China, research or academic institutions are leading in patent applications.

However, the number of nano-related patents China applied overseas is still very low, accounting for only 2.61% of its total patent applications for the last 20 years cumulatively, whereas the proportion in the United States is nearly 50%. In some European countries, including the UK and France, more than 70% of patent applications are filed overseas.

China has high numbers of patent applications in several popular technical areas for nanotechnology use, and is strongest in patents for polymer compositions and macromolecular compounds. In comparison, nano-related patent applications in the United States, South Korea and Japan are mainly for electronics or semiconductor devices, with the United States leading the world in the cumulative number of patents for semiconductor devices.

Outlook, opportunities and challenges

The white paper highlights that the rapid rise of China’s research output and patent applications has painted a rosy picture for the development of Chinese nanoscience, and in both the traditionally strong subjects and newly emerging areas, Chinese nanoscience shows great potential.

Several interviewed experts in the survey identify catalysis and catalytic nanomaterials as the most promising nanoscience area for China. The use of nanotechnology in the energy and medical sectors was also considered very promising.

Some of the interviewed experts commented that the industrial impact of China’s nanotechnology is limited and there is still a gap between nanoscience research and the industrialization of nanotechnologies. Therefore, they recommended that the government invest more in applied research to drive the translation of nanoscience research and find ways to encourage enterprises to invest more in R&D.

As more and more young scientists enter the field, the competition for research funding is becoming more intense. However, this increasing competition for funding was not found to concern most interviewed young scientists, rather, they emphasized that the soft environment is more important. They recommended establishing channels that allow the suggestions or creative ideas of the young to be heard. Also, some interviewed young researchers commented that they felt that the current evaluation system was geared towards past achievements or favoured overseas experience, and recommended the development of an improved talent selection mechanism to ensure a sustainable growth of China’s nanoscience.

I have taken a look at the white paper and found it to be well written. It also provides a brief but thorough history of nanotechnology/nanoscience even adding a bit of historical information that was new to me. As for the rest of the white paper, it relies on bibliometrics (number of published papers and number of citations) and number of patents filed to lay the groundwork for claiming Chinese leadership in nanotechnology. As I’ve stated many times before, these are problematic measures but as far as I can determine they are almost the only ones we have. Frankly, as a Canadian, it doesn’t much matter to me since Canada no matter how you slice or dice it is always in a lower tier relative to science leadership in major fields. It’s the Americans who might feel inclined to debate leadership with regard to nanotechnology and other major fields and I leave it to to US commentators to take up the cudgels should they be inclined. The big bonuses here are the history, the glimpse into the Chinese perspective on the field of nanotechnology/nanoscience, and the analysis of weaknesses and strengths.

Coming up fast on Google and Amazon

A November 16, 2017 article by Christina Bonnington for Slate explores the possibility that a Chinese tech giant, Baidu,  will provide Google and Amazon serious competition in their quests to dominate world markets (Note: Links have been removed,

raven_h
The company took a playful approach to the form—but it has functional reasons for the design, too. Baidu

 

One of the most interesting companies in tech right now isn’t based in Palo Alto, or San Francisco, or Seattle. Baidu, a Chinese company with headquarters in Beijing, is taking on America’s biggest and most innovative tech titans—with style.

Baidu, a titan in its own right, leapt onto the scene as a competitor to Google in the search engine space. Since then, the company, largely underappreciated here in the U.S., has focused on beefing up its artificial intelligence efforts. Former AI chief Andrew Ng, upon leaving the company in March, credited Baidu’s CEO Robin Li on being one of the first technology leaders to fully appreciate the value of deep learning. Baidu now has a 1,300 person AI group, and that investment in AI has helped the company catch up to older, more established companies like Google and Amazon—both in emerging spaces, such as autonomous vehicles, and in consumer tech, as its latest announcement shows.

On Thursday [November 16, 2017], Baidu debuted its entrants to the popular virtual assistant space: a connected speaker and two robots. Baidu aims for the speaker to compete against options such as Amazon’s Echo line, Google Home, and Apple HomePod. Inside, the $256 device will utilize Baidu’s DuerOS conversational artificial intelligence platform, which is already used in more than 100 different smart home brands’ products. DuerOS will let you use your voice to do things like ask the speaker for information, play music, or hail a cab. Called the Raven H, the speaker includes high-end audio components from Tymphany and a unique design jointly created by acquired startup Raven Tech and Swedish consumer electronics company Teenage Engineering.

While the focus is on exciting new technology products from Baidu, the subtext, such as it is, suggests US companies had best keep an eye on its Chinese competitor(s).

Dutch/Chinese partnership to produce nanoparticles at the touch of a button

Now back to China and nanotechnology leadership and the production of nanoparticles. This announcement was made in a November 17, 2017 news item on Azonano,

Delft University of Technology [Netherlands] spin-off VSPARTICLE enters the booming Chinese market with a radical technology that allows researchers to produce nanoparticles at the push of a button. VSPARTICLE’s nanoparticle generator uses atoms, the worlds’ smallest building blocks, to provide a controllable source of nanoparticles. The start-up from Delft signed a distribution agreement with Bio-Sun to make their VSP-G1 nanoparticle generator available in China.

A November 16, 2017 VSPARTICLE press release, which originated the news item,

“We are honoured to cooperate with VSPARTICLE and bring the innovative VSP-G1 nanoparticle generator into the Chinese market. The VSP-G1 will create new possibilities for researchers in catalysis, aerosol, healthcare and electronics,” says Yinghui Cai, CEO of Bio-Sun.

With an exponential growth in nanoparticle research in the last decade, China is one of the leading countries in the field of nanotechnology and its applications. Vincent Laban, CFO of VSPARTICLE, explains: “Due to its immense investments in IOT, sensors, semiconductor technology, renewable energy and healthcare applications, China will eventually become one of our biggest markets. The collaboration with Bio-Sun offers a valuable opportunity to enter the Chinese market at exactly the right time.”

NANOPARTICLES ARE THE BUILDING BLOCKS OF THE FUTURE

Increasingly, scientists are focusing on nanoparticles as a key technology in enabling the transition to a sustainable future. Nanoparticles are used to make new types of sensors and smart electronics; provide new imaging and treatment possibilities in healthcare; and reduce harmful waste in chemical processes.

CURRENT RESEARCH TOOLKIT LACKS A FAST WAY FOR MAKING SPECIFIC BUILDING BLOCKS

With the latest tools in nanotechnology, researchers are exploring the possibilities of building novel materials. This is, however, a trial-and-error method. Getting the right nanoparticles often is a slow struggle, as most production methods take a substantial amount of effort and time to develop.

VSPARTICLE’S VSP-G1 NANOPARTICLE GENERATOR

With the VSP-G1 nanoparticle generator, VSPARTICLE makes the production of nanoparticles as easy as pushing a button. . Easy and fast iterations enable researchers to fast forward their research cycle, and verify their hypotheses.

VSPARTICLE

Born out of the research labs of Delft University of Technology, with over 20 years of experience in the synthesis of aerosol, VSPARTICLE believes there is a whole new world of possibilities and materials at the nanoscale. The company was founded in 2014 and has an international sales network in Europe, Japan and China.

BIO-SUN

Bio-Sun was founded in Beijing in 2010 and is a leader in promoting nanotechnology and biotechnology instruments in China. It serves many renowned customers in life science, drug discovery and material science. Bio-Sun has four branch offices in Qingdao, Shanghai, Guangzhou and Wuhan City, and a nationwide sale network.

That’s all folks!

The volatile lithium-ion battery

On the heels of Samsung’s Galaxy Note 7 recall due to fires (see Alex Fitzpatrick’s Sept. 9, 2016 article for Time magazine for a good description of lithium-ion batteries and why they catch fire; see my May 29, 2013 posting on lithium-ion batteries, fires [including the airplane fires], and nanotechnology risk assessments), there’s new research on lithium-ion batteries and fires from China. From an Oct. 21, 2016 news item on Nanotechnology Now,

Dozens of dangerous gases are produced by the batteries found in billions of consumer devices, like smartphones and tablets, according to a new study. The research, published in Nano Energy, identified more than 100 toxic gases released by lithium batteries, including carbon monoxide.

An Oct. 20, 2016 Elsevier Publishing press release (also on EurekAlert), which originated the news item, expands on the theme,

The gases are potentially fatal, they can cause strong irritations to the skin, eyes and nasal passages, and harm the wider environment. The researchers behind the study, from the Institute of NBC Defence and Tsinghua University in China, say many people may be unaware of the dangers of overheating, damaging or using a disreputable charger for their rechargeable devices.

In the new study, the researchers investigated a type of rechargeable battery, known as a “lithium-ion” battery, which is placed in two billion consumer devices every year.

“Nowadays, lithium-ion batteries are being actively promoted by many governments all over the world as a viable energy solution to power everything from electric vehicles to mobile devices. The lithium-ion battery is used by millions of families, so it is imperative that the general public understand the risks behind this energy source,” explained Dr. Jie Sun, lead author and professor at the Institute of NBC Defence.

The dangers of exploding batteries have led manufacturers to recall millions of devices: Dell recalled four million laptops in 2006 and millions of Samsung Galaxy Note 7 devices were recalled this month after reports of battery fires. But the threats posed by toxic gas emissions and the source of these emissions are not well understood.

Dr. Sun and her colleagues identified several factors that can cause an increase in the concentration of the toxic gases emitted. A fully charged battery will release more toxic gases than a battery with 50 percent charge, for example. The chemicals contained in the batteries and their capacity to release charge also affected the concentrations and types of toxic gases released.

Identifying the gases produced and the reasons for their emission gives manufacturers a better understanding of how to reduce toxic emissions and protect the wider public, as lithium-ion batteries are used in a wide range of environments.

“Such dangerous substances, in particular carbon monoxide, have the potential to cause serious harm within a short period of time if they leak inside a small, sealed environment, such as the interior of a car or an airplane compartment,” Dr. Sun said.

Almost 20,000 lithium-ion batteries were heated to the point of combustion in the study, causing most devices to explode and all to emit a range of toxic gases. Batteries can be exposed to such temperature extremes in the real world, for example, if the battery overheats or is damaged in some way.

The researchers now plan to develop this detection technique to improve the safety of lithium-ion batteries so they can be used to power the electric vehicles of the future safely.

“We hope this research will allow the lithium-ion battery industry and electric vehicle sector to continue to expand and develop with a greater understanding of the potential hazards and ways to combat these issues,” Sun concluded.

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

Toxicity, a serious concern of thermal runaway from commercial Li-ion battery by Jie Sun, Jigang Li, Tian Zhou, Kai Yang, Shouping Wei, Na Tang, Nannan Dang, Hong Li, Xinping Qiu, Liquan Chend. Nano Energy Volume 27, September 2016, Pages 313–319  http://dx.doi.org/10.1016/j.nanoen.2016.06.031

This paper appears to be open access.