中大工程學院新技術提高液流電池使用壽命

香港中文大學指機械與自動化工程學系副教授盧怡君領導研究團隊成功研發可用於硫基液流電池之新型「電荷增強型離子選擇性膜」;電池在利用新技術和沒有明顯容量衰減下,運作時間提高至逾2千小時、每次充滿電後可持續使用達15小時;而研究結果已刊登於國際期刊《自然‧能源》。

Date: 
Wednesday, May 5, 2021
Media: 
香港商報

CUHK Engineering Develops New Technology Extending the Lifetime of Redox Flow Batteries and the Development of Grid-scale Energy Storage

Date: 
2021-05-04
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A research team led by Professor Yi-Chun Lu, Associate Professor, Department of Mechanical and Automation Engineering, Faculty of Engineering has successfully developed a novel charge-reinforced ion-selective (CRIS) membrane for sulphur-based redox flow batteries, with 15 consecutive hours of runtime and over 2,000 hours cycling without obvious capacity decay. The new battery has taken a significant step forward in the practical application of redox flow batteries in grid-scale storage for renewable energy, and in its commercialisation, by resolving the problems posed by its poor lifetime and low cost-effectiveness. The breakthrough has been recently published in the world-leading scientific journal Nature Energy.
 
Conventional sulphur-based redox flow batteries with poor lifetime are not optimal for grid-scale energy storage
 
Aqueous redox flow batteries are a novel energy storage technology in which electricity is generated by electron transfer between two electrolytes. Compared to lithium-ion batteries, redox flow batteries are distinguished by the high safety, high power density and high flexibility in design which can be widely applied to devices including large-scale storage for renewable energy generated from solar or wind, and fast switching electric vehicles. However, the low energy density and high cost of conventional redox flow batteries determine their market penetration.
 
In 2016, a promising polysulfide-iodide redox flow battery was first invented by Professor Lu’s team taking advantage of the eco-friendly and low-cost sulphur element to greatly improve its energy density and cost effectiveness. However, the sulphur-based redox flow batteries adopting a commercial ion-selective membrane are compromised by the crossover and precipitation of active materials, leading to a rapid capacity decay and poor lifetime, and its application in large-scale energy storage fails to be effective. For example, a polysulfide-bromine project was launched by a UK technology company in the 1990s, but it was abandoned eventually due to its low energy density and poor lifetime. This led to a long-unsolved challenge for scientists.
 
CRIS membrane enables record stability and cycling
 
Based on the findings on the polysulfide-iodide redox flow batteries, Professor Lu has made efforts to improve the cycling stability and lifetime of the batteries. The team has designed the CRIS membrane, a simple and readily applicable incorporation of polymer-bounded carbon to create an ultra-thin cover, and place it on the commercial Nafion membranes (product model N117) to keep the two electrodes apart. The absorption of negatively charged species in porous carbon has strengthened the negative charge of the membrane and reduced the loss of active materials, which dramatically increased the stability and the lifetime of batteries. It is the first time in the world this goal has been achieved by using this new membrane, and it promises an effective application in grid-scale energy storage devices.
 
The demonstrated polysulfide-iodide redox flow batteries revealed an ultralow capacity decay rate (0.005% per day) for 1,200 cycles, and also achieved record high cycling stability and calendar lifetime with over 2,000 hours cycling (approximately 3 months), in comparison to only 160 hours cycling (approximately 6.7 days) with the commercial membrane N117, given that there is no obvious capacity decay. In addition, the coulombic efficiency of new batteries is more than 99.9% for every cycle with 15 consecutive hours of runtime on being fully charged, while there is 3 to 4 consecutive hours of runtime for sulphur-based redox flow batteries with a commercial membrane N117, and for lithium-ion batteries.
 
Under the condition in which the energy storage device with a new battery continuously operated and discharged for more than 15 hours, the surprising result yielded, in turn, a levelised cost of storage (LCOS) for this system which is competitive with other state-of-the-art redox flow techniques for long-duration energy storage applications. This approach can be universally applied in other redox flow batteries using sulphur-bromine or sulphur-iron materials, and in organic redox flow batteries, which goes further to fulfil a high stability of cycling.
 
Professor Lu said, “This approach successfully addresses the long-unresolved problems in the crossover of active materials in redox flow batteries and their poor lifetime. This encouraging membrane design strategy will enable practical commercialisation of sulphur-based redox flow batteries and guide the future development of highly-selective ion exchange membrane.”
 
The full text of journal can be founded at: https://doi.org/10.1038/s41560-021-00804-x

The CRIS membrane developed by Professor Yi-Chun Lu (right) and her team member Dr. Zhejun Li (left) can enhance the lifetime of sulphur-based redox flow batteries, and can be widely applied to large-scale storage devices.

Crossover comparison between CUHK’s charge-reinforced ion-selective membrane (CRIS, right) and commercial Nafion membrane (N117, left).

The prototype of demonstrated polysulfide-iodide redox flow batteries with CRIS membrane.

Diagram of a polysulfide-iodide redox flow battery. CRIS membrane repels the crossover of active materials themselves which dramatically improve the stability and lifetime of batteries, while the commercial membrane is challenged by the crossover of active materials which accelerates the capacity decay of battery.

 

 

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台處理出軌事故 顯港軟實力優勢

四月二日台灣花蓮發生嚴重鐵路事故,滿載乘客的火車與從山坡滑下的工程車相撞,造成四十九人死亡,包括年輕的火車司機。台灣國家運輸安全委員會翻看列車的「黑盒」,了解到司機看到工程車時已盡全力煞車。死者已矣,台灣鐵路管理局堅持殉職司機英勇盡責,然而,卻有部分人認為司機若於意外發生之前能及早慢駛,撞擊力便會大大減輕,死傷人數便不會如此慘重。雖然筆者對此評論不盡同意,但這一說法也是當頭棒喝,引發幾個技術問題:為甚麽司機是唯一的把關者?為甚麽到了最後七秒鐘,他才知道前面有失事工程車?這些問題,也值得香港設計運輸糸統的人思考。

Date: 
Monday, April 26, 2021
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Commentary
Media: 
Sing Tao Daily

CUHK x ASTRI Industry Talk Series: Data Analytics vs. Data Privacy, Synergy or Compromise?

Speakers:

Dr. KH Shum has over 25 years of working experience in both the industry and academia. He is currently the Director of Applied Cryptosystems, Cybersecurity, Cryptography & Trusted Technology Division in Hong Kong Applied Science and Technology Research Institute (ASTRI). His experience in the IT industry includes CTO and technical director positions in various IT companies, specializing in the areas of security, e-payment, and fintech solutions. The IT systems designed by him had been deployed widely in Singapore, Hong Kong, Korea, Japan, Thailand and other countries in Asia.
 
Prof. Sherman Chow got his Ph.D. from Courant Institute of Mathematical Sciences, New York University. He publishes in and serves as a program committee of many top-tier conferences in cryptography and security, including AsiaCrypt, CCS, and Usenix Security, and fintech and privacy conferences, such as PETS and Financial Cryptography. He is the Deputy Editor in Cryptography of IET Information Security, and served on the award committee of The Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies in 2019. He is a European Alliance for Innovation (EAI) Fellow (2019, inaugural), and named as one of the 100 Most Influential Scholars (Security and Privacy, 2018) by ArnetMiner (AMiner). He is also a founding member of IEEE SMC's Blockchain technical committee and a research committee of the Hong Kong Blockchain Society.

Event Details : http://edm.erg.cuhk.edu.hk/cuhk-x-astri-series2/

Registration : https://cuhk.zoom.us/webinar/register/WN_aso8eQbERWuAthOgWUmNvw

Organiser: 
Hosted by: CUHK x ASTRI
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Live Webinar
Date: 
Thursday, April 29, 2021
Time
Thursday, April 29, 2021 to 17:00
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Data Analytics vs. Data Privacy, Synergy or Compromise?
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中大研發AI分析CT影像 僅0.04秒辨別病灶位置 有助判斷出罕見病情

疫情下為病人「照肺」是其中一項找出確診者的重要方法,但解讀醫學影像需時,難免對臨床工作造成負擔。中大工程學院及醫學院組成聯合研究團隊,開發出人工智能(AI)系統,可自動檢測胸部電腦斷層掃描(CT)影像上的肺炎病灶,平均每次僅需0.04秒即能辨別病灶位置,並計算出佔全肺範圍比例,便利醫生分析及診斷。團隊預料,系統可大幅提高相關診斷的效率,協助監察病人病情變化,並減少人為失誤。

Date: 
Tuesday, April 20, 2021
Media: 
蘋果日報網

中大研 AI 助篩查新冠病人徵狀 40 毫秒完成 準確率達 96%

人工智能或可協助診斷新冠肺炎!香港中文大學研究團隊開發了一個人工智能系統(AI),可快速及準確地自動檢測胸部電腦斷層掃描(CT)影像上的新冠肺炎感染徵狀,其準確度高達96%,料可用於診斷、監察治療病情進展及預測治療成效。
 
Date: 
Thursday, April 22, 2021
Media: 
On.CC

AI system ‘rapidly and accurately’ detects Covid

The Chinese University has developed an artificial intelligence system that it says can detect Covid-19 
infections in tomography images, computer-generated cross-sections of people's bodies. It says the 
rapid and accurate system is already being used on the mainland and in Germany. One of the 
researchers, Dr Tiffany So, from the Department of Imaging and Interventional Radiology, told 
Annemarie Evans how it works.
Date: 
Thursday, April 22, 2021
Media: 
RTHK English

中大AI 「驗毒」 40毫秒即知

香港中文大學昨日表示,中大研究團隊開發了一個人工智能系統(AI),可快速自動檢測胸部電腦斷層掃描(CT)影像上的新冠病毒徵狀,準確度高達96%,料可用於診斷、監察治療病情進展及預測治療成效。
 
中大醫學院影像及介入放射學系助理教授蘇宛彤表示,AI具有明顯的速度優勢。傳統臨床閱片流程上,醫生檢查一個CT影像需時約5分鐘至10分鐘,而AI在40毫秒內即可完成,可提高臨床診斷效率及減省相關人手。中大醫學院影像及介入放射學系系主任余俊豪則指,現時AI主要收集未變異的病毒數據,相信如未來再有變種病毒相關確診個案,可加入AI系統內
Date: 
Thursday, April 22, 2021
Media: 
文匯報

AI分析新冠患者CT影像 僅0.04秒揪出肺部病灶

香港中文大學研究團隊開發了一個人工智能(AI)系統,可快速及準確地自動檢測胸部電腦斷層掃描(CT)影像上的新冠肺炎感染病灶,為臨牀醫生提供即時可靠的診斷結果,而系統亦僅需四十毫秒,即百分之四秒內即可準確評估整個三維CT影像,較傳統的臨牀閱片流程需時五至十分鐘更具效率。該項研究近期已發表在「Nature」旗下綜合期刊《npj Digital Medicine》。

Date: 
Thursday, April 22, 2021
Media: 
Sing Tao Daily

CUHK Research Team Develops an AI System for Detecting COVID-19 Infections in CT with a Privacy Preserving Multinational Validation Study

Date: 
2021-04-21
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A multidisciplinary research team from CUHK has developed an artificial intelligence (AI) system for the automated, rapid and accurate detection of COVID-19 infections in chest computed tomography (CT) images. The team was led by Professor Qi DOU and Professor Pheng Ann HENG from the Department of Computer Science and Engineering, Faculty of Engineering, Dr. Tiffany SO and Professor Simon YU from the Department of Imaging and Interventional Radiology, Faculty of Medicine. 
 
Using new federated learning techniques, the AI system is trained on multicentre data in Hong Kong without the need to centralise data in one place, thus protecting patient privacy. “The established AI system is validated on multiple, unseen, independent external cohorts from mainland China and Europe, showing the potential and feasibility to build large-scale medical datasets with privacy protection, so as to rapidly develop reliable AI models amidst a global disease outbreak such as the COVID-19 pandemic,” said Professor Qi DOU from the Department of Computer Science and Engineering. A recent research article describing the outcomes from the study has been published in the npj Digital Medicine, part of the Nature Partner Journals series. 
 
Accurate COVID-19 CT lesion detection with federated deep learning
 
COVID-19 has presented a public health crisis worldwide. In Hong Kong, even with advanced healthcare service systems, the rapidly evolving COVID-19 pandemic has been overwhelming the existing clinical systems and burdening frontline radiologists with an unprecedentedly large emergency workload for data analysis and medical image interpretation. Given this situation, automated diagnostic methods with AI are extremely helpful to facilitate effective management of COVID-19. Radiological imaging can play a complementary role together with clinical diagnostic testing in COVID-19 diagnosis, and can effectively assess the severity and progression in the course of disease. The team in close collaboration with engineering and clinical experts has developed an accurate AI system for automated detection of COVID-19 lesions from CT images, which can provide immediately available results, alleviating the burden of clinicians in interpreting images. Professor Pheng Ann HENG from the Department of Computer Science and Engineering said, “Making use of the cutting-edge federated learning techniques, the AI system can effectively coordinate the patient data across multiple clinical centres in Hong Kong, including Prince of Wales Hospital, for model development. Given the unavoidable challenge of data heterogeneity in medical images, multicentre collaborative effort is essential to capture diverse data distributions, which enhances model reliability for unleashing the potential of AI-powered medical image diagnosis in complex clinical practice.”
 
Model robustness and generalisability on multi-national validation cohorts
 
The established AI model has been externally validated on multiple unseen cohorts from mainland China and Germany. Experimental outcomes revealed that the AI model yields a competitive performance in lesion detection in comparison with radiologist interpretation of chest CT across local, regional and global patients. This wide validation and applicability on cohorts with various imaging scanners and different demographics show outstanding robustness and generalisability of the established AI model in complex real-world situations. Dr. Tiffany SO, Assistant Professor from the Department of Imaging and Interventional Radiology at the Faculty of Medicine, stated, “Besides a high diagnostic accuracy, the AI system can also present a remarkable speed advantage to clinician interpretation. In traditional clinical diagnosis, review and interpretation of a single chest CT takes at least 5-10 minutes for clinicians. In contrast, the AI system can accurately evaluate the same CT data in around 40 ms, showing immense potential to support real-time clinical practice.”
 
This latest study demonstrates the use of privacy preserving AI in responding to a global disease outbreak. In the rapidly evolving pandemic of COVID-19, there is apparently no time to set up complicated data sharing agreements across institutions or even countries. Professor Simon YU, Professor and Chairman of the Department of Imaging and Interventional Radiology at the Faculty of Medicine, added, “Privacy preserving machine learning acts as an important enabler under such situations to gather efforts on digital medicine technology for providing reliable clinical assistance for timely patient care. This study demonstrates the feasibility and efficacy of federated learning for COVID-19 image analysis, where collaborative effort is especially valuable at a time of global crisis. More importantly, beyond assisting COVID-19 management, we believe that AI, which protects patient privacy and achieves reliable generalisability in practice, has enormous potential to revolutionise smart hospitals and healthcare systems in Hong Kong and worldwide.”

A multidisciplinary research team from CUHK has developed an artificial intelligence (AI) system for the automated, rapid and accurate detection of COVID-19 infections in chest computed tomography (CT) images.The research team includes: Mr. Quande LIU (1st from right), PhD student, and Prof. Qi DOU (2nd from left), Assistant Professor, Department of Computer Science and Engineering; and Dr. Tiffany SO (1st from left), Assistant Professor, and Prof. Simon YU (2nd from right), Chairman, Department of Imaging and Interventional Radiology, Faculty of Medicine; CUHK.

Prof. Simon YU believes that AI, which protects patient privacy and achieves reliable generalisability in practice, has enormous potential to revolutionise smart hospitals and healthcare systems in Hong Kong and worldwide.

Dr. Tiffany SO states that review and interpretation of a single chest CT takes at least 5-10 minutes for clinicians in traditional clinical diagnosis. In contrast, the AI system can accurately evaluate the same CT data in around 40 ms, showing immense potential to support real-time clinical practice. 

Prof. Qi DOU states that the established AI system is validated on multiple independent cohorts, showing the potential and feasibility to build large-scale medical datasets with privacy protection, so as to rapidly develop reliable AI models amidst a global disease outbreak such as the COVID-19 pandemic.

 

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