A Mathematical Theory of Subwavelength Resonances in Elasticity with Applications and Beyond
Hong Kong Principal Investigator: Prof Hongyu Liu (City University of Hong Kong)
Mainland Principal Investigator: Prof Youjun Deng (Central South University)
In this project, we aim to establish a comprehensive mathematical theory of the subwavelength resonances in linear elasticity. By the subwavelength scale, we mean the size of the material device is smaller than the operating wavelength. Based on the obtained resonance results, we consider their applications to the studies of several cutting-edge applications including the effective constructions of elastic metamaterials, super-resolution elastic wave imaging and guiding elastic wave propagations. Moreover, two completely new and highly intriguing spectral problems arise from the above proposed studies. We shall also conduct a systematic study and achieve a thorough understanding of these spectral problems. Then we shall consider the applications of the obtained spectral results to tackle several challenging inverse problems and elastic wave imaging problems of practical significances that go beyond the subwavelength regime.
Design and Preparation of Phosphorescent Lifetime-Responsive Polymeric Probes for Enzyme Targeting and Catalytic Activity Sensing in Living Cells
Hong Kong Principal Investigator: Prof Kenneth Kam-wing Lo (City University of Hong Kong)
Mainland Principal Investigator: Prof Qiang Zhao (Nanjing University of Posts and Telecommunications)
This project aims to develop novel strategies to study the activity of enzymes in live cells by making use of: 1) amphiphilic peptides and polymers, and 2) phosphorogenic bioorthogonal probes derived from transition metal complexes. Enzymes are responsible for numerous important cellular processes and play central roles in a number of diseases and conditions. Thus, probes that reveal the activity of enzymes in live cells are urgently required. The existing methods for the detection of enzyme activity have limitations such as poor spatial distribution accuracy, low reproducibility, and susceptibility to the complex microenvironment in live cells. In this project, we will utilize specially designed peptide- and polymer-based amphiphiles, intriguing photophysical characteristics of transition metal complexes, and specific bioorthogonal reactions to develop two innovative strategies for imaging enzyme activity in live cells. In the first strategy, sensing and imaging will reply on enzyme-induced morphological transformation of spherical micelles of a hydrophobic core–hydrophilic shell structure to expose an embedded chemical reporter. The exposed chemical reporter will then undergo a specific bioorthogonal reaction with our proposed transition metal complexes, producing a phosphorogenic response for detection. In the second strategy, the chemical reporter is caged with a peptide-based enzyme substrate and a hydrophilic oligopeptide. Upon enzyme-catalyzed cleavage of the substrate, the released chemical reporter can gain access to the hydrophobic core of the micelles and react with the embedded phosphorogenic bioorthogonal probes. In both strategies, the resulting emission turn-on of the transition metal complexes can be readily monitored by photophysical measurements and laser-scanning confocal microscopy techniques. Additionally, due to the long-lived phosphorescence nature of our target transition metal complexes after the bioorthogonal reaction, time-resolved detection and phosphorescence lifetime imaging microscopy (PLIM) will be exploited, which will significantly enhance the sensitivity, reliability, and accuracy of the detection and imaging of enzyme activity in live cells. We believe that the utilization of these interesting peptide- and polymer-based amphiphiles and phosphorogenic bioorthogonal probes derived from transition metal complexes will further enhance the capability, significance, and diversity of bioanalytical probes and imaging reagents. We are confident that the outcomes of this project will not only provide new and exciting research results, but also bring enormous impact such as the training and career development of research personnel, deliver new knowledge and information to the general public, and produce innovative diagnostic and therapeutic reagents that will greatly benefit patients suffering from enzyme-related diseases and conditions.
Financial systemic risk measures based on Monte Carlo simulation: Theory and Methods
Hong Kong Principal Investigator: Prof Guangwu Liu (City University of Hong Kong)
Mainland Principal Investigator: Prof Liu Hong (Fudan Univerisity)
Systemic risk has been one of the most important issues in financial risk management, and plays a central role in regulatory frameworks of financial systems. As a useful tool in measurement of systemic risk, Monte Carlo simulation allows from complex structure of systemic models, which is appealing in practical applications. However, application of Monte Carlo simulation to systemic risk measurement is far from straightforward, and research in this area has been underdeveloped. This project aims to fill this gap.
In this project, we propose to study Monte Carlo methods for systemic risk measures, including the commonly used conditional value-at-risk (CoVaR) and marginal expected shortfall (MES). We focus on the development of efficient simulation methods and variance-reduction techniques that produce efficient estimators for these systemic risk measures, and their sensitivities. With the proposed methods for sensitivity analysis, we propose to further study the quantification of model uncertainty in systemic risk measurement, portfolio optimization under systemic risk constraints and approaches to distributionally robustifying these models. The project focuses on both the design of practically useful methods and algorithms, and their theoretical analysis. It is expected that research outputs of this project may lead to a set of useful quantitative tools for systemic risk measurement with sound theoretical guarantees.
Two-Dimensional (2D) Metallic Glasses: From Synthesis, Physical/Mechanical Properties to Alloy Design
Hong Kong Principal Investigator: Prof Yong Yang (City University of Hong Kong)
Mainland Principal Investigator: Prof Pengfei Guan (China Academy of Engineering Physics)
Bulk metallic-glasses (BMGs) are amorphous alloys with a typical size > 1 mm in three different directions. They have been attracting tremendous research interest over the past decades because of their superb strength, high elastic strain limit and excellent thermo-plastic formability. However, BMGs are also notorious for their lack of ductility at room temperature, particularly under tension, which hinders their engineering applications. By comparison, two-dimensional metallic glasses (2D MGs) have a thickness < 100 nm while a width and length > 1 mm. Compared to BMGs, 2D MGs are strong but still ductile because of their nano-scale thickness, which can effectively avoid catastrophic shear banding that leads to brittle fracture of BMGs. More importantly, the physical properties of 2D MGs are mainly determined by their surface layers. Therefore, 2D MGs are a good candidate material for many advanced applications, such as flexible electronics, catalysis, clean energy, and biomedical engineering, in which the mechanical flexibility and the high surface area of a material are pre-requisite. In this project, we will first synthesize different type of 2D MGs using the method of polymer surface buckling enabled exfoliation (PSBEE) we recently developed. After that, we will perform a systematic study of the mechanical and thermal properties of the 2D MGs by combining experiments, simulations and data-based modelling. The outcome of our research could spur innovations with new applications of amorphous alloys in the areas which have not been explored before.
Towards Secure and Privacy-enhanced Machine Learning as a Service
Hong Kong Principal Investigator: Prof Cong Wang (City University of Hong Kong)
Mainland Principal Investigator: Prof Chao Shen (Xi’an Jiaotong University)
Nowadays, the emerging cloud Machine-Learning-as-a-Service (MLaaS) platforms, driven by leading companies like Google and Amazon, have established the foundation for many real-world Internet applications. On the rise of this ML marketplace, how to properly ensure the data usage, transfer, and rigorous protection has attracted wide attention from the public. Latest studies have shown that ensuring MLaaS security and privacy is particularly difficult, and today’s understanding on these problems is still underdeveloped. Firstly, most prior studies stand upon simplified threat assumptions. They often overlook how significantly the background information (i.e., the readily-available open-source models or public benchmark datasets) and compound attacks (e.g., model extraction followed by membership inference) would help lower the attack bound against existing MLaaS in practice, preventing us from approaching the real upper bound of potential threats. Secondly, existing countermeasures are likely to become ineffective on practical MLaaS platforms, because the existing threat models can no longer capture the capability of real-world adversaries. Even solutions under strong privacy guarantees, such as differential privacy, have obvious shortcomings, such as significant model accuracy decay, and unexpected side effects, such as higher model bias. Yet, most countermeasures are incompatible with existing cloud MLaaS platforms. These observations demand new defense mechanisms, especially for real-world cloud MLaaS platforms. In this project, we propose to advance the frontier of MLaaS security and privacy issues, with focus on practical exploitation attacks and effective countermeasures. Our research thrusts include: 1) Investigate efficient exploitation attacks of black-box MLaaS in practice, including new model extraction and adversarial evasion attacks in real-world settings; 2) Comprehensively analyze unexpected information leakage for MLaaS, including both group-level and record-level privacy breaches respectively; 3) Investigate practical and effective solutions on hardening security and privacy for MLaaS, including both output perturbation and parameter perturbation defences accordingly. Our results will contribute new insights into the practical threats to MLaaS, and benefit all deep learning applications involving sensitive or strongly regulated data.
Invasive Species in Greater Bay Area: Population Genomics of a Freshwater Snail (Biomphalaria Straminea) and Risk Assessment of Imported Schistosomiasis Transmission
Hong Kong Principal Investigator: Prof Jerome Ho-lam Hui (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Zhongdao Wu (Sun Yat-Sen University)
Schistosomiasis, or also generally known as bilharzia or snail fever, is a parasitic disease caused by trematode flatworms of the genus Schistosoma. Schistosomiasis is considered by the World Health Organisation as the second most prevalent parasitic diseases after malaria affecting millions of people worldwide. According to the Law of Communicable Diseases Prevention and Control in China, schistosomiasis is currently listed in the category B of notifiable diseases to be reported. The infection of S. mansoni in humans are caused by the release of cercariae larvae by freshwater snails Biomphalaria, which penetrate the skin of human when exposed in water.
In our previous published works, the detailed geographical and habitat distribution of Biomphalaria straminea in different districts in the Greater Bay Area (GBA) are revealed. Moreover, by comparison of their spatiotemporal distribution, a relatively fast expansion of snails B. straminea is undergoing. Considering the unavoidable trading and travel between different places in the globe, such as businessmen, workers and tourists coming from the schistosomiasis endemic areas in Africa, and increasing travels within the GBA; it is important to understand the schistosomiasis vector B. straminea.
In our preliminary study, we have obtained a high-quality genome of B. straminea for the first time providing an excellent genomic resource as backbone to carry out population genomic study of B. straminea in the GBA. A set of microRNAs were also identified via transcriptomes which support the feasibility of the understanding the following questions:
1) Are there any distinct populations of B. straminea in the Greater Bay Area?
2) What and how are microRNAs involved in the biological interactions of S. mansoni and B. straminea?
3) What and how are microRNAs involved in the interactions of abiotic factors and B. straminea?
In this project, we will unravel the population genomics and roles of understudied microRNAs in schistosomiasis vector. Our working hypothesis is that there are different populations of B. straminea in the Greater Bay Area that have different responses to S. mansoni and abiotic factors.
Understanding the biology of snail B. straminea is important to understand the potential spread of schistosomiasis as well as provide information for necessary measures to control this disease vector. In addition, in this case, it also allows the understanding of a disease-spreading invasive species in the GBA. This project will provide valuable new insights into the understudied population genomics and noncoding RNAs of schistosomiasis vector.
Differential Roles of CGRP in Osteoarthriti Pain and Pathology
Hong Kong Principal Investigator: Prof Ling Qin (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Di Chen (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences)
Osteoarthritis (OA) is one of the most common painful and disabling musculoskeletal diseases, currently affecting over 250 million people worldwide. It severely affects the quality of the patient’s life and imposes a huge economic burden on their families and society. With population aging and the increase in life span, OA is now becoming a major challenge to public health and the social economy. Pain is the main cause in patients with OA who are seeking a clinical diagnosis. In clinics, a dissociation of pathological changes and pain symptoms in OA patients often occurs. This dissociation was also found in our pilot study when evaluated the clinical cases. However, the molecular mechanisms of this dissociation are still unknown, which affects the effective diagnosis and treatment of OA, and poses a challenge in current clinics. In our previous collaborative study published in Nature Medicine, we found that calcitonin gene-related peptide (CGRP) is a key mediator between the musculoskeletal system and nerve system, and a potential target for OA pain management. The current collaborative project is aiming to investigate the effect and molecular mechanisms of CGRP on pathological progression and pain production in OA, to identify novel molecular targets for the treatment of OA. In our pilot study, we found that mice with CGRP deficiency showed differential effects on OA pain and its pathology, namely, alleviating OA pain with less nerve fiber in synovium but accelerating articular cartilage degradation with upregulation of MMP13 and Adamts5. We postulate that inhibition of CGRP may also exacerbate joint pathology, possibly promoting local inflammatory responses and triggering sensory nerve activation in other tissues (not joint tissues), thus counteracting its role in blocking pain at the administration site. Therefore, there is an urgent need to study the role and mechanisms of CGRP in OA pain and pathology to develop innovative therapy that can both relieve pain and attenuate OA joint damage. We hypothesize that inhibition of CGRP may reduce the sensory nerve fiber invasion in joint tissues to alleviate OA pain by downregulating NGF and TRPA1. Meanwhile, inhibition of CGRP promotes cartilage matrix degradation through Runx2, β-catenin, and NF-κB pathways to upregulate MMP13 and ADAMTS5. Based on our preliminary data and established collaborative platforms, this preclinical study will explore a novel strategy for OA treatment and pain relief by targeting CGRP downstream genes.
Synergistic Coupling of Thermogalvanic and Thermodiffusion Effects and System Optimization of Liquid Thermocells for Low-Grade Heat Harvesting
Hong Kong Principal Investigator: Prof Dongyan Xu (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Run Hu (Huazhong University of Science and Technology)
The proposed research aims to develop liquid thermocells with high thermopower and large current density through synergistically coupling thermogalvanic and thermodiffusion effects via the selective ion-exchange membrane and synthesizing graphene-oxide-based porous electrode materials for low-grade heat harvesting.
Low-grade heat exists ubiquitously and provides an abundant energy source for wearable devices and wireless sensor nodes. Liquid thermocells are promising for low-grade heat harvesting due to their merits of the relatively high thermopowers, low cost, and high flexibility. To date, two types of liquid thermocells, i.e., thermogalvanic and thermodiffusion cells, which convert heat to electricity through redox reactions and thermodiffusion of cations and anions, respectively, have drawn much attention. Despite of great advance in the enhancement of either thermogalvanic or thermodiffusion thermopower, coupling of these two effects in one system is still rare and challenging. Only a recent work reported a giant thermopower of 17 mV/K for ionic gelatin by combining thermogalvanic and thermodiffusion effects, which opens a new avenue to achieve liquid thermocells with high thermopower.
In the preliminary work, the Hong Kong and Mainland PIs have developed a polarized ternary electrolyte containing I−/I3− redox couple, methylcellulose, and KCl, which achieves ultrahigh thermopowers of 8.18 mV/K for n-type and 9.62 mV/K for p-type. The thermopowers mainly come from the thermogalvanic effect of I−/I3− redox couple enhanced by methylcellulose and KCl, while the thermodiffusion effect of KCl only contributes marginally. In this project, we propose to develop ion-exchange membranes that selectively allow the passage of cations or anions to boost the thermodiffusion thermopower of the ternary electrolyte. Aside from a high thermopower, a large current density is crucial to enlarge the power output of a liquid thermocell. Porous graphene-oxide-based functional electrode materials will be developed to simultaneously enhance the current density, stability, and thermodiffusion thermopower of liquid thermocells. An ion transport model will be developed to guide the synergistic coupling of thermogalvanic and thermodiffusion effects and molecular dynamics simulations will be conducted to understand the complex interactions at the electrode-electrolyte interface. High-performance liquid thermocells will be fabricated through a hydraulic crimping technique for low-grade heat harvesting.
The proposed research will enhance fundamental understanding on how to synergistically couple thermogalvanic and thermodiffusion effects in liquid thermocells. The ion-exchange membranes and porous electrode materials developed in this project can be extended to other electrolyte systems. The success of the project will advance technology development of liquid thermocells for low-grade heat harvesting.
Hybrid Integration of Layered Group Ten Transition Metal Dichalcogenides on Planar Waveguides for Long Wavelength Optical Communications
Hong Kong Principal Investigator: Prof Hon-ki Tsang (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Zhenzhou Cheng (Tianjin University)
We investigate the hybrid integration of group ten transition metal dichalcogenides (TMD) on silicon and germanium waveguides for optoelectronic devices operating in the 2-micron wavelength band. Our approach is based on the use of the layered dichalcogenides formed by the combination of a group ten transition metal element with a chalcogens element. We plan to develop high-speed and high-responsivity photodetectors and optical modulators by the hybrid integration of atomically thin two-dimensional (2D) TMD materials on optical waveguides and plasmonic structures designed for operation in the 2-micron wavelength band. By taking advantage of the long interaction lengths made possible by the in-plane interaction between the evanescent optical field from the waveguide and the layered 2D TMD material, we aim to attain high quantum efficiencies in the photodetectors and high-speed response by minimizing the path length for the extraction of photogenerated carriers. We shall address the main challenges of engineering the heterointerfaces and investigate the use of Schottky barriers or tunnel barriers for reducing the dark current of the photodetectors, and explore the use of bound state in the continuum waveguide structures to maintain defect-free planar 2D structures without perturbation from the sharp etched corners of conventional waveguides, and maintain the high carrier mobility in the 2D materials. We shall also explore the use of plasmonic slot structures to concentrate light in the few-layer TMDs which may further improve the speed and responsivity of the photodetectors. Apart from making high-speed and efficient low-noise photodetectors in the 2-micron wavelength band, we shall also develop hybrid integrated structures for making high-speed optical modulators.
Thermokarst Landforms on the Qinghai-Tibet Plateau: Spatio-temporal Evolution and Future Changes
Hong Kong Principal Investigator: Prof Lin Liu (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Xiaoqing Peng (Lanzhou University)
Permafrost has been undergoing fast degradation on the Qinghai-Tibet Plateau due to climatic warming and anthropogenic disturbance. One significant indicator of thawing ice-rich permafrost is the development of thermokarst landforms. These landforms and associated dynamic processes play important roles in permafrost-carbon feedback and have strong impacts on the local and regional ecosystem and infrastructure in cold regions. However, thermokarst landforms on the vast Qinghai-Tibet Plateau are not being mapped systematically. Accordingly, we lack basic knowledge about their spatial-temporal evolution or how they will change in the future.
To address these problems, this joint research project will map and quantify two typical thermokarst landforms, namely, thaw slumps and thermokarst lakes over the Qinghai-Tibet Plateau, quantify their spatial and temporal variabilities, determine the underlying controlling factors, and predict their future changes. Synthesizing the complementary expertise of the mainland and Hong Kong teams, we will integrate field measurements, long-term permafrost monitoring, new remote sensing imagery, state-of-the-art deep learning algorithms, machine-learning-based statistical modeling to carry out an interdisciplinary, comprehensive, up-to-date, forward-looking study on the spatial-temporal dynamics of typical thermokarst landforms over the Qinghai-Tibet Plateau.
The specific objectives of our proposed research are (1) to obtain the spatial distribution and temporal changes of thermokarst lakes and thaw slumps on the Qinghai-Tibet Plateau since 2000, (2) to determine the controlling factors of the spatio-temporal variabilities of the mapped thermokarst landforms, and (3) to predict the future changes of thermokarst-affected areas on the Qinghai-Tibet Plateau till 2100 under different scenarios of global socioeconomic changes. We will produce an inventory of thermokarst landforms over the entire plateau permafrost area, the first of this kind enabled by deep learning and high-resolution imagery. We will also elucidate the climatic, topographic, soil, and vegetation factors that affect distribution and dynamics of thermokarst landforms in a quantitative manner.
This project will improve the quantitative understanding of climatic impacts on permafrost and associated feedbacks, provide scientific support for engineering project planning, natural resource management, and environmental protection on the fragile Qinghai-Tibet Plateau.
Decoding Diverse Motor Intentions from Macro and Micro Features of High-Density Electromyographic Signals for Naturalistic Control of Neural Prostheses
Hong Kong Principal Investigator: Prof Vincent Chi-kwan Cheung (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Guanglin Li (Shenzhen Institute of Advanced Technology, China Academy of Science)
This is a neural engineering project from a Hong Kong-Shenzhen team with the ultimate goal of enabling amputees and stroke survivors to intuitively and naturally control a neural prosthesis or a rehabilitative robot for any actions they wish to perform.
Millions of people in the world suffer from upper-limb motor disability following amputation or brain injury. Clinically efficacious rehabilitative robots and flexible limb prostheses driven by the wearer’s motor intentions are still lacking. It remains formidably challenging to design an assistive robot or prosthesis whose actions match the motor intentions of the device’s wearer. One design direction that should permit intuitive and flexible device control is to directly drive the robot or prosthesis with signals recorded from the wearer’s central nervous system (CNS). Since limb muscles are activated by neurons in the spinal cord and brain, activities of multiple judiciously chosen muscles should contain information that indicates the exact motor tasks intended by the wearer. As such, we aim to build decoders of muscle signals, called electromyographic signals (EMGs), that can accurately recognize diverse motor intentions from amputees and stroke survivors through real-time, intelligent EMG analysis. The decoder’s outputs can then drive the device’s actuation.
To further increase the decoder’s capability to recognize diverse motor intentions, we will employ three strategies. First, we will extract features known as muscle synergies from EMGs for motor-task classification. Since muscle synergies are the natural activation units utilized by CNS for motor control, muscle-synergy features should result in with higher task recognition accuracy. Second, we will record high-density EMGs (hd-EMGs) in addition to traditional EMGs, and extract features known as motor unit spike trains (MUSTs) from hd-EMGs. Since MUSTs represent the firing of individual motoneurons that recruit muscle fibers, these features should permit estimation of the wearer’s intended joint motions for accomplishing the willed task. Third, to minimize the data needed for retraining the decoder in new situations, we will employ a machine-learning technique known as transfer learning so that our decoder can be quickly adapted to recognize new tasks beyond the predefined tasks.
When validated, our novel approach of combining macro muscle-synergy-based features and micro MUST-based features for motor-intention decoding may be readily incorporated into the design of next-generation rehabilitative robots and neural prostheses. The project will not only be impactful in rehabilitation science and motor neuroscience, but also contribute to the growth of neural engineering in the Greater Bay Area.
Theranostic and Biophysical Lanthanide Approaches to Evaluate Protein-Protein Interactions in Combat Cancer Disease
Hong Kong Principal Investigator: Prof Ka-Leung Wong (The University of Hong Kong)
Mainland Principal Investigator: Prof Yong Fan (Fudan University)
Theranostics is a new field of medicine which combines therapy based on specifically targeted diagnostic tests and imaging techniques enabling simultaneous follow-up of the treatment effects. With a key focus on individual patients, theranostics provides a transition from conventional medicine to a contemporary personalised and precision medicinal approach. There are limited examples of using thermal imaging probes as theranostics due to them being limited by a poor imaging resolution of only 1 micron. NSFC proposed a new thermal sensor in however, its target specificity in vitro/in vivo still has room for improvement. Our proposal focuses on developing the first generation of thermal-imaging based theranostic agents that are cancer-specific in vitro and in vivo. Our cancer of interest is Epstein–Barr virus (EBV)-associated tumours. EBV is aetiologically linked to at least seven distinct types of human cancers and nasopharyngeal cancer is one of the cancers that is common in Hong Kong. The dimerization of EBNA1 has been found to be essential for EBV-associated cancer growth, thus HK PI recently an organic EBNA1-specific binding peptide sequence to hinder the dimerization, but the auto-fluorescence of organic chromophore diminishes the quality of in vitro imaging. NSFC partner’s nanothermometers has been proved to detect a change of 10 degrees in the physiological range and sensitive enough for monitoring the binding process upon addition of target protein. The proposed joint project herein helps both PIs to overcome the disadvantages of their own bioprobes and obtain a practical theranostic agents combining therapeutic and thermal-imaging techniques for monitoring and inhibiting EBV associated cancers.
Preliminary studies have shown that our dual-functional lanthanide bioprobes conjugated with EBNA1-specific peptides are promising in imaging and controlling the growth of EBV-related tumorigenesis by pinpointing the dimerization step as the main process to inhibit. The potential of this project lies in unravelling the missing connection between EBNA1 and the tumorigenesis of EBV-associated cancers, leading to cure for tumours like the Hodgkin lymphoma. This proposed study concentrates on developing polyfunctional agents that could perform drug delivery and imaging. Temperature sensitive lanthanide-based agents/bioconjugates will be designed that, preferably, can be triggered by physical dimerization (heat change during the binding process between our designed probe and EBNA1) for imaging and subsequently inhibit EBV associated cancer growth. A perfect complementation of the thermal imaging probe in the NSFC partner and EBNA1-targeting on EBV associated cancers in the HK side will be our final goal.
The project proposes the design and fabrication of light-emitting lanthanide-based NPs (including persistent luminescence NPs) and heater-thermometer nanostructures (either highly absorbing NPs with intentionally low emission quantum yields or two different NPs linked together in a dual heater-thermometer nanoplatform) with relative thermal sensitivity Sr >1%K−1, resolution <0.1 K and efficiency of light-(or magnetic field) to-heat conversion >50% (figures of merit well above the currently achieved values). For in vivo experiments, NIR-emitting NPs will be used to take advantage of the low optical attenuation of tissues in the second biological window (BW-II, 1000-1350 nm). This would provide large subtissue penetration depths, while remotely performing luminescence thermal sensing. For in vitro experiments, two different NPs (one heating and one sensing) operating in BW-I (650-1000 nm) will be linked together in a heater-thermometer nanoplatform, either by polymeric encapsulation or by molecular linking.
According to these preliminary findings, our current proposal will focus on the development of lanthanide nanomaterials with impressive and responsive photophysical properties, cell permeability and EBNA1 specificity which will be suitable for long-term live cell/animal imaging, creating a new generation of smart dual-function (imaging and inhibition) agents for EBNA1-specific peptide delivery into EBV-related tumour cells via EBNA1. This project is divided into three stages:
Stage 1. Synthesis of a library of EBNA1-specific thermal sensitive theranostic agents and study of the mechanism of their binding to EBNA1 both in vitro and in vivo;
Stage 2. In vitro studies of the newly proposed thermal sensitive agents. Evaluation of their cytotoxicity and subcellular localization (via thermal imaging) in EBV +ve and -ve cells;
Stage 3. In vivo thermal evaluation of the theranostic effect, and pharmacokinetic studies of in a mouse model with EBV-associated cancer xenograft.
Developing Machine Learning Methods for Industrial Big Data Analysis and Traceability of Product Quality Abnormality
Hong Kong Principal Investigator: Prof Yiu Ming Cheung (The University of Hong Kong)
Mainland Principal Investigator: Prof Qiang Liu (Northeastern University)
Fault detection and traceability of product quality abnormality (FDT-PQA) play a significant role in maintaining product quality in industrial manufacturing processes such as those in the petrochemical, mineral processing, and steel manufacturing industries. Recently, several data-driven-based FDT-PQA techniques have been proposed and investigated in power distribution networks, the Tennessee Eastman process, and other domains, but most of them focus on small-scale and static fault location tasks. They are generally ineffective or may even fail when applied to real-world complex industrial processes. In general, industrial processes characterized by large-scale multiple processes with unclear latent mechanisms and operations in a dynamic environment result in three key issues for FDT-PQA: (1) Multiple processes and complex reactions generate high-dimensional heterogeneous data with categorical and numerical attributes (i.e. mixed-type attributes). Selecting discriminative features from such data is a challenge; (2) Cross-coupled industrial processes often feature complex mutual reactions with multiple variables, making the traceability of PQA challenging; (3) Dynamic manufacturing environments will occasionally cause the occurrence of concept drift, i.e. the patterns that a model have learned are not held any more. Furthermore, it is often expensive or even impossible to obtain the labels of the generated data from the manufacturing process. Another challenging issue is therefore how to quickly update the FDT-PQA model under concept drift with few labeled data.
This project will develop machine learning methods to address the three above-mentioned challenges. First, we will design feature selection methods for high-dimensional heterogeneous data with mixed-type attributes. Then, a generative adversarial network-based one-classification model and a hierarchical representation learning method will be proposed for FDT-PQA. Furthermore, we will design a local similarity measure and a contrastive learning-based method to address the problem of concept drift in dynamic environments with insufficient labeled samples. Finally, we will design and build a semi-physical simulation platform to evaluate the effectiveness of the proposed method.
Through this project, we will gain a thorough understanding of the underlying mechanism of FDT-PQA. The project’s output will be an FDT-PQA solution for the industrial manufacturing process. Furthermore, the research findings will provide the basis for the development of FDT-PQA. The techniques developed in this project will also be essentially applicable to a variety of applications in machine learning and data science.
Automatic Understanding and Generation of Long Descriptive Text
Hong Kong Principal Investigator: Dr Lingpeng Kong (The University of Hong Kong)
Mainland Principal Investigator: Dr Yansong Feng (Peking University)
In the field of artificial intelligence (AI) there is long-standing and ever-increasing demand for automatic understanding and generation of long descriptive text, found for example in scientific articles, instruction books, or recipes. Machines and robots have been facilitating both industrial applications, such as automotive manufacturing, and civil applications, such as smart homes. These intelligent systems therefore need to understand the description of a procedure in natural language and generate descriptive text telling human users how they plan to carry out certain tasks. This project is about machine-learning algorithms that uncover useful internal structures in long descriptive text and generate long descriptive text in a consistent, controllable, and comprehensive manner. These methods will be used in various real-world applications and will lead to advances in communications with intelligent systems.
Molecular insights of cell therapy for skeletal conditions of low bone mass
Hong Kong Principal Investigator: Prof Danny Chan (The University of Hong Kong)
Mainland Principal Investigator: Prof Guangdun Peng (Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences)
Our bones become weak with age, changing shape and break easily. It is well-known that osteoporosis and kyphosis is associated with ageing, evident from the posture and gait in elderlies. However, many of us may not know many children are born with weak bones that never recover, and they will live with deformed bones that are broken frequently for their entire life, such as osteogenesis imperfecta (OI), or “glass bones”. It is a genetic condition due to the production of poor collagens needed to build strong bones. Bone is constantly turned over, a remodeling process where old bones are removed and replaced with new. It is a balanced process keeping our bone mass constant for most of our life, until we get old. Thus, our bones are completely “renewed” every decade. This task is carried out by bone cells called osteoblasts that build bone. There are pharmacological treatments to slow the breakdown of bones, favoring bone building to improve bone mass for OI patients, but it is far from satisfactory as the “bone quality” remains poor. A promising treatment is to incorporate normal cells into the bones of OI patients, so that these cells can build normal bone during remodeling, and over time replace the poor bone with good bone. This project leverage on the technology of making mouse chimeras to mimic cell therapy containing a mixture of OI and normal cells in all tissues and organs including skin and bone, and to assess the outcome over time to determine how and to what extend can the presence of normal cells improve the quality of bone. We will use state-of-the-art technologies in single cell transcriptomics and innovative cell tracking from genomic information to investigate the tissues. The completion of this project will provide critical biological insights and information for formulating cell therapy strategies to treat OI patients, that can also apply to elderlies with osteoporosis.
Micro-circuitry and beneficial effects of auditory perceptual learning
Hong Kong Principal Investigator: Dr Sau Wan Lai (The University of Hong Kong)
Mainland Principal Investigator: Prof Xiaoming Zhou (East China Normal University)
Auditory perceptual learning tasks are not only closely associated with auditory perception functions, but are also involved in memory retrieval and decision-making components. This leads to the hypothesis that besides the auditory cortex, auditory perceptual learning induces synaptic plasticity in brain regions involved in decision making and memory acquisition, storage, and retrieval. However, the dynamic neural networks and the excitatory and inhibitory micro-circuit plasticity, and the molecular mechanisms involved in auditory perceptual learning are still largely unknown.
Aging is a natural process and is accompanied by a steady decline of cognitive abilities in the domains of perception, attention, and working memory. In human studies, attention-demanding, speed-challenged auditory or visual training were found to improve the speed of processing and spatiotemporal signal resolution performance in aging. Animal studies also demonstrated that intensive training could enhance attention control, suppress distractors, and improve processing accuracy and speed in models of aging. These studies suggest that the physical or functional status of the brain machinery in aging can be substantially rejuvenated via simple forms of intensive training. Nonetheless, the mechanism and the beneficial effects of auditory perceptual learning in aging remain elusive. The overall goals of this project are to elucidate the neural circuits and mechanisms underlying the beneficial effects of auditory perceptual learning in aging.
Deciphering recurrent mutation combinations in myelodysplasia syndromes using zebrafish – mechanistic and therapeutic insights
Hong Kong Principal Investigator: Prof Anskar Yu-Hung Leung (The University of Hong Kong)
Mainland Principal Investigator: Prof Yiyue Zhang (South China University of Technology)
Myelodysplasia syndromes (MDS) are a group of heterogeneous clonal haematological disorders, however, the mechanisms underlying the disease heterogeneity is still unclear. Current studies have shown MDS patients often harbour more than one mutation and acquisition of specific mutations during disease initiation and progression may significantly influence the clonal heterogeneity and therapeutic response of the disease. The two parties in this project will combine their respective expertise and resources to tackle the scientific question of " Do specific combinations of MDS-associated mutations exert synergistic effects to drive disease pathogenesis and leukemogenesis?”. Specifically, we will use zebrafish to model common mutation combinations in MDS and investigate the effects of specific mutation combinations on haematopoiesis and disease progression. Furthermore, the model can be used as the whole animal platform to screen novel agents for personalized treatment of MDS. The expected results will not only provide a comprehensive understanding of the mechanisms that underlie MDS heterogeneity but also provide valuable insights into disease diagnosis and prognosis. Importantly, the research platform can be applied to the research of other haematological other haematological disorders and diseases in other organ systems.
Mechanism research of borosilicate bioactive glass modulating the microenvironment of bone regeneration and inhibiting on the bone tumors
Hong Kong Principal Investigator: Prof William Weijia Lu (The University of Hong Kong)
Mainland Principal Investigator: Prof Haobo Pan (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences)
Bone tumors are prone to metastasis and recurrence, which results in a low survival rate and poor quality of life to patients after surgery. Meanwhile, acidic metabolism products of tumor cells enhance cell functions of osteoclasts, thus makes the persistent destruction of bone, and ultimately raise the risk of bone fractures. Alkaline microenvironment, previously reported by us, is an effective approach to bone metabolism regulation, which can inhibit the activity of osteoclasts and suppress bone resorption, while simultaneously stimulating osteogenesis differentiation of bone marrow mesenchymal stem cell. This is a phenomenon called “switch on/off effect” related to bone formation/resorption, which finally restores bone homeostasis. Furthermore, alkaline microenvironment destroys the extracellular acidic environment of tumor. Hereby, on the basis of our research collaborated with Mainland, this project aims to develop a high-strength borosilicate scaffolds which can maintain high pH to the surroundings and intervene the normal metabolism of tumor cells via blocking the communication between tumor cells and osteoclast, regulating the polarization of macrophage, restoring immune surveillance and response, and ultimately inhibiting the biological activity of tumor cells. Besides, borosilicate scaffolds made osteoimmunology, osteogenic differentiation and angiogenesis via alkaline microenvironment and releasing of functional ions e.g. Sr2+ and Mg2+. Thus, the restoration of normal bone homeostasis can potentially be anticipated. This bilateral cooperation between Hong Kong and Mainland for bone tissue engineering has been continued for more than 10 years and has made remarkable achievement in different areas. The joint group has postulated the concept of alkaline effect to bone regeneration and more than 50 cooperated articles have been published. Furthermore, a newly developed bioactive bone cement based on borosilicate materials has been applied clinically. In summary, the successful implementation of the project will probably provide new therapeutic approach to the regeneration of bone tissue and the treatment of bone tumors after surgery. The synergic effect via pH and Fenton reaction seems to be an interesting concept to the next generation of bone implants design for bone tumor.
Roles of biogenic volatile organic compounds (BVOCs) for canopy and understorey trophic interactions
Hong Kong Principal Investigator: Dr Louise Amy Ashton (The University of Hong Kong)
Mainland Principal Investigator: Prof Akihiro Nakamura (Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences)
In response to herbivory, plants often emit various kinds of gasses (biogenic volatile organic compounds or BVOCs) which are organic molecules with high vapour pressures. These compounds are believed to repel herbivores and attract predators and parasitoids of herbivores. There is large uncertainty about whether forests are more structured by ‘bottom-up effects’ such as BVOC chemical defence by plants or ‘top-down effects’, such as the interactions between predators and herbivores. This remains a fundamental question in ecology. How the emission of BVOCs by plants will shift under climate change is uncertain, as are the cascading effects for processes like herbivory. There are complex feedback loops between plants, the environment and biotic interactions which complicate predictions, particularly for understudied ecosystems such as tropical and subtropical rainforests.
Due to the complexity of these interactions, it is essential to integrate multiple approaches across multiple spatial and temporal scales to understand how biodiversity and associated ecosystem functions are shifting. Our project proposes an integrative and multidisciplinary approach encompassing experimental field ecology and drones to investigate the dynamics of one of the last frontiers in biodiversity research: the forest canopy.
We will conduct experiments on greenhouse seedlings to investigate the effects of drought and herbivory. We will then sample BVOCs in tropical and subtropical forests to investigate the dynamics of BVOCs across scales of time and space in both the forest canopy and understorey. We will also use a remote sensing drone to extend our sampling across much larger areas of forest. Finally, we will prepare blends of BVOCs which will be deployed in the field to test relationships among BVOCs, insect herbivores and predators. The proposed research is the first of this type to capitalize on a network of two canopy cranes located in tropical and subtropical rainforests across different biogeographical regions in Mainland China.
Sex chromosome evolution in Pungitius sticklebacks
Hong Kong Principal Investigator: Prof Juha Merilä (The University of Hong Kong)
Mainland Principal Investigator: Prof Baocheng Guo (Institute of Zoology, Chinese Academy of Sciences)
Sex chromosomes are involved in several fundamental biological processes such as sex determination, sexual selection, and dimorphism, as well as in speciation, and thus, understanding their evolution can provide insights to understand many important biological phenomena. While sex chromosomes in mammals are extremely conserved and fully differentiated, those in fishes and reptiles are very fast evolving. The reason and evolutionary significance of this phenomenon is largely unknown. The applicants’ have earlier shown that the turnover of sex chromosomes in Pungitius sticklebacks is frequent, making these fish a good model system to study sex chromosome evolution. In this project the applicants will work in collaborative fashion to study turnover of sex chromosomes in Pungitius sticklebacks. The aim is to identify sex determination systems in three additional Pungitius species and two divergent lineages within one species, as well as assemble their sex chromosomes. We will also investigate accumulation of deleterious mutations on sex chromosomes to understand dynamics of their molecular evolution and their evolutionary significance in speciation.
Hyperspectral Image Restoration, Unmixing, and Classification by Low-Rank Tensor Recovery, Nonlocal Self-Similarity, and Deep Priors
Hong Kong Principal Investigator: Prof Michael Kwok Po Ng (The University of Hong Kong)
Mainland Principal Investigator: Prof Lianru Gao (Aerospace Informationr Research Institute, Chinese Academy of Sciences)
Hyperspectral remote sensing images, recording electromagnetic information of subjects on Earth surface, have played an important role in countless applications, such as earth observation, environmental protection, and natural disaster monitoring. New-generation hyperspectral imagers tend to have a higher spectral resolution (i.e., to acquire images with more color channels), which enables precise material identification with higher accuracy. For example, a Chinese GaoFen-5 satellite, equipped with the Advanced Hyperspectral Imager (AHSI) and launched in May 2018, has 330 color channels covering the spectrum from visible to short-wave infrared. However, the ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of noisy images, thus calling for effective denoising methods and data processing techniques. To address the complex noise and to improve the accuracy of information extracted from HSIs, this project will use China’s newly launched Gaofen-5 satellite image as the main data source, combine the advantages of cooperation between the mainland and Hong Kong teams, and study theoretical problems of hyperspectral image restoration, spectral unmixing, and classification by combining traditional machine learning and deep learning techniques. Our main focus will be proposing new mathematical models and algorithms for three typical inverse problems (restoration, spectral unmixing, and classification) in hyperspectral images by exploiting the data structure of hyperspectral images. We propose the following directions of research. (i) Hyperspectral image restoration by low-rank tensor recovery, nonlocal self-similarity, and deep priors. (ii) Hyperspectral image unmixing by low-rank tensor decomposition and training deep prior for abundance maps. (iii) Low-rank tensor decomposition and deep learning-based hyperspectral image classification. The results of this project are expected to greatly improve the quality of information extracted from HSIs. Therefore, the results of this project will be beneficial to earth observation research by improving the accuracy of information extracted from HSIs. Furthermore, it can expand the applications of HSIs and discover new remote sensing applications, which had been precluded by the poor quality of images.
Exploring novel crystalline topological quantum matter with gauge fields
Hong Kong Principal Investigator: Prof Zidan Wang (The University of Hong Kong)
Mainland Principal Investigator: Prof Yu-Xin Zhao (Nanjing University)
The beauty of crystals comes from their symmetries, such as a snowflake being invariant under the rotation by 60 degrees. However, the microscopic world preserves symmetries more complex than the macroscopic world does, as microscopic particles/entities are actually governed by their wavefunctions following quantum mechanics, distinctly different from our classical world. A wavefunction has an intrinsic phase factor, which is at the center of quantum physics. As is known, symmetry groups are enriched by phase factors into a projective symmetry algebra. In this project, we shall focus on crystalline symmetries considering the phase factors, namely the projective crystalline symmetry algebra, which actually consists of an inherent integration of thematic melodies of 20th century in physics: quantization, symmetry, and phase factor.
The phase factors can be tuned by gauge fields, such as electromagnetic fields, and therefore the interference patterns of wavefunctions can be manipulated by gauge fields. Hence, the projective crystalline symmetry algebra can be modulated by gauge fields. Recently, engineerable gauge fields have realized in artificial crystals, such as photonic/acoustic crystals, and cold atoms in optical lattices. Thus, it is highly demanding to systematically develop theories based on the projective crystalline symmetry algebras for analyzing such physical systems. On the other hand, gauge fields also emerge naturally at low energies for some condensed matter systems.
Remarkably, physics has developed the fourth melody in the 21st century, namely topology, to characterize the global ‘shape’ of physical systems. This is because physicists have discovered various quantum phases of matter, such as quantum hall effects, topological insulators and semimetals, which cannot be understood by the traditional theory in the framework of Landau’s paradigm of symmetry breaking, but topological physics, particularly symmetry-protected topological orders, have been successful in characterizing such novel quantum phases of matter. This project is expected to further include the fourth melody, i.e., to explore topological properties of matter preserving the projective crystalline symmetry algebra. Especially, it is planned to construct a library for novel topological matter preserving projective crystalline symmetry algebra, which has extraordinary topological properties never witnessed with classical crystal symmetries. These properties may be used for designing advanced devices, such as mid-infrared lasers, and next-generation chips.
Using 3-D Remote Sensing and Numerical Simulations to Investigate Boundary Layer Evolution and Improve Prediction of Extreme Air Pollution Episodes
Hong Kong Principal Investigator: Prof Alexis Kai-Hon Lau (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Prof Cheng-Cai Li (Peking University)
Exposure to severe air pollution is associated with a broad range of adverse health effects. Accurate prediction of the occurrence and evolution of extreme air pollution episodes using air quality models is of critical importance for protecting public health, particularly for protecting people in susceptible subgroups. Atmospheric boundary layer structure plays a critical role in controlling the occurrence and evolution of extreme air pollution episodes. Lack of knowledge on the boundary layer structure is a key cause for inaccurate prediction of extreme pollution episodes by air quality models. The recent development of lidar technology has enabled accurate remote sensing of vertical variation in air pollutant concentrations and meteorological values in the boundary layer. Meanwhile, new-generation geostationary satellites such as the Geostationary Environment Monitoring Spectrometer (GEMS) launched by South Korea in 2020, can now simultaneously monitor aerosol, ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2), and provide spatial patterns of their column densities over East Asia at an hourly resolution. The proposed study will conduct synergistic vertical measurements of air pollutants and meteorological factors in the boundary layer using a full set of lidar systems (including aerosol, ozone, and wind lidar) and drones in Hong Kong and Beijing. Boundary layer structure and factors governing its variation will be investigated. Through lidar and satellite data fusion, we will construct an hourly three-dimensional (3D) remote sensing database on the variation in aerosol and gaseous pollutants. By assimilating the hourly 3D remote sensing database and optimizing the boundary layer parameterization schemes, the capability of air quality models to predict extreme air pollution episodes will be improved more effectively. Subsequently, the effects of boundary layer dynamics on the evolution of extreme air pollution episodes in key regions of East Asia will be investigated using the improved air quality models.
Design, Synthesis and Characterization of Metal-Organic Assembled Quantum Spin Systems
Hong Kong Principal Investigator: Prof Nian Lin (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Prof Pei-Nian Liu (East China University of Science and Technology)
Certain quantum magnetic materials exhibit an exotic state called quantum spin liquid. This concept was first proposed by Nobel Prize winner P. W. Anderson in 1973. Theoretical predictions indicate that there is long-range quantum entanglement between the spins in quantum spin liquids, which may be used to realize quantum computing. In the past forty years, although a lot of progress has been made in theories and experiments of quantum spin liquids, there is still no material recognized as having a quantum spin liquid ground state. This project innovatively uses coordination assembly of organic radical ligands and ferromagnetic metals on solid surfaces to generate a new type of spin lattice with a low-dimensional metal-organic assembly structure. After the mainland research group has successfully developed a quantum spin system based on a metal-organic assembly structure, especially a controllable preparation method for the two-dimensional Kagome spin frustrated lattice system, the Hong Kong research group will prepare the same quantum spin system, and use low-temperature STM, nc-AFM, STS, XMCD and other means to measure the chemical structure, electronic state, magnetic properties, and quantum behavior, and compare the experimental results with theoretical calculations. This project is expected to realize the quantum spin liquid state and discover new quantum effects.
Magnetic Two-Dimensional Materials for Spin-Orbit Torque and Device Study
Hong Kong Principal Investigator: Prof Qiming Shao (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Prof Guoqiang Yu (Institute of Physics, Chinese Academy of Science State Key Laboratory of Magnetism)
Spintronic devices utilizing the spin degree of freedom of electrons have advantages such as non-volatility of data, small unit device size, low power consumption, and high speed. Magnetic materials are the key component to storing information in functional spintronic devices. Recently discovered magnetic two-dimensional (2D) materials add exotic properties of magnetism on top of their excellent electronic properties and controllability. The development of heterostructures and devices based on magnetic 2D materials, as well as the study of electrical manipulation, are key to promoting the application of magnetic 2D materials in spintronic devices. Our two teams in the Mainland and Hong Kong intend to conduct a joint in-depth study of the spin-orbit torques (SOTs) in magnetic 2D material-based heterostructures, built upon our previous and collaborative works. Here, SOT provides an efficient method to manipulate magnetic moment or magnetization of magnetic 2D materials. The project will combine the advantages of both teams to jointly develop two types of heterostructures of magnetic 2D materials/heavy metals and magnetic 2D materials/non-magnetic 2D materials with high interface quality. We will characterize current-induced SOTs to systematically study the correlation between SOT and crystal/interface/device structure. We will investigate the physical mechanisms and dynamics of SOT-driven magnetization switching and explore SOT switching without the assistance of an external magnetic field. We will prepare magnetic tunnel junctions based on magnetic 2D materials and study the tunneling magnetoresistance effect. Finally, we will explore the complete SOT unit cell device for data storage, logic, neuromorphic computing, and probabilistic computing applications. The anticipated results of this project will open a new avenue to future research and application of spintronic devices based on magnetic 2D materials and contribute to the development of materials and information science.
Fundamental Principles of How the Interplay between a Substrate-adsorbed Polymer Layer and Free Surface Affects the Dynamics of Polymer Films
Hong Kong Principal Investigator: Prof Ophelia Kwan Chui Tsui (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Prof Xinping Wang (Zhejiang Sci-Tech University)
Fundamental understanding of the dynamics of polymers under nano-confinement constitutes an important foundation for the development of polymer nanomaterials and nanotechnology. Although the subject has been extensively researched in the past 30 years, there are still confusing and sometimes inconsistent experimental results that cannot be explained by current understanding. Based on current development of the field, it is noted that two key issues continue to be poorly understood or little addressed. One is the interplay between the effects of the polymer/air surface and the polymer/substrate surface of a polymer film on the overall dynamics of the film. The other is how the polymer conformations, namely the way by which the polymer chains wrap around itself and the neighboring chains, near the free surface and in the polymer layer adherent to the substrate (i.e., the so-called adsorbed polymer layer) may affect the surface effects. Drawing on the respective strengths of the two principle investigators, this project will address the key issues by designing polymer films with different chain conformations near the free surface and in the adsorbed layer, and measuring the characteristic penetration distances, i.e., the distances over which individual surface effects propagate in the film. To disentangle the interplay between the two surface effects, correlations will be examined between the chain conformations and penetration distances, while perturbations to these two quantities upon reduction of the thickness of the polymer films will be investigated. The ultimate aim of this study is to build a physical model to describe the surface effects and their interplay, and thereby polymer dynamics under confinement. In the long run, it is hoped that the progress achieved will aid the development of a theoretical basis for the design and adjustment of the desirable physical properties for polymer nanomaterials.
Quantum manipulation of superconducting electronic states in orbital ordered materials
Hong Kong Principal Investigator: Prof Jingdi Zhang (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Prof Tao Wu (University of Science and Technology of China)
One enduring challenge in modern condensed matter physics is the unconventional superconductivity, first discovered in cuprate, heavy fermion superconductors and, more recently, in iron-based and nickel-based superconductors. Much effort, theoretically and experimentally, has shown that the underlying physics of unconventional superconductivity is far beyond the reach of a phonon-mediated picture, i.e., Bardeen-Cooper-Schrieffer (BCS) theory, and requires the cooperative interaction among multiple degrees of freedom (charge, spin, lattice and perhaps orbital) to invoke a novel mechanism to pair up electrons into superfluid. In spite of the extensive but nearly saturated experimental observations in high-Tc superconductors, the pairing mechanism therein remains elusive. Therefore, it is desirable to develop and explore alternative compounds, displaying phenomenologically similar phase competition but bearing novel symmetry-breaking states. One such candidate mechanism with novel symmetry-breaking state is referred to as bond order, that is, ordering of electronic bonds between transition metal cations and ligand anions far below the Fermi level. With the aim of bringing forward new insights on the intriguing unconventional superconductivity from a comparative perspective, we propose to synthesis, characterize and control superconductors with bond order—particularly in titanium pnictide oxide—using state-of-the-art nuclear magnetic resonance (NMR) spectroscopy and ultrafast terahertz (THz) pump-probe spectroscopy. In the short term, we aim to elucidate the intriguing phenomenon of unconventional superconductivity from the new perspective of bond-order–superconductivity competition in BiTi2Pn2O compounds. In the long run, such study may enrich our understanding of high-Tc superconductivity in sibling systems with symmetry-breaking states of other types.
The Molecular Mechanism and Function of RNA Helicase DDX21 in Regulating the Virus-induced Immune Response
Hong Kong Principal Investigator: Dr Jinqing Huang (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Prof Jixi Li (Fudan University)
Infectious disease remains one of the most severe threats to human health. Viruses enter host cells to replicate and transcribe their own genome, in most cases, RNA. As a cytosolic RNA sensor to regulate gene transcription, the multifunctional RNA helicase DDX21 plays an important role in anti-virus infection responses. Specifically, DDX21 inhibits influenza A virus replication in the early infection stage, upon binding with the influenza virus PB1 protein to inhibit the assembly of the viral polymerase. Interestingly, this antiviral activation of DDX21 is diminished in a later stage of infection, due to the interaction between DDX21 and the viral non-structural (NS1) protein. More importantly, we have identified an unconventional RNA dependency in the interaction between DDX21 and NS1, which may provide a new perspective for interpreting the complicated yet crucial virus–host interface. Nevertheless, studies on the thermodynamics and dynamics of these processes and the associated biofunctions are scarce. Questions such as “how DDX21 recognizes and unwinds different RNA?”, “how DDX21 interacts with the viral proteins?”, and “how viral NS1 affects the activities of DDX21?” remain unclear.
In this proposal, we will use single-molecule manipulation techniques to investigate the interaction between DDX21 and NS1 as well as the specificity of the associated RNA binding, monitor the unwinding activity and conformational dynamics of DDX21, and assess the downstream activities of polymerase and RNA transcription in real-time. In particular, a single DDX21 will be trapped and manipulated by optical tweezers to measure the dynamic force versus molecular extension with the presence of NS1 and the specific RNA at sub-millisecond temporal resolution and sub-nanometer spatial resolution for the conformational dynamic analysis. Moreover, a single RNA hairpin structure will be held by optical tweezers under similar configuration with the loading of DDX21 and NS1 to monitor the unwinding step in real-time. Furthermore, a single RNA-DNA template will be stretched by optical tweezers with the assembly of polymerase to study the pausing and backtracking dynamics in response to the DDX21 and NS1 interaction. By combining the structural details obtained from the structural biology methods (X-ray crystallography and cryo-EM) and the dynamic information generated from the single-molecule manipulation techniques (optical tweezers), we expect to reveal the molecular mechanism and function of DDX21 in antiviral signal response and shed light on the development of new therapeutics against influenza virus infections.
Study of the Synergistic Catalytic Effect in Palladium-based Alloy Nanomaterials for Electrochemical Carbon Dioxide Reduction
Hong Kong Principal Investigator: Dr Bolong Huang (The Hong Kong Polytechnic University)
Mainland Principal Investigator: Prof Yanguang Li (Soochow University)
Currently, reducing and conversion of greenhouse gas CO2 become the pivotal challenges to decrease the emission for reaching carbon neutrality. In recent year, the electrochemical CO2 reduction reactions (CO2RR) enable the effective conversion of CO2 to other value-added chemical or carbon fuels, which has become one of the most promising strategies to capture and utilize the greenhouse gas CO2. Through the CO2RR, different chemicals can be obtained including formate (HCOOH), carbon oxide (CO), ethylene (C2H4) and ethanol (C2H5OH), etc. in which the multi-carbon products still shows relatively low conversion efficiency. In comparison, the formate has been considered as the most commercially profitable product of CO2RR and the current demands of formate are quickly increasing in industrial applications. Previous reports of electrocatalysts with different metal electrocatalysts have been reported, where Pd is the only candidate is able to produce formate with high Faradaic efficiency near the equilibrium potential. However, the severe CO poisoning effect during the catalysis often results in the rapid degradation of electrocatalyst performances regarding both selectivity and stability in long-term applications. In this project, we plan to collaborate with the research team from Soochow University to investigate the potential of Pd-based alloys as electrocatalysts for efficient CO2RR. This alloying strategy enables the flexible modulations of electronic structures in the Pd-based alloys to weaken their affinity towards the CO to suppress catalyst poisoning. We will develop high-throughput multiscale theoretical simulation methods to design, screen and optimize a series of bimetallic Pd-based nanomaterials. By carefully tuning their compositions, crystal structures, surface properties, we will modulate their electronic structures and consequently interactions with key reaction intermediates, and ultimately achieve much improved formate selectivity and catalytic stability. Moreover, we will also combine electrochemical characterizations with theoretical simulations and in-situ spectroscopic analysis for the in-depth explanations of the structure-property correlations in these Pd-based alloys as well as the unique synergistic effect during the electrocatalysis. This project will offer advanced insights into the electrocatalysts of CO2RR, which further accelerate the potential commercial applications of designed electrocatalyst in the future.
Construction of Bismuth-based Hierarchical Nano/microstructured Electrodes and their Applications in Photoelectrochemical Cells for Solar-driven Ambient Ammonia Production
Hong Kong Principal Investigator: Dr Liang An (The Hong Kong Polytechnic University)
Mainland Principal Investigator: Prof Rong Chen (Chongqing University)
Ammonia is an important chemical in the industry and agriculture as well as an emerging energy carrier with a large hydrogen content for long-term energy storage. Traditional industrial ammonia synthesis is accomplished through the Haber-Bosch process which however will annually consume 1-2 % of the global energy supply in order to create the reaction condition of high temperatures and pressures and release 1% of total emissions of carbon dioxide. In response to energy and environmental consequences, therefore, renewable energy/power driven approach appears to be one of the most promising candidates for green ammonia production. This project is concerned with the development of an emerging ammonia production technology - photoelectrochemical cells, which can utilize solar energy to drive the conversion from nitrogen to ammonia under ambient condition, achieving energy, environmental and economic sustainability. Before making this emerging technology viable, however, the performance must be boosted. In addition to development of novel photocatalytic and electrocatalytic materials, the performance improvement also depends on the optimization of the photoelectrode/electrode structures and the cell configuration designs. Accomplishing this project will enable a revolutionary performance improvement, allowing the technology to become commercialized.