Research Progress----Shanghai Advanced Research Institute,Chinese Academy of Sciences

Researchers Developed Novel Molecular Electrocatalysts for Hydrogen Peroxide Electrosynthesis

A research team at Shanghai Advanced Research Institute of the Chinese Academy of Sciences proposed a strategy to promote H2O2 selectivity by designing a cobalt porphyrin supported on reduced graphene oxide molecular catalyst (CoTPP@RGO), which enabled the stable electrosynthesis of H2O2 at industrial-scale current in PEM electrolyzer. The results were published in Angewandte Chemie International Edition in June, 2024.

Molecular electrocatalysts are regarded as a promising alternative for the electrolysis of hydrogen peroxide (H2O2) through two-electron oxygen reduction reaction (2e-ORR). And oxygen-functionalized groups (OFGs) on the carbon supports are proven to have a great influence on the molecular center.
　　However, such catalysts are always limited by their poor electrocatalytic activity and durability in acidic solution and little work has been done to investigate the interaction mechanism when the specific OFG interacts with the active center.
　　Recently, a research team led by Prof. YANG Hui at Shanghai Advanced Research Institute (SARI) of the Chinese Academy of Sciences proposed a strategy to promote H2O2 selectivity by designing a cobalt porphyrin supported on reduced graphene oxide molecular catalyst (CoTPP@RGO), which enabled the stable electrosynthesis of H2O2 at industrial-scale current in PEM electrolyzer.
　　The results were published in Angewandte Chemie International Edition in June, 2024.
　　Scientists tailored the variation of the single oxygen functional group (OFG) on the reduced graphene oxide (RGO) carrier at the specific temperature and prepared several CoTPP@RGO-T catalysts.
　　Advanced characterization techniques combined with density functional theory calculations revealed the regulation effects of different oxygen functional groups on the electronic structure of active center and unraveled the enhancement mechanism of 2e-ORR activity on the developed CoTPP@RGO catalyst.
　　It was found that PEM electrolyzer with the CoTPP@RGO-160 catalyst could continuously produce pure H2O2 aqueous solution with a high concentration of up to ca. 7 wt% at 400 mA cm-2 over 200 h with a low cell voltage of ~2.1 V, demonstrating the breakthrough of exceptional stability.
　　The findings provide a new strategy for the rational design of the active centers for molecular catalyst, highlighting the enormous potential of supported molecular catalysts for the electrosynthesis of H2O2 in acidic environment as well as its application in industrial-scale H2O2 electrosynthesis.
　　Interaction mechanism investigation and PEM performance evaluation(Image by SARI)
　　Contact: YANG Hui
　　Shanghai Advanced Research Institute
　　Email: yangh@sari.ac.cn
　　

2024-06-20 more+

Scientists Propose a Novel Artificial Intelligence Approach for Lipid Nanoparticles Screening in mRNA Delivery

A joint research team proposed a deep learning model named TransLNP, which based on self-attention mechanisms that maps the three-dimensional microstructure and biochemical properties of mRNA-LNPs to enable high-precision automated screening of LNPs.The research findings were published in Briefings in Bioinformatics.

　　Messenger RNA (mRNA) vaccines targeting pan-cancer therapy hold significant academic and economic value in drug research. A key challenge in mRNA design is the construction of delivery systems called lipid nanoparticles (LNPs), which serve as carriers to deliver mRNA therapies or vaccines to target cells. The preparation and screening of LNPs components involve long cycles and high costs.
　　Driven by this challenge, a joint research team led by Prof. LIU Lizhuang from the Shanghai Advanced Research Institute (SARI) of the Chinese Academy of Sciences proposed a deep learning model named TransLNP, which based on self-attention mechanisms that maps the three-dimensional microstructure and biochemical properties of mRNA-LNPs to enable high-precision automated screening of LNPs.
　　The research findings were published in Briefings in Bioinformatics.
　　The designed TransLNP used a cross-molecule automatic learning approach to extract knowledge from existing molecular data, enabling small-sample training for LNPs and facilitating model transfer across different molecule types.
　　Figure 1 The overall architecture of TransLNP (Image by SARI)
　　To construct the mapping relationship between the 3D microstructure and biochemical properties of mRNA-LNPs, the model fully leveraged coarse-grained atomic sequence information and fine-grained atomic spatial correspondences. It extractd molecular-level features through the interaction of atomic information (atom types, coordinates, relative distance matrices, edge type matrices) based on a self-attention mechanism.
　　To address the imbalance caused by limited LNP data, scientists designed the BalMol (balance molecule) module. This module balanced the data by smoothing label distributions and molecular feature distributions. TransLNP achieved a mean squared error (MSE) of less than 5 for predicting LNP transfection efficiency. Compared with various mainstream graph convolutional neural networks and machine learning algorithms, TransLNP showd superior performance in terms of MSE, R2 (the larger the value, the better), and Pearson correlation coefficient, achieving top-tier metrics in the field.
　　This work is helpful for the rapid and accurate prediction of mRNA-LNP transfection efficiency and the prediction of new lipid nanoparticle structures, and sheds light on the application of mRNA drugs in gene therapy, vaccine development, and drug delivery.
　　

2024-06-07 more+

Chinese Scientists Call for Using Consumption-based Accounting of Carbon Emissions to Increase Fairness

A new study by Chinese scientists, released on May 29 in Shanghai, has called for the use of consumption-based accounting (“CBA”) emissions in calculating global carbon emissions in order to help make allocating responsibility for reducing emissions just and fair.The study,"Research Report on Consumption-based Carbon Emissions (2024)" ("the Report"), was jointly completed by scientists from Shanghai Advanced Research Institute, CAS, University of Chinese Academy of Sciences, Institute of Urban Environment, CAS and Tsinghua University.

A new study by Chinese scientists, released on May 29 in Shanghai, has called for the use of consumption-based accounting (“CBA”) emissions in calculating global carbon emissions in order to help make allocating responsibility for reducing emissions just and fair.
　　The study,"Research Report on Consumption-based Carbon Emissions (2024)" ("the Report"), was jointly completed by scientists from Shanghai Advanced Research Institute, CAS, University of Chinese Academy of Sciences, Institute of Urban Environment, CAS and Tsinghua University.
　　The Report presents the latest research results on global carbon emissions from the consumption perspective. The scientists analyzed the evolution of CBA emissions in major developed and developing countries from 1990 to 2019, with focus on assessing the carbon transfer effects of key trade products.
　　"Carbon emission accounting is the key basis for global emission reduction and climate change governance," said WEI Wei, one of the lead authors of the Report and also a researcher at the Shanghai Advanced Research Institute of CAS. WEI noted that the widely adopted PBA (production-based accounting) method does not consider the implicit contribution of economic activities-especially international trade-to carbon emissions. WEI said that the CBA method could help clarify how the responsibility for global emissions reduction could be fairly attributed to producers and customers.
　　The Report points out that from 1990 to 2019, the CBA emissions of major developed countries were higher than PBA emissions throughout the period, while the opposite was true for major developing countries.
　　For major developing countries, the gap between CBA and PBA emissions gradually increased from 1.47 Gt in 1990 to 4.17 Gt in 2019.
　　According to the Report, China’s CBA had been lowering than its PBA from 1990 to 2019. The gap between China’s PBA and CBA emissions increased from 0.7 Gt in 1990 to 1.8 Gt in 2019. Meanwhile, China’s embodied carbon intensity in exported products decreased by 83.3% during this period, showing that China is providing more and more green and low-carbon products to the world.
　　In 2021, China bore 100 million tons of net carbon emissions from trade in steel products and 250 million tons from trade in photovoltaic products for other countries.
　　In addition, the Report suggests to improve CBA emission methods with broader products range and to establish a CBA methodology that combines top-down and bottom-up approaches that focus on region-level emissions and product-level emissions, respectively, with the goal of obtaining more in-depth, accurate and comprehensive results.The Report also noted that in order to achieve global carbon reduction goals, all countries across the world should work together to promote science and technology advancement. Countries need to jointly deal with climate change by taking common but differentiated carbon reduction responsibilities.
　　

2024-05-29 more+

Scientists Discover Novel Strategy for Selectivity Tuning in Alcohol Synthesis by Crystal Phase Engineering

A research team at Shanghai Advanced Research Institute (SARI) of the Chinese Academy of Sciences reported for the first time an efficient FeZn-based catalyst for methanol synthesis via CO2 hydrogenation and by crystal structure, achieving high methanol selectivity of 84.5%.The research results were published in the latest issue of Chem.

　　A research team led by Prof. GAO Peng and Prof. LI Shenggang at Shanghai Advanced Research Institute (SARI) of the Chinese Academy of Sciences developed an efficient FeZn-based catalyst for methanol synthesis via CO2 hydrogenation. After investigating the crystal structure engineering of FeZn catalysts, the research team reported for the first time that ZnFe2O4 spinel enables high methanol selectivity of 84.5% in CO2 hydrogenation.
　　The research results were published in the latest issue of Chem.
　　The catalytic conversion of CO2 into alcohols using low-cost green hydrogen is a promising solution for mitigating CO2 emissions. Substantial progress has been made in developing efficient CO2-to-methanol catalysts and revealing the reaction mechanisms, in which highly selective CO2 hydrogenation into alcohols remains a great challenge, due to the difficulty in controlling the C-C coupling steps.
　　Noble metal-based catalysts were reported to exhibit high ethanol selectivity in CO2 hydrogenation to higher alcohols containing two or more carbon atoms (C2+OH). The research team investigated how crystal structure engineering of FeZn catalysts transformed selective product formation from methanol to C2+OH, realizing unprecedented higher alcohol synthesis (HAS) performance from CO2 hydrogenation.
　　Researchers first synthesized the single-phase crystal structure of ZnFe2O4 spinel. The ZnFe2O4 oxide sample was activated in situ prior to the CO2 hydrogenation reaction.
　　The experimental observation showed that more than 97% of the reaction products consisted of methanol and ZnFe2O4 catalyst exhibited a high CH3OH selectivity and CO2 conversion. ZnO and Fe2O3 oxides were synthesized and tested for comparison study, which shown a much lower CO2 conversion and CH3OH selectivity.
　　The results indicated the ZnFe2O4 spinel phase enabled high methanol selectivity during CO2 hydrogenation. The experimental analysis suggested the oxygen vacancy on the surface of ZnFe2O4 spinel is the active site for CO2 and H2 activation.
　　It was also found that spinel ZnFe2O4 plays a vital role in the formation of oxygenates from CO2 hydrogenation, whereas the formation of the Fe5C2 phase facilitates both carbon chain growth and the insertion of C1 oxygen-containing intermediates to yield C2+OH after further hydrogenation, which greatly increases the selectivity of C2+OH in addition to olefins.
　　Moreover, introducing Fe5C2 phase to the formation of the ZnFe2O4/Fe5C2 interface greatly promotes C2+OH selectivity in oxygenates to 98.2%, giving a very high C2+OH productivity and an unprecedented C3+OH yield of 5.3%, which surpass the current reported maximum C3+OH yield of 2.1%.
　　Theoretical calculations further revealed the role of the ZnFe2O4/Fe5C2 interface in the C-C coupling. The ZnFe2O4/Fe5C2 interface drives selective transformation to C2+OH by promoting the facile migration of alkyl species to the interface and coupling with CHO* species, which contribute to the breaking of the Anderson-Schulz-Flory(ASF) distribution and result in the much lower methanol selectivity than expected.
　　This work demonstrates a new strategy for selectivity tuning in alcohol synthesis by crystal structure engineering.
　　Reaction networks for CO2 hydrogenation to methanol over the ZnFe2O4 and CO2 to C2+OH over the ZnFe2O4/Fe5C2 interface (Image by SARI)
　　

2024-04-17 more+

Researcher Propose Solvent Effects on Metal-free Covalent Organic Frameworks in Oxygen Reduction Reaction

A research group at Shanghai Advanced Research Institute (SARI) of the Chinese Academy of Sciences, represents the first investigation into the solvent effect on COFs for catalyzing the oxygen reduction reaction (ORR). They found all COFs synthesized with different nitrogen atoms exhibited good crystallinity and high surface areas, but displayed different binding abilities towards water molecules.This work was published in Angew. Chem. In. Ed.

2024-04-08 more+

Researchers Develop an Adaptive NOMA-based Spectrum Sensing for Next Generation of IoT Networks

An international collaborative research team led by the Shanghai Advanced Research Institute (SARI) of the Chinese Academy of Sciences proposed an innovative NOMA-based spectrum sensing algorithm for uplink IoT networks, which improves the efficiency of targeting frequency usage in multi-user systems.

As the Internet of Things (IoT) network continues to expand, the challenge of managing limited spectrum resources intensifies. Studies have shown that both non-orthogonal multiple access (NOMA) and spectrum sensing have the potential to increase spectrum utilization.
　　While NOMA and spectrum sensing can ease the spectrum shortage, the combination of them does not reach the upper bound of spectrum utilization due to certain reasons. Also, it faces the challenge of detecting the signal states when multiple users superpose.
　　To address these problems, a collaborative research team led by Prof. XU Tianhong and HU Honglin from the Shanghai Advanced Research Institute (SARI) of the Chinese Academy of Sciences (CAS), in partnership with VTT Technical Research Centre of Finland, the University of Electro-Communications in Japan, and Shanghai University, proposed an innovative NOMA-based spectrum sensing algorithm for uplink IoT networks, which improves the efficiency of targeting frequency usage in multi-user systems.
　　The research results were published in the latest issue of IEEE Transactions on Cognitive Communications and Networking.
　　Focusing on inter-system orthogonal/non-orthogonal aliasing coexistence scenarios, researchers propose an adaptive spectrum sensing technology for the multi-user uplink NOMA system, which harmonizes the advancements in both static and dynamic spectrum efficiency.
　　Moreover, researchers have derived the closed-form expressions among the number of primary users, user transmission willingness, the power ratio and the false-alarm probability in various sensing processes. These formulas have been validated through numerous simulations.
　　Furthermore, an adaptive NOMA-based sensing algorithm is designed and showcases an impressive 38.20% improvement in system throughput, compared with state-of-the-art techniques.
　　This research holds the promise of transforming the landscape of spectrum utilization in the emerging IoT era.
　　Schematic diagram of inter-system orthogonal/non-orthogonal aliasing coexistence IoT sensing scenarios (Image by SARI)
　　Contact: XU Tianheng
　　Shanghai Advanced Research Institute
　　Email: xuth@sari.ac.cn
　　

2024-01-12 more+