The recent COVID-19 crisis caught many by surprise. Yet some firms were better prepared to weather the downturn than others. Using a comprehensive data set that observes over 15,000 firms in 27 ...countries, including several developing countries, shortly before and after the pandemic, we document that pre-crisis innovation affected firm's survival odds and performance thereafter. The results show that innovative firms are less likely to close and perform better than non-innovators during the pandemic crisis. Innovative firms are also more optimistic about the future than non-innovators. Our results further indicate that firm's adaptability mediates the relationship between innovation and survival outcomes. The study finding shows that innovative firms are more likely to introduce new products, remote work arrangements, increase delivery, pivoting, and online activities than non-innovators during the pandemic crisis.
High CO FE and good stability in large-scale CO2ER device with 495 cm2 electrode area was realized by IL-based electrolytes
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•An upscaling device of CO2ER with the biggest electrode ...area was established.•IL-based electrolytes were firstly used in upscaling device to boost CO2 conversion.•The superior CO2ER performance of 83.9% CO FE with only 2% H2 FE was achieved.•CO selectivity keeps good stability in IL systems on a continuous 10 hr running.
Electric-driven CO2 reduction to high value-added chemicals is a potential way to solve the carbon emissions. However, the current studies on CO2 electroreduction (CO2ER) are mainly focused on design and preparation of novel electrocatalysts and electrolytes. The large-scale of CO2ER is puzzled in the serious hydrogen evolution reaction (HER) in aqueous electrolytes and inferior reaction stability in an enlarged CO2 electrolyzer. Ionic liquids (ILs) as electrolytes have opened great opportunities for CO2ER due to their unique advantages. Herein, a large-scale CO2ER device containing an upscaling modified H-type flow cell using IL-based electrolytes (UHFC-IL) with the largest electrode active area of 495 cm2 was established for CO2ER studies. The influences of key operating parameters, such as compositions of IL-based electrolytes, electrolytes velocity, CO2 gas flow rate and cell voltage on CO2ER performance were systematically investigated. A high CO2ER performance under the optimum operating conditions achieves 83.9% Faraday Efficiency (FE) for CO with a reaction current of 6.32 A, suppressing HER to only 2% FE. After 10 hr continuous operation, the CO selectivity in IL-based electrolytes is 51.3% higher than that in 0.1 M KHCO3 aqueous electrolytes, which maintains excellent stability with a high CO generation rate of 1.7 L hr-1. In addition, the mechanism of CO2ER to CO boosted by IL-based electrolytes in UHFC-IL was proposed. This study provides experimental parameters and guidance for future research on the amplification process of CO2ER.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Electric-driven reduction of CO2 to high value-added chemicals is considered to be one of the most promising methods for CO2 utilization. In this work, a novel aprotic electrolyte consisting of ...phenoxy-nitrile bifunctional ionic liquid (TBPp-CN-PhO) and acetonitrile was designed for CO2 electroreduction to oxalate, which shows the maximum Faradaic efficiency (FE) of 84.9% at −2.6 V. Importantly, a high oxalate formation rate of 233.7 μmol·cm–2·h–1 and oxalate FE of 73.9% were simultaneously achieved to break the trade-off effect at −2.8 V in the bifunctional ionic liquid electrolyte, which is also one of the state-of-the-art performances for CO2 electroreduction to oxalate. The significant performance of CO2 electroreduction was attributed to the strong interaction energies of phenoxy and nitrile groups with CO2 molecules in the bifunctional ionic liquid, which greatly facilitates CO2 activation to form CO2 – radicals by a new bulk-phase pathway and then dimerizes into C2O4 2– at a lower energy barrier.
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IJS, KILJ, NUK, PNG, UL, UM
Degradation of target proteins has been considered to be a promising therapeutic approach, but the rational design of compounds for degradation remains a challenge. In this study, we reasonably ...designed and synthesized only 10 compounds to discover effective CDK4/6 protein degraders. Among the newly synthesized compounds, 7f achieved dual degradation of CDK4/6 protein, with DC50 values of 10.5 and 2.5 nM, respectively. Compound 7f also exhibited inhibitory proliferative activity against Jurkat cells with an IC50 value of 0.18 μM. Furthermore, 7f induced cell apoptosis and G1 phase cell cycle arrest in a dose-dependent manner in Jurkat cells. In conclusion, these findings demonstrate the potential of 7f as a CDK4/6 degrader and a potential therapeutic strategy against cancer, thereby expanding the potential of CDK4/6 dual PROTACs.
Morphology and synergistic effect of constituents are the two important factors that greatly influence the physical and chemical properties of materials. In the present work, we report the enhanced ...magnetic performance of highly ordered octahedral like NiFe2O4 synthesized via novel multistep hydrothermal approach. SEM, XRD and EDX characterization confirmed an octahedral like morphology of the nickel ferrites. The effects of temperature, hydrazine hydrate, ferric salt and sodium hydroxide on the nickel ferrite were investigated. Results demonstrated the prepared nickel ferrites to be readily regulated in their morphologies from particles to spheres to octahedral by changing experimental conditions. The formation mechanism of octahedral like NiFe2O4 was proposed. The magnetic properties of nickel ferrite crystals can be controlled by changing the morphologies of crystals. The octahedral like nickel ferrite crystals exhibited a better ferromagnetic behavior at room temperature, with Ms and Hc values of 34.55 emu/g and 112 Oe respectively.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Porous silver spheres were successfully prepared by reducing silver-ammonia solution and using rape pollen as novel bio-templates. The synthesized porous silver spheres with diameter of 50 μm have ...nanoparticles of ca. 25 nm. The reaction temperature and AgNO3 concentration have important influence on the morphology and electrical properties of the final products. Volume resistivity results revealed that the volume resistivity was decreased with increasing the amount of AgNO3 concentration. The volume resistivity of porous silver was decreased two orders of magnitude while the AgNO3 increased twice.
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•Porous silver spheres were successfully prepared by using rape pollen as novel bio-templates.•Porous silver spheres with diameter of 50 μm have nanoparticles of ca. 25 nm.•Volume resistivity of porous silver was decreased two orders of magnitude while the AgNO3 increased twice.
Porous silver spheres were successfully prepared by reducing silver-ammonia solution and using rape pollen as novel bio-templates. The synthesized porous silver spheres with diameter of 50 μm have nanoparticles of ca. 25 nm. The samples were characterized by X-ray diffraction, scanning electron microscope (SEM), and transmission electron microscope (TEM) techniques. The thermal and electrical properties were also studied. We find that the reaction temperature and AgNO3 concentration have important influence on the morphology and electrical properties of the final products. TEM observations showed that the as-synthesized products are composed of nanoparticles with size around 25 nm. Volume resistivity results revealed that the volume resistivity was decreased with increasing the amount of AgNO3 concentration. The volume resistivity of porous silver was decreased two orders of magnitude while the AgNO3 increased twice.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
During the drug discovery and design process, the acid-base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, ...distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of pKa values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular pKa values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge pKa prediction model named GR-pKa (Graph Retention pKa), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular pKa values. The GR-pKa model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model's ability to capture and update molecular information. Our GR-pKa model outperforms several state-of-the-art models in predicting macro-pKa values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R2 of 0.937 on the SAMPL7 dataset.During the drug discovery and design process, the acid-base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of pKa values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular pKa values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge pKa prediction model named GR-pKa (Graph Retention pKa), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular pKa values. The GR-pKa model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model's ability to capture and update molecular information. Our GR-pKa model outperforms several state-of-the-art models in predicting macro-pKa values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R2 of 0.937 on the SAMPL7 dataset.
Abstract During the drug discovery and design process, the acid–base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, ...distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of pKa values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular pKa values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge pKa prediction model named GR-pKa (Graph Retention pKa), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular pKa values. The GR-pKa model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model’s ability to capture and update molecular information. Our GR-pKa model outperforms several state-of-the-art models in predicting macro-pKa values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R2 of 0.937 on the SAMPL7 dataset.
Carbon sink afforestation (CSA) has become one of the most concerned issues of countries around the world under the background of climate change. The northern forest ecosystem, located in mid- and ...high latitudes, is a huge terrestrial carbon pool and is very sensitive to climate change. Studying the carbon emission accounting of CSA in northern forests helps clarify the contribution of CSA to forestry carbon sequestration and forecasts the carbon sink effect of forest ecosystems. It is of great significance for the assessment of forest carbon sink and carbon cycling by providing a scientific basis and reference for the development, utilization, and management of carbon sink afforestation, as well as the coordinated development of ecology and social economy. At present, research on the carbon emission accounting of the CSA in northern China is still lacking. According to the relevant models and parameters of estimating live biomasses with the default method from the IPCC’s (Intergovernmental Panel on Climate Change) Technical Guidelines for National Forestry Carbon Sink Accounting and Monitoring, carbon stock, carbon emission, and carbon leakage of the Weihe CSA (carbon sink afforestation) pilot demonstration area in the boreal Longjiang Forest Industry in a baseline scenario and CSA scenario were measured, and the CSA’s net carbon sink was estimated. The conclusions were as follows: (1) By the end of the crediting period of the project’s baseline, carbon fixation reached 101.85 t CO2, with an average annual CO2 fixation of 5.09 t. By the end of the CSA term, carbon sequestration was accumulated as 382.13 t CO2, with an average annual sequestration of 19.11 t CO2, nearly four times that of the baseline period. (2) By the end of the CSA term, the carbon sequestration of the coniferous standing forest was 46.2% higher than that of the broad-leaved standing forest, accounting for 65% of the total carbon sequestration of the forest. The carbon sequestration of the tree species in the coniferous forest in descending order is Picea koraiensis, Pinus koraiensis, Larix olgensis, Fraxinus mandshurica, and Populus cathayana. The carbon sink density of the coniferous standing forest was 8.7% higher than that of the broad-leaved standing forest. (3) The carbon fixation of the coniferous standing forest nearly doubled that of the broad-leaved standing forest. The highest carbon fixation belongs to Fraxinus mandshurica, closely followed by Picea koraiensis and Pinus koraiensis at a high level, and then Larix olgensis and Populus cathayana. The carbon fixation of Fraxinus mandshurica was 20 times that of Populus cathayana. (4) The accumulated greenhouse gas emissions within the boundary during the CSA period were 2.53 t CO2-e. The accumulated greenhouse gas leakage outside the boundary was 0.05 t CO2-e. Carbon emissions occurred in the first, second, and third years of the crediting period, while carbon leakage occurred only in the first year. (5) This result appeared as carbon sources during the first three years of the CSA period but changed to carbon sink from the fourth year and then accumulated to 280.28 t (70.07 t CO2-e·hm−2) as a net carbon sink by the end of the term. The Weihe CSA appeared to have a relatively strong ability of carbon sequestration in temperate forests. The CSA activity is influenced by factors such as policies, environment, management, etc., resulting in uncertainties in carbon sequestration accounting. Therefore, it is suggested that comparison studies and investigations should be strengthened, and multiple methods should be integrated into carbon sequestration estimation and accounting, leading the carbon accounting of forest ecosystems to a high-level and comprehensive development.
