Potassium Tethered Carbons with Unparalleled Adsorption Capacity and Selectivity for Low-Cost Carbon Dioxide Capture from Flue Gas

Carbons are considered less favorable for postcombustion CO₂ capture because of their low affinity toward CO₂, and nitrogen doping was widely studied to enhance CO₂ adsorption, but the results are still unsatisfactory. Herein, we report a simple, scalable, and controllable strategy of tethering potassium to a carbon matrix, which can enhance carbon–CO₂ interaction effectively, and a remarkable working capacity of ca. 4.5 wt % under flue gas conditions was achieved, which is among the highest for carbon-based materials. More interestingly, a high CO₂/N₂ selectivity of 404 was obtained. Density functional theory calculations evidenced that the introduced potassium carboxylate moieties are responsible for such excellent performances. We also show the effectiveness of this strategy to be universal, and thus, cheaper precursors can be used, holding great promise for low-cost carbon capture from flue gas

Quantum Chemistry Calculation-Aided Structural Optimization of Combretastatin A‑4-like Tubulin Polymerization Inhibitors: Improved Stability and Biological Activity

A potent combretastatin A-4 (CA-4) like tubulin polymerization inhibitor <b>22b</b> was found with strong antitumor activity previously. However, it easily undergoes <i>cis–trans</i> isomerization under natural light, and the resulting decrease in activity limits its further applications. In this study, we used quantum chemistry calculations to explore the molecular basis of its instability. Aided by the calculations, two rounds of structural optimization of <b>22b</b> were conducted. Accelerated quantitative light stability testing confirmed that the stability of these designed compounds was significantly improved as predicted. Among them, compounds <b>1</b> and <b>3b</b> displayed more potent inhibitory activity on tumor cell growth than <b>22b</b>. In addition, the potent <i>in vivo</i> antitumor activity of compound <b>1</b> was confirmed. Quantum chemistry calculations were used in the optimization of stilbene-like molecules, providing new insight into stilbenoid optimization and important implications for the future development of novel CA-4-like tubulin polymerization inhibitors

Highly Active Isolated Single-Atom Pd Catalyst Supported on Layered MgO for Semihydrogenation of Acetylene

Selective hydrogenation of acetylene to ethylene is an important industrial reaction. In this work, we report a highly efficient Pd/MgO catalyst loaded with ultralow (0.05 wt %) levels of palladium for the selective hydrogenation of acetylene to ethylene. Palladium single-atom catalysts exhibited an excellent catalytic performance for semihydrogenation of acetylene with the highest ethylene selectivity of about 82% at 200 °C due to facile desorption of ethylene against the overhydrogenation to unwanted ethane. This work provides a very simple route to prepare a highly active, selective, and low-cost Pd catalyst for the semihydrogenation of acetylene

Worm Generator: A System for High-Throughput <i>in Vivo</i> Screening

Large-scale screening of molecules in organisms requires high-throughput and cost-effective evaluating tools during preclinical development. Here, a novel in vivo screening strategy combining hierarchically structured biohybrid triboelectric nanogenerators (HB-TENGs) arrays with computational bioinformatics analysis for high-throughput pharmacological evaluation using Caenorhabditis elegans is described. Unlike the traditional methods for behavioral monitoring of the animals, which are laborious and costly, HB-TENGs with micropillars are designed to efficiently convert animals’ behaviors into friction deformation and result in a contact–separation motion between two triboelectric layers to generate electrical outputs. The triboelectric signals are recorded and extracted to various bioinformation for each screened compound. Moreover, the information-rich electrical readouts are successfully demonstrated to be sufficient to predict a drug’s identity by multiple-Gaussian-kernels-based machine learning methods. This proposed strategy can be readily applied to various fields and is especially useful in in vivo explorations to accelerate the identification of novel therapeutics

Worm Generator: A System for High-Throughput <i>in Vivo</i> Screening

Large-scale screening of molecules in organisms requires high-throughput and cost-effective evaluating tools during preclinical development. Here, a novel in vivo screening strategy combining hierarchically structured biohybrid triboelectric nanogenerators (HB-TENGs) arrays with computational bioinformatics analysis for high-throughput pharmacological evaluation using Caenorhabditis elegans is described. Unlike the traditional methods for behavioral monitoring of the animals, which are laborious and costly, HB-TENGs with micropillars are designed to efficiently convert animals’ behaviors into friction deformation and result in a contact–separation motion between two triboelectric layers to generate electrical outputs. The triboelectric signals are recorded and extracted to various bioinformation for each screened compound. Moreover, the information-rich electrical readouts are successfully demonstrated to be sufficient to predict a drug’s identity by multiple-Gaussian-kernels-based machine learning methods. This proposed strategy can be readily applied to various fields and is especially useful in in vivo explorations to accelerate the identification of novel therapeutics

MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery

Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimization, and compound targeting, as well as abundant analyzing tools to check the results. MolProphet is an accessible and user-friendly web-based platform that is fully designed according to the practices in the drug discovery industry. The molecule screened, generated, or optimized by the MolProphet is purchasable and synthesizable at low cost but with good drug-likeness. More than 400 users from industry and academia have used MolProphet in their work. We hope this platform can provide a powerful solution to assist each normal researcher in drug design and related research areas. It is available for everyone at https://www.molprophet.com/

Worm Generator: A System for High-Throughput <i>in Vivo</i> Screening

Large-scale screening of molecules in organisms requires high-throughput and cost-effective evaluating tools during preclinical development. Here, a novel in vivo screening strategy combining hierarchically structured biohybrid triboelectric nanogenerators (HB-TENGs) arrays with computational bioinformatics analysis for high-throughput pharmacological evaluation using Caenorhabditis elegans is described. Unlike the traditional methods for behavioral monitoring of the animals, which are laborious and costly, HB-TENGs with micropillars are designed to efficiently convert animals’ behaviors into friction deformation and result in a contact–separation motion between two triboelectric layers to generate electrical outputs. The triboelectric signals are recorded and extracted to various bioinformation for each screened compound. Moreover, the information-rich electrical readouts are successfully demonstrated to be sufficient to predict a drug’s identity by multiple-Gaussian-kernels-based machine learning methods. This proposed strategy can be readily applied to various fields and is especially useful in in vivo explorations to accelerate the identification of novel therapeutics

Worm Generator: A System for High-Throughput <i>in Vivo</i> Screening

Large-scale screening of molecules in organisms requires high-throughput and cost-effective evaluating tools during preclinical development. Here, a novel in vivo screening strategy combining hierarchically structured biohybrid triboelectric nanogenerators (HB-TENGs) arrays with computational bioinformatics analysis for high-throughput pharmacological evaluation using Caenorhabditis elegans is described. Unlike the traditional methods for behavioral monitoring of the animals, which are laborious and costly, HB-TENGs with micropillars are designed to efficiently convert animals’ behaviors into friction deformation and result in a contact–separation motion between two triboelectric layers to generate electrical outputs. The triboelectric signals are recorded and extracted to various bioinformation for each screened compound. Moreover, the information-rich electrical readouts are successfully demonstrated to be sufficient to predict a drug’s identity by multiple-Gaussian-kernels-based machine learning methods. This proposed strategy can be readily applied to various fields and is especially useful in in vivo explorations to accelerate the identification of novel therapeutics

Worm Generator: A System for High-Throughput <i>in Vivo</i> Screening

Large-scale screening of molecules in organisms requires high-throughput and cost-effective evaluating tools during preclinical development. Here, a novel in vivo screening strategy combining hierarchically structured biohybrid triboelectric nanogenerators (HB-TENGs) arrays with computational bioinformatics analysis for high-throughput pharmacological evaluation using Caenorhabditis elegans is described. Unlike the traditional methods for behavioral monitoring of the animals, which are laborious and costly, HB-TENGs with micropillars are designed to efficiently convert animals’ behaviors into friction deformation and result in a contact–separation motion between two triboelectric layers to generate electrical outputs. The triboelectric signals are recorded and extracted to various bioinformation for each screened compound. Moreover, the information-rich electrical readouts are successfully demonstrated to be sufficient to predict a drug’s identity by multiple-Gaussian-kernels-based machine learning methods. This proposed strategy can be readily applied to various fields and is especially useful in in vivo explorations to accelerate the identification of novel therapeutics