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Bis(imino)pyridine iron bis(alkoxide) complexes have been synthesized and utilized in the polymerization of (rac)-lactide. The activities of the catalysts were particularly sensitive to the identity of the initiating alkoxide with more electron-donating alkoxides resulting in faster polymerization rates. The reaction displayed characteristics of a living polymerization with production of polymers that exhibited low molecular weight distributions, linear relationships between molecular weight and conversion, and polymer growth observed for up to fifteen sequential additions of lactide monomer to the polymerization reaction. Mechanistic experiments revealed that iron bis(aryloxide) catalysts initiate polymerization with one alkoxide ligand, while iron bis(alkylalkoxide) catalysts initiate polymerization with both alkoxide ligands. Oxidation of an iron(II) catalyst precursor lead to a cationic iron(III) bis-alkoxide complex that was completely inactive toward lactide polymerization. When redox reactions were carried out during lactide polymerization, catalysis could be switched off and turned back on upon oxidation and reduction of the iron catalyst, respectively.

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The development of functional organic fluorescent materials calls for fast and accurate predictions of photophysical parameters for processes such as high-throughput virtual screening, while the task is challenged by the limitations of quantum mechanical calculations. We establish a database covering >4,300 solvated organic fluorescent dyes and develop new machine learning (ML) approach aimed at efficient and accurate predictions of emission wavelength and photoluminescence quantum yield (PLQY). Our feature engineering has given rise to Functionalized Structure Descriptor (FSD) and Comprehensive General Solvent Descriptor (CGSD), whereby a highly black-box computational framework is realized with consistently good accuracy across different dye families, ability of describing substitution effects and solvent effects, efficiency for large-scale predictions and workability with on-the-fly learning. Evaluations with unseen molecules suggests a remarkable MAE of 0.13 for PLQY and 0.080 eV for emission energy, the latter comparable to time-dependent density functional theory (TD-DFT) calculations. An online prediction platform was constructed based on the ensemble model to make prediction in various solvents (https://www.chemfluor.top/). Our statistical learning methodology will complement quantum mechanical calculations as an efficient alternative approach for the prediction of these parameters. File list (2) download file view on ChemRxiv Manuscript_20201029.pdf (1.05 MiB) download file view on ChemRxiv SupportingInformation_update.pdf (2.71 MiB)

Small-angle neutron scattering (SANS), helium pycnometry, and low-pressure gas sorption isotherm experiments were used to investigate the pore characteristics of Longmaxi shale, the leading producing formation in China. This work is the first microstuctural study of Chinese marine shales by neutron scattering technique. The polydisperse spheres model (PDSP) was used for SANS data analysis to obtain porosity and pore size distribution (PSD), and the results were compared with those from gas (CO 2 , N 2 ) adsorption and helium pycnometry. By evaluating the difference of porosities determined from these methods, the closed fraction of shale pores are derived and discussed. Moreover, the porosity has a positive correlation with total organic carbon (TOC) in Longmaxi shale samples. The fractal dimension of shale samples was derived, and results indicated that Longmaxi shale is a mass fractal. A lower mass fractal dimension means less closed pores with correspondingly more open structures.