Data_Sheet_1_A Theoretical Study on Laser Cooling Feasibility of Group IVA Hydrides XH (X = Si, Ge, Sn, and Pb): The Role of Electronic State Crossing.PDF

The feasibility of direct laser cooling of SiH, GeH, SnH, and PbH is investigated and assessed based upon first principles. The internally contracted multi-reference configuration interaction method with the Davidson correction is applied. Very good agreement is obtained between our computed spectroscopic constants and the available experimental data. We find that the locations of crossing point between the B2Σ− and A2Δ states have the tendency of moving downwards from CH to SnH relative to the bottom of the corresponding A2Δ potential, which precludes the laser cooling of GeH, SnH, and PbH. By including the spin-orbit coupling effects and on the basis of the A2Δ5/2→X2Π3/2 transition, we propose a feasible laser cooling scheme for SiH using three lasers with wavelengths varying from 400 to 500 nm, which features a very large vibrational branching ratio (0.9954) and a very short radiative lifetime (575 ns). Moreover, similar studies are extended to carbon monosulfide (CS) with a feasible laser cooling scheme proposed. The importance of electronic state crossing in molecular laser cooling is underscored, and our work suggests useful caveats to the choice of promising candidates for producing ultracold molecules.</p

The Role of Transition Metal and Nitrogen in Metal–N–C Composites for Hydrogen Evolution Reaction at Universal pHs

For the first time, we demonstrated that transition metal and nitrogen codoped carbon nanocomposites synthesized by pyrolysis and heat treatment showed excellent catalytic activity toward hydrogen evolution reaction (HER) in both acidic and alkaline media. The overpotential at 10 mA cm^–2 was 235 mV in a 0.5 M H₂SO₄ solution at a catalyst loading of 0.765 mg cm^–2 for Co–N–C. In a 1 M KOH solution, the overpotential was only slightly increased by 35 mV. The high activity and excellent durability (negligible loss after 1000 cycles in both acidic and alkaline media) make this carbon-based catalyst a promising alternative to noble metals for HER. Electrochemical and density functional theory (DFT) calculation results suggested that transition metals and nitrogen played a critical role in activity enhancement. The active sites for HER might be associated with metal/N/C moieties, which have been also proposed as reaction centers for oxygen reduction reaction

Palladium–Platinum Core–Shell Electrocatalysts for Oxygen Reduction Reaction Prepared with the Assistance of Citric Acid

Core–shell structure is a promising alternative to solid platinum (Pt) nanoparticles as electrocatalyst for oxygen reduction reaction (ORR) in proton exchange membrane fuel cells (PEMFCs). A simple method of preparing palladium (Pd)–platinum (Pt) core–shell catalysts (Pd@Pt/C) in a gram-batch was developed with the assistance of citric acid. The Pt shell deposition involves three different pathways: galvanic displacement reaction between Pd atoms and Pt cations, chemical reduction by citric acid, and reduction by negative charges on Pd surfaces. The uniform ultrathin (∼0.4 nm) Pt shell was characterized by in situ X-ray diffraction (XRD) and high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) images combined with electron energy loss spectroscopy (EELS). Compared with state-of-the-art Pt/C, the Pd@Pt/C core–shell catalyst showed 4 times higher Pt mass activity and much better durability upon potential cycling. Furthermore, both the mass activity and durability were comparable to that of Pd@Pt/C synthesized by a Cu-mediated-Pt-displacement method, which is more complicated and difficult for mass production

In silico Prediction of Chemical Ames Mutagenicity

Mutagenicity is one of the most important end points of toxicity. Due to high cost and laboriousness in experimental tests, it is necessary to develop robust in silico methods to predict chemical mutagenicity. In this paper, a comprehensive database containing 7617 diverse compounds, including 4252 mutagens and 3365 nonmutagens, was constructed. On the basis of this data set, high predictive models were then built using five machine learning methods, namely support vector machine (SVM), C4.5 decision tree (C4.5 DT), artificial neural network (ANN), k-nearest neighbors (kNN), and naïve Bayes (NB), along with five fingerprints, namely CDK fingerprint (FP), Estate fingerprint (Estate), MACCS keys (MACCS), PubChem fingerprint (PubChem), and Substructure fingerprint (SubFP). Performances were measured by cross validation and an external test set containing 831 diverse chemicals. Information gain and substructure analysis were used to interpret the models. The accuracies of fivefold cross validation were from 0.808 to 0.841 for top five models. The range of accuracy for the external validation set was from 0.904 to 0.980, which outperformed that of Toxtree. Three models (PubChem-kNN, MACCS-kNN, and PubChem-SVM) showed high and reliable predictive accuracy for the mutagens and nonmutagens and, hence, could be used in prediction of chemical Ames mutagenicity

In silico Prediction of Chemical Ames Mutagenicity

Mutagenicity is one of the most important end points of toxicity. Due to high cost and laboriousness in experimental tests, it is necessary to develop robust <i>in silico</i> methods to predict chemical mutagenicity. In this paper, a comprehensive database containing 7617 diverse compounds, including 4252 mutagens and 3365 nonmutagens, was constructed. On the basis of this data set, high predictive models were then built using five machine learning methods, namely support vector machine (SVM), C4.5 decision tree (C4.5 DT), artificial neural network (ANN), <i>k</i>-nearest neighbors (<i>k</i>NN), and naïve Bayes (NB), along with five fingerprints, namely CDK fingerprint (FP), Estate fingerprint (Estate), MACCS keys (MACCS), PubChem fingerprint (PubChem), and Substructure fingerprint (SubFP). Performances were measured by cross validation and an external test set containing 831 diverse chemicals. Information gain and substructure analysis were used to interpret the models. The accuracies of fivefold cross validation were from 0.808 to 0.841 for top five models. The range of accuracy for the external validation set was from 0.904 to 0.980, which outperformed that of Toxtree. Three models (PubChem-<i>k</i>NN, MACCS-<i>k</i>NN, and PubChem-SVM) showed high and reliable predictive accuracy for the mutagens and nonmutagens and, hence, could be used in prediction of chemical Ames mutagenicity

In Silico Assessment of Chemical Biodegradability

Biodegradation is the principal environmental dissipation process. Due to a lack of comprehensive experimental data, high study cost and time-consuming, in silico approaches for assessing the biodegradable profiles of chemicals are encouraged and is an active current research topic. Here we developed in silico methods to estimate chemical biodegradability in the environment. At first 1440 diverse compounds tested under the Japanese Ministry of International Trade and Industry (MITI) protocol were used. Four different methods, namely support vector machine, <i>k</i>-nearest neighbor, naïve Bayes, and C4.5 decision tree, were used to build the combinatorial classification probability models of ready versus not ready biodegradability using physicochemical descriptors and fingerprints separately. The overall predictive accuracies of the best models were more than 80% for the external test set of 164 diverse compounds. Some privileged substructures were further identified for ready or not ready biodegradable chemicals by combining information gain and substructure fragment analysis. Moreover, 27 new predicted chemicals were selected for experimental assay through the Japanese MITI test protocols, which validated that all 27 compounds were predicted correctly. The predictive accuracies of our models outperform the commonly used software of the EPI Suite. Our study provided critical tools for early assessment of biodegradability of new organic chemicals in environmental hazard assessment