Recovering palladium and gold by peroxydisulfate-based advanced oxidation process.

Palladium (Pd) and gold (Au) are the most often used precious metals (PMs) in industrial catalysis and electronics. Green recycling of Pd and Au is crucial and difficult. Here, we report a peroxydisulfate (PDS)-based advanced oxidation process (AOPs) for selectively recovering Pd and Au from spent catalysts. The PDS/NaCl photochemical system achieves complete dissolution of Pd and Au. By introducing Fe(II), the PDS/FeCl2·4H2O solution functioned as Fenton-like system, enhancing the leaching efficiency without xenon (Xe) lamp irradiation. Electron paramagnetic resonance (EPR), 18O isotope tracing experiments, and density functional theory calculations revealed that the reactive oxidation species of SO4·-, ·OH, and Fe(IV)═O were responsible for the oxidative dissolution process. Lixiviant leaching and one-step electrodeposition recovered high-purity Pd and Au. Strong acids, poisonous cyanide, and volatile organic solvents were not used during the whole recovery, which enables an efficient and sustainable precious metal recovery approach and encourage AOP technology for secondary resource recycling.

Recent advances in the removal of radioactive iodine by bismuth-based materials.

Nowadays, the demand for nuclear power is continue increasing due to its safety, cleanliness, and high economic benefits. Radioactive iodine from nuclear accidents and nuclear waste treatment processes poses a threat to humans and the environment. Therefore, the capture and storage of radioactive iodine are vital. Bismuth-based (Bi-based) materials have drawn much attention as low-toxicity and economical materials for removing and immobilizing iodine. Recent advances in adsorption and immobilization of vapor iodine by the Bi-based materials are discussed in this review, in addition with the removal of iodine from solution. It points out the neglected areas in this research topic and provides suggestions for further development and application of Bi-based materials in the removal of radioactive iodine.

ZnCl2: A Green Brønsted Acid for Selectively Recovering Rare Earth Elements from Spent NdFeB Permanent Magnets.

The spent neodymium-iron-boron (NdFeB) magnet is a highly valuable secondary resource of rare earth elements (REEs). Hydrometallurgical processes are widely used in recovering REEs from spent NdFeB magnets, but they will consume large amounts of organic chemicals, leading to severe environmental pollution. This work developed an alternative green route to selectively recover REEs from spent NdFeB permanent magnets using a purely inorganic zinc salt. The Hammett acidity measurement showed that concentrated ZnCl2 solutions could be regarded as a strong Brønsted acid. Concentrated ZnCl2 solutions achieved a high separation factor (>1 × 105) between neodymium and iron through simple dissolution of their corresponding oxide mixture. In the simulated recovery process of spent NdFeB magnets, the Nd2O3 product was successfully recovered with a purity close to 100% after selective leaching by ZnCl2 solution, sulfate double-salt precipitation, and oxalic acid precipitation. The separation performance of the ZnCl2 solution for Nd2O3 and Fe2O3 remained almost unchanged after four cycles. The energy consumption and chemical inputs of this process are about 1/10 and half of the traditional hydrometallurgy process separately. This work provides a promising approach for the green recovery of secondary REE resources.

An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression.

In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.

Efficient Recovery of End-of-Life NdFeB Permanent Magnets by Selective Leaching with Deep Eutectic Solvents.

The NdFeB permanent magnet is a critical material in digital electronics and clean energy industry. Traditional recovery processes based on the solvent extraction technique would consume high energy and large amounts of chemicals as well as resulting in abundant secondary organic wastes. In this work, a green process using deep eutectic solvents (DESs) in the selective leaching technology was designed to recover NdFeB permanent magnets. Nine kinds of DESs composed of guanidine were prepared and screened as the leachants. The guanidine hydrochloride-lactic acid (GUC-LAC) combined DES achieved the highest separation factor (>1300) between neodymium and iron through simple dissolution of their corresponding oxide mixture. The mass concentration of Nd dissolved in the GUC-LAC DES could reach 6.7 × 104 ppm. The viscosity of this type of DES at 50 °C was 36 cP, which was comparable to many common organic solvents. In a practical recovery of roasted magnet powders, the Nd2O3 product with 99% purity was facilely obtained with only one dissolution step, followed by a stripping process with oxalic acid. Even after 3 cycles, the GUC-LAC DES kept the same dissolution property and chemical stability. With such superior performances in selective leaching of rare earth elements from transition metals, the GUC-LAC DES is greatly promising in the rare earth element recovery field.

Comprehensive anticancer drug response prediction based on a simple cell line-drug complex network model.

BACKGROUND: Accurate prediction of anticancer drug responses in cell lines is a crucial step to accomplish the precision medicine in oncology. Although many popular computational models have been proposed towards this non-trivial issue, there is still room for improving the prediction performance by combining multiple types of genome-wide molecular data. RESULTS: We first demonstrated an observation on the CCLE and GDSC datasets, i.e., genetically similar cell lines always exhibit higher response correlations to structurally related drugs. Based on this observation we built a cell line-drug complex network model, named CDCN model. It captures different contributions of all available cell line-drug responses through cell line similarities and drug similarities. We executed anticancer drug response prediction on CCLE and GDSC independently. The result is significantly superior to that of some existing studies. More importantly, our model could predict the response of new drug to new cell line with considerable performance. We also divided all possible cell lines into "sensitive" and "resistant" groups by their response values to a given drug, the prediction accuracy, sensitivity, specificity and goodness of fit are also very promising. CONCLUSION: CDCN model is a comprehensive tool to predict anticancer drug responses. Compared with existing methods, it is able to provide more satisfactory prediction results with less computational consumption.