Enhanced Separation Performance of Radioactive Cesium and Cobalt in Graphene Oxide Membrane via Cationic Control

The great applications of nuclear power for the most promising clean energy sources have been challenged by a large amount of radioactive wastewater generated, specifically the Cs+/Co2+ separation for nuclear waste storage, retreatment or recycling of radioactive wastewater, because of their wide difference in half-life and high heat release. In this work, graphene oxide membranes (GOMs) with interlayer spacing controlled by cations were used to separate mixed Cs+/Co2+ ions. The separation factors of Cs+/Co2+ for K+-controlled graphene oxide membranes (K-GOMs) was 2∼3 times higher than that of GOMs without treatment. In addition, the separation factors of Cs+/Co2+ for K-GOMs can be further enhanced with the increase of membranes thickness and change the initial ratios of the two ions. Typically, the separation factors of K-GOMs with a thickness of ∼300 nm reached up to 73.7 ± 3.9. Moreover, the K-GOM showed outstanding stability of the separation performance under long-term operation within 7 days. First-principles calculation revealed that the enhanced ionic selectivity of controlled GOM is induced by the difference of adsorption energies between the hydrated cations and aromatic rings, resulting in a significant increase in the mobility differences between Cs+ and Co2+ through a fixed narrow interlayer spacing. This study demonstrated excellent separation performances of GO-based membranes based on their size-exclusion effect rather than electrostatic repulsion effect, and we believe this work can enable potential efficient treatment technologies for radioactive wastewater needed urgently

Unexpected Ion Sieving in Graphene Oxide Membranes

Ion sieving is significantly important in many fields such as life science, resource utilization, and environmental protection. However, unlike biological ion channels, realizing efficient ion sieving using two-dimensional (2D) membranes with artificial ion channels is severely restrained because of swelling. In this work, we demonstrated that graphene oxide membranes (GOMs) with the interlayer spacings controlled by K+ (K-GOMs) provide a remarkably enhanced ion sieving performance in mono-/multi-valent mixed ion systems, in which ion selectivity is more than 1 order of magnitude than that of untreated GOMs. The highest selectivity of Na+/Fe3+ for K-GOMs reached up to 159.1, which is superior to most of the state-of the art NF or 2D membranes, and the competitive permeation rate of Na+ ions still remained at 0.48 mol m–2 h–1. Additionally, the K-GOMs also showed an outstanding stability in the long-term test. Further, the K-GOMs also presented high selectivity for multi-valent/multi-valent mixed ion systems. Overall, this study gives a full picture of the excellent performance of K-GOMs based on the size effect for ion sieving and exhibits great value in real-world applications such as desalination, water treatment, battery, and even in life science

Impact of −C₂H₅ and −OH Functionalizations on the Water Flow Blockage in Carbon Nanotubes

Carbon nanotube (CNT) filter membranes are excellent promising materials for efficient desalination. In our previous studies (<i>Phys. Rev. Lett.</i> <b>2015,</b> <i>115</i>, 164502) we showed that Na⁺ cations in seawater would easily bind at the entrance of the pristine CNT due to cation−π interaction, resulting in the blocking of water flow through the nanotube. Here, we systematically investigate the binding behavior of ions and blockage effects of water flow in much more chemically realistic CNTs that are functionalized at the ends with various density of hydrophilic −OH or hydrophobic −C₂H₅ groups. Our findings show that hydrophobic −C₂H₅ groups will weaken the cation−π interaction between Na⁺ ions and CNTs, and accordingly, water flows through the CNTs fluently. CNTs functionalized with −C₂H₅ groups in moderate density are expected to work excellently in desalination application, whereas functionalization with hydrophilic −OH groups cannot prevent the blockage of water. This finding brings insights in designing efficient desalination filter materials based on CNT

Evaluation of the inferences using a list of 943 drugs based on original indication and clinical trials.

<p>Evaluation of the inferences using a list of 943 drugs based on original indication and clinical trials.</p

Different types of knowledge used in our approach and their sources.

<p>Different types of knowledge used in our approach and their sources.</p

A diagrammatic view of (a) direct and (b) indirect inferences for dipyridamole and tazarotene as novel cancer indications.

<p>A diagrammatic view of (a) direct and (b) indirect inferences for dipyridamole and tazarotene as novel cancer indications.</p

Treatment distribution for the 296 inferred drugs that neither have cancer as the original indication nor in clinical trials for cancer.

<p>Treatment distribution for the 296 inferred drugs that neither have cancer as the original indication nor in clinical trials for cancer.</p

Logic forms for the classes and entities involved in the drug mechanism domain.

<p>Logic forms for the classes and entities involved in the drug mechanism domain.</p

Examples of extracted gene-disease relationships and protein-protein interactions with their support evidences.

<p>Examples of extracted gene-disease relationships and protein-protein interactions with their support evidences.</p

Identifying Novel Drug Indications through Automated Reasoning

<div><h3>Background</h3><p>With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using <em>in silico</em> approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.</p> <h3>Methodology</h3><p>In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.</p> <h3>Conclusion/Significance</h3><p>To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.</p> </div