Nanjing University

About: Nanjing University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.

Integrated Nanozymes with Nanoscale Proximity for in Vivo Neurochemical Monitoring in Living Brains

Abstract: Nanozymes, the nanostructures with enzymatic activities, have attracted considerable attention because, in comparison with natural enzymes, they offer the possibility of lowered cost, improved stability, and excellent recyclability. However, the specificity and catalytic activity of current nanozymes are still far lower than that of their natural counterparts, which in turn has limited their use such as in bioanalysis. To address these challenges, herein we report the design and development of integrated nanozymes (INAzymes) by simultaneously embedding two cascade catalysts (i.e., a molecular catalyst hemin and a natural enzyme glucose oxidase, GOx) inside zeolitic imidazolate framework (ZIF-8) nanostructures. Such integrated design endowed the INAzymes with major advantage in improved catalytic efficiency as the first enzymatic reaction occurred in close (nanoscale) proximity to the second enzyme, so products of the first reaction can be used immediately as substrates for the second reaction, thus overco...

Application potential of carbon nanotubes in water treatment: A review.

Abstract: Water treatment is the key to coping with the conflict between people's increasing demand for water and the world-wide water shortage. Owing to their unique and tunable structural, physical, and chemical properties, carbon nanotubes (CNTs) have exhibited great potentials in water treatment. This review makes an attempt to provide an overview of potential solutions to various environmental challenges by using CNTs as adsorbents, catalysts or catalyst support, membranes, and electrodes. The merits of incorporating CNT to conventional water-treatment material are emphasized, and the remaining challenges are discussed.

Column sampling based discrete supervised hashing

Abstract: By leveraging semantic (label) information, supervised hashing has demonstrated better accuracy than unsupervised hashing in many real applications. Because the hashing-code learning problem is essentially a discrete optimization problem which is hard to solve, most existing supervised hashing methods try to solve a relaxed continuous optimization problem by dropping the discrete constraints. However, these methods typically suffer from poor performance due to the errors caused by the relaxation. Some other methods try to directly solve the discrete optimization problem. However, they are typically time-consuming and unscalable. In this paper, we propose a novel method, called column sampling based discrete supervised hashing (COSDISH), to directly learn the discrete hashing code from semantic information. COSDISH is an iterative method, in each iteration of which several columns are sampled from the semantic similarity matrix and then the hashing code is decomposed into two parts which can be alternately optimized in a discrete way. Theoretical analysis shows that the learning (optimization) algorithm of COSDISH has a constant-approximation bound in each step of the alternating optimization procedure. Empirical results on datasets with semantic labels illustrate that COSDISH can outperform the state-of-the-art methods in real applications like image retrieval.