Nanjing University

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

Adsorption of nonionic aromatic compounds to single-walled carbon nanotubes: effects of aqueous solution chemistry.

Abstract: We systematically studied effects of pH, ionic strength, and presence of Cu2+ (50 mg/L) or a dissolved humic acid (HA, Fluka) (50 mg/L) on adsorption of three nonionic aromatic compounds, naphthalene, 1,3-dinitrobenzene, and 1,3,5-trinitrobenzene to single-walled carbon nanotubes. Presence of Cu2+ or variance in the ionic strength between 0.02 and 0.1 M (NaNO3) only slightly affected adsorption affinities. Presence of HA reduced adsorption of the three compounds by 29−57% for CNTs, as measured by change in distribution coefficient (Kd), and by 80−95% for graphite. In contrast to nonporous graphite, whose surface area was completely accessible in adsorption, CNTs formed aggregates with microporous interstices in aqueous solution, which blocked large HA molecules from competing with the surface area. Changing the pH from 2 to 11 did not affect adsorption of naphthalene, while it increased adsorption of 1,3-dinitrobenzene and 1,3,5-trinitrobenzene by 2−3 times. Increasing pH apparently facilitated deprotonat...

Semiconductor/relaxor 0–3 type composites without thermal depolarization in Bi 0.5 Na 0.5 TiO 3 -based lead-free piezoceramics

Abstract: Piezoelectric materials are used as sensors or actuators in many devices. Here, the authors demonstrate that semiconducting ZnO particles embedded into a Bi0.5Na0.5TiO3-based matrix improve its piezoelectric properties, promising an alternative to presently used lead-based materials.

Semi-supervised regression with co-training

Abstract: In many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as co-training have attracted much attention. Previous research mainly focuses on semi-supervised classification. In this paper, a co-training style semi-supervised regression algorithm, i.e. COREG, is proposed. This algorithm uses two k-nearest neighbor regressors with different distance metrics, each of which labels the unlabeled data for the other regressor where the labeling confidence is estimated through consulting the influence of the labeling of unlabeled examples on the labeled ones. Experiments show that COREG can effectively exploit unlabeled data to improve regression estimates.