JOURNAL ARTICLE

Semi-supervised hierarchical clustering algorithms

Amine AmarN. Tazi LabzourA. Bensaid

Year: 1998 Journal:   Scandinavian Conference on AI Vol: 554 Pages: 232-239

Abstract

A one-pot simple procedure for the synthesis of uniform, ellipsoidal Eu3+-doped sodium lanthanum tungstate and molybdate (NaLa(XO4)2, X = W, Mo) nanophosphors, functionalized with carboxylate groups, is described. The method is based on a homogeneous precipitation process at 120 °C from appropriate Na+, Ln3+ and tungstate or molybdate precursors dissolved in ethylene glycol/water mixtures containing polyacrylic acid. A comparative study of the luminescent properties of both luminescent materials as a function of the Eu3+ doping level has been performed to find the optimum nanophosphor, whose efficiency as X-ray computed tomography contrast agent is also evaluated and compared with that of a commercial probe. Finally, the cell viability and colloidal stability in physiological pH medium of the optimum samples have also been studied to assess their suitability for biomedical applications.

Keywords:
Computer science Cluster analysis Hierarchical clustering Artificial intelligence Algorithm Data mining Machine learning Pattern recognition (psychology)

Metrics

11
Cited By
0.63
FWCI (Field Weighted Citation Impact)
0
Refs
0.75
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Advanced Clustering Algorithms Research
Physical Sciences →  Computer Science →  Artificial Intelligence
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