Dendrimers are well-defined, highly branched macromolecules with hollow cores and dense shells. They are constructed from an initiator core to which radially branched layers are covalently attached. Dendrimers have become the subject of extensive study [1] because their multifunctional structure and specific shape have been recognized as powerful tools in the synthesis of new structures. The special properties of dendritic boxes make them useful in many applications, such as in drug release, molecular labels, probe moieties, chemical sensors, holographic data storage, and molecular shuttles for transporting guest molecules between two different phases.[2] Most of the dendritic containers reported so far use the concept of closing the dendritic box in order to keep the guest molecules inside the dendrimer host. Thus, many researchers have addressed this kind of core±shell structure by modifying the dendrimer surface, so that molecules can be reversibly imprisoned in internal cavities of the dendrimer and then site-selectively liberated by a suitable external or internal stimulus. For such applications, a shell should be ªopenedº and ªclosedº reversiblyÐcontrolled by means of a simple external or internal stimulus, such as light, pH, or ionic strength.[3] Vögtle and co-workers prepared a photoswitchable dendritic box by modifying terminal groups with light-switchable units. [3] Poly(N-isopropylacrylamide) (PNIPAAm) is a well-known water-soluble polymer that shows reversible hydration±dehy-dration changes in response to small solution-temperature changes. Okano and co-workers have already reported thermally controlled surface attachment and detachment of cells using a surface-grafted PNIPAAm. The thermally responsive polymeric micelles maintain passive targeting of tissue sites via their small size, and the potential for active targeting via their PNIPAAm-switchable physicochemical character. [4] Jiang and co-workers have achieved reversible switching between super-hydrophilicity and super-hydrophobicity by grafting PNIPAAm to a surface.[5] PNIPAAm and its copolymers have found several applications, such as extraction, controlled release, and enzyme-activity control. Thus, attaching a sensitive PNIPAAm shell onto a dendrimer surface will produce dendritic core±shell nanostructures with special properties for applications in drug delivery and smart catalysis. However, it has been very difficult to link a PNIPAAm chain to the dendrimer surface to form a dendritic core±shell nanostructure, although some kinds of core±shell structures have been successfully prepared by polymer-chain modification of the dendrimer surface. In addition, the shells of the previously reported dendritic core±shell nanostructures are not sensitive to stimuli. [6] We are therefore engaged in a research program aimed at synthesizing a dendritic core±shell nanostructure with a temperature-sensitive shell by PNIPAAm-chain modification of the dendrimer surface.As is well-known, many dendrimers with hydroxyl-terminal groups can easily be syn...

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Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact Airborne Spectrographic Imager) hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity) and CASI data (48 bands) with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA) methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM) and maximum likelihood (MLC) classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions): (1) the layer stacking fusion data; (2) the PCA fusion data; (3) the LiDAR data alone; and (4) the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964) using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the SVM classifier generally performed better than the MLC when classifying multisource data; however, none of the classifiers consistently produced higher accuracies at all spatial resolutions.

Water-soluble multiwalled carbon nanotubes (MWNTs) with temperature-responsive shells were successfully prepared by grafting poly (N-isopropylacrylamide) (PNIPAM) from the sidewalls of MWNTs, via surface reversible addition-fragmentation chain transfer (RAFT) polymerization using RAFT agent functionalized MWNTs as the chain transfer agent. Thermogravimetric analysis (TGA) measurements showed that the weight composition of the as-grown PNIPAM polymers on the MWNTs can be well controlled by the feed ratio (in weight) of NIPAM to RAFT agent functionalized MWNTs (MWNT-SC(S)Ph). The MWNT-g-PNIPAM has good solubility in water, chloroform, and tetrahydrofuran (THF). Transmission electron microscope (TEM) and scanning electron microscope (SEM) images also showed that the MWNT-g-PNIPAM was dispersed individually and eventually bonded with the polymer layer by surface RAFT polymerization. The PNIPAM shell is very sensitive to a change of temperature. This method could find potential applications by grafting other functional polymer chains onto MWNTs.