Semiconducting metal oxide nanocrystals are an important class of materials that have versatile applications because of their useful properties and high stability. Here, we developed a simple route to synthesize nanocrystals (NCs) of copper oxides such as Cu2O and CuO using a hot-soap method, and applied them to H2S sensing. Cu2O NCs were synthesized by simply heating a copper precursor in oleylamine in the presence of diol at 160 °C under an Ar flow. X-ray diffractometry (XRD), dynamic light scattering (DLS), and transmission electron microscopy (TEM) results indicated the formation of monodispersed Cu2O NCs having approximately 5 nm in crystallite size and 12 nm in colloidal size. The conversion of the Cu2O NCs to CuO NCs was undertaken by straightforward air oxidation at room temperature, as confirmed by XRD and UV-vis analyses. A thin film Cu2O NC sensor fabricated by spin coating showed responses to H2S in dilute concentrations (1–8 ppm) at 50–150 °C, but the stability was poor because of the formation of metallic Cu2S in a H2S atmosphere. We found that Pd loading improved the stability of the sensor response. The Pd-loaded Cu2O NC sensor exhibited reproducible responses to H2S at 200 °C. Based on the gas sensing mechanism, it is suggested that Pd loading facilitates the reaction of adsorbed oxygen with H2S and suppresses the irreversible formation of Cu2S.

We propose a new method SSED (State Segmentation based on Euclidean Distance) to categorize continuous numeric percepts for Q-learning, where percept vectors are classified into categories and Q-learning uses categories as states to acquire rules for agent behavior. In SSED, categories are represented by hyper-spheres. A percept vector is classified into a category that covers the vector and is the nearest to it. For efficient reinforcement learning, category merging is provided with SSED, where the number of parameters to control category merging in SSED is fewer than that in fuzzy ART with category merging. In addition, match tracking is incorporated into SSED in order to specialize a category. SSED is combined with Q-learning and it is compared with some state segmentation methods. Experimental results show that Q-learning with SSED learns good rules for agent behavior more efficiently than other methods.

Abstract. Previous studies have indicated pro-tumor functions of macrophages in tumor progression in different types of malignant tumors. The detailed mechanisms of cell-cell interaction between macrophages and tumor cells have been investigated by means of in vitro co-culture experiments. The present study developed magnetite nanoparticles modified with gelatin that are specifically engulfed by macrophages and investigated methods to deplete these macrophages in co-culture experiments using a magnet. T98G glioma cell line and human monocyte-derived macrophages were mixed and co-cultured for 2 days. The T98G cells were isolated by depletion of the macrophages using the magnetite nanoparticles. mRNA expression of a number of pro-tumor molecules in the isolated T98G cells, with or without co-culture with macrophages, was then evaluated. The mRNA expression levels of chemokine (CC motif) ligand 2, interleukin-6 and macrophage-colony stimulating factor receptor (M-CSFR) were significantly upregulated in T98G cells by co-culture with macrophages (P<0.01). M-CSFR protein expression was also increased by co-culture with macrophages. The conditioned medium of co-cultured cells increased M-CSFR expression in T98G cells. Magnetite nanoparticles may be a novel tool not only for investigating the unique activation status of tumor cells in co-culture conditions, but also for targeting pro-tumor macrophages in tumor tissues.

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