RESUMEN
A trade-off between the carrier concentration and carrier mobility is an inherent problem of traditional transparent conducting oxide (TCO) films. In this study, we demonstrate that the electron concentration of TCO films can be increased without deteriorating the carrier mobility by embedding Ag nanoparticles (NPs) into Al-doped ZnO (AZO) films. An increment of Ag NP content up to 0.7 vol % in the AZO causes the electron concentration rising to 4 × 1020 cm-3. A dependence of the conductivity on temperature suggests that the energy barrier for the electron donation from Ag NPs at room temperature is similar to the Schottky barrier height at the Ag-AZO interface. In spite of an increase in the electron concentration, embedded Ag NPs do not compromise the carrier mobility at room temperature. This is evidence showing that this electron donation mechanism by Ag NPs is different from impurity doping, which produces both electrons and ionized scattering centers. Instead, an increase in the Fermi energy level of the AZO matrix partially neutralizes Al impurities, and the carrier mobility of Ag NP embedded AZO film is slightly increased. The optical transmittance of mixture films with resistivity less than 1 × 10-3 Ω·cm still maintains above 85% in visible wavelengths. This opens a new paradigm to the design of alternative TCO composite materials which circumvent an inherent problem of the impurity doping.
RESUMEN
Pattern formation during evaporation of a colloidal sessile droplet is a phenomenon relevant to a wide variety of scientific disciplines. The patterns remaining on the substrate are indicative of the transport mechanisms and phase transitions occurring during evaporation and may reflect the solution chemistry of the fluid [1-18]. Pattern formation during evaporation of droplets of biofluids has also been examined and these complex patterns may reflect the health of the patient [23-31]. Automatic detection of variations in the fluid composition based on these deposit patterns could lead to rapid screening for diagnostic or quality control purposes. In this study, a pattern recognition algorithm is presented to differentiate between deposits containing various solution compositions. The deposits studied are from droplets of simplified, model biological fluids of aqueous lysozyme and NaCl solutions. For the solution concentrations examined here, the deposit patterns are dependent upon the initial solution composition. Deposit images are represented by extracting features using the Gabor wavelet, similar to the method used for iris recognition. Two popular pattern recognition algorithms are used to classify the deposits. The k-means clustering algorithm is used to test if incremental changes in solution concentration result in reproducible and statistically interpretable variations in the deposit patterns. The k-nearest neighbor algorithm is also used to classify the deposit images by solution concentration based on a set of training images for each class. Here, we demonstrate that the deposit patterns may act as a "fingerprint" for identification of solution chemistry. The results of this study are very promising, with classification accuracies of 90-97.5%.