Structurally stabilized mesoporous TiO2 nanofibres for efficient dye-sensitized solar cells

One-dimensional (1D) TiO2 nanostructures are very desirable for providing fascinating properties and features, such as high electron mobility, quantum confinement effects, and high specific surface area. Herein, 1D mesoporous TiO2 nanofibres were prepared using the electrospinning method to verify their potential for use as the photoelectrode of dye-sensitized solar cells (DSSCs). The 1D mesoporous nanofibres, 300 nm in diameter and 10-20 μm in length, were aggregated from anatase nanoparticles 20-30 nm in size. The employment of these novel 1D mesoporous nanofibres significantly improved dye loading and light scattering of the DSSC photoanode, and resulted in conversion cell efficiency of 8.14%, corresponding to an ∼35% enhancement over the Degussa P25 reference photoanode.

Continually adjustable oriented 1D TiO2 nanostructure arrays with controlled growth of morphology and their application in dye-sensitized solar cells

Oriented, single-crystalline, one-dimensional (1D) TiO2 nanostructures would be most desirable for providing fascinating properties and features, such as high electron mobility or quantum confinement effects, high specific surface area, and even high mechanical strength, but achieving these structures has been limited by the availability of synthetic techniques. In this study, a concept for precisely controlling the morphology of 1D TiO2 nanostructures by tuning the hydrolysis rate of titanium precursors is proposed. Based on this innovation, oriented 1D rutile TiO2 nanostructure arrays with continually adjustable morphologies, from nanorods (NRODs) to nanoribbons (NRIBs), and then nanowires (NWs), as well as the transient state morphologies, were successfully synthesized. The proposed method is a significant finding in terms of controlling the morphology of the 1D TiO2 nano-architectures, which leads to significant changes in their band structures. It is worth noting that the synthesized rutile NRIBs and NWs have a comparable bandgap and conduction band edge height to those of the anatase phase, which in turn enhances their photochemical activity. In photovoltaic performance tests, the photoanode constructed from the oriented NRIB arrays possesses not only a high surface area for sufficient dye loading and better light scattering in the visible light range than for the other morphologies, but also a wider bandgap and higher conduction band edge, with more than 200% improvement in power conversion efficiency in dye-sensitized solar cells (DSCs) compared with NROD morphology.

Modelling Cued-Target Recall using Quantum Inspired Models of Target Activation

This article presents and evaluates Quantum Inspired models of Target Activation using Cued-Target Recall Memory Modelling over multiple sources of Free Association data. Two components were evaluated: Whether Quantum Inspired models of Target Activation would provide a better framework than their classical psychological counterparts and how robust these models are across the different sources of Free Association data. In previous work, a formal model of cued-target recall did not exist and as such Target Activation was unable to be assessed directly. Further to that, the data source used was suspected of suffering from temporal and geographical bias. As a consequence, Target Activation was measured against cued-target recall data as an approximation of performance. Since then, a formal model of cued-target recall (PIER3) has been developed [10] with alternative sources of data also becoming available. This allowed us to directly model target activation in cued-target recall with human cued-target recall pairs and use multiply sources of Free Association Data. Featural Characteristics known to be important to Target Activation were measured for each of the data sources to identify any major differences that may explain variations in performance for each of the models. Each of the activation models were used in the PIER3 memory model for each of the data sources and was benchmarked against cued-target recall pairs provided by the University of South Florida (USF). Two methods where used to evaluate performance. The first involved measuring the divergence between the sets of results using the Kullback Leibler (KL) divergence with the second utilizing a previous statistical analysis of the errors [9]. Of the three sources of data, two were sourced from human subjects being the USF Free Association Norms and the University of Leuven (UL) Free Association Networks. The third was sourced from a new method put forward by Galea and Bruza, 2015 in which pseudo Free Association Networks (Corpus Based Association Networks - CANs) are built using co-occurrence statistics on large text corpus. It was found that the Quantum Inspired Models of Target Activation not only outperformed the classical psychological model but was more robust across a variety of data sources.