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In the present dissertation, the interband optical transition and carrier lifetime of InAs/GaAsSb QDs with different silicon delta-doping densities have been first studied by time-integrated and time-resolved photoluminescence (PL). It is found that an optimized silicon delta-doping density in the QDs enables to fill the QD electronic states with electrons for sub-bandgap photon absorption and to improve carrier lifetime of the QDs.
After that, the crystal quality and QD morphology of single- and multi-stack InAs/GaAsSb QDs with different Sb compositions have been investigated by transmission electron microscopy (TEM) and x-ray diffraction (XRD). The TEM studies reveal that QD morphology of single-stack QDs is affected by Sb composition due to strain reducing effect of Sb incorporation. The XRD studies confirm that the increase of Sb composition increases the lattice mismatch between GaAs matrix and GaAsSb spacers, resulting in increase of the strain relaxation in GaAsSb of the multi-stack QDs. Furthermore, the increase of Sb composition causes a PL redshift and increases carrier lifetime of QDs.
Finally, the spacer layer thickness of multi-stack InAs/GaAsSb QDs is optimized for the growth of InAs/GaAsSb QD solar cells (QDSCs). The InAs/GaAsSb QDSCs with GaP strain compensating layer are grown and their device performances are characterized. The increase of GaP coverage is beneficial to improve the conversion efficiency of the QDSCs. However, the conversion efficiency is reduced when using a relatively large GaP coverage.
Many children born preterm exhibit frontal executive dysfunction, behavioral problems including attentional deficit/hyperactivity disorder and attention related learning disabilities. Anomalies in regional specificity of cortico-striato-thalamo-cortical circuits may underlie deficits in these disorders. Nonspecific volumetric deficits of striatal structures have been documented in these subjects, but little is known about surface deformation in these structures. For the first time, here we found regional surface morphological differences in the preterm neonatal ventral striatum. We performed regional group comparisons of the surface anatomy of the striatum (putamen and globus pallidus) between 17 preterm and 19 term-born neonates at term-equivalent age. We reconstructed striatal surfaces from manually segmented brain magnetic resonance images and analyzed them using our in-house conformal mapping program. All surfaces were registered to a template with a new surface fluid registration method. Vertex-based statistical comparisons between the two groups were performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We found statistically significant differences in regional morphology between the two groups that are consistent across statistics, but more extensive for multivariate measures. Differences were localized to the ventral aspect of the striatum. In particular, we found abnormalities in the preterm anterior/inferior putamen, which is interconnected with the medial orbital/prefrontal cortex and the midline thalamic nuclei including the medial dorsal nucleus and pulvinar. These findings support the hypothesis that the ventral striatum is vulnerable, within the cortico-stiato-thalamo-cortical neural circuitry, which may underlie the risk for long-term development of frontal executive dysfunction, attention deficit hyperactivity disorder and attention-related learning disabilities in preterm neonates.
Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.
Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.
Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.