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.
appears in a smaller region inside of the tumor. Simulations show that if the aggressive strain focuses its efforts on proliferating and does not contribute to angiogenesis signaling when in a hypoxic state, a hypertumor will form. More importantly, this resultant aggressive tumor is paradoxically prone to extinction and hypothesize is the cause of necrosis in many vascularized tumors.
Predicting the timing of a castrate resistant prostate cancer is critical to lowering medical costs and improving the quality of life of advanced prostate cancer patients. We formulate, compare and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). We accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). While these models are simplifications of a previously published model, they fit data with similar accuracy and improve forecasting results. Both models describe the progression of androgen resistance. Although Model 1 is simpler than the more realistic Model 2, it can fit clinical data to a greater precision. However, we found that Model 2 can forecast future PSA levels more accurately. These findings suggest that including more realistic mechanisms of androgen dynamics in a two population model may help androgen resistance timing prediction.
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.
