Analysis of Structure, Composition and Growth of Semiconductor Nanowires by Transmission Electron Microscopy

Nanowires have the potential to be a very flexible platform for the design of semiconductor devices. In nanowires it is possible to form crystal structures not found in the bulk materials under normal conditions, and to combine different III-V and group IV materials into axial or radial heterostructures. As quite complex structures can be formed, both intentionally and unintentionally, characterization of the crystal structure and composition is important. In this thesis, various transmission electron microscopy techniques are presented for this purpose. High resolution imaging can directly visualize the crystal structure, including twinning and stacking faults. The polar nature of the III-V materials leaves one more parameter to be determined. In order to determine polarity from high resolution images it is not only necessary to improve the resolution further by aberration correction, but in addition the local orientation of the sample must be determined. Convergent beam electron diffraction is an alternative method with much lower demands on the microscope and operator, and can be adapted to suit most materials and crystal structures. Transmission electron microscopy also provides several methods for determining and mapping the composition of the nanowires. It is important in all cases to avoid damaging the nanowires during the acquisition of the analytical signal. In the most commonly used method, energy dispersive X-ray spectroscopy, this can be achieved by spreading the electron dose over as large an area as possible. If there is only a single unknown parameter for the composition, alternative methods such as the shift in plasmon energy with composition can be used instead, as they have higher collection efficiencies. In order to improve the nanowires in terms of crystal structure and composition, these must be connected to the dynamic processes occurring during growth. Occasionally these processes can be inferred from the fully formed nanowires after growth, but ideally one would like to observe the growth in-situ in the microscope. This is usually possible only with highly specialized environmental microscopes. In this thesis, nanowire growth in much simpler closed cells is demonstrated. Although the growth conditions could neither be precisely measured nor controlled, the closed cells made it possible to observe for the first time growing InAs nanowires in-situ in a conventional transmission electron microscope

Intellectual Capital's Importance for Corporate Performance

The purpose of the thesis is to empirically investigate the relationship between intellectual capital and corporate performance. The estimated models will be used tp predict future corporate performance. Definitions of Intellectual Capital are presented as well as measurements of the concept, with focus on VAIC. Prior research investigating the relationship between intellectual capital and corporate performance are brought forward. A quantitative approach is used to investiagte the relationship between intellectual capital and corporate performance. Panel data regressions are used to analyze the relationship and estimate prediction models. 823 obseravtions during the period 1998-2007 are collected. The analyze shows a positive relationship between intellctual capital and profitability. For accurate predictions of corporate performance, more factors than intellectual capital, form size and leverage, are needed to be included in the model

Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning

This paper presents an approach for learning invariant features for object affordance understanding. One of the major problems for a robotic agent acquiring a deeper understanding of affordances is finding sensory-grounded semantics. Being able to understand what in the representation of an object makes the object afford an action opens up for more efficient manipulation, interchange of objects that visually might not be similar, transfer learning, and robot to human communication. Our approach uses a metric learning algorithm that learns a feature transform that encourages objects that affords the same action to be close in the feature space. We regularize the learning, such that we penalize irrelevant features, allowing the agent to link what in the sensory input caused the object to afford the action. From this, we show how the agent can abstract the affordance and reason about the similarity between different affordances

Catastrophic chromosomal restructuring during genome elimination in plants.

Genome instability is associated with mitotic errors and cancer. This phenomenon can lead to deleterious rearrangements, but also genetic novelty, and many questions regarding its genesis, fate and evolutionary role remain unanswered. Here, we describe extreme chromosomal restructuring during genome elimination, a process resulting from hybridization of Arabidopsis plants expressing different centromere histones H3. Shattered chromosomes are formed from the genome of the haploid inducer, consistent with genomic catastrophes affecting a single, laggard chromosome compartmentalized within a micronucleus. Analysis of breakpoint junctions implicates breaks followed by repair through non-homologous end joining (NHEJ) or stalled fork repair. Furthermore, mutation of required NHEJ factor DNA Ligase 4 results in enhanced haploid recovery. Lastly, heritability and stability of a rearranged chromosome suggest a potential for enduring genomic novelty. These findings provide a tractable, natural system towards investigating the causes and mechanisms of complex genomic rearrangements similar to those associated with several human disorders

Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice