Advanced Numerical Simulation of Silicon-Based Solar Cells

Photovoltaic (PV) conversion is the direct production of electrical energy from sun without involving the emission of polluting substances. In order to be competitive with other energy sources, cost of the PV technology must be reduced ensuring adequate conversion efficiencies. These goals have motivated the interest of researchers in investigating advanced designs of crystalline silicon solar (c-Si) cells. Since lowering the cost of PV devices involves the reduction of the volume of semiconductor, an effective light trapping strategy aimed at increasing the photon absorption is required. Modeling of solar cells by electro-optical numerical simulation is helpful to predict the performance of future generations devices exhibiting advanced light-trapping schemes and to provide new and more specific guidelines to industry. The approaches to optical simulation commonly adopted for c-Si solar cells may lead to inaccurate results in case of thin film and nano-stuctured solar cells. On the other hand, rigorous solvers of Maxwell equations are really cpu- and memory-intensive. Recently, in optical simulation of solar cells, the RCWA method has gained relevance, providing a good trade-off between accuracy and computational resources requirement. This thesis is a contribution to the numerical simulation of advanced silicon solar cells by means of a state-of-the-art numerical 2-D/3-D device simulator, that has been successfully applied to the simulation of selective emitter and the rear point contact solar cells, for which the multi-dimensionality of the transport model is required in order to properly account for all physical competing mechanisms. In the second part of the thesis, the optical problems is discussed. Two novel and computationally efficient RCWA implementations for 2-D simulation domains as well as a third RCWA for 3-D structures based on an eigenvalues calculation approach have been presented. The proposed simulators have been validated in terms of accuracy, numerical convergence, computation time and correctness of results.

Analysis of charge-transport properties in GST materials for next generation phase-change memory devices

The quest for universal memory is driving the rapid development of memories with superior all-round capabilities in non-volatility, high speed, high endurance and low power. The memory subsystem accounts for a significant cost and power budget of a computer system. Current DRAM-based main memory systems are starting to hit the power and cost limit. To resolve this issue the industry is improving existing technologies such as Flash and exploring new ones. Among those new technologies is the Phase Change Memory (PCM), which overcomes some of the shortcomings of the Flash such as durability and scalability. This alternative non-volatile memory technology, which uses resistance contrast in phase-change materials, offers more density relative to DRAM, and can help to increase main memory capacity of future systems while remaining within the cost and power constraints. Chalcogenide materials can suitably be exploited for manufacturing phase-change memory devices. Charge transport in amorphous chalcogenide-GST used for memory devices is modeled using two contributions: hopping of trapped electrons and motion of band electrons in extended states. Crystalline GST exhibits an almost Ohmic I(V) curve. In contrast amorphous GST shows a high resistance at low biases while, above a threshold voltage, a transition takes place from a highly resistive to a conductive state, characterized by a negative differential-resistance behavior. A clear and complete understanding of the threshold behavior of the amorphous phase is fundamental for exploiting such materials in the fabrication of innovative nonvolatile memories. The type of feedback that produces the snapback phenomenon is described as a filamentation in energy that is controlled by electron–electron interactions between trapped electrons and band electrons. The model thus derived is implemented within a state-of-the-art simulator. An analytical version of the model is also derived and is useful for discussing the snapback behavior and the scaling properties of the device.

Multicomponent nanodevices based on molecular and nanocrystal moieties

Nanoscience is an emerging and fast-growing field of science with the aim of manipulating nanometric objects with dimension below 100 nm. Top down approach is currently used to build these type of architectures (e.g microchips). The miniaturization process cannot proceed indefinitely due to physical and technical limitations. Those limits are focusing the interest on the bottom-up approach and construction of nano-objects starting from “nano-bricks” like atoms, molecules or nanocrystals. Unlike atoms, molecules can be “fully programmable” and represent the best choice to build up nanostructures. In the past twenty years many examples of functional nano-devices able to perform simple actions have been reported. Nanocrystals which are often considered simply nanostructured materials, can be active part in the development of those nano-devices, in combination with functional molecules. The object of this dissertation is the photophysical and photochemical investigation of nano-objects bearing molecules and semiconductor nanocrystals (QDs) as components. The first part focuses on the characterization of a bistable rotaxane. This study, in collaboration with the group of Prof. J.F. Stoddart (Northwestern University, Evanston, Illinois, USA) who made the synthesis of the compounds, shows the ability of this artificial machine to operate as bistable molecular-level memory under kinetic control. The second part concerns the study of the surface properties of luminescent semiconductor nanocrystals (QDs) and in particular the effect of acid and base on the spectroscopical properties of those nanoparticles. In this section is also reported the work carried out in the laboratory of Prof H. Mattoussi (Florida State University, Tallahassee, Florida, USA), where I developed a novel method for the surface decoration of QDs with lipoic acid-based ligands involving the photoreduction of the di-thiolane moiety.

Gpu-based many-core architecture emulation: A double level approach

The efficient emulation of a many-core architecture is a challenging task, each core could be emulated through a dedicated thread and such threads would be interleaved on an either single-core or a multi-core processor. The high number of context switches will results in an unacceptable performance. To support this kind of application, the GPU computational power is exploited in order to schedule the emulation threads on the GPU cores. This presents a non trivial divergence issue, since GPU computational power is offered through SIMD processing elements, that are forced to synchronously execute the same instruction on different memory portions. Thus, a new emulation technique is introduced in order to overcome this limitation: instead of providing a routine for each ISA opcode, the emulator mimics the behavior of the Micro Architecture level, here instructions are date that a unique routine takes as input. Our new technique has been implemented and compared with the classic emulation approach, in order to investigate the chance of a hybrid solution.

Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques

In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following: - Capability of monitoring the fault evolution continuously over time under any transient operating condition; - Speed/slip measurement or estimation is not required; - Higher accuracy in filtering frequency components around the fundamental in case of rotor faults; - Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band); - Low memory requirement due to low sampling frequency; - Reduction in the latency of time processing (no requirement of repeated sampling operation).

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Shape memory and elastoplastic materials: from constitutive and numerical to fatigue modeling

Shape memory materials (SMMs) represent an important class of smart materials that have the ability to return from a deformed state to their original shape. Thanks to such a property, SMMs are utilized in a wide range of innovative applications. The increasing number of applications and the consequent involvement of industrial players in the field have motivated researchers to formulate constitutive models able to catch the complex behavior of these materials and to develop robust computational tools for design purposes. Such a research field is still under progress, especially in the prediction of shape memory polymer (SMP) behavior and of important effects characterizing shape memory alloy (SMA) applications. Moreover, the frequent use of shape memory and metallic materials in biomedical devices, particularly in cardiovascular stents, implanted in the human body and experiencing millions of in-vivo cycles by the blood pressure, clearly indicates the need for a deeper understanding of fatigue/fracture failure in microsize components. The development of reliable stent designs against fatigue is still an open subject in scientific literature. Motivated by the described framework, the thesis focuses on several research issues involving the advanced constitutive, numerical and fatigue modeling of elastoplastic and shape memory materials. Starting from the constitutive modeling, the thesis proposes to develop refined phenomenological models for reliable SMA and SMP behavior descriptions. Then, concerning the numerical modeling, the thesis proposes to implement the models into numerical software by developing implicit/explicit time-integration algorithms, to guarantee robust computational tools for practical purposes. The described modeling activities are completed by experimental investigations on SMA actuator springs and polyethylene polymers. Finally, regarding the fatigue modeling, the thesis proposes the introduction of a general computational approach for the fatigue-life assessment of a classical stent design, in order to exploit computer-based simulations to prevent failures and modify design, without testing numerous devices.

Microstructure of epitaxial thin films of the ferromagnetic shape memory alloy Ni 2 MnGa

Gegenstand dieser Arbeit ist die Präparation und die ausführliche Charakterisierung epitaktischer Dünnschicht-Proben der Heusler Verbindung Ni2MnGa. Diese intermetallische Verbindung zeigt einen magnetischen Formgedächtnis-Effekt (MFG), der sowohl im Bezug auf mögliche Anwendungen, als auch im Kontext der Grundlagenforschung äußerst interessant ist. In Einkristallen nahe der Stöchiometrie Ni2MnGa wurden riesige magnetfeldinduzierte Dehnungen von bis zu 10 % nachgewiesen. Der zugrundeliegende Mechanismus basiert auf einer Umverteilung von kristallographischen Zwillings-Varianten, die eine tetragonale oder orthorhombische Symmetrie besitzen. Unter dem Einfluss des Magnetfeldes bewegen sich die Zwillingsgrenzen durch den Kristall, was eine makroskopische Formänderung mit sich bringt. Die somit erzeugten reversiblen Längenänderungen können mit hoher Frequenz geschaltet werden, was Ni2MnGa zu einem vielversprechenden Aktuatorwerkstoff macht. rnDa der Effekt auf einem intrinsischen Prozess beruht, eignen sich Bauteile aus MFG Legierungen zur Integration in Mikrosystemen (z.B. im Bereich der Mikrofluidik). rnrnBislang konnten große magnetfeldinduzierte Dehnungen nur für Einkristalle und Polykristalle mit hoher Porosität („foams") nachgewiesen werden. Um den Effekt für Anwendungen nutzbar zu machen, werden allerdings Konzepte zur Miniaturisierung benötigt. Eine Möglichkeit bieten epitaktische dünne Filme, die im Rahmen dieser Arbeit hergestellt und untersucht werden sollen. Im Fokus stehen dabei die Optimierung der Herstellungsparameter, sowie die Präparation von freitragenden Schichten. Zudem werden verschiedene Konzepte zur Herstellung freistehender Mikrostrukturen erprobt. Mittels Röntgendiffraktometrie konnte die komplizierte Kristallstruktur für verschiedene Wachstumsrichtungen verstanden und die genaue Verteilung der Zwillingsvarianten aufgedeckt werden. In Verbindung mit Mikroskopie-Methoden konnte so die Zwillingsstruktur auf verschiedenen Längenskalen geklärt werden. Die Ergebnisse erklären das Ausbleiben des MFG Effekts in den Proben mit (100) Orientierung. Andererseits wurde für Schichten mit (110) Wachstum eine vielversprechende Mikrostruktur entdeckt, die einen guten Ausgangspunkt für weitere Untersuchungen bietet.rnDurch die spezielle Geometrie der Proben war es möglich, Spektroskopie-Experimente in Transmission durchzuführen. Die Ergebnisse stellen den ersten experimentellen Nachweis der Änderungen in der elektronischen Struktur einer metallischen Verbindung während des martensitischen Phasenübergangs dar. Durch Messen des magnetischen Zirkulardichroismus in der Röntgenabsorption konnten quantitative Aussagen über die magnetischen Momente von Ni und Mn getroffen werden. Die Methode erlaubt überdies die Beiträge von Spin- und Bahn-Moment separat zu bestimmen. Durch winkelabhängige Messungen gelang es, die mikroskopische Ursache der magnetischen Anisotropie aufzuklären. Diese Ergebnisse tragen wesentlich zum Verständnis der komplexen magnetischen und strukturellen Eigenschaften von Ni2MnGa bei.rn

Conceptual and normative issues of memory enhancement

Our growing understanding of human mind and cognition and the development of neurotechnology has triggered debate around cognitive enhancement in neuroethics. The dissertation examines the normative issues of memory enhancement, and focuses on two issues: (1) the distinction between memory treatment and enhancement; and (2) how the issue of authenticity concerns memory interventions, including memory treatments and enhancements. rnThe first part consists of a conceptual analysis of the concepts required for normative considerations. First, the representational nature and the function of memory are discussed. Memory is regarded as a special form of self-representation resulting from a constructive processes. Next, the concepts of selfhood, personhood, and identity are examined and a conceptual tool—the autobiographical self-model (ASM)—is introduced. An ASM is a collection of mental representations of the system’s relations with its past and potential future states. Third, the debate between objectivist and constructivist views of health are considered. I argue for a phenomenological account of health, which is based on the primacy of illness and negative utilitarianism.rnThe second part presents a synthesis of the relevant normative issues based on the conceptual tools developed. I argue that memory enhancement can be distinguished from memory treatment using a demarcation regarding the existence of memory-related suffering. That is, memory enhancements are, under standard circumstances and without any unwilling suffering or potential suffering resulting from the alteration of memory functions, interventions that aim to manipulate memory function based on the self-interests of the individual. I then consider the issue of authenticity, namely whether memory intervention or enhancement endangers “one’s true self”. By analyzing two conceptions of authenticity—authenticity as self-discovery and authenticity as self-creation, I propose that authenticity should be understood in terms of the satisfaction of the functional constraints of an ASM—synchronic coherence, diachronic coherence, and global veridicality. This framework provides clearer criteria for considering the relevant concerns and allows us to examine the moral values of authenticity. rn

Bitext alignment: building and evaluating a bilingual corpus and translation memory of academic course descriptions

Following the internationalization of contemporary higher education, academic institutions based in non-English speaking countries are increasingly urged to produce contents in English to address international prospective students and personnel, as well as to increase their attractiveness. The demand for English translations in the institutional academic domain is consequently increasing at a rate exceeding the capacity of the translation profession. Resources for assisting non-native authors and translators in the production of appropriate texts in L2 are therefore required in order to help academic institutions and professionals streamline their translation workload. Some of these resources include: (i) parallel corpora to train machine translation systems and multilingual authoring tools; and (ii) translation memories for computer-aided tools. The purpose of this study is to create and evaluate reference resources like the ones mentioned in (i) and (ii) through the automatic sentence alignment of a large set of Italian and English as a Lingua Franca (ELF) institutional academic texts given as equivalent but not necessarily parallel (i.e. translated). In this framework, a set of aligning algorithms and alignment tools is examined in order to identify the most profitable one(s) in terms of accuracy and time- and cost-effectiveness. In order to determine the text pairs to align, a sample is selected according to document length similarity (characters) and subsequently evaluated in terms of extent of noisiness/parallelism, alignment accuracy and content leverageability. The results of these analyses serve as the basis for the creation of an aligned bilingual corpus of academic course descriptions, which is eventually used to create a translation memory in TMX format.

Genomic analysis of non-splenic marginal zone lymphomas (MZL) indicates similarities between nodal and extranodal MZL and supports their derivation from memory B-cells

Three distinct categories of marginal zone lymphomas (MZLs) are currently recognized, principally based on their site of occurrence. They are thought to represent unique entities, but the relationship of one subtype with another is poorly understood. We investigated 17 non-splenic MZLs (seven nodal, 10 extranodal) by gene expression profiling to distinguish between subtypes and determine their cell of origin. Our findings suggest biological inter-relatedness of these entities despite occurrence at different locations and associations with possibly different aetiologies. Furthermore, the expression profiles of non-splenic MZL were similar to memory B cells.

Single-cell gene-expression profiling reveals qualitatively distinct CD8 T cells elicited by different gene-based vaccines

CD8 T cells play a key role in mediating protective immunity against selected pathogens after vaccination. Understanding the mechanism of this protection is dependent upon definition of the heterogeneity and complexity of cellular immune responses generated by different vaccines. Here, we identify previously unrecognized subsets of CD8 T cells based upon analysis of gene-expression patterns within single cells and show that they are differentially induced by different vaccines. Three prime-boost vector combinations encoding HIV Env stimulated antigen-specific CD8 T-cell populations of similar magnitude, phenotype, and functionality. Remarkably, however, analysis of single-cell gene-expression profiles enabled discrimination of a majority of central memory (CM) and effector memory (EM) CD8 T cells elicited by the three vaccines. Subsets of T cells could be defined based on their expression of Eomes, Cxcr3, and Ccr7, or Klrk1, Klrg1, and Ccr5 in CM and EM cells, respectively. Of CM cells elicited by DNA prime-recombinant adenoviral (rAd) boost vectors, 67% were Eomes(-) Ccr7(+) Cxcr3(-), in contrast to only 7% and 2% stimulated by rAd5-rAd5 or rAd-LCMV, respectively. Of EM cells elicited by DNA-rAd, 74% were Klrk1(-) Klrg1(-)Ccr5(-) compared with only 26% and 20% for rAd5-rAd5 or rAd5-LCMV. Definition by single-cell gene profiling of specific CM and EM CD8 T-cell subsets that are differentially induced by different gene-based vaccines will facilitate the design and evaluation of vaccines, as well as enable our understanding of mechanisms of protective immunity.

Cannabidiol inhibits THC-elicited paranoid symptoms and hippocampal-dependent memory impairment

Community-based studies suggest that cannabis products that are high in Δ⁹-tetrahydrocannabinol (THC) but low in cannabidiol (CBD) are particularly hazardous for mental health. Laboratory-based studies are ideal for clarifying this issue because THC and CBD can be administered in pure form, under controlled conditions. In a between-subjects design, we tested the hypothesis that pre-treatment with CBD inhibited THC-elicited psychosis and cognitive impairment. Healthy participants were randomised to receive oral CBD 600 mg (n=22) or placebo (n=26), 210 min ahead of intravenous (IV) THC (1.5 mg). Post-THC, there were lower PANSS positive scores in the CBD group, but this did not reach statistical significance. However, clinically significant positive psychotic symptoms (defined a priori as increases ≥ 3 points) were less likely in the CBD group compared with the placebo group, odds ratio (OR)=0.22 (χ²=4.74, p<0.05). In agreement, post-THC paranoia, as rated with the State Social Paranoia Scale (SSPS), was less in the CBD group compared with the placebo group (t=2.28, p<0.05). Episodic memory, indexed by scores on the Hopkins Verbal Learning Task-revised (HVLT-R), was poorer, relative to baseline, in the placebo pre-treated group (-10.6 ± 18.9%) compared with the CBD group (-0.4% ± 9.7 %) (t=2.39, p<0.05). These findings support the idea that high-THC/low-CBD cannabis products are associated with increased risks for mental health.

The MHC-haplotype influences primary, but not memory, immune responses to an immunodominant peptide containing T- and B-cell epitopes of the caprine arthritis encephalitis virus Gag protein

In this report, we describe a short peptide, containing a T helper- and a B-cell epitope, located in the Gag protein of the caprine arthritis encephalitis virus (CAEV). This T-cell epitope is capable of inducing a robust T-cell proliferative response in vaccinated goats with different genetic backgrounds and to provide help for a strong antibody response to the B-cell epitope, indicating that it may function as a universal antigen-carrier for goat vaccines. The primary immune response of goats homozygous for MHC class I and II genes showed an MHC-dependent partitioning in rapid-high and slow-low responses, whereas the memory immune response was strong in both groups, demonstrating that a vaccine based on this immunodominant T helper epitope is capable to overcome genetic differences.

Relation of white matter anisotropy to visual memory in 17 healthy subjects

We performed a Rey visual design learning test (RVDLT) in 17 subjects and measured intervoxel coherence (IC) by DTI as an indication of connectivity to investigate if visual memory performance would depend on white matter structure in healthy persons. IC considers the orientation of the adjacent voxels and has a better signal-to-noise ratio than the commonly used fractional anisotropy index. Voxel-based t-test analysis of the IC values was used to identify neighboring voxel clusters with significant differences between 7 low and 10 high test performers. We detected 9 circumscribed significant clusters (p< .01) with lower IC values in low performers than in high performers, with centers of gravity located in left and right superior temporal region, corpus callosum, left superior longitudinal fascicle, and left optic radiation. Using non-parametric correlation analysis, IC and memory performance were significantly correlated in each of the 9 clusters (r< .61 to r< .81; df=15, p< .01 to p< .0001). The findings provide in vivo evidence for the contribution of white matter structure to visual memory in healthy people.