Orientador: Alexandre Reily Rocha / Resumo: Os excitons desempenham um papel fundamental em aplicações fotovoltaicas (FV). Atualmente, a tecnologia FV de filme fino possui 9% da produção mundial. Em particular, o telúrio foi ligado ao cádmio (Cd-Te) e utilizado no fabrico de células solares de película fina. No entanto, a tecnologia de telúrio pode ser melhorada se estruturas de menor dimensão forem usadas, por exemplo, a forma de monocamada conhecida como telureno. O telureno pode ser sintetizado com sucesso em um substrato (por exemplo, grafeno), tem alta mobilidade dos portadores, a condutividade térmica mais baixa entre monocamadas de átomos e um gap de banda óptica sintonizável que o torna em um candidato proeminente para o desenvolvimento de tecnologia. Neste trabalho, realizamos cálculos ab-initio da teoria da perturbação do muitos corpos (QPMBPT) para analisar os efeitos excitônicos na absorção de luz pelo telureno. Como telúrio é um elemento pesado, nossa análise foi estendida para a presença de acoplamento spin-órbita, que faz uma mudança significativa na estrutura da banda, bem como na parte imaginária da constante dielétrica. A anisotropia da telurena é evidente no espectro de absorção, que é semelhante ao fósforo preto, com a mais forte excitação ao longo da direção em ziguezague e energias de ligação de excitons semelhantes a outros semicondutores 2D. Além disso, comparamos nossos resultados com um modelo efetivo de hidrogênio, no qual o elétron e o buraco interagem através de uma interação anisotrópica d... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre

<div>Since the debut of graphene, many 2D materials have emerged as promising candidates for silicon alternatives to extend Moore’s Law, such as MoS₂ and phosphorene. However, some common shortcomings such as low mobility, instability and lack of massive production methods limit the exploration and applications of these materials. Here, we introduce a novel member to the 2D category – high-mobility air-stable 2D tellurium film (tellurene).</div><div><br></div><div>Tellurium (Te) is a narrow bandgap semiconductor with unique one-dimensional chiral structure. Recently, a hydrothermal synthesizing method was developed to produce large-area tellurene nanofilms with thickness ranging from tens of nanometers down to few layers. In this thesis, a thorough investigation of Te properties in 2D quantum region was first carried out by various material characterization techniques including TEM and Raman spectroscopy. Potential applications of Te-based electronics, optoelectronic and thermoelectric devices were explored, and high-performance Te FETs were achieved with record-high drive current over 1 A/mm via device scaling and contact engineering. Magneto-transport, including weak anti-localization and Shubnikov-de-Haas oscillations was studied at cryogenic temperature. Quantum Hall effect was observed for the first time in both 2D electron and hole gases with mobility of 6,000 and 3,000 cm²/Vs, and non-trivial Berry phase in Te 2D electron system was detected as the first experimental evidence of massive Weyl fermions. This work not only demonstrates the great potential of tellurene films for electronics and quantum device applications, but also expands the spectrum of topological matters into a new material species - Weyl semiconductors.</div>

<p dir="ltr">Broadly, the focus of this work is on efficient statistical estimation and optimization of data arising from experimental data, particularly motivated by nanomanufacturing experiments on the material tellurene. Tellurene is a novel material for transistors with reliable attributes that enhance the performance of electronics (e.g., nanochip). As a solution-grown product, two-dimensional (2D) tellurene can be manufactured through a scalable process at a low cost. There are three main throughlines to this work, data augmentation, optimization, and equality constraint, and three distinct methodological projects, each of which addresses a subset of these throughlines. For the first project, I apply transfer learning in the analysis of data from a new tellurene experiment (process B) using the established linear regression model from a prior experiment (process A) from a similar study to combine the information from both experiments. The key of this approach is to incorporate the total equivalent amounts (TEA) of a lurking variable (experimental process changes) in terms of an observed (base) factor that appears in both experimental designs into the prespecified linear regression model. The results of the experimental data are presented including the optimal PVP chain length for scaling up production through a larger autoclave size. For the second project, I develop a multi-armed bandit Bayesian optimization (BO) approach to incorporate the equality constraint that comes from a mixture experiment on tellurium nanoproduct and account for factors with categorical levels. A more complex optimization approach was necessitated by the experimenters’ use of a neural network regression model to estimate the response surface. Results are presented on synthetic data to validate the ability of BO to recover the optimal response and its efficiency is compared to Monte Carlo random sampling to understand the level of experimental design complexity at which BO begins to pay off. The third project examines the potential enhancement of parameter estimation by utilizing synthetic data generated through Generative Adversarial Networks (GANs) to augment experimental data coming from a mixture experiment with a small to moderate number of runs. Transfer learning shows high promise for aiding in tellurene experiments, BO’s value increases with the complexity of the experiment, and GANs performed poorly on smaller experiments introducing bias to parameter estimates.</p>