A liquid crystalline copper phthalocyanine derivative for high performance organic thin film transistors

This journal is © The Royal Society of Chemistry 2012Bottom-gate, bottom-contact organic thin film transistors (OTFTs) were fabricated using solvent soluble copper 1,4,8,11,15,18,22,25-octakis(hexyl)phthalocyanine as the active semiconductor layer. The compound was deposited as 70 nm thick spin-coated films onto gold source–drain electrodes supported on octadecyltrichlorosilane treated 250 nm thick SiO2 gate insulators. The performance of the OTFTs was optimised by investigating the effects of vacuum annealing of the films at temperatures between 50 0C and 200 0C, a range that included the thermotropic mesophase of the bulk material. These effects were monitored by ultraviolet-visible absorption spectroscopy, atomic force microscopy and XRD measurements. Device performance was shown to be dependent upon the annealing temperature due to structural changes of the film. Devices heat treated at 100 0C under vacuum (≥10-7 mbar) were found to exhibit the highest field-effect mobility, 0.7 cm2 V^-1 s^-1, with an on–off current modulation ratio of~107, a reduced threshold voltage of 2.0 V and a sub-threshold swing of 1.11 V per decade.UK Technology Strategy Board (Project no: TP/6/EPH/6/S/K2536J) and UK National Measurement System (Project IRD C02 ‘‘Plastic Electronics’’, 2008–2011)

Tetrabenzoporphyrin and -mono-, - Cis -di- and tetrabenzotriazaporphyrin derivatives: Electrochemical and spectroscopic implications of meso CH Group replacement with nitrogen

Nonperipherally hexyl-substituted metal-free tetrabenzoporphyrin (2H-TBP, 1a) tetrabenzomonoazaporphyrin (2H-TBMAP, 2a), tetrabenzo-cis-diazaporphyrin (2H-TBDAP, 3a), tetrabenzotriazaporphyrin (2H-TBTAP, 4a), and phthalocyanine (2H-Pc, 5a), as well as their copper complexes (1b-5b), were synthesized. As the number of meso nitrogen atoms increases from zero to four, Îmax of the Q-band absorption peak becomes red-shifted by almost 100 nm, and extinction coefficients increased at least threefold. Simultaneously the blue-shifted Soret (UV) band substantially decreased in intensity. These changes were related to the relative electron-density of each macrocycle expressed as the group electronegativity sum of all meso N and CH atom groups, âχR. X-ray photoelectron spectroscopy differentiated between the three different types of macrocyclic nitrogen atoms (the Ninner, (NH)inner, and Nmeso) in the metal-free complexes. Binding energies of the Nmeso and Ninner,Cu atoms in copper chelates could not be resolved. Copper insertion lowered especially the cathodic redox potentials, while all four observed redox processes occurred at larger potentials as the number of meso nitrogens increased. Computational chemical methods using density functional theory confirmed 1b to exhibit a Cu(II) reduction prior to ring-based reductions, while for 2b, Cu(II) reduction is the first reductive step only if the nonperipheral substituents are hydrogen. When they are methyl groups, it is the second reduction process; when they are ethyl, propyl, or hexyl, it becomes the third reductive process. Spectro-electrochemical measurements showed redox processes were associated with a substantial change in intensity of at least two main absorbances (the Q and Soret bands) in the UV spectra of these compounds

Eigenvector Spatial Filtering and Lasso: Theory and Applications

This thesis focuses on Eigenvector Spatial Filtering (ESF), developed by Griffith (2000, 2003) as a methodology designed to handle general cross-sectional/spatial dependence. ESF uses a subset of eigenvectors from a spatial weights matrix in a linear regression framework to approximate/control for any spatially correlated terms in the underlying data-generating process. Thus, ESF has the key advantage that it does not require the researcher to specify which parts of the model are spatially correlated. This advantage is the main driver behind ESF’s recent increasing popularity with applied economists. I extend the theory around ESF and its Lasso (Tibshirani, 1996) implementation for the cases when the structural equation being studied includes and excludes endogenous variables, and demonstrate how the method can be applied in several empirical applications. This thesis is comprised of four chapters, the first is a literature review, the second and third are in econometrics, and the fourth is in environmental economics. The econometrics chapters propose Moran’s i based Lasso procedures for estimating exogenous and endogenous right-hand-side regression parameters when the data is spatially dependent. The last chapter of this thesis uses the procedure proposed in the second chapter to account for spatial dependence when testing for the presence of the environmental Kuznets curve for forests. The first chapter provides a review of some common spatial economic models and how they are conventionally estimated, an overview of Kojevnikov et al. (2021) limit theorems for cross-sectionally dependent random variables (used in Chapter 3), and a summary of penalised regressions with a focus on Lasso. The second chapter, entitled “Moran’s i Lasso: for spatially correlated models” provides a theoretical contribution. After an extensive evaluation of existing procedures to select the relevant subset of eigenvectors for ESF, I develop a new selection method called Moran’s i Lasso (Mi-Lasso). The procedure uses information about the overall level of spatial dependence present in the underlying data-generating process, contained in the Moran’s i, to determine a point estimate for the Lasso tuning parameter. I derive performance bounds and show the necessary conditions for consistent eigenvector selection. The key advantages of the proposed estimator are that it is intuitive and substantially faster than Lasso based on cross-validation or any proposed forward stepwise procedure. Our main simulation results show the proposed selection procedure performs well in finite samples and an application on house prices. Compared to existing selection procedures, I find, Mi-Lasso has one of the smallest biases and mean squared errors across a range of sample sizes and levels of spatial correlation. Additionally, through an evaluation of the properties of the spectral decomposition, I note that ESF can also handle higher-order spatial lags, which is confirmed in a simulation experiment. The third chapter, entitled “Moran’s i 2-Stage Lasso: for spatial models with endogenous variables” also provides a theoretical contribution, is co-authored work with Dr. Sylvain Barde and Dr. Guy Tchuente. It proposes a new way of estimating a spatial model that includes endogenous variables when the researchers’ main concerns are estimating only the direct effect and/or misspecification of the spatial weights matrix and spatial model. The proposed procedure uses Mi-Lasso to select the first and second-stage relevant eigenvectors and then uses the union of selected eigenvectors as controls in a two-stage least squares regression. The procedure is called Moran’s i 2-Stage Lasso (Mi-2SL). We show the conditions necessary for consistent and asymptotically normal parameter estimation assuming the support (relevant) set of eigenvectors is known. Our Monte Carlo simulation results also show that Mi-2SL performs well when the spatial weights matrix has a high degree of misspecified links. Our empirical application replicates Cadena and Kovak (2016) instrumental variables estimates using Mi-2SL and shows that Mi-2SL can boost the performance of the first stage. Finally, the fourth chapter, entitled “The Environmental Kuznets Curve for forests: an application of Mi-Lasso” is an application of Mi-Lasso to a hotly debated question in environmental economics. Does the relationship between a country’s economic development (proxied by per capita GDP) and its deforestation rate follow the inverse U-shaped curve postulated by the classic environmental Kuznets curve for forests? I use the Mi-Lasso methodology proposed in chapter 2 to account for spatial dependence of an unknown functional form when testing for the presence of the environmental Kuznets curve for forests. I find evidence of a non-linear relationship, which in some cases is a more complicated predicted inverse U-shaped curve, the average peak rate of deforestation appears to be falling with time, while the income required for the deforestation rate to start falling is increasing with time. Additionally, I find, that if the spatial dependence is not accounted for, the OLS estimates of income exhibit an absolute upward bias

A structural investigation of side chain liquid crystal polymers and cyclic ologomers using x-ray diffraction

SIGLEAvailable from British Library Document Supply Centre- DSC:DX178255 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

An Environmental Kuznets Curve for Global Forests: An Application of the Mi-Lasso Estimator

In this study, we employ a Moran’s i based Lasso (Mi-Lasso) methodology to address the spatial dependence of an unspecified functional form, investigating the association between a country’s economic growth and the rate of deforestation. Our aim is to explore the existence of a forestry environmental Kuznets curve (EKC). Our approach to handling spatial dependence overcomes limitations identified in existing EKC literature. We estimate a series of cross-sectional data models spanning the period from 1990 to 2020 for 146 countries. Our findings indicate a non-linear relationship, revealing a change peak rate of deforestation over time. Additionally, we observe that the income threshold at which the deforestation rate begins to decrease changes over time with differences observed between model specifications. Crucially, our results highlight that failing to account for spatial dependence leads to a significant absolute upward bias in ordinary least squares (OLS) estimates of income and worse model fit