Integrating Contorted Aromatic Molecules into Molecular Electronics and Optoelectronic Devices

This thesis has focused on the optical and electronic properties of organic semiconductors and their application in molecular electronic and optoelectronic devices. The studies have featured new and useful properties from a series of perylene diimide (PDI) nanoribbons and conjugated macrocycles. These novel strained carbon-based materials are highly promising as n-type semiconductors in organic gas sensor, organic solar cells and organic photodetectors. In Chapter 2, I describe a new molecular design that enables high performance organic photodetectors. We use a rigid, conjugated macrocycle as the electron acceptor in devices to obtain high photocurrent and low dark current. We make a direct comparison between the devices made with the macrocyclic acceptor and an acyclic control molecule; we find that the superior performance of the macrocycle originates from its rigid, conjugated, and cyclic structure. The macrocycle’s rigid structure reduces the number of charged defects originating from deformed sp2 carbons and covalent defects from photo/thermo-activation. With this molecular design we are able to suppress dark current density while retaining high responsivity in an ultra-sensitive non-fullerene OPD. Importantly, we achieve a detectivity of ~1014 Jones at near zero bias voltage. This is without the need for extra carrier blocking layers commonly employed in fullerene-based devices. Our devices are comparable to the best fullerene-based photodetectors, and the sensitivity at low working voltages (< 0.1 V) is a record for non-fullerene OPDs. In Chapter 3, I describe a capsule-shaped molecule that assembles itself into a cellular semiconducting material. The interior space of the capsule with a volume of ~415 Å3 is a nanoenvironment that can accommodate a guest. To self-assemble these capsules into electronic materials, we functionalize the thiophene rings with bromines, which encode self-assembly into two-dimensional layers held together through halogen bonding interactions. In the solid state and in films, these two-dimensional layers assemble into the three-dimensional crystalline structure. This hollow material is able to form the active layer in field effect transistor devices. We find that the current of these devices has strong response to the guest’s interaction within the hollow spaces in the film. These devices are remarkable in their ability to distinguish, through their electrical response, between small differences in the guest. In Chapter 4, I describe a new molecular design for the efficient synthesis of donor-acceptor, cove-edge graphene nanoribbons and their properties in solar cells. These nanoribbons are long (~5 nm), atomically precise, and soluble. The design is based on the fusion of electron deficient perylene diimide oligomers with an electron rich alkoxy pyrene subunit. This strategy of alternating electron rich and electron poor units facilitates a visible light fusion reaction in >95% yield, while the cove-edge nature of these nanoribbons results in a high degree of twisting along the long axis. The rigidity of the backbone yields a sharp longest wavelength absorption edge. These nanoribbons are exceptional electron acceptors, and organic photovoltaics fabricated with the ribbons show efficiencies of ~8% without optimization. In Chapter 5, I describe a new molecular design that yields ultra-narrowband organic photodetectors. The design is based on a series of helically-twisted molecular ribbons as the optoelectronic material. We fabricate charge collection narrowing photodetectors based on four different helical ribbons that differ in the wavelength of their response. The photodetectors made from these materials have narrow spectral response with full-width at half maxima of < 20 nm. The devices reported here are superior by approximately a factor of 5 to those from traditional organic materials due to the narrowness of their response. Moreover, the active layers for the helical ribbon-based photodetectors are solution cast but have performance that is comparable to the state-of-the-art narrowband photodetectors made from methylammonium lead trihalide perovskite single crystals. The ultra-narrow bandwidth for detection results from the helical ribbons’ high absorption coefficient, good electron mobility, and sharp absorption edges that are defined by the twisted molecular conformation. In Chapter 6, I describe the direct connection between the molecular conformation of a conjugated macrocycle and its macroscopic charge transport properties. The macrocycles studied here are new examples of a growing class of electronically active, conjugated macrocycles that have been utilized in materials applications. Here, we incorporate chiral, helical perylene diimide ribbons into the two separate macrocycles as the n-type, electron transporting material. As the macrocycles’ films and electronic structures are analogous, the important finding is that the macrocycles’ molecular structures and their associated dynamics determine device performance in organic field effect transistors. We show the more flexible macrocycle has a four-fold increase in electron mobility in field effect transistor devices. Using a combination of 1H-NMR, spectroscopy, and density functional theory calculations, we find that the origin of the difference in device performance is the ability of more flexible isomer to make intermolecular contacts relative to the more rigid counterpart. In Chapter 7, I discuss that intramolecular conductivity can play a role in controlling device characteristics of organic field effect transistors made with macrocycle building blocks. We use two isomeric macrocyclic semiconductors that consist of perylene diimides linked with bithiophenes and find that the trans-linked macrocycle has a higher mobility than the cis-based device. Through a combination of single molecule junction conductance measurements of the components of the macrocycles, control experiments with acyclic counterparts to the macrocycles, and analyses of each of the materials using spectroscopy, electrochemistry, and density functional theory, we attribute the difference in electron mobility of the OFETs created with the two isomers to the difference in intramolecular conductivity of the two macrocycles

Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models

The assumption of group heterogeneity has become popular in panel data models. We develop a constrained Bayesian grouped estimator that exploits researchers' prior beliefs on groups in a form of pairwise constraints, indicating whether a pair of units is likely to belong to a same group or different groups. We propose a prior to incorporate the pairwise constraints with varying degrees of confidence. The whole framework is built on the nonparametric Bayesian method, which implicitly specifies a distribution over the group partitions, and so the posterior analysis takes the uncertainty of the latent group structure into account. Monte Carlo experiments reveal that adding prior knowledge yields more accurate estimates of coefficient and scores predictive gains over alternative estimators. We apply our method to two empirical applications. In a first application to forecasting U.S. CPI inflation, we illustrate that prior knowledge of groups improves density forecasts when the data is not entirely informative. A second application revisits the relationship between a country's income and its democratic transition; we identify heterogeneous income effects on democracy with five distinct groups over ninety countries

Exploring the impact of demand and supply-side interventions on energy decarbonization of freight transportation: a research based on G20 nations

Through quantitative modeling, the study established a dynamic supply and demand system from freight demand, renewable energy production, alternative new energy, renewable energy consumption and carbon dioxide emissions to assess the impact of demand-side and supply-side changes on energy decarbonization. The results indicate that adjusting the freight volumes of railway and aviation, renewable energy electricity supply, and the use of alternative new energy sources have varying degrees of impact on decarbonization in transportation. Through interventions on the demand side of freight volumes, CO2 emissions from transportation decrease to levels below those before the intervention-induced fluctuations, while consumption of renewable energy increases to levels above those before the adjustment

Path Planning for Autonomous Vehicle in Off-Road Scenario

The road topography information, such as bank angle and road slope, can significantly affect the trajectory tracking performance of the autonomous vehicle, so this information needs to be considered in the trajectory planning and tracking control for off-road autonomous vehicle. In this chapter, a two-level real-time dynamically integrated spatiotemporal-based trajectory planning and control method for off-road autonomous vehicle is proposed. In the upper-level trajectory planner, the most suitable time-parameterised trajectory with the minimum values of road slope and bank angle can be selected from a set of candidate trajectories. In the lower-level trajectory tracking controller, the sliding-mode control (SMC) technique is applied to control the vehicle and achieve the desired trajectory. Finally, simulation results are presented to verify the proposed integrated trajectory planning and control method and prove that the proposed integrated method has better overall tracking control and dynamics control performance than the conventional method both in the highway scenario and off-road scenario. Furthermore, the four-wheel-independent-steering (4WIS) and four-wheel-independent-driving (4WID) vehicle shows better tracking control performance than vehicle based on two-wheel model

Role of Acupuncture in the Treatment of Drug Addiction

This review systematically assessed the clinical evidence for and against acupuncture as a treatment for drug addiction. The existing scientific rationale and possible mechanisms for the effectiveness of acupuncture on drug addiction were also evaluated. We used computerized literature searches in English and Chinese and examined texts written before these computerized databases existed. We also used search terms of treatment and neurobiology for drug abuse and dependence. Acupuncture showed evidence for relevant neurobiological mechanisms in the treatment of drug addiction. Although positive findings regarding the use of acupuncture to treat drug dependence have been reported by many clinical studies, the data do not allow us to make conclusions that acupuncture was an effective treatment for drug addiction, given that many studies reviewed here were hampered by small numbers of patients, insufficient reporting of randomization and allocation concealment methods, and strength of the inference. However, considering the potential of acupuncture demonstrated in the included studies, further rigorous randomized controlled trials with long follow-up are warranted

On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates

We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters (even the ex post best forecaster) are outperformed by our regularized mixtures. From the Great Recession onward, the optimal regularization tends to move density forecasts' probability mass from the centers to the tails, correcting for overconfidence

Bayesian Estimation of Panel Models under Potentially Sparse Heterogeneity

We incorporate a version of a spike and slab prior, comprising a pointmass at zero ("spike") and a Normal distribution around zero ("slab") into a dynamic panel data framework to model coefficient heterogeneity. In addition to homogeneity and full heterogeneity, our specification can also capture sparse heterogeneity, that is, there is a core group of units that share common parameters and a set of deviators with idiosyncratic parameters. We fit a model with unobserved components to income data from the Panel Study of Income Dynamics. We find evidence for sparse heterogeneity for balanced panels composed of individuals with long employment histories

Dynamically integrated spatiotemporal-based trajectory planning and control for autonomous vehicles

In the literature, the intensive research effort has been made on the trajectory planning for autonomous vehicles, while the integration of the trajectory planner with the trajectory controller is less focused. This study proposes the spatiotemporal-based trajectory planner and controller by a two-level dynamically integrated structure. In the upper level, the best trajectory is selected among a group of candidate time-parameterised trajectories, while the target vehicle ending position and velocity can be satisfied. Then the planned trajectory is evaluated by checking the feasibility when the actual vehicle dynamic motion constraints are considered. After that, the lower level trajectory controller based on the vehicle dynamics model will control the vehicle to follow the desired trajectory. Numerical simulations are used to validate the effectiveness of the proposed approach, where the scenario of an intersection and the scenario of overtaking are applied to show that the proposed trajectory controller can successfully achieve the control targets. In addition, compared with the potential field method, the proposed method based on the four-wheel independent steering and four-wheel independent driving electric vehicle shows great advantages in guaranteeing the vehicle handling and stability