Efficient Organic Photovoltaic Cells Employing Squaraines and Their Aggregates: Experiment and Theory

Organic photovoltaics (OPVs) have continued to attract attention over the past two decades, promising solution processable and aesthetically pleasing solar energy harvesting devices. The power conversion efficiency of OPV has improved rapidly owing to the development of novel conjugated polymers and functional molecules. Recently, donor-acceptor push-pull type materials have been investigated ubiquitously for OPV applications due to their high extinction coefficients in the near-infrared region of the solar spectrum. At RIT, a series of donor-acceptor-donor type squaraine (SQ) materials have been systematically synthesized and investigated for their potential in bulk heterojunction (BHJ) OPV devices. This dissertation presents both experimental and theoretical work associated with these squaraines. In the first part, the dependence of solar cell performance on BHJ morphology is discussed, with the emphasis on how SQ aggregation dominates the morphological behavior of the BHJ upon spin coating and post annealing treatments. SQ aggregates in the BHJ films represents crystalline domains which should benefit the charge transport toward the electrodes. At the same time, SQ aggregation induces phase separation and leads to formation of large SQ or PCBM domains. Domain size is a critical factor determining the solar cell efficiency as the exciton diffusion length in SQ films is believed to be small. The extent of phase separation can be controlled through varying SQ:PCBM weight ratio; a more homogeneously mixed BHJ morphology is obtained when PCBM content is high, leading to an improved solar cell efficiency. Film crystallinity and SQ aggregation is disrupted at high PCBM weight ratio but can be recovered via thermal annealing. Controlling the trade-off between crystallinity and phase separation of the BHJ is identified as critical for device optimization of SQ-based solar cells. In addition, different SQ molecules have been comparatively investigated to reveal the correlation between the molecular structure and the aggregation properties. In this way, this dissertation connects SQ structure to aggregation properties, then to BHJ morphology and finally to OPV performance. The second half of this dissertation focuses on using an essential state model to fully understand the intermolecular interactions within the SQ aggregates. The model has been constructed based on three main charge resonant structures associated with the zwitterionic nature of the SQ conjugation backbone. Molecular aggregates of the SQ chromophores were built based on the experimentally obtained single crystal structures. Specifically, we found that, in as-cast BHJ films, the SQ-SQ interaction is dominated by Coulombic coupling (CC) while in annealed BHJ films the intermolecular charge transfer (ICT) strongly influences the electronic properties. The type of aggregation is shown to greatly influence the solar cell performance. Specifically, CC-aggregates formed in the as cast films yield better solar cell efficiency as compared to ICT-coupled aggregates (which is of higher ordered and more crystalline). Finally, the sub-picosecond transient absorption spectroscopy results reveal how the excitons in the CC-aggregates are highly mobile, which rationalizes the high solar cell efficiency obtained from such aggregates

Spectral Properties of Squaraines and Their Aggregates, Targeted for Use in Bulk Hetero-junction Solar Cells

While Organic photovoltaics (OPV) offer great promise as a low-cost renewable energy source, the relative low efficiency still challenges its commercialization potential. This challenge can be addressed with squaraine (SQ) molecules through unique material de-sign. Advantages of SQ over other materials, such as conjugate polymers, include its high extinction coefficients (\u3e105), decent photo-stability, good synthetic reproducibility, and tunable molecular structure. The chemical properties of SQ dyes make them very suitable to form two pronounced aggregates in film: H- and J-aggregates. With small chemical modifications, the squaraines can have substantial impact on photophysical properties and aggregation pattern, and thus on operational OPV efficiency. In this work we comprehen-sively assign the spectral features of two squaraines: a dihydroxy squaraine, 2,4-bis-(4-dibutylamino-2,6-dihydroxyphenyl) cyclobutane-1,3-dione (DBSQ(OH)2) and a corre-sponding deshydroxy squaraine, 2,4-bis-(4-dibutylamino-phenyl)cyclobutane-1,3-dione (DBSQ) as the squaraines interact with each other in a range of different environments spanning dilute liquid solution with completely isolated molecules, concentrated solid so-lutions by working with polymethylmethacrylate films, and neat and blended films analo-gous to active layers for OPV devices. We then verified these assignments with materials characterization and testing of associated OPV devices. In general, the data follows a trend seen for many squaraines synthesized in our group that the presence (absence) of the OH groups. The superior performance of DBSQ(OH)2-based OPV devices suggests the benefit of incorporating OH groups into SQ molecule for OPV application

Design and simulation of a pneumatic actuator bending soft robotics based on 3D printing

Robots mechatronic devices are able to replicate human actions, especially in dangerous environments and in manufacturing. Recently, the development of robotics has been inspired by bionics. The advanced robotics allow advanced robots to be used in new environments where they were not traditionally applicable, such as narrow and small spaces. Compared to traditional rigid robots, soft robots are made by deformable materials and possess high dexterity and adaptivity in harsh working environments. Traditional soft robots are made by casting. The method implies that the molds of soft robots should be designed and printed by a 3D printer first, before casting. In this thesis, a pneumatic bending actuator will be designed and printed by 3D printer directly. The direct 3D printing method saves abundant time in the overall design and printingas in rubber casting. The printing material is a hyperelastic material called NinjaFlex. Moreover, this thesis simplified the physical model to a cantilever beam with uniform distributed load. Based on the cantilever mathematical model, two types of simulation have been designed with linear material properties and nonlinear material properties. The wall thickness of the original design was set as the optimization parameters. By adjusting the thickness, the relationship between the wall thickness and the deformation of the bending actuator was obtained. By comparing the results of experiments, simulation, and theoretical modeling, we propose 3D printing of soft actuators as a novel technique to be used in the new frontier of soft robotics

Toward Understanding Generative Data Augmentation

Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model, boosts classification performance in various learning tasks including (semi-)supervised learning, few-shot learning, and adversarially robust learning. However, little work has theoretically investigated the effect of generative data augmentation. To fill this gap, we establish a general stability bound in this not independently and identically distributed (non-i.i.d.) setting, where the learned distribution is dependent on the original train set and generally not the same as the true distribution. Our theoretical result includes the divergence between the learned distribution and the true distribution. It shows that generative data augmentation can enjoy a faster learning rate when the order of divergence term is $o(\max\left( \log(m)\beta_m, 1 / \sqrt{m})\right)$, where $m$ is the train set size and $\beta_m$ is the corresponding stability constant. We further specify the learning setup to the Gaussian mixture model and generative adversarial nets. We prove that in both cases, though generative data augmentation does not enjoy a faster learning rate, it can improve the learning guarantees at a constant level when the train set is small, which is significant when the awful overfitting occurs. Simulation results on the Gaussian mixture model and empirical results on generative adversarial nets support our theoretical conclusions. Our code is available at https://github.com/ML-GSAI/Understanding-GDA.Comment: 39 page

Ultra-low power energy harvesting wireless sensor network design

Master of ScienceDepartment of Electrical and Computer EngineeringWilliam B. Kuhn and Balasubramaniam NatarajanThis thesis presents an energy harvesting wireless sensor network (EHWSN) architecture customized for use within a space suit. The contribution of this research spans both physical (PHY) layer energy harvesting transceiver design and appropriate medium access control (MAC) layer solutions. The EHWSN architecture consists of a star topology with two types of transceiver nodes: a powered Gateway Radio (GR) node and multiple energy harvesting (EH) Bio-Sensor Radio (BSR) nodes. A GR node works as a central controller to receive data from BSR nodes and manages the EHWSN via command packets; low power BSR nodes work to obtain biological signals, packetize the data and transmit it to the GR node. To demonstrate the feasibility of an EHWSN at the PHY layer, a representative BSR node is designed and implemented. The BSR node is powered by a thermal energy harvesting system (TEHS) which exploits the difference between the temperatures of a space suit's cooling garment and the astronaut's body. It is shown that through appropriate control of the duty-cycle in transmission and receiving modes, it is possible for the transceiver to operate with less than 1mW power generated by the TEHS. A super capacitor, energy storage of TEHS, acts as an energy buffer between TEHS and power consuming units (processing units and transceiver radio). The super capacitor charges when a BSR node is in sleep mode and discharges when the node is active. The node switches from sleep mode to active mode whenever the super capacitor is fully charged. A voltage level monitor detects the system's energy level by measuring voltage across the super capacitor. Since the power generated by the TEHS is extremely low(less than 1mW) and a BSR node consumes relatively high power (approximately 250mW) during active mode, a BSR node must work under an extremely low duty cycle (approximately 0.4%). This ultra-low duty cycle complicates MAC layer design because a BSR node must sleep for more than 99.6% of overall operation time. Another challenge for MAC layer design is the inability to predict when the BSR node awakens from sleep mode due to unpredictability of the harvested energy. Therefore, two feasible MAC layer designs, CSA (carrier sense ALOHA based)-MAC and GRI (gateway radio initialized)-MAC, are proposed in this thesis

Institutional Perspectives on Women’s Entrepreneurship in the UK Equity Funding Sector

This research aims to understand the institutional embeddedness of women’s entrepreneurship in the UK equity financing sector. In specific, it seeks to explore institutional barriers faced by women entrepreneurs in the UK when securing equity funding and examine how the issue of under-representation of women in the UK equity financing sector has been tackled. Through a lens of institutional theory, nine detailed case studies were conducted over a three-month period to achieve research objectives. It has identified several institutional barriers in the industry from macro, meso, and motherhood levels. By advancing the understanding of the relationship between women in institutional practices and their strategic choices in a dynamic entrepreneurial environment, the issue of gender gap in the UK equity financing sector has been tackled exogenously and endogenously through institutional change and institutional work. Thus, the study contributes to our understanding about the role of women investors play in a specific institutional context, and also have an influence by advancing the acceptance of gender difference as a social construct

The Application of the “Three Accordance” Principles in the Translation of Foreign Publicity Texts: Taking the translation of Chinese leaders’ epidemic speech as examples

This paper takes the epidemic speech of Chinese leaders as the research object, analyzes the English translation of the epidemic speech of Chinese leaders in detail based on the “Three Accordance” Principles for External Publicity. The translation techniques of foreign publicity texts are discussed - comparing words with each other, turning images into meanings and adapting to local customs. These three techniques are used in order to show the strengths of China and avoid the prejudice and better publicize Chinese opinions and attitudes

DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines

Multi-task model training has been adopted to enable a single deep neural network model (often a large language model) to handle multiple tasks (e.g., question answering and text summarization). Multi-task training commonly receives input sequences of highly different lengths due to the diverse contexts of different tasks. Padding (to the same sequence length) or packing (short examples into long sequences of the same length) is usually adopted to prepare input samples for model training, which is nonetheless not space or computation efficient. This paper proposes a dynamic micro-batching approach to tackle sequence length variation and enable efficient multi-task model training. We advocate pipeline-parallel training of the large model with variable-length micro-batches, each of which potentially comprises a different number of samples. We optimize micro-batch construction using a dynamic programming-based approach, and handle micro-batch execution time variation through dynamic pipeline and communication scheduling, enabling highly efficient pipeline training. Extensive evaluation on the FLANv2 dataset demonstrates up to 4.39x higher training throughput when training T5, and 3.25x when training GPT, as compared with packing-based baselines. DynaPipe's source code is publicly available at https://github.com/awslabs/optimizing-multitask-training-through-dynamic-pipelines.Comment: 18 pages, 18 figure

Expediting the accuracy-improving process of SVMs for class imbalance learning

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ