Quantifying and Elucidating the effect of CO2 on the Thermodynamics, Kinetics and Charge Transport of AEMFCs

Anion exchange membrane fuel cells (AEMFCs) have shown significant promise to provide clean, sustainable energy for grid and transportation applications – and at a lower theoretical cost than more established proton exchange membrane fuel cells (PEMFCs). Adding to the excitement around AEMFCs is the extremely high peak power that can now be obtained (\u3e 3 W cm-2) and continuously improving durability (1000+ h), which has made the future deployment of AEMFCs in real-world applications a serious consideration. For some applications (e.g. automotive), the most critical remaining practical issue with AEMFCs is understanding and mitigating the effects of atmospheric CO2 (in the air supply) on cell behavior and performance. Most literature discussion around AEMFC carbonation has hypothesized: 1) that the effect of carbonation is limited to an increase in the Ohmic resistance because carbonate has lower mobility than hydroxide; and/or 2) that the so-called “self-purging” mechanism could effectively decarbonate the cell and eliminate CO2-related voltage losses during operation at a reasonable operating current density (\u3e 1 A cm-2). However, this study definitively shows that neither of these assertions are correct. This study is the first comprehensive experimental investigation into the effects of CO2 on operating AEMFCs. It is also the first study to be able to quantitatively determine the root causes for performance decline when CO2 is added to the system, where cell behavior is directly linked to cell chemistry and reaction dynamics. This work, the first experimental examination of its kind, studies the dynamics of cell carbonation and its effect on AEMFC performance over a wide range of operating currents (0.2 – 2.0 A cm-2), operating temperatures (60 – 80°C), and CO2 concentrations (5 – 3200 ppm) in the reactant gases. I have also investigated the influence of reactant gas flowrates (0.2 – 1 L/min) and dew points (50 – 57°C at 60°C cell temperature) on cell carbonation. The resulting data provides for new fundamental relationships to be developed and for the root causes of increased polarization in the presence of CO2 to be quantitatively probed and deconvoluted into Ohmic, Nernstian and charge transfer components, with the Nernstian and charge transfer components controlling the cell behavior under conditions of practical interest. In addition to the demonstrated technology, the lessons learned in this work can also provide transformational insights to other air breathing and/or AEM-based electrochemical systems such as metal air batteries, regenerative fuel cells, electrochemical CO2 capture, CO2 separator and concentrator, CO2 reduction reactors and dialyzers

Quantifying and Elucidating the Effect of CO\u3csub\u3e2\u3c/sub\u3e on AEMFCs

Anion exchange membrane fuel cells (AEMFCs) have recently received significant attention as a future high efficiency, environmentally friendly energy conversion device. This attention is due to the potential advantages that AEMFCs can offer compared the much more common, and commercialized, proton exchange membrane fuel cells (PEMFCs) – most notably lower cost. However, there are several remaining roadblocks for the AEMFC technology to be widely adopted, such as: i) the stability of the anion exchange membranes (AEMs) and anion exchange ionomer (AEIs); ii) the development of highly active catalysts with either low platinum group metal (PGM) loading or catalysts that are completely PGM-free; iii) the discovery of water management strategies to prevent electrodes from flooding or drying out; and iv) reducing the negative effect of CO2 on performance. This last issue, CO2 poisoning in AEMFCs, is considered by many to be the most serious hurdle to overcome. In an AEMFC operating on ambient air, CO2 reacts with the OHanions created from the oxygen reduction reaction at the cathode, forming HCO3 - and CO3 2- . These carbonates are transported from the cathode to the anode during operation. As shown in Chapter 1 of this thesis, the presence of carbonate anions has multiple impacts on the operating AEMFC; carbonates decrease the conductivity and water uptake of AEM, introduce additional charge transfer resistance at the hydrogen oxidation anode and change the anode pH (resulting in a thermodynamic decrease in the cell operating voltage). In total, the CO2-related overpotential can be up to 400 mV, which is unacceptable from a practical perspective. This work will present an extensive array of experiments that deconvolutes the fundamental electrochemical mechanism for carbonate “poisoning” in AEMFCs. The dynamics of CO2 uptake and removal and dynamics in these systems – with a particular focus on the impact of CO2 concentration in the reacting gas, gas flowrates, backpressure, fuel cell hydration level and temperature, AEM thickness and AEM chemistry, which are the focus of Chapters 2 - 4. With this new understanding strategies to reduce the CO2 related overpotential below 100 mV will be shown. Finally, as shown in Chapter 5, the chemical mechanisms for how carbonation leads to voltage loss in operating AEMFCs have even been used to design systems that minimize the exposure of operating AEMFCs to CO2. Such a device can be called an anion exchange CO2 separator (AECS) which i) is able to generate power; and ii) takes advantage of the carbonation phenomena that harms AEMFCs. In this work, the effectiveness of an AECS in lowering the CO2 concentration of an incoming stream of 400ppm air is investigated. In addition to showing significant CO2 removal, an AECS that operates with a stable output for over 150 h is shown. AEMFC operation on AECS-purified industrial air is successfully demonstrated. Chapters 6 is a summary of all the fundamental findings in this work. Lastly, Chapter 7 of this thesis is meant to provide some perspective on the state of the technology and where it is going. It also proposes future work that can be done to achieve a AEMFCs with high CO2 resistance in the near future

Multivariable Linear Regression Model for Promotional Forecasting:The Coca Cola - Morrisons Case

This paper describes a promotional forecasting model, built by linear regression module in Microsoft Excel. It intends to provide quick and reliable forecasts with a moderate credit and to assist the CPFR between the Coca Cola Enterprises (CCE) and the Morrisons. The model is derived from previous researches and literature review on CPFR, promotion, forecasting and modelling. It is designed as a multivariable linear regression model, which involves several promotional mix as variables including percentage discount, display, and holidays. Before modelling, all data and variables have been tested for their validity by two tests: the trend test and the up/downlift-average test. The model has also been conducted twice: the first time is to use a part of the data to define the structure of the model and the second time is to use all the data to finalize the model by deciding its coefficients. The model is capable to make forecast for 26 products and to forecast for several new promotions. The performance of this model is satisfactory in terms of the adjusted R2 (over 80%) and the MAPE (lower than 20%). A user-friendly interface is also provided to facilitate the use of the model in the actual forecasting. However, the model can be further improved both from the modelling method and the variable refining

A novel algorithm of posture best fit based on key characteristics for large components assembly

Measurement and variation control of geometrical Key Characteristics (KCs), such as flatness and gap of joint faces, coaxiality of cabin sections, is the crucial issue in large components assembly from the aerospace industry. Aiming to control geometrical KCs and to attain the best fit of posture, an optimization algorithm based on KCs for large components assembly is proposed. This approach regards the posture best fit, which is a key activity in Measurement Aided Assembly (MAA), as a two-phase optimal problem. In the first phase, the global measurement coordinate system of digital model and shop floor is unified with minimum error based on singular value decomposition, and the current posture of components being assembly is optimally solved in terms of minimum variation of all reference points. In the second phase, the best posture of the movable component is optimally determined by minimizing multiple KCs' variation with the constraints that every KC respectively conforms to its product specification. The optimal models and the process procedures for these two-phase optimal problems based on Particle Swarm Optimization (PSO) are proposed. In each model, every posture to be calculated is modeled as a 6 dimensional particle (three movement and three rotation parameters). Finally, an example that two cabin sections of satellite mainframe structure are being assembled is selected to verify the effectiveness of the proposed approach, models and algorithms. The experiment result shows the approach is promising and will provide a foundation for further study and application. © 2013 The Authors

Tailoring Intermolecular Interactions Towards High‐Performance Thermoelectric Ionogels at Low Humidity

Development of ionic thermoelectric (iTE) materials is of immense interest for efficient heat-to-electricity conversion due to their giant ionic Seebeck coefficient (Si), but challenges remain in terms of relatively small Si at low humidity, poor stretchability, and ambiguous interaction mechanism in ionogels. Herein, a novel ionogel is reported consisting of polyethylene oxide (PEO), polyethylene oxide-polypropylene oxide-polyethylene oxide (P123), and 1-ethyl-3-methylimidazolium acetate (Emim:OAC). By delicately designing the interactions between ions and polymers, the migration of anions is restricted due to their strong binding with the hydroxyl groups of polymers, while the transport of cations is facilitated through segmental motions due to the increased amorphous regions, thereby leading to enlarged diffusion difference between the cations and anions. Moreover, the plasticizing effect of P123 and Emim:OAC can increase the elongation at break. As a consequence, the ionogel exhibits excellent properties including high Si (18 mV K−1 at relative humidity of 60%), good ionic conductivity (1.1 mS cm−1), superior stretchability (787%), and high stability (over 80% retention after 600 h). These findings show a promising strategy to obtain multifunctional iTE materials by engineering the intermolecular interactions and demonstrate the great potential of ionogels for harvesting low-grade heat in human-comfortable humidity environments

Tile Classification Based Viewport Prediction with Multi-modal Fusion Transformer

Viewport prediction is a crucial aspect of tile-based 360 video streaming system. However, existing trajectory based methods lack of robustness, also oversimplify the process of information construction and fusion between different modality inputs, leading to the error accumulation problem. In this paper, we propose a tile classification based viewport prediction method with Multi-modal Fusion Transformer, namely MFTR. Specifically, MFTR utilizes transformer-based networks to extract the long-range dependencies within each modality, then mine intra- and inter-modality relations to capture the combined impact of user historical inputs and video contents on future viewport selection. In addition, MFTR categorizes future tiles into two categories: user interested or not, and selects future viewport as the region that contains most user interested tiles. Comparing with predicting head trajectories, choosing future viewport based on tile's binary classification results exhibits better robustness and interpretability. To evaluate our proposed MFTR, we conduct extensive experiments on two widely used PVS-HM and Xu-Gaze dataset. MFTR shows superior performance over state-of-the-art methods in terms of average prediction accuracy and overlap ratio, also presents competitive computation efficiency.Comment: This paper is accepted by ACM-MM 202

UniCATS: A Unified Context-Aware Text-to-Speech Framework with Contextual VQ-Diffusion and Vocoding

The utilization of discrete speech tokens, divided into semantic tokens and acoustic tokens, has been proven superior to traditional acoustic feature mel-spectrograms in terms of naturalness and robustness for text-to-speech (TTS) synthesis. Recent popular models, such as VALL-E and SPEAR-TTS, allow zero-shot speaker adaptation through auto-regressive (AR) continuation of acoustic tokens extracted from a short speech prompt. However, these AR models are restricted to generate speech only in a left-to-right direction, making them unsuitable for speech editing where both preceding and following contexts are provided. Furthermore, these models rely on acoustic tokens, which have audio quality limitations imposed by the performance of audio codec models. In this study, we propose a unified context-aware TTS framework called UniCATS, which is capable of both speech continuation and editing. UniCATS comprises two components, an acoustic model CTX-txt2vec and a vocoder CTX-vec2wav. CTX-txt2vec employs contextual VQ-diffusion to predict semantic tokens from the input text, enabling it to incorporate the semantic context and maintain seamless concatenation with the surrounding context. Following that, CTX-vec2wav utilizes contextual vocoding to convert these semantic tokens into waveforms, taking into consideration the acoustic context. Our experimental results demonstrate that CTX-vec2wav outperforms HifiGAN and AudioLM in terms of speech resynthesis from semantic tokens. Moreover, we show that UniCATS achieves state-of-the-art performance in both speech continuation and editing