THE DEVELOPMENT OF HIGH MASS-SPECIFIC ACTIVITY RHODIUM SULFIDE CATALYSTS FOR HOR/HER IN HYDROGEN-BROMINE FUEL CELL

The integration of intermittent energies into the electrical grid like wind and solar power demands the participation of efficient and cost-effective energy storage systoms. H2-Br2 fuel cell is one of the promising technologies due to the advantage from the fast kinetics of Br2/Br- and low price of HBr. The corrosive nature of Br2/Br- determines that the metallic Pt is not a good choice for the HOR/HER catalysis on the negative electrode in the long-term operation of the fuel cell. The RhxSy is free from the corrosion of the bromine and bromide. The low mass-specific surface area and activity of existing RhxSy catalyst for HOR/HER, however, obstruct the deployment of the H2-Br2 fuel cell. In this dissertation, a clear roadmap to solve those problems has been demonstrated. Core-shell structure was the first attempt to increase the mass-specific surface area of rhodium sulfide catalyst. Catalysts with RhxSy as shell and different percentages (5%, 10%, and 20%) of platinum on carbon as core material were synthesized. The TEM and EDX results confirm the existence of the core-shell structure. Cyclic voltammetry was used to evaluate the Pt-equivalent mass specific ECSA and durability of these catalysts. Cycling test and polarization curve measurement in the H2-Br2 fuel cell were used to assess the catalyst stability and performance in a real fuel cell. The results show that the catalyst with core-shell structure has higher mass-specific ECSA (50 m2/gm-Rh) compared to a commercial catalyst RhxSy/C catalyst from BASF, 6.9 m2/gm-Rh). It also shows better HOR/HER performance in the fuel cell. Compared to the platinum catalyst, the core-shell catalysts show more stable performance in the fuel cell cycling test. The cheap metal core material was also explored to replace the expensive Pt material. However, the CV test suggests that the cheap metal catalyst was dissolved in the acidic RhCl3 solution and then precipitated by the sulfide ion during the synthesis, which results in a lower ECSA/mass compared to the commercial catalyst. Supporting carbon materials were functionalized to create more suitable nucleation sites for the precipitation of rhodium sulfide nanoparticles resulting in a catalyst with smaller particle size, better particle distribution, and higher hydrogen oxidation and evolution reaction performance. XPS and FT-IR confirm that the dominant functional group on the carbon is the ketone group, which is more suitable group for rhodium sulfide particles formation than the carboxylic group. TEM and cyclic voltammetry results confirm that the catalysts with treated carbon have a smaller average particle size (7.2 nm vs. 13.2 nm) and higher mass-specific ECSA (21.8 m2/g-Rh vs. 9.1 m2/g-Rh) than those with the untreated carbon. The H2-Br2 fuel cell test results show that the catalysts with functionalized carbon have better performance in the kinetic region but poorer performance in the mass transfer dominant region. The issue was identified to be due to the weak affinity of the ketone group on the carbon surface with the Nafion ionomer in the catalyst ink. The mass-specific surface area of RhxSy was further increased by the selection of a more effective nanoparticle growth mechanism. The diffusion-controlled nanoparticle growth mechanism was created by controlling the concentration of Rh2S3 monomer in the synthesis of rhodium sulfide catalyst, which decreases the average particle size from 13.2 nm to 3.2 nm by the TEM measurements. The mass-specific ECSA is improved from 9.1 m2/g-Rh to 43 m2/g-Rh with this approach. Moreover, the crystal phase composition in the mixed RhxSy was modified by the usage of a new sulfur source, Na2S, which results in an increase in the active area specific exchange current (0.59 A/cm2 vs 0.51 A/cm2). The mass-specific exchange current density increases from 0.58 A/mg-Rh of RhxSy/untreated carbon synthesized by the traditional sulfur source) to 2.8 A/mg-Rh with the combination of the usage of functionalized carbon material, the diffusion-controlled of nanoparticle growth mechanism, and the new sulfur source. The affinity issue between the ketone functional group on the carbon surface and the Nafion ionomer in the catalyst ink was resolved by using the Baeyer-Villiger reaction and ester hydrolysis to convert the Nafion-unfriendly ketone group to the Nafion-friendly carboxylic group. After the organic reactions, the modification of the surface functional group was validated by the FT-IR method. Furthermore, the TEM, XRD, and cyclic voltammetry methods confirm that the organic reactions have no negative effect on the catalyst and carbon surface in the process of organic reactions. The H2-Br2 fuel cell tests confirm that the mass transfer resistance observed in the fuel cell with the RhxSy on the ketone-dominated pretreated carbon was significantly reduced by this approach. The discharge performance of hydrogen-bromine fuel cell with the RhxSy /pretreated carbon with surface functional groups conversion is improved by 2.8 times compared to that of the fuel cell with the commercial RhxSy catalyst

Robotic Speech Synthesis: Perspectives on Interactions, Scenarios, and Ethics

In recent years, many works have investigated the feasibility of conversational robots for performing specific tasks, such as healthcare and interview. Along with this development comes a practical issue: how should we synthesize robotic voices to meet the needs of different situations? In this paper, we discuss this issue from three perspectives: 1) the difficulties of synthesizing non-verbal and interaction-oriented speech signals, particularly backchannels; 2) the scenario classification for robotic voice synthesis; 3) the ethical issues regarding the design of robot voice for its emotion and identity. We present the findings of relevant literature and our prior work, trying to bring the attention of human-robot interaction researchers to design better conversational robots in the future.Comment: Accepted for the HRI 2022 Workshop "Robo-Identity: Exploring Artificial Identity and Emotion via Speech Interactions" at HRI 2022, 7 March 202

High Hydrogen Evolution Reaction (HER) and Hydrogen Oxidation Reaction (HOR) Activity RhxSy Catalyst Synthesized with Na2S for Hydrogen-Bromine Fuel Cell

A RhxSy/C catalyst with high mass-specific electrochemical surface area (ECSA/mass), high hydrogen oxidation reaction (HOR)/hydrogen evolution reaction (HER) activity, and high Nafion® ionomer-affinity was synthesized and evaluated. A new sulfur source, Na2S instead of (NH4)2S2O3, was applied to prepare the rhodium sulfide precursor Rh2S3 that resulted in a RhxSy catalyst with higher HOR/HER catalytic activity after thermal treatment. The higher activity was attributed to the higher quantity formation of the more active phase Rh3S4, in addition to the other active Rh17S15 phase, in the RhxSy catalyst. Using this new sulfur source, carbon substrate functionalization, and the mass-transfer-controlled nanoparticle growth process, the average particle size of this catalyst was reduced from 13.5 nm to 3.2 nm, and its ECSA/mass was increased from 9.3 m2/g-Rh to 43.0 m2/g-Rh. Finally, by applying the Baeyer–Villiger and ester hydrolysis process to convert the Nafion® ionomer-unfriendly ketone group on the carbon support surface to the Nafion ionomer-friendly carboxylic group, which increases the Nafion® affinity of this catalyst, its use in the hydrogen electrode of an H2-Br2 fuel cell resulted in a performance that is 2.5× higher than that of the fuel cell with a commercial RhxSy catalyst

Fusing ASR Outputs in Joint Training for Speech Emotion Recognition

Alongside acoustic information, linguistic features based on speech transcripts have been proven useful in Speech Emotion Recognition (SER). However, due to the scarcity of emotion labelled data and the difficulty of recognizing emotional speech, it is hard to obtain reliable linguistic features and models in this research area. In this paper, we propose to fuse Automatic Speech Recognition (ASR) outputs into the pipeline for joint training SER. The relationship between ASR and SER is understudied, and it is unclear what and how ASR features benefit SER. By examining various ASR outputs and fusion methods, our experiments show that in joint ASR-SER training, incorporating both ASR hidden and text output using a hierarchical co-attention fusion approach improves the SER performance the most. On the IEMOCAP corpus, our approach achieves 63.4% weighted accuracy, which is close to the baseline results achieved by combining ground-truth transcripts. In addition, we also present novel word error rate analysis on IEMOCAP and layer-difference analysis of the Wav2vec 2.0 model to better understand the relationship between ASR and SER.Comment: Accepted for ICASSP 202

Transfer Learning for Personality Perception via Speech Emotion Recognition

Holistic perception of affective attributes is an important human perceptual ability. However, this ability is far from being realized in current affective computing, as not all of the attributes are well studied and their interrelationships are poorly understood. In this work, we investigate the relationship between two affective attributes: personality and emotion, from a transfer learning perspective. Specifically, we transfer Transformer-based and wav2vec2-based emotion recognition models to perceive personality from speech across corpora. Compared with previous studies, our results show that transferring emotion recognition is effective for personality perception. Moreoever, this allows for better use and exploration of small personality corpora. We also provide novel findings on the relationship between personality and emotion that will aid future research on holistic affect recognition.Comment: Accepted to INTERSPEECH 202

Multimodal Dyadic Impression Recognition via Listener Adaptive Cross-Domain Fusion

As a sub-branch of affective computing, impression recognition, e.g., perception of speaker characteristics such as warmth or competence, is potentially a critical part of both human-human conversations and spoken dialogue systems. Most research has studied impressions only from the behaviors expressed by the speaker or the response from the listener, yet ignored their latent connection. In this paper, we perform impression recognition using a proposed listener adaptive cross-domain architecture, which consists of a listener adaptation function to model the causality between speaker and listener behaviors and a cross-domain fusion function to strengthen their connection. The experimental evaluation on the dyadic IMPRESSION dataset verified the efficacy of our method, producing concordance correlation coefficients of 78.8% and 77.5% in the competence and warmth dimensions, outperforming previous studies. The proposed method is expected to be generalized to similar dyadic interaction scenarios.Comment: Accepted to ICASSP2023. arXiv admin note: substantial text overlap with arXiv:2203.1393

Molecular Tensile Machines: a New Tool for Quantitative Mechanochemistry

Controlling force at molecular length scales is a challenge in mechanochemistry. Well-defined macromolecules and tools that allow accurate control of both magnitude and direction of bond tension have been developed during the past two decades. Our contribution to this endeavor has been the design of molecular tensile machines that are highly branched macromolecular architectures (e.g. bottlebrushes, pom-poms, and dendrimers), which are able to generate bond tensions up to nanoNewton range due to steric repulsion between densely grafted branches. Furthermore, these macromolecular devices allow accurate variation of the bond tension through changes in the surrounding environment such as temperature, solvent quality, and salinity, making them ideal systems for the study of quantitative mechanochemistry. In this dissertation, we focus on bottlebrushes that can generate bond tension along the backbone and also amplify & focus this tension to a chemical group of interest. We have explored two complementary effects of the self-generated tension: activation of chemical reactions and electronic properties. Specifically, we have studied homolytic cleavage and reduction of disulfides under controlled force and analyze the bond activation parameters quantitatively. We have shown that the scission rate of disulfide increases exponentially with tension as reported by others but decreases with temperature. This anti-Arrhenius behavior is ascribed to the decrease of backbone tension with temperature, which can overpower thermal effect. Moreover, the reduction rate constant at zero force was found significantly lower than that in bulk solution, which suggests an acidic composition of the water surface with pH=3.7. To investigate the effect of tension on electronic structures, we have synthesized bottlebrushes with a polythiophene backbone and constructed a unique experimental set-up enabling measurements of fluorescence spectra of sub-monolayer films as a function of backbone tension. The energy band gap was found decreasing with tension due to the increase of conjugation length and then increasing due to the deformation of bond lengths and angles, which agrees with our prediction by DFT calculations. In addition to bottlebrushes, we have explored other types of strained molecular architectures including spoked-wheel macromolecules and block-copolymer brushes. Molecular dimensions and emission spectra of these unique molecular architectures have been characterized.Doctor of Philosoph

Exploration of A Self-Supervised Speech Model: A Study on Emotional Corpora

Self-supervised speech models have grown fast during the past few years and have proven feasible for use in various downstream tasks. Some recent work has started to look at the characteristics of these models, yet many concerns have not been fully addressed. In this work, we conduct a study on emotional corpora to explore a popular self-supervised model -- wav2vec 2.0. Via a set of quantitative analysis, we mainly demonstrate that: 1) wav2vec 2.0 appears to discard paralinguistic information that is less useful for word recognition purposes; 2) for emotion recognition, representations from the middle layer alone perform as well as those derived from layer averaging, while the final layer results in the worst performance in some cases; 3) current self-supervised models may not be the optimal solution for downstream tasks that make use of non-lexical features. Our work provides novel findings that will aid future research in this area and theoretical basis for the use of existing models.Comment: Accepted to SLT 202