Cathode materials for high performance lithium-sulfur batteries

Since the late 20th century, energy crises have acquired worldwide attention. In the last two decades a lot of renewable energy sources have been fully developed and used, including solar energy, wind energy, tide energy and so on. However, the application of these energy sources are hindered by time and space restrictions. For example, solar energy can only be used at day time with relatively clear whether. To make full use of these energy resources, a variety of energy storage devices have been developed. Among them, lithium-ion batteries (LIBs) are the most successful commercialized energy storage devices and are widely used in our daily life, including phones, computers, electric vehicles and so on. However, the energy density of LIBs is hindered by the theoretical specific capacity of the lithium transition metal oxide cathode. Lithium-sulfur batteries (LSBs) with a theoretical specific capacity of 1675 mA h g-1 are regarded as the most promising next generation energy storage devices. But several obstacles, including the low conductivity of S and Li2S, the big volume change of S during charge and discharge and the notorious shuttle effect, stand in the road of commercialization of LSBs. In the thesis, two different strategies have been applied to solve these problems. First, ZIF-67, one kind of metal-organic framework (MOF), was used as a template to synthesis porous carbon frameworks. The carbon frameworks were used as a S host to accommodate the volume change of S and improve the conductivity of the electrode. What’s more, the Co centers in ZIF- 67 transferred into cobalt phosphide and cobalt sulphides, based on the detailed experiment condition. Cobalt phosphide and cobalt sulphides with high catalyst activity accelerate the reactions in the electrodes and alleviated the shuttle effect and thus improved the electrochemical performance. Second, sulfurized poly acrylonitrile (SPAN) was used as a source of S for LSBs. The covalent C-S bonds in SPAN alleviated the shuttle effect through reducing the formation of lithium polysulfides. Carbon nanotubes (CNTs) and Se-doping further improved the electrochemical performance of SPAN through improving the conductivity and accelerating the reactions. Samples with different levels of Se-doping were synthesized and characterized to find the best conditions. Meanwhile, the structure of the as-synthesized SPAN samples was characterized by a variety of methods to gain some insight about structure of SPAN, which is a subject of debate among researchers. Through these two strategies, the shuttle effect in LSBs was reduced and the performance of LSBs were improved. A higher specific capacity and a better cyclic stability were achieved. At the same time, a better understanding of the mechanism of LSBs was gained

Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance

Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recently, deep learning based terrain generation has emerged, notably the ones based on generative adversarial networks (GAN). However, these methods often struggle to fulfill the requirements of flexible user control and maintain generative diversity for realistic terrain. Therefore, we propose a novel diffusion-based method, namely terrain diffusion network (TDN), which actively incorporates user guidance for enhanced controllability, taking into account terrain features like rivers, ridges, basins, and peaks. Instead of adhering to a conventional monolithic denoising process, which often compromises the fidelity of terrain details or the alignment with user control, a multi-level denoising scheme is proposed to generate more realistic terrains by taking into account fine-grained details, particularly those related to climatic patterns influenced by erosion and tectonic activities. Specifically, three terrain synthesisers are designed for structural, intermediate, and fine-grained level denoising purposes, which allow each synthesiser concentrate on a distinct terrain aspect. Moreover, to maximise the efficiency of our TDN, we further introduce terrain and sketch latent spaces for the synthesizers with pre-trained terrain autoencoders. Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. Our code and dataset will be publicly available

Experimental study of needle-tissue interaction forces: effect of needle geometries, insertion methods and tissue characteristics.

A thorough understanding of needle-tissue interaction mechanics is necessary to optimize needle design, achieve robotically needle steering, and establish surgical simulation system. It is obvious that the interaction is influenced by numerous variable parameters, which are divided into three categories: needle geometries, insertion methods, and tissue characteristics. A series of experiments are performed to explore the effect of influence factors (material samples n=5 for each factor) on the insertion force. Data were collected from different biological tissues and a special tissue-equivalent phantom with similar mechanical properties, using a 1-DOF mechanical testing system instrumented with a 6-DOF force/torque (F/T) sensor. The experimental results indicate that three basic phases (deformation, insertion, and extraction phase) are existent during needle penetration. Needle diameter (0.7-3.2mm), needle tip (blunt, diamond, conical, and beveled) and bevel angle (10-85°) are turned out to have a great influence on insertion force, so do the insertion velocity (0.5-10mm/s), drive mode (robot-assisted and hand-held), and the insertion process (interrupted and continuous). Different tissues such as skin, muscle, fat, liver capsule and vessel are proved to generate various force cures, which can contribute to the judgement of the needle position and provide efficient insertion strategy

LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-Speech

Recent advances in neural text-to-speech (TTS) models bring thousands of TTS applications into daily life, where models are deployed in cloud to provide services for customs. Among these models are diffusion probabilistic models (DPMs), which can be stably trained and are more parameter-efficient compared with other generative models. As transmitting data between customs and the cloud introduces high latency and the risk of exposing private data, deploying TTS models on edge devices is preferred. When implementing DPMs onto edge devices, there are two practical problems. First, current DPMs are not lightweight enough for resource-constrained devices. Second, DPMs require many denoising steps in inference, which increases latency. In this work, we present LightGrad, a lightweight DPM for TTS. LightGrad is equipped with a lightweight U-Net diffusion decoder and a training-free fast sampling technique, reducing both model parameters and inference latency. Streaming inference is also implemented in LightGrad to reduce latency further. Compared with Grad-TTS, LightGrad achieves 62.2% reduction in paramters, 65.7% reduction in latency, while preserving comparable speech quality on both Chinese Mandarin and English in 4 denoising steps.Comment: Accepted by ICASSP 202

Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation

The recently rising markup-to-image generation poses greater challenges as compared to natural image generation, due to its low tolerance for errors as well as the complex sequence and context correlations between markup and rendered image. This paper proposes a novel model named "Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment" (FSA-CDM), which introduces contrastive positive/negative samples into the diffusion model to boost performance for markup-to-image generation. Technically, we design a fine-grained cross-modal alignment module to well explore the sequence similarity between the two modalities for learning robust feature representations. To improve the generalization ability, we propose a contrast-augmented diffusion model to explicitly explore positive and negative samples by maximizing a novel contrastive variational objective, which is mathematically inferred to provide a tighter bound for the model's optimization. Moreover, the context-aware cross attention module is developed to capture the contextual information within markup language during the denoising process, yielding better noise prediction results. Extensive experiments are conducted on four benchmark datasets from different domains, and the experimental results demonstrate the effectiveness of the proposed components in FSA-CDM, significantly exceeding state-of-the-art performance by about 2%-12% DTW improvements. The code will be released at https://github.com/zgj77/FSACDM.Comment: Accepted to ACM MM 2023. The code will be released at https://github.com/zgj77/FSACD

ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs

In this paper, we present ZeroPrompt (Figure 1-(a)) and the corresponding Prompt-and-Refine strategy (Figure 3), two simple but effective \textbf{training-free} methods to decrease the Token Display Time (TDT) of streaming ASR models \textbf{without any accuracy loss}. The core idea of ZeroPrompt is to append zeroed content to each chunk during inference, which acts like a prompt to encourage the model to predict future tokens even before they were spoken. We argue that streaming acoustic encoders naturally have the modeling ability of Masked Language Models and our experiments demonstrate that ZeroPrompt is engineering cheap and can be applied to streaming acoustic encoders on any dataset without any accuracy loss. Specifically, compared with our baseline models, we achieve 350 $\sim$ 700ms reduction on First Token Display Time (TDT-F) and 100 $\sim$ 400ms reduction on Last Token Display Time (TDT-L), with theoretically and experimentally equal WER on both Aishell-1 and Librispeech datasets.Comment: accepted by interspeech 202

Sensitivity Enhancement of Strain Sensing Utilizing a Differential Pair of Fiber Bragg Gratings

In strain measurement applications, the matched fiber Bragg gratings (FBG) method is generally used to reduce temperature dependence effects. The FBG parameters have to be designed to meet the requirements by the particular application. The bandwidth and slope of the FBG has to be balanced well, according to the measurement range, accuracy and sensitivity. A sensitivity enhanced strain demodulation method without sacrificing the measurement range for FBG sensing systems is proposed and demonstrated utilizing a pair of reference FBGs. One of the reference FBGs and the sensing FBG have almost the same Bragg wavelength, while the other reference FBGs has a Bragg wavelength offset relative to the sensing FBG. Reflected optical signals from the sensing FBG pass through two reference FBGs, and subtract from each other after the detection. Doubled strain measurement sensitivity is obtained by static rail load experiments compared to the general matched grating approach, and further verified in dynamic load experiments. Experimental results indicate that such a method could be used for real-time rail strain monitoring applications

A proteomic analysis of the chromoplasts isolated from sweet orange fruits [Citrus sinensis (L.) Osbeck]

Here, a comprehensive proteomic analysis of the chromoplasts purified from sweet orange using Nycodenz density gradient centrifugation is reported. A GeLC-MS/MS shotgun approach was used to identify the proteins of pooled chromoplast samples. A total of 493 proteins were identified from purified chromoplasts, of which 418 are putative plastid proteins based on in silico sequence homology and functional analyses. Based on the predicted functions of these identified plastid proteins, a large proportion (∼60%) of the chromoplast proteome of sweet orange is constituted by proteins involved in carbohydrate metabolism, amino acid/protein synthesis, and secondary metabolism. Of note, HDS (hydroxymethylbutenyl 4-diphosphate synthase), PAP (plastid-lipid-associated protein), and psHSPs (plastid small heat shock proteins) involved in the synthesis or storage of carotenoid and stress response are among the most abundant proteins identified. A comparison of chromoplast proteomes between sweet orange and tomato suggested a high level of conservation in a broad range of metabolic pathways. However, the citrus chromoplast was characterized by more extensive carotenoid synthesis, extensive amino acid synthesis without nitrogen assimilation, and evidence for lipid metabolism concerning jasmonic acid synthesis. In conclusion, this study provides an insight into the major metabolic pathways as well as some unique characteristics of the sweet orange chromoplasts at the whole proteome level