Silicon Anode Materials for Next Generation Lithium-Ion Batteries

Lithium Ion Batteries (LIBs) are a promising green energy storage system with application toward portable electronic devices, electronic vehicles (EVs) and smart grid energy storage. High energy density is one of the most important advantages for LIB, however, improvements toward performance measures, such as energy density, power and rate capability, cycling life, safety and cost, need to meet the different requirements for various applications. It is well known that battery performance is highly dependent on the type of electrode material that is employed. Currently most LIBs are predominantly fabricated using graphite as an anode material. Therefore, finding a qualified candidate of anode material to replace graphite is the key to achieve a higher energy density of LIB. Silicon is a hopeful candidate for use as an anode material due to its super-high specific capacity (4200 mAh/g). However, this material suffers from severe volume change during the lithiation/delithiation process, thereby limiting its practical application in LIB. In this thesis, three sections of work were made for the practical application of Si anode in LIB. The first section was to develop a novel method to synthesis nanostructured Si anode materials, which is easy to handle, low cost and environmental friendly. The second section is focused on electrode optimization based on an engineering point of view, including electrode mass loading and density, binder selection and optimized cycle mode. The last section pertains to the development of electrolyte, which can further improve battery performance. A better Si-based electrode is presented LIB after this study

The Complexity of Distributed Edge Coloring with Small Palettes

The complexity of distributed edge coloring depends heavily on the palette size as a function of the maximum degree $\Delta$. In this paper we explore the complexity of edge coloring in the LOCAL model in different palette size regimes. 1. We simplify the \emph{round elimination} technique of Brandt et al. and prove that $(2\Delta-2)$-edge coloring requires $\Omega(\log_\Delta \log n)$ time w.h.p. and $\Omega(\log_\Delta n)$ time deterministically, even on trees. The simplified technique is based on two ideas: the notion of an irregular running time and some general observations that transform weak lower bounds into stronger ones. 2. We give a randomized edge coloring algorithm that can use palette sizes as small as $\Delta + \tilde{O}(\sqrt{\Delta})$, which is a natural barrier for randomized approaches. The running time of the algorithm is at most $O(\log\Delta \cdot T_{LLL})$, where $T_{LLL}$ is the complexity of a permissive version of the constructive Lovasz local lemma. 3. We develop a new distributed Lovasz local lemma algorithm for tree-structured dependency graphs, which leads to a $(1+\epsilon)\Delta$-edge coloring algorithm for trees running in $O(\log\log n)$ time. This algorithm arises from two new results: a deterministic $O(\log n)$-time LLL algorithm for tree-structured instances, and a randomized $O(\log\log n)$-time graph shattering method for breaking the dependency graph into independent $O(\log n)$-size LLL instances. 4. A natural approach to computing $(\Delta+1)$-edge colorings (Vizing's theorem) is to extend partial colorings by iteratively re-coloring parts of the graph. We prove that this approach may be viable, but in the worst case requires recoloring subgraphs of diameter $\Omega(\Delta\log n)$. This stands in contrast to distributed algorithms for Brooks' theorem, which exploit the existence of $O(\log_\Delta n)$-length augmenting paths

The Energy Complexity of Broadcast

Energy is often the most constrained resource in networks of battery-powered devices, and as devices become smaller, they spend a larger fraction of their energy on communication (transceiver usage) not computation. As an imperfect proxy for true energy usage, we define energy complexity to be the number of time slots a device transmits/listens; idle time and computation are free. In this paper we investigate the energy complexity of fundamental communication primitives such as broadcast in multi-hop radio networks. We consider models with collision detection (CD) and without (No-CD), as well as both randomized and deterministic algorithms. Some take-away messages from this work include: 1. The energy complexity of broadcast in a multi-hop network is intimately connected to the time complexity of leader election in a single-hop (clique) network. Many existing lower bounds on time complexity immediately transfer to energy complexity. For example, in the CD and No-CD models, we need $\Omega(\log n)$ and $\Omega(\log^2 n)$ energy, respectively. 2. The energy lower bounds above can almost be achieved, given sufficient ($\Omega(n)$) time. In the CD and No-CD models we can solve broadcast using $O(\frac{\log n\log\log n}{\log\log\log n})$ energy and $O(\log^3 n)$ energy, respectively. 3. The complexity measures of Energy and Time are in conflict, and it is an open problem whether both can be minimized simultaneously. We give a tradeoff showing it is possible to be nearly optimal in both measures simultaneously. For any constant $\epsilon>0$, broadcast can be solved in $O(D^{1+\epsilon}\log^{O(1/\epsilon)} n)$ time with $O(\log^{O(1/\epsilon)} n)$ energy, where $D$ is the diameter of the network

Chemical Oscillation and Morphological Oscillation in Catalyst-Embedded Lyotropic Liquid Crystalline Gels

Liquid crystalline gels offer promising means in generating smart materials due to programmable mechanics and reversible shape changes in response to external stimuli. We demonstrate a simple and convenient method of constructing catalyst-embedded lyotropic liquid crystalline (LLC) gels and achieve chemomechanical oscillator by converting chemical waves in Belousov-Zhabotinsky (BZ) reaction. We observe the directed chemical oscillations on LLC sticks accompanied by small-scale oscillatory swellings-shrinkages that are synchronized with the chemical waves of an LLC stick. To amplify the mechanical oscillations, we further fabricate small LLC fibers and achieve macroscopically oscillatory bending-unbending transition of the LLC fiber driven by a BZ reaction

Enable Dynamic Parameters Combination to Boost Linear Convolutional Neural Network for Sensitive Data Inference

As cloud computing matures, Machine Learning as a Service(MLaaS) has received more attention. In many scenarios, sensitive information also has a demand for MLaaS, but it should not be exposed to others, which brings a dilemma. In order to solve this dilemma, many works have proposed some privacy-protected machine learning frameworks. Compared with plain-text tasks, cipher-text inference has higher computation and communication overhead. In addition to the difficulties caused by cipher-text calculations, the nonlinear activation functions in machine learning models are not friendly to Homomorphic Encryption(HE) and Secure Multi-Party Computation(MPC). The nonlinear activation function can effectively improve the performance of the network, and it seems that the high overhead brought by it is inevitable. In order to solve this problem, this paper re-explains the mechanism of the nonlinear activation function in forward propagation from another perspective, and based on this observation, proposed a dynamic parameters combination scheme as an alternative, called DPC. DPC allows the decoupling of nonlinear operations and linear operations in neural networks. This work further uses this feature to design the HE-based framework and MPC-based framework, so that non-linear operations can be completed locally by the user through pre-computation, which greatly improves the efficiency of privacy protection data prediction. The evaluation result shows that the linear neural networks with DPC can perform high accuracy. Without other optimizations, the HE-based proposed in this work shows 2x faster executions than CryptoNets only relying on the advantage of the DPC. The MPC-based framework proposed in this work can achieve similar efficiency to plain-text prediction, and has advantages over other work in terms of communication complexity and computational complexity

Hemoglobin to red cell distribution width ratio as a prognostic marker for ischemic stroke after mechanical thrombectomy

BackgroundThe hemoglobin to red cell distribution width ratio (HRR) has been experimentally associated with the prognosis of acute ischemic stroke (AIS). However, its relationship with mechanical thrombectomy (MT) for AIS remains unclear. Therefore, this study aimed to investigate the relationship between HRR at admission, follow-up HRR, and clinical outcomes in patients undergoing MT.MethodsAcute ischemic stroke patients undergoing MT were consecutively enrolled from January 2017 to December 2022. Demographic, clinical, and laboratory information were collected. HRR was measured by dividing hemoglobin (Hb) by red cell distribution width (RDW) at admission and after 24 h of MT. Clinical outcomes after 3 months were evaluated using the modified Rankin Scale (mRS). The primary outcome was poor prognosis (mRS > 2) at 3 months, while the secondary outcome was death within 3 months.ResultsA total of 310 patients were analyzed, of whom 216 patients (69.7%) had poor prognosis, and 92 patients (29.6%) died. Patients with a poor prognosis and death had significantly lower HRR levels at admission and after 24 h. HRR at admission was not associated with clinical outcomes according to multivariable logistic regression analysis. However, HRR after 24 h was significantly associated with poor prognosis (adjusted odds ratio [OR]: 0.646, 95% confidence interval [CI]: 0.520–0.803, p < 0.001) and death (adjusted OR: 0.615, 95% CI: 0.508–0.744, p < 0.001). Receiver-operating characteristic curve analysis demonstrated the predictive ability of HRR after 24 h, with areas under the curves of 0.790 for poor prognosis and 0.771 for death.ConclusionRapidly measurable HRR levels are an independent marker of outcome after MT in AIS patients. This may provide a reliable auxiliary outcome measure for clinical routine and interventional therapy

GRACE: Loss-Resilient Real-Time Video through Neural Codecs

In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements. To counter packet losses without retransmission, two primary strategies are employed -- encoder-based forward error correction (FEC) and decoder-based error concealment. The former encodes data with redundancy before transmission, yet determining the optimal redundancy level in advance proves challenging. The latter reconstructs video from partially received frames, but dividing a frame into independently coded partitions inherently compromises compression efficiency, and the lost information cannot be effectively recovered by the decoder without adapting the encoder. We present a loss-resilient real-time video system called GRACE, which preserves the user's quality of experience (QoE) across a wide range of packet losses through a new neural video codec. Central to GRACE's enhanced loss resilience is its joint training of the neural encoder and decoder under a spectrum of simulated packet losses. In lossless scenarios, GRACE achieves video quality on par with conventional codecs (e.g., H.265). As the loss rate escalates, GRACE exhibits a more graceful, less pronounced decline in quality, consistently outperforming other loss-resilient schemes. Through extensive evaluation on various videos and real network traces, we demonstrate that GRACE reduces undecodable frames by 95% and stall duration by 90% compared with FEC, while markedly boosting video quality over error concealment methods. In a user study with 240 crowdsourced participants and 960 subjective ratings, GRACE registers a 38% higher mean opinion score (MOS) than other baselines

CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving

As large language models (LLMs) take on complex tasks, their inputs are supplemented with longer contexts that incorporate domain knowledge. Yet using long contexts is challenging as nothing can be generated until the whole context is processed by the LLM. While the context-processing delay can be reduced by reusing the KV cache of a context across different inputs, fetching the KV cache, which contains large tensors, over the network can cause high extra network delays. CacheGen is a fast context-loading module for LLM systems. First, CacheGen uses a custom tensor encoder, leveraging KV cache's distributional properties to encode a KV cache into more compact bitstream representations with negligible decoding overhead, to save bandwidth usage. Second, CacheGen adapts the compression level of different parts of a KV cache to cope with changes in available bandwidth, in order to maintain low context-loading delay and high generation quality. We test CacheGen on popular LLMs and datasets. Compared to the recent systems that reuse the KV cache, CacheGen reduces the KV cache size by 3.5--4.3x and the total delay in fetching and processing contexts by 3.2--3.7x with negligible impact on the LLM response quality. Our code is at: https://github.com/UChi-JCL/CacheGen.</p

Graphene‐Like Conjugated Molecule as Hole‐Selective Contact for Operationally Stable Inverted Perovskite Solar Cells and Modules

Further enhancing the operational lifetime of inverted-structure perovskite solar cells (PSCs) is crucial for their commercialization, and the design of hole-selective contacts at the illumination side plays a key role in operational stability. In this work, the self-anchoring benzo[rst]pentaphene (SA-BPP) is developed as a new type of hole-selective contact toward long-term operationally stable inverted PSCs. The SA-BPP molecule with a graphene-like conjugated structure shows a higher photostability and mobility than that of the frequently-used triphenylamine and carbazole-based hole-selective molecules. Besides, the anchoring groups of SA-BPP promote the formation of a large-scale uniform hole contact on ITO substrate and efficiently passivate the perovskite absorbers. Benefiting from these merits, the champion efficiencies of 22.03% for the small-sized cells and 17.08% for 5 × 5 cm2 solar modules on an aperture area of 22.4 cm2 are achieved based on this SA-BPP contact. Also, the SA-BPP-based device exhibits promising operational stability, with an efficiency retention of 87.4% after 2000 h continuous operation at the maximum power point under simulated 1-sun illumination, which indicates an estimated T80 lifetime of 3175 h. This novel design concept of hole-selective contacts provides a promising strategy for further improving the PSC stability.journal articl