Regulating the size and assembled structure of graphene building blocks for high-performance silicon nanocomposite anodes

Silicon-based composites have received significant interest as a high-capacity anode material for high-performance lithium-ion batteries. However, the large volume change during prolonged charge/discharge cycles, poor electric conductivity, and unstable solid electrolyte interface of the Si electrodes lead to performance degradations, such as fast capacity decay and low coulombic efficiency (CE). It\u27s promising but challenging to fabricate Si-based composite anodes with a high Si active material, which enables high energy density, high-rate capability, and good cycling stability. Herein, the size effect of mechanically robust and highly conductive graphene sheets was investigated to effectively regulate the charge transport kinetics, volume changes, first cycle CE, and stable solid-electrolyte-interphase of the Si-anode for improved electrochemical performance. Specifically, our developed nanocomposite electrode (Si@ULG) consisting of Si nanoparticles (NPs) enveloped by ultra-large graphene sheets (ULG) can deliver a specific capacity of 1478 mA h g−1 even after 200 cycles at C/5, with a low capacity loss of 0.23% per cycle. This outstanding cycling performance surpasses that of electrodes wrapped by small (SG) or large graphene sheets (LG). By further assembling ULG sheets as building blocks into a three-dimensional (3D) graphene framework to load a high weight percentage of graphene-wrapped Si materials (e.g., Si@ULG), the as-prepared binder-free 3D Si@ULG-ULG nanocomposite electrode (with a high mass loading of 3 mg cm−2) enabled an areal capacity of 2.1 mA h cm−2 after 200 cycles at C/5, which is much higher than the slurry coating thin-film anodes (e.g., 0.12 mA h cm−2) at low areal mass loading (0.49 mg cm−2)

ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model

During the development of large language models (LLMs), the scale and quality of the pre-training data play a crucial role in shaping LLMs' capabilities. To accelerate the research of LLMs, several large-scale datasets, such as C4 [1], Pile [2], RefinedWeb [3] and WanJuan [4], have been released to the public. However, most of the released corpus focus mainly on English, and there is still lack of complete tool-chain for extracting clean texts from web data. Furthermore, fine-grained information of the corpus, e.g. the quality of each text, is missing. To address these challenges, we propose in this paper a new complete tool-chain EvalWeb to extract Chinese clean texts from noisy web data. First, similar to previous work, manually crafted rules are employed to discard explicit noisy texts from the raw crawled web contents. Second, a well-designed evaluation model is leveraged to assess the remaining relatively clean data, and each text is assigned a specific quality score. Finally, we can easily utilize an appropriate threshold to select the high-quality pre-training data for Chinese. Using our proposed approach, we release the largest and latest large-scale high-quality Chinese web text ChineseWebText, which consists of 1.42 TB and each text is associated with a quality score, facilitating the LLM researchers to choose the data according to the desired quality thresholds. We also release a much cleaner subset of 600 GB Chinese data with the quality exceeding 90%

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way

Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has made it possible to adapt these models to specific applications for enhanced performance. However, the substantial resources required for training these models necessitate efficient solutions. This paper introduces CoLLiE, an efficient library that facilitates collaborative training of large language models using 3D parallelism, parameter-efficient fine-tuning (PEFT) methods, and optimizers such as Lion, Adan, Sophia, LOMO and AdaLomo. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization. CoLLiE has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. Furthermore, we provide an empirical evaluation of the correlation between model size and GPU memory consumption under different optimization methods, as well as an analysis of the throughput. Lastly, we carry out a comprehensive comparison of various optimizers and PEFT methods within the instruction-tuning context. CoLLiE is available at https://github.com/OpenLMLab/collie.Comment: To appear at EMNLP 2023 Demo; Code is available at https://github.com/OpenLMLab/colli

Association between complete blood count-derived inflammatory markers and the risk of frailty and mortality in middle-aged and older adults

ObjectiveThis study aimed to evaluate the association between six complete blood count (CBC)-derived inflammatory markers [neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammatory index (SII), systemic inflammatory response index (SIRI), and pan-immune inflammation value (PIV)] and the risk of frailty and mortality.MethodsData were obtained from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. Mortality was identified using the National Death Index until December 31, 2019. Multiple logistic regression analysis was conducted to evaluate the association between six CBC-derived inflammatory markers and frailty. The Cox regression model assessed the association between six CBC-derived inflammatory markers and mortality in frail populations. Restricted cubic spline (RCS) was used to visualize the association of the six CBC-derived inflammatory markers with mortality risk. The predictive value of CBC-derived inflammatory markers for mortality was further assessed using a random survival forest (RSF) approach.ResultsThis study analyzed data from a total of 16,705 middle-aged and older participants. Among them, 6,503 participants were frail, with a mortality rate of 41.47%. Multiple logistic regression analysis showed that NLR, MLR, PLR, SII, SIRI, and PIV were positively associated with frailty risk. The Cox regression model revealed that participants in the highest quartile had a significantly increased risk of death compared to those in the lowest quartile: NLR (HR = 1.73, 95% CI:1.54, 1.94), MLR (HR = 1.71, 95% CI:1.51, 1.93), PLR (HR = 1.28, 95%CI: 1.15, 1.43), SII (HR = 1.50, 95%CI:1.34, 1.68), SIRI (HR = 1.88, CI 95%:1.67, 2.12), PIV (HR = 1.55, 95%CI:1.38, 1.73). Random survival forest (RSF) analyses demonstrated that MLR had the highest predictive value for mortality risk middle-aged and older adult frail participants.ConclusionThe results suggest that CBC-derived inflammatory markers are associated with a higher risk of frailty as well as mortality in the middle and old-aged population of the United States

Chemical modifications of perovskite solar cells at interfaces

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.Science, Faculty ofChemistry, Department ofGraduat

Experience reverses the red effect among Chinese stockbrokers.

Recent research has shown that the color red influences psychological functioning. Red is hypothesized to be linked to aggression and danger in evolution, and these links are enhanced by culture-specific uses of red. Thus, color meanings are thought to be grounded in biologically based proclivities and learned associations. However, to date, there has been no direct evidence for the influence of experience on the red effect. This study focused on whether experience could change the psychological effects of the color red. In the context of the Chinese stock market, contrary to the meaning generally associated with red as negative and green as positive, red represents a rise in stock price and green stands for a decrease. An experiment using a 2×2 between subjects factorial design demonstrated that red (compared with green) impaired Chinese college students' performance on an IQ test (in accordance with the red effect), but the opposite effect was found among stockbrokers. These results provide direct evidence of learned color meanings, in support of the general model of color effect

Red color in flags: A signal for competition

The color-in-context theory and ecological valence theory suggest that color preference depends on the context and ecological object that define the psychological meanings of colors. The present study was conducted to identify the preference for the color red in national flags across the world. We explored 192 national flags across the world and found that red was the most frequently used color. Through a systemic examination of symbolic meanings behind use of the color red in flags, it was also found that the color red was often attached with an aggressive connotation. In contrast, the flags of the selected international collaborative organizations did not appear to prefer red. These results support the hypothesis of red flag preference in real-world competitive contexts. Limitations and future research directions are also discussed.</p

The effect of color on performance on Raven’s Standard Progressive Matrices The college students in the red group (n = 12) performed worse than did those in the green group (n = 12).

<p>Conversely, the stockbrokers in the red group (n = 12) performed better than did those in the green group (n = 12). Error bars indicate standard error of test scores.</p