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)

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

Motion Responses of a Berthed Tank under Resonance Coupling Effect of Internal Sloshing and Gap Flow

The growth of global energy transportation has promoted the rapid increase of large-scale LNG (liquefied natural gas) carriers, and concerns around the safety of LNG ships has attracted significant attention. Such a floating structure is affected by the external wave excitation and internal liquid sloshing. The interaction between the structure’s motion and the internal sloshing under wave actions may lead to the ship experiencing an unexpected accident. In this research, a hydrodynamic experiment is conducted to investigate the motion responses of a floating tank mooring, both close to and away from a dock. The resonance coupling effect of the internal sloshing and gap flow on the tank’s motion is considered. Based on the measured motion trajectory of the floating tank, the stability and safety of the floating tank are estimated. The results show that the sloshing resonance and narrow gap resonance are beneficial to the stability of the ship. This is helpful for controlling the motion of a berthed ship under wave action with a reasonable selection of the gap distance and the liquid level

Naphthothiadiazole-Based Near-Infrared Emitter with a Photoluminescence Quantum Yield of 60% in Neat Film and External Quantum Efficiencies of up to 3.9% in Nondoped OLEDs

Fluorescent emitters have regained intensive attention in organic light emitting diode (OLED) community owing to the breakthrough of the device efficiency and/or new emitting mechanism. This provides a good chance to develop new near-infrared (NIR) fluorescent emitter and high-efficiency device. In this work, a D-p-A-p-D type compound with naphthothiadiazole as acceptor, namely, 4,4'-(naphtho[2,3-c][1,2,5] thiadiazole-4,9-diyl) bis(N,N-diphenylaniline) (NZ2TPA), is designed and synthesized. The photophysical study and density functional theory analysis reveal that the emission of the compound has obvious hybridized local and charge-transfer (HLCT) state feature. In addition, the compound shows aggregation-induced emission (AIE) characteristic. Attributed to its HLCT mechanism and AIE characteristic, NZ2TPA acquires an unprecedentedly high photoluminescent quantum yield of 60% in the neat film, which is the highest among the reported organic small-molecule NIR emitters and even exceeds most phosphorescent NIR materials. The nondoped devices based on NZ2TPA exhibit excellent performance, achieving a maximum external quantum efficiency (EQE) of 3.9% with the emission peak at 696 nm and a high luminance of 6330 cd m(-2), which are among the highest in the reported nondoped NIR fluorescent OLEDs. Moreover, the device remains a high EQE of 2.8% at high brightness of 1000 cd m(-2), with very low efficiency roll-off

A Red Fluorescent Emitter with a Simultaneous Hybrid Local and Charge Transfer Excited State and Aggregation-Induced Emission for High-Efficiency, Low Efficiency Roll-Off OLEDs

Most red/deep-red fluorescent organic light-emitting diodes (OLEDs) suffer from a low exciton utilization efficiency (eta(gamma)) and a drastic efficiency roll-off at high brightness. This work reports a new red fluorescent emitter with a D-pi-A-pi-D architecture, namely, 4,9-bis(4-(9,9-dimethylacridin-10(9H)-yl)phenyl)naphtho[2,3-c][1,2,5]thiadiazole (NZ2AC). The new emitter shows a hybrid local and charge transfer (HLCT) excited state, which can utilize the triplet excitons by the reverse intersystem cross process via the high-lying triplet channel. A red OLED with an emission peak at 612 nm achieves a maximum external quantum efficiency (EQE) of 6.2% at a doping concentration of 8 wt% NZ2AC in a 4,4'-bis(9-carbazolyl)-2,2'-biphenyl host. Moreover, the new emitter reveals a typical aggregation-induced emission (AIE) property, and consequently, the nondoped OLEDs exhibit a deep-red emission at 663 nm with a maximum EQE of 2.8%, corresponding to a maximum exciton utilization ratio of 93%. Attributed to the simultaneous HLCT and AIE features, both the doped and nondoped devices exhibit low efficiency roll-off at high brightness, with their EQEs remaining at high values of 3.0% and 2.3% at the high luminance of 5000 cd m(-2), respectively, which are among the highest efficiencies at such high luminance for red/deep-red OLEDs

Noninvasive <i>In-Vivo</i> Quantification of Mechanical Heterogeneity of Invasive Breast Carcinomas

<div><p>Heterogeneity is a hallmark of cancer whether one considers the genotype of cancerous cells, the composition of their microenvironment, the distribution of blood and lymphatic microvasculature, or the spatial distribution of the desmoplastic reaction. It is logical to expect that this heterogeneity in tumor microenvironment will lead to spatial heterogeneity in its mechanical properties. In this study we seek to quantify the mechanical heterogeneity within malignant and benign tumors using ultrasound based elasticity imaging. By creating <i>in-vivo</i> elastic modulus images for ten human subjects with breast tumors, we show that Young’s modulus distribution in cancerous breast tumors is more heterogeneous when compared with tumors that are not malignant, and that this signature may be used to distinguish malignant breast tumors. Our results complement the view of cancer as a heterogeneous disease on multiple length scales by demonstrating that mechanical properties within cancerous tumors are also spatially heterogeneous.</p></div

Noninvasive <i>In-Vivo - Fig 4 </i> Quantification of Mechanical Heterogeneity of Invasive Breast Carcinomas

<p>(A) and (C): B-mode ultrasound images of two typical invasive ductal carcinomas. (B) and (D): Corresponding Young’s modulus images generated using elasticity imaging. The tumor boundary is represented by a black curve that is drawn using 50 peak tumor modulus value. The modulus distribution within the tumors is heterogeneous and the margins of the tumors are rough.</p

Schematic diagram of tumorigenesis in breast cancer (adapted from [7, 8]).

<p>(a) Healthy milk duct. (b) Proliferation of tumor cells within the duct is accompanied by desmoplasia in the extra-cellular matrix. (c) Changes in the morphology collagen fiber bundles from a wavy and tortuous state to a straight and taut state, and the emergence of a site where the fiber orientation is predominantly radial with respect to the tumor boundary. (d) Invasion of cancer cells to surrounding glandular tissue from this site. (e) Invasion of the cancer cells to nearby ducts. (f) A fully invasive tumor state. The dashed red curve represents the envelope of the tumor components that would appear as a region with elevated Young’s modulus.</p

Schematic diagram of experimental setup: Ultrasound-based quasi-static elasticity imaging is a compression-based method to evaluate the mechanical properties of tissue <i>in-vivo</i>.

<p>First, an ultrasound transducer is gently pressed into the tissue, while acquiring a sequence of images. These images are used in a cross-correlation algorithm in order to determine the displacement field within the tissue. This deformation is then used in an inverse problem to determine the spatial distribution of elastic parameters.</p