In-situ synthesis of interconnected SWCNT/OMC framework on silicon nanoparticles for high performance lithium-ion batteries

AbstractIn spite of silicon has a superior theoretical capacity, the large volume expansion of Si anodes during Li+ insertion/extraction is the bottle neck that results in fast capacity fading and poor cycling performance. In this paper, we report a silicon, single-walled carbon nanotube, and ordered mesoporous carbon nanocomposite synthesized by an evaporation-induced self-assembly process, in which silicon nanoparticles and single-walled carbon nanotubes were added into the phenolic resol with F-127 for co-condensation. The ordered mesoporous carbon matrix and single-walled carbon nanotubes network could effectively accommodate the volume change of silicon nanoparticles, and the ordered mesoporous structure could also provide efficient channels for the fast transport of Li-ions. As a consequence, this hybrid material exhibits a reversible capacity of 861 mAh g−1 after 150 cycles at a current density of 400 mA g−1. It achieves significant improvement in the electrochemical performance when compared with the raw materials and Si nanoparticle anodes

Preserving In-Context Learning ability in Large Language Model Fine-tuning

Pretrained large language models (LLMs) are strong in-context learners that are able to perform few-shot learning without changing model parameters. However, as we show, fine-tuning an LLM on any specific task generally destroys its in-context ability. We discover an important cause of this loss, format specialization, where the model overfits to the format of the fine-tuned task and is unable to output anything beyond this format. We further show that format specialization happens at the beginning of fine-tuning. To solve this problem, we propose Prompt Tuning with MOdel Tuning (ProMoT), a simple yet effective two-stage fine-tuning framework that preserves in-context abilities of the pretrained model. ProMoT first trains a soft prompt for the fine-tuning target task, and then fine-tunes the model itself with this soft prompt attached. ProMoT offloads task-specific formats into the soft prompt that can be removed when doing other in-context tasks. We fine-tune mT5 XXL with ProMoT on natural language inference (NLI) and English-French translation and evaluate the in-context abilities of the resulting models on 8 different NLP tasks. ProMoT achieves similar performance on the fine-tuned tasks compared with vanilla fine-tuning, but with much less reduction of in-context learning performances across the board. More importantly, ProMoT shows remarkable generalization ability on tasks that have different formats, e.g. fine-tuning on a NLI binary classification task improves the model's in-context ability to do summarization (+0.53 Rouge-2 score compared to the pretrained model), making ProMoT a promising method to build general purpose capabilities such as grounding and reasoning into LLMs with small but high quality datasets. When extended to sequential or multi-task training, ProMoT can achieve even better out-of-domain generalization performance

ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent

Answering complex natural language questions often necessitates multi-step reasoning and integrating external information. Several systems have combined knowledge retrieval with a large language model (LLM) to answer such questions. These systems, however, suffer from various failure cases, and we cannot directly train them end-to-end to fix such failures, as interaction with external knowledge is non-differentiable. To address these deficiencies, we define a ReAct-style LLM agent with the ability to reason and act upon external knowledge. We further refine the agent through a ReST-like method that iteratively trains on previous trajectories, employing growing-batch reinforcement learning with AI feedback for continuous self-improvement and self-distillation. Starting from a prompted large model and after just two iterations of the algorithm, we can produce a fine-tuned small model that achieves comparable performance on challenging compositional question-answering benchmarks with two orders of magnitude fewer parameters.Comment: 19 pages, 4 figures, 4 tables, 8 listing

Imaging defects and their evolution in a metal–organic framework at sub-unit-cell resolution

© 2019, The Author(s), under exclusive licence to Springer Nature Limited. Defect engineering of metal–organic frameworks (MOFs) offers promising opportunities for tailoring their properties to specific functions and applications. However, determining the structures of defects in MOFs—either point defects or extended ones—has proved challenging owing to the difficulty of directly probing local structures in these typically fragile crystals. Here we report the real-space observation, with sub-unit-cell resolution, of structural defects in the catalytic MOF UiO-66 using a combination of low-dose transmission electron microscopy and electron crystallography. Ordered ‘missing linker’ and ‘missing cluster’ defects were found to coexist. The missing-linker defects, reconstructed three-dimensionally with high precision, were attributed to terminating formate groups. The crystallization of the MOF was found to undergo an Ostwald ripening process, during which the defects also evolve: on prolonged crystallization, only the missing-linker defects remained. These observations were rationalized through density functional theory calculations. Finally, the missing-cluster defects were shown to be more catalytically active than their missing-linker counterparts for the isomerization of glucose to fructose

Nearly a decade-long repeatable seasonal diversity patterns of bacterioplankton communities in the eutrophic Lake Donghu (Wuhan, China).

Uncovering which environmental factors govern community diversity patterns and how ecological processes drive community turnover are key questions related to understand the community assembly. However, the ecological mechanisms regulating long-term variations of bacterioplankton communities in lake ecosystems remain poorly understood. Here we present nearly a decade-long study of bacterioplankton communities from the eutrophic Lake Donghu (Wuhan, China) using 16S rRNA gene amplicon sequencing with MiSeq platform. We found strong repeatable seasonal diversity patterns in terms of both common (detected in more than 50% samples) and dominant (relative abundance >1%) bacterial taxa turnover. Moreover, community composition tracked the seasonal temperature gradient, indicating that temperature is a key environmental factor controlling observed diversity patterns. Total phosphorus also contributed significantly to the seasonal shifts in bacterioplankton composition. However, any spatial pattern of bacterioplankton communities across the main lake areas within season was overwhelmed by their temporal variabilities. Phylogenetic analysis further indicated that 75%-82% of community turnover was governed by homogeneous selection due to consistent environmental conditions within seasons, suggesting that the microbial communities in Lake Donghu are mainly controlled by niche-based processes. Therefore, dominant niches available within seasons might be occupied by similar combinations of bacterial taxa with modest dispersal rates throughout different lake areas

The cAMP pathway is important for controlling the morphological switch to the pathogenic yeast form of Paracoccidioides brasiliensis

Paracoccidioides brasiliensis is a human pathogenic fungus that switches from a saprobic mycelium to a pathogenic yeast. Consistent with the morphological transition being regulated by the cAMP-signalling pathway, there is an increase in cellular cAMP levels both transiently at the onset (< 24 h) and progressively in the later stages (> 120 h) of the transition to the yeast form, and this transition can be modulated by exogenous cAMP. We have cloned the cyr1 gene encoding adenylate cyclase (AC) and established that its transcript levels correlate with cAMP levels. In addition, we have cloned the genes encoding three Gα (Gpa1–3), Gβ (Gpb1) and Gγ (Gpg1) G proteins. Gpa1 and Gpb1 interact with one another and the N-terminus of AC, but neither Gpa2 nor Gpa3 interacted with Gpb1 or AC. The interaction of Gpa1 with Gpb1 was blocked by GTP, but its interaction with AC was independent of bound nucleotide. The transcript levels for gpa1, gpb1 and gpg1 were similar in mycelium, but there was a transient excess of gpb1 during the transition, and an excess of gpa1 in yeast. We have interpreted our findings in terms of a novel signalling mechanism in which the activity of AC is differentially modulated by Gpa1 and Gpb1 to maintain the signal over the 10 days needed for the morphological switch

A double-layered Ge/carbon cloth integrated anode for high performance lithium ion batteries

A double-layered Ge coated carbon cloth composite was synthesized by electrodepositing Ge from an ionic liquid using carbon cloth as substrate. As an integrated electrode, the composite exhibits a high initial charge capacity of 1169 mA h g(-1) and retains 989 mA h g(-1) after 100 cycles at 300 mA g(-1)

Nature-Inspired 2D-Mosaic 3D-Gradient Mesoporous Framework: Bimetal Oxide Dual-Composite Strategy toward Ultrastable and High-Capacity Lithium Storage

In allusion to traditional transition-metal oxide (TMO) anodes for lithium-ion batteries, which face severe volume variation and poor conductivity, herein a bimetal oxide dual-composite strategy based on two-dimensional (2D)-mosaic three-dimensional (3D)-gradient design is proposed. Inspired by natural mosaic dominance phenomena, Zn1-xCoxO/ZnCo2O4 2D-mosaic-hybrid mesoporous ultrathin nanosheets serve as building blocks to assemble into a 3D Zn-Co hierarchical framework. Moreover, a series of derivative frameworks with high evolution are controllably synthesized, based on which a facile one-pot synthesis process can be developed. From a component-composite perspective, both Zn1-xCoxO and ZnCo2O4 provide superior conductivity due to bimetal doping effect, which is verified by density functional theory calculations. From a structure-composite perspective, 2D-mosaic-hybrid mode gives rise to ladder-type buffering and electrochemical synergistic effect, thus realizing mutual stabilization and activation between the mosaic pair, especially for Zn1-xCoxO with higher capacity yet higher expansion. Moreover, the inside-out Zn-Co concentration gradient in 3D framework and rich oxygen vacancies further greatly enhance Li storage capability and stability. As a result, a high reversible capacity (1010 mA h g(-1)) and areal capacity (1.48 mA h cm(-2)) are attained, while ultrastable cyclability is obtained during high-rate and long-term cycles, rending great potential of our 2D-mosaic 3D-gradient design together with facile synthesis.</p

Significantly improvement in formability and ductility of AZ31 Mg alloy by differential temperature rolling

The implementation of asymmetric deformation proves to be an effective approach towards optimizing the basal texture of wrought Mg alloys, ultimately leading to improved plasticity and formability. This work performs differential temperature rolling (DTR) on AZ31 Mg alloy to achieve asymmetric deformation. After the DTR process, the ductility and formability of the RT-200 sample are comparable to the traditional cold rolling and annealing combination process. Combined with the annealing treatment (AT), the fracture elongation and Erichsen value of the RT-200/AT sample are reached 32.4% and 4.49 mm, respectively. The improvement of the ductility and formability is mainly attributed to the texture weakening, which originates from the temperature gradient formed in the DTR process. The gradient temperature field modifies the dynamic recrystallization behavior during the rolling deformation process, as well as the static recrystallization behavior during annealing. The microstructure evolution and the texture weakening mechanism are discussed in detail. The findings can serve as a theoretical reference for the development of high-ductility Mg alloys and for optimizing the continuous hot-rolling process of Mg alloys