Novel Nanomaterials for Lithium Ion Batteries and Oxygen Reduction Reactions

Undoubtedly, one of the most pressing issues that world is dealing with today is global warming. It requires long-term potential actions for sustainable development to achieve solutions to address this kind of environmental problems that we are facing today. In this regard, renewable energy resources appear to be the one of the most efficient and effective solutions to reduce reliance on fossil fuels that cause release of excess amounts of carbon dioxide, the main contributor to global warming. As an alternative, electrochemical energy production has long been explored due to its intrinsic nature of more sustainable and more environmentally friendly. Lithium-ion batteries and fuel cells are two of the most studied systems for electrochemical energy storage and conversion. Although the energy storage and conversion mechanisms of lithium-ion batteries and fuel cells are different, there are “electrochemical similarities” of these two systems, all consisting of two electrodes in contact with an electrolyte solution. In order to improve the electrochemical performance of rechargeable lithium-ion batteries, various anode materials including Si and Sn have attracted tremendous interests to replace currently used graphite anode due to their high theoretical specific capacity. However, up to 400% volume expansion/contraction causes cracking and pulverization leading to repaid capacity fading during charge and discharge process. On the other side, the searching of advanced nanocatalysts with unprecedented catalytic efficiency at low-cost limps toward commercialization of highly efficient fuel cells and lithium-air batteries, whereas the pivotal challenges lie in the kinetically sluggish oxygen reduction reaction (ORR) at the cathode. Nanostructured materials have offered new opportunities to design high capacity lithium-ion battery anodes and efficient catalysts to replace traditional noble metal such as Pt based materials. The objective of this study is to demonstrate high performance anode with superior rate capacity and long-cycle-life and effective nanocatalyst for oxygen reduction reaction with superior electrocatalytic activity and stability through rational design of novel nanomaterials. First, a three-dimensionally interconnected carbon nanotube/layered MoS2 nanohybrid network is reported with best-so-far rate capability and outstanding long cycle life. The monolayer and bilayer MoS2 ultrathin nanosheets with large surface to volume ratio facilitate fast Li ion transport further boosting high power capability, while incorporating high conductive CNT enhances the electronic conductivity and retains the structural integrity. The nanohybrid delivers discharge capacity as high as 512 mAh g-1 at 100 A g-1 and 1679 mAh g-1 over 425 cycles at 1 A g-1 with 96% discharge capacity retention of the initial cycle. Then a novel 3D carbon coated Si NPs loaded on high conductive ultrathin graphene nanosheets was fabricated for potential use as anode material for high performance LIBs. The unique structural design of Si@C/NRGO has the combined merits of the carbon layer coating and graphene nanosheets which not only provides volume buffer and improve the conductivity but also separates the Si particles from direct exposure to electrolyte to form a stable SEI layer. Thus, the Si@C/NRGO nanohybrid demonstrates a superior electrochemical performance which is an ideal candidate for high performance LIBs. The nanohybrid delivers discharge/charge capacity of 3079 and 2522 mAh g-1 in the initial cycle at 100 mA g-1, corresponding to a Coulombic efficiency of 82%. A reversible capacity of 2312 mAh g-1 with an approximate Coulombic efficiency of 92% is retained when the current density increases to 1 A g-1 at the 3rd cycle. After 250 cycles, the nanohybrid still retains charge capacity of 1525 mAh g-1, close to a 60% capacity retention of the first cycle at 100 mA g-1. Moreover, the Si@C/NRGO nanohybrid demonstrates proficient cyclic stability with reversible capacities of 1932, 1507, and 1245 mAh g−1 for 2, 4, and 8 A g−1, respectively. Subsequently, a three-dimensionally core-shell structured edge enriched Fe3C@C nanocrystals on graphene network is demonstrated with superior electrocatalytic activity and stability. The graphene nanosheets provide host and vital support for locally grown edge enriched Fe3C nanocrystals, which in-turn perform like separator/spacer to avoid the stacking of ultrathin graphene sheets, leading to a high surface area and super-stable Fe3C@C/rGO hybrid structure. The unique structural design of Fe3C@C/rGO nanohybrid with large surface area enables fast mass transport and a large number of active sites for catalytic reactions. The Fe3C@C/rGO nanohybrid exhibits excellent ORR catalytic activity with a high positive onset potential close to 1.0 V, a Tafel slope of 65 mV/decade, and excellent durability with only ~8% current density decay at 0.8 V after 20,000 seconds’ continuous operation, which is superior to that of a commercial Pt/C in an alkaline electrolyte

Reliability of environmental sampling culture results using the negative binomial intraclass correlation coefficient.

The Intraclass Correlation Coefficient (ICC) is commonly used to estimate the similarity between quantitative measures obtained from different sources. Overdispersed data is traditionally transformed so that linear mixed model (LMM) based ICC can be estimated. A common transformation used is the natural logarithm. The reliability of environmental sampling of fecal slurry on freestall pens has been estimated for Mycobacterium avium subsp. paratuberculosis using the natural logarithm transformed culture results. Recently, the negative binomial ICC was defined based on a generalized linear mixed model for negative binomial distributed data. The current study reports on the negative binomial ICC estimate which includes fixed effects using culture results of environmental samples. Simulations using a wide variety of inputs and negative binomial distribution parameters (r; p) showed better performance of the new negative binomial ICC compared to the ICC based on LMM even when negative binomial data was logarithm, and square root transformed. A second comparison that targeted a wider range of ICC values showed that the mean of estimated ICC closely approximated the true ICC

Knowledge Prompt-tuning for Sequential Recommendation

Pre-trained language models (PLMs) have demonstrated strong performance in sequential recommendation (SR), which are utilized to extract general knowledge. However, existing methods still lack domain knowledge and struggle to capture users' fine-grained preferences. Meanwhile, many traditional SR methods improve this issue by integrating side information while suffering from information loss. To summarize, we believe that a good recommendation system should utilize both general and domain knowledge simultaneously. Therefore, we introduce an external knowledge base and propose Knowledge Prompt-tuning for Sequential Recommendation (\textbf{KP4SR}). Specifically, we construct a set of relationship templates and transform a structured knowledge graph (KG) into knowledge prompts to solve the problem of the semantic gap. However, knowledge prompts disrupt the original data structure and introduce a significant amount of noise. We further construct a knowledge tree and propose a knowledge tree mask, which restores the data structure in a mask matrix form, thus mitigating the noise problem. We evaluate KP4SR on three real-world datasets, and experimental results show that our approach outperforms state-of-the-art methods on multiple evaluation metrics. Specifically, compared with PLM-based methods, our method improves NDCG@5 and HR@5 by \textcolor{red}{40.65\%} and \textcolor{red}{36.42\%} on the books dataset, \textcolor{red}{11.17\%} and \textcolor{red}{11.47\%} on the music dataset, and \textcolor{red}{22.17\%} and \textcolor{red}{19.14\%} on the movies dataset, respectively. Our code is publicly available at the link: \href{https://github.com/zhaijianyang/KP4SR}{\textcolor{blue}{https://github.com/zhaijianyang/KP4SR}.

Strengthening and weakening of methane hydrate by water vacancies

Gas clathrate hydrates show promising applications in sustainable technologies such as future energy resources, gas capture and storage. The stability of clathrate hydrates under external load is of great crucial to those important applications, but remains unknown. Water vacancy is a common structural defect in clathrate hydrates. Herein, the mechanical characteristics of sI methane hydrates containing three types of water vacancy are investigated by molecular dynamics simulations with four different water forcefields. Mechanical properties of methane hydrates such as tensile strength are dictated not only by the density but also the type of water vacancy. Surprisingly, the tensile strength of methane hydrates can be weakened or strengthened, depending on the adopted water model and water vacancy density. Strength enhancement mainly results from the formation of new water cages. This work provides critical insights into the mechanics and microstructural properties of methane clathrate hydrates under external load, which is of primary importance in the recovery of natural gas from methane hydrate reservoirs.Cited as: Lin, Y., Liu, Y., Xu, K., Li, T., Zhang, Z., Wu, J. Strengthening and weakening of methane hydrate by water vacancies. Advances in Geo-Energy Research, 2022, 6(1): 23-37. https://doi.org/10.46690/ager.2022.01.0

Validation of Reference Genes for RT-qPCR Studies of Gene Expression in Preharvest and Postharvest Longan Fruits under Different Experimental Conditions

Reverse transcription quantitative PCR (RT-qPCR), a sensitive technique for quantifying gene expression, relies on stable reference gene(s) for data normalization. Although a few studies have been conducted on reference gene validation in fruit trees, none have been done on preharvest and postharvest longan fruits. In this study, 12 candidate reference genes, namely, CYP, RPL, GAPDH, TUA, TUB, Fe-SOD, Mn-SOD, Cu/Zn-SOD, 18SrRNA, Actin, Histone H3 and EF-1a, were selected. Expression stability of these genes in 150 longan samples was evaluated and analyzed using geNorm and NormFinder algorithms. Preharvest samples consisted of seven experimental sets, including different developmental stages, organs, hormone stimuli (NAA, 2,4-D and ethephon) and abiotic stresses (bagging and girdling with defoliation). Postharvest samples consisted of different temperature treatments (4 and 22 °C) and varieties. Our findings indicate that appropriate reference gene(s) should be picked for each experimental condition. Our data further showed that the commonly used reference gene Actin does not exhibit stable expression across experimental conditions in longan. Expression levels of the DlACO gene, which is a key gene involved in regulating fruit abscission under girdling with defoliation treatment, was evaluated to validate our findings. In conclusion, our data provide a useful framework for choice of suitable reference genes across different experimental conditions for RT-qPCR analysis of preharvest and postharvest longan fruits

Dynamic Gradient Reactivation for Backward Compatible Person Re-identification

We study the backward compatible problem for person re-identification (Re-ID), which aims to constrain the features of an updated new model to be comparable with the existing features from the old model in galleries. Most of the existing works adopt distillation-based methods, which focus on pushing new features to imitate the distribution of the old ones. However, the distillation-based methods are intrinsically sub-optimal since it forces the new feature space to imitate the inferior old feature space. To address this issue, we propose the Ranking-based Backward Compatible Learning (RBCL), which directly optimizes the ranking metric between new features and old features. Different from previous methods, RBCL only pushes the new features to find best-ranking positions in the old feature space instead of strictly alignment, and is in line with the ultimate goal of backward retrieval. However, the sharp sigmoid function used to make the ranking metric differentiable also incurs the gradient vanish issue, therefore stems the ranking refinement during the later period of training. To address this issue, we propose the Dynamic Gradient Reactivation (DGR), which can reactivate the suppressed gradients by adding dynamic computed constant during forward step. To further help targeting the best-ranking positions, we include the Neighbor Context Agents (NCAs) to approximate the entire old feature space during training. Unlike previous works which only test on the in-domain settings, we make the first attempt to introduce the cross-domain settings (including both supervised and unsupervised), which are more meaningful and difficult. The experimental results on all five settings show that the proposed RBCL outperforms previous state-of-the-art methods by large margins under all settings.Comment: Submitted to Pattern Recognition on Dec 06, 2021. Under Revie