Mesoscale Interaction In Electrodes for Energy Storage

The electrode microstructure in rechargeable lithium batteries, particularly Lithium-ion battery and Lithium-sulfur batteries, plays an important role in determining the adhesive strength and electrochemical performance of the battery. The overall objective of the present research is to develop mesoscale computational models to understand the effects of mesoscale interactions on electrode structure evolution. For Lithium-ion battery, the electrode microstructure is significantly affected by the multiphase slurry properties and solvent evaporation. The most important slurry properties are nanoparticle loading, interparticle interactions, and the shape and the size of nanoparticles. Computational results from the present study indicate that the small-sized active material nanoparticles are beneficial to improve the electronic conductivity of electrode microstructure due to its high conductive interfacial area ratio, and high evaporation rate is harmful for achieving good cooperation between the active material and conductive additives. The mixing sequence also affects electrode microstructure. It is found that stepwise mixing sequence can significantly increase the conductive interfacial area ratio in the electrode microstructure to reduce resistance. A severe challenge for Lithium-sulfur battery is that the discharge product Li2S is an insulator for both electrons and Li ions. The precipitation of Li2S varies porosity and tortuosity of cathode microstructure and corresponding electrochemical properties. In this research, it is proposed to develop a mesoscale modeling strategy to investigate Li2S precipitation-electrode interactions. A first-principle study is performed to fundamentally understand the interaction mechanism between polysulfides and solid Li2S substrate. Results reveal that Li2S molecule direct deposition is energetically favored over the Li2S2 molecule deposition/reduction process. Li2S film formation on graphene is also studied by the first-principles approach and it is found that Li2S molecule adsorption on graphene is weaker than adsorption on crystalline Li2S surface. Atomic structure evolution of Li2S film formation on graphene is also studied by first-principle calculation. It is found that Li2S (111) layer on the graphene is energetically favored. Based on results from first-principles calculations, a coarse-grained model accompanied by kinetic Monte Carlo algorithm is developed to study cathode surface passivation caused by Li2S precipitation, which is affected by reactants concentrations, electrode porosity, electrolyte/solid interfacial area, and operating temperature

Research on the structure function recognition of PLOS

PurposeThe present study explores and investigates the efficiency of deep learning models in identifying discourse structure and functional features and explores the potential application of natural language processing (NLP) techniques in text mining, information measurement, and scientific communication.MethodThe PLOS literature series has been utilized to obtain full-text data, and four deep learning models, including BERT, RoBERTa, SciBERT, and SsciBERT, have been employed for structure-function recognition.ResultThe experimental findings reveal that the SciBERT model performs outstandingly, surpassing the other models, with an F1 score. Additionally, the performance of different paragraph structures has been analyzed, and it has been found that the model performs well in paragraphs such as method and result.ConclusionThe study's outcomes suggest that deep learning models can recognize the structure and functional elements at the discourse level, particularly for scientific literature, where the SciBERT model performs remarkably. Moreover, the NLP techniques have extensive prospects in various fields, including text mining, information measurement, and scientific communication. By automatically parsing and identifying structural and functional information in text, the efficiency of literature management and retrieval can be improved, thereby expediting scientific research progress. Therefore, deep learning and NLP technologies hold significant value in scientific research

Key protein identification by integrating protein complex information and multi-biological features

Identifying key proteins based on protein-protein interaction networks has emerged as a prominent area of research in bioinformatics. However, current methods exhibit certain limitations, such as the omission of subcellular localization information and the disregard for the impact of topological structure noise on the reliability of key protein identification. Moreover, the influence of proteins outside a complex but interacting with proteins inside the complex on complex participation tends to be overlooked. Addressing these shortcomings, this paper presents a novel method for key protein identification that integrates protein complex information with multiple biological features. This approach offers a comprehensive evaluation of protein importance by considering subcellular localization centrality, topological centrality weighted by gene ontology (GO) similarity and complex participation centrality. Experimental results, including traditional statistical metrics, jackknife methodology metric and key protein overlap or difference, demonstrate that the proposed method not only achieves higher accuracy in identifying key proteins compared to nine classical methods but also exhibits robustness across diverse protein-protein interaction networks

GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts

In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts. These models have been trained on an extensive dataset that encompasses both simplified and traditional Chinese characters, allowing them to effectively handle various natural language processing tasks related to ancient books, including but not limited to automatic sentence segmentation, punctuation, word segmentation, part-of-speech tagging, entity recognition, and automatic translation. Notably, these models have exhibited exceptional performance across a range of validation tasks using publicly available datasets. Our research findings highlight the efficacy of employing self-supervised methods to further train the models using classical text corpora, thus enhancing their capability to tackle downstream tasks. Moreover, it is worth emphasizing that the choice of font, the scale of the corpus, and the initial model selection all exert significant influence over the ultimate experimental outcomes. To cater to the diverse text processing preferences of researchers in digital humanities and linguistics, we have developed three distinct categories comprising a total of nine model variations. We believe that by sharing these foundational language models specialized in the domain of ancient texts, we can facilitate the intelligent processing and scholarly exploration of ancient literary works and, consequently, contribute to the global dissemination of China's rich and esteemed traditional culture in this new era.Comment: 22pages,0 figur

Dopant Segregation Boosting High‐Voltage Cyclability of Layered Cathode for Sodium Ion Batteries

As a widely used approach to modify a material’s bulk properties, doping can effectively improve electrochemical properties and structural stability of various cathodes for rechargeable batteries, which usually empirically favors a uniform distribution of dopants. It is reported that dopant aggregation effectively boosts the cyclability of a Mg‐doped P2‐type layered cathode (Na0.67Ni0.33Mn0.67O2). Experimental characterization and calculation consistently reveal that randomly distributed Mg dopants tend to segregate into the Na‐layer during high‐voltage cycling, leading to the formation of high‐density precipitates. Intriguingly, such Mg‐enriched precipitates, acting as 3D network pillars, can further enhance a material’s mechanical strength, suppress cracking, and consequently benefit cyclability. This work not only deepens the understanding on dopant evolution but also offers a conceptually new approach by utilizing precipitation strengthening design to counter cracking related degradation and improve high‐voltage cyclability of layered cathodes.Improved cyclability of Mg‐doped P2‐NMM layered cathode is mainly due to suppression of cracking. Randomly distributed Mg dopants tend to segregate into precipitates during high‐voltage cycling, which can further strengthen the layered cathode and suppress cracking, leading to superior cycling stability at elevated voltage. Dopant precipitate is a new design concept to improve layered cathode cyclability.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153093/1/adma201904816.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153093/2/adma201904816-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153093/3/adma201904816_am.pd

Comparative genomics reveals Cyclospora cayetanensis possesses coccidia-like metabolism and invasion components but unique surface antigens

Assessment of the completeness of sequenced Toxoplasma gondii, Eimeria tenella and Cyclospora cayetanensis genomes based on core eukaryotic protein-encoding genes search using BUSCO. (DOCX 14 kb

Coexistence of Histologically Confirmed Hashimoto's Thyroiditis with Different Stages of Papillary Thyroid Carcinoma in a Consecutive Chinese Cohort

Purpose. To determine the relationship between Hashimoto's thyroiditis (HT) and all stages of papillary thyroid carcinoma (PTC) with or without local lymph node metastasis (LNM). Methods:. We conducted a retrospective study of thyroidectomies from 2008–2013 in First Affiliated Hospital of Nanjing Medical University. We categorized patients according to the presence of histopathologically proven HT. The prevalence of mPTC (maximum diameter ≤ 10 mm) and crPTC (clinical relevant PTC) and local LNM rates were compared. Results:. We evaluated 6,432 consecutive thyroidectomies. In total, 1,328 specimens were confirmed as HT. The prevalence of PTC in this HT cohort was 43.8%, significantly higher than non-HT group. After adjustment of gender and age, the prevalence of PTC was still higher in HT group. HT was a risk factor for PTC in multivariate analysis with odds ratio 2.725 (95% CI, 2.390–3.109) (P < 0.001). However, no correlation was found between HT and LNM of PTC. Conclusion:. HT was associated with an increased prevalence of all stages of PTC, independent of tumor size, gender, and age. In contrast, locally advanced disease defined by LNM was unrelated to HT. These data suggest an association of HT with low risk PTC and a potential protective immunologic effect from further disease progression

Transcriptome Analysis of Arabidopsis thaliana in Response to Plasmodiophora brassicae during Early Infection

Clubroot disease is a serious threat to cruciferous plants worldwide, especially to oilseed rape. However, knowledge on pathogenic molecular mechanisms and host interaction is limited. We presume that the recognition between Arabidopsis thaliana and Plasmodiophora brassicae occurs at the early stage of infection and within a relatively short period. In this study, we demonstrated changes on gene expression and pathways in A. thaliana during early infection with P. brassicae using transcriptome analysis. We identified 1,903 and 1,359 DEGs at 24 and 48 h post-inoculation (hpi), respectively. Flavonoids and the lignin synthesis pathways were enhanced, glucosinolates, terpenoids, and proanthocyanidins accumulated and many hormonal- and receptor-kinase related genes were expressed, caused by P. brassicae infection during its early phase. Therefore, the early interaction between A. thaliana and P. brassicae plays an important role in the entire infection process. The results provide a new contribution to a better understanding of the interaction between host plants and P. brassicae, as well as the development of future measures for the prevention of clubroot

An in vitro vesicle formation assay reveals cargo clients and factors that mediate vesicular trafficking

The fidelity of protein transport in the secretory pathway relies on the accurate sorting of proteins to their correct destinations. To deepen our understanding of the underlying molecular mechanisms, it is important to develop a robust approach to systematically reveal cargo proteins that depend on specific sorting machinery to be enriched into transport vesicles. Here, we used an in vitro assay that reconstitutes packaging of human cargo proteins into vesicles to quantify cargo capture. Quantitative mass spectrometry (MS) analyses of the isolated vesicles revealed cytosolic proteins that are associated with vesicle membranes in a GTP-dependent manner. We found that two of them, FAM84B (also known as LRAT domain containing 2 or LRATD2) and PRRC1, contain proline-rich domains and regulate anterograde trafficking. Further analyses revealed that PRRC1 is recruited to endoplasmic reticulum (ER) exit sites, interacts with the inner COPII coat, and its absence increases membrane association of COPII. In addition, we uncovered cargo proteins that depend on GTP hydrolysis to be captured into vesicles. Comparing control cells with cells depleted of the cargo receptors, SURF4 or ERGIC53, we revealed specific clients of each of these two export adaptors. Our results indicate that the vesicle formation assay in combination with quantitative MS analysis is a robust and powerful tool to uncover novel factors that mediate vesicular trafficking and to uncover cargo clients of specific cellular factors.</p

ROS-scavenging hydrogel as protective carrier to regulate stem cells activity and promote osteointegration of 3D printed porous titanium prosthesis in osteoporosis

Stem cell-based therapy has drawn attention as an alternative option for promoting prosthetic osteointegration in osteoporosis by virtue of its unique characteristics. However, estrogen deficiency is the main mechanism of postmenopausal osteoporosis. Estrogen, as an effective antioxidant, deficienncy also results in the accumulation of reactive oxygen species (ROS) in the body, affecting the osteogenic differentiation of stem cells and the bone formation i osteoporosis. In this study, we prepared a ROS-scavenging hydrogel by crosslinking of epigallocatechin-3-gallate (EGCG), 3-acrylamido phenylboronic acid (APBA) and acrylamide. The engineered hydrogel can scavenge ROS efficiently, enabling it to be a cell carrier of bone marrow-derived mesenchymal stem cells (BMSCs) to protect delivered cells from ROS-mediated death and osteogenesis inhibition, favorably enhancing the tissue repair potential of stem cells. Further in vivo investigations seriously demonstrated that this ROS-scavenging hydrogel encapsulated with BMSCs can prominently promote osteointegration of 3D printed microporous titanium alloy prosthesis in osteoporosis, including scavenging accumulated ROS, inducing macrophages to polarize toward M2 phenotype, suppressing inflammatory cytokines expression, and improving osteogenesis related markers (e.g., ALP, Runx-2, COL-1, BSP, OCN, and OPN). This work provides a novel strategy for conquering the challenge of transplanted stem cells cannot fully function in the impaired microenvironment, and enhancing prosthetic osteointegration in osteoporosis