An Elastomeric Lithium-Conducting Interlayer for High-Performance LATP-Based Lithium Metal Batteries.

In response to the critical challenges of interfacial impedance and volumetric changes in Li(1+x)AlxTi(2­x)(PO4)3 (LATP)-based lithium metal batteries, an elastomeric lithium-conducting interlayer fabricates from fluorinated hydrogenated nitrile butadiene rubber (F-HNBR) matrix is introduced herein. Owing to the vulcanization, vapor-phase fluorination, and plasticization processes, the lithium-conducting interlayer exhibits a high elasticity of 423%, exceptional fatigue resistance (10 000 compression cycles), superior ionic conductivity of 6.3 × 10-4 S cm-1, and favorable lithiophilicity, rendering it an ideal buffer layer. By integrating the F-HNBR interlayer, the LATP-based lithium symmetric cells demonstrate an extended cycle life of up to 1600 h at 0.1 mA cm-2 and can also endure deep charge/discharge cycles (0.5 mAh cm-2) for the same duration. Furthermore, the corresponding lithium metal full cells achieve 500 cycles at 0.5 C with 98.3% capacity retention and enable a high-mass-loading cathode of 11.1 mg cm-2 to operate at room temperature.

APOE Genotype Modifies the Association between Midlife Adherence to the Planetary Healthy Diet and Cognitive Function in Later Life among Chinese Adults in Singapore.

BACKGROUND: It remains unclear if adherence to the planetary healthy diet (PHD), designed to improve human and environmental health, is associated with better cognitive function in aging, and if this association differs by apolipoprotein E (APOE) genotype. OBJECTIVES: We aimed to examine the association between the PHD pattern and risk of poor cognitive function, and to further assess whether the APOE ε4 allele could modify this association. METHODS: The study included 16,736 participants from the Singapore Chinese Health Study. The PHD score was calculated using data from a validated 165-item food frequency questionnaire at baseline (1993-1998), with higher scores indicating greater adherence to the PHD. Cognitive function was assessed by the Singapore-modified Mini-Mental State Examination at follow-up 3 visits (2014-2016). A subset of 9313 participants had APOE genotype data. Logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs), with adjustment for potential confounders. RESULTS: We identified 2397 (14.3%) cases of poor cognitive function. In the total population, OR (95% CI) of poor cognitive function for each one-SD increment in the PHD score was 0.89 (0.85, 0.93). Carriers of APOE ε4 allele had increased risk of poor cognitive function (OR: 1.36, 95% CI: 1.15, 1.61). There was a significant interaction between the PHD score and the APOE ε4 allele (P-interaction = 0.042). Each one-SD increment in the PHD score was significantly associated with lower risk of poor cognitive function (OR: 0.89; 95% CI: 0.83, 0.96) in non-carriers of APOE ε4 allele, but not in APOE ε4 allele carriers (OR: 1.04, 95% CI: 0.89, 1.23). CONCLUSIONS: Midlife adherence to the PHD was associated with reduced risk of poor cognitive function in later life. However, this was not observed in carriers of APOE ε4 allele who had higher risk of poor cognitive function.

Evaluation and obstacle analysis of sustainable development in small towns based on multi-source big data: A case study of 782 top small towns in China.

Evaluating the sustainable development level and obstacle factors of small towns is an important guarantee for implementing China's new-type urbanization and rural revitalization strategies, and is also a key path to promoting the United Nations Sustainable Development Goal 11 (SDG11). Traditional evaluation methods (such as Analytic Hierarchy Process, AHP, and Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) mainly calculate the comprehensive score of each indicator through weighting. These methods have limitations in handling multidimensional data and system nonlinearity, and they cannot fully reveal the complex relationships and interactions within the sustainability systems of small towns. In contrast, the evaluation model combining Principal Component Analysis (PCA) and Catastrophe Progression Method (CPM) used in this study can better handle multidimensional data and system nonlinear relationships, reducing subjectivity in evaluation and improving the accuracy and reliability of the assessment results. The specific research process is as follows: First, based on the United Nations SDG11 framework, using multi-source big data, a theoretical framework and evaluation index system for the sustainable development of small towns suitable for the Chinese context were established. The impact of county-level factors on the sustainable development of small towns was also considered, and an entropy weight-grey correlation model was used to measure these impacts, resulting in a town-level dataset incorporating county-level influences. Secondly, the sustainability levels of 782 top small towns in China were evaluated using the comprehensive evaluation model based on PCA-CPM Model. Finally, an improved diagnostic model was used to identify obstacles influencing the sustainable development of small towns. The main findings include: 52.69% of the small towns have a sustainable development score exceeding 0.7255, indicating that the overall performance of small towns is at a medium to high development level. The development of small towns exhibits significant differences across regions and types, which are closely linked to county-level effects. Economic and social factors are the main obstacles to the sustainable development of small towns, and the impact of these obstacles intensifies from the eastern to the central, western, and northeastern regions. This study provides valuable insights for policymakers and scholars, promoting a deeper understanding of the sustainable development of small towns.

Studying the mechanism underlying lipid metabolism in osteosarcoma based on transcriptomic RNA sequencing and single-cell data.

BACKGROUND: We aimed to provide a new typing method for osteosarcoma (OS) based on single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the perspective of lipid metabolism and examine its potential mechanisms in the onset and progression of OS. METHODS: Scores for six lipid metabolic pathways were calculated by single-sample gene set enrichment analysis (ssGSEA) based on a scRNA-seq dataset and three microarray expression profiles. Subsequently, cluster typing was conducted using unsupervised consistency clustering. Furthermore, single-cell clustering and dimensionality-reduction analyses identified cell subtypes. Finally, an analysis of cellular receptors was performed using CellphoneDB to identify cellular communication. RESULTS: OS was classified into three subtypes based on lipid metabolic pathways. Among them, patients in clust3 showed poor prognoses, whereas those in clust1 and clust2 exhibited good prognoses. In addition, ssGSEA analysis showed that patients in clust3 had lower immune cell scores. Moreover, the Th17 cell differentiation pathway was significantly differentially enriched between clust2 and clust3, with lower enrichment scores for metabolic pathways in the former relative to clust1 and clust2. In total, 24 genes were upregulated between clust1 and clust2, whereas 20 were downregulated in clust3. These observations were validated by single-cell data analysis. Finally, through scRNA-seq data analysis, we identified nine ligand-receptor pairs particularly critical for communication between normal and malignant cells. CONCLUSIONS: Three clusters were identified and the single-cell analysis revealed that malignant cells dominated lipid metabolism patterns in tumors, thereby influencing the tumor microenvironment.

Bioaugmentation of intertidal sludge enhancing the development of salt-tolerant aerobic granular sludge.

Three parallel bioreactors were operated with different inoculation of activated sludge (R1), intertidal sludge (ItS) (R2), and ItS-added AS (R3), respectively, to explore the effects of ItS bioaugmentation on the formation of salt-tolerant aerobic granular sludge (SAGS) and the enhancement of COD removal performance. The results showed that compared to the control (R1-2), R3 promoted a more rapid development of SAGS with a cultivation time of 25 d. Following 110-day cultivation, R3 exhibited a higher granular diameter of 1.3 mm and a higher hydrophobic aromatic protein content than that in control. Compared to the control, the salt-tolerant performance in R3 was also enhanced with the COD removal efficiency of 96.4% due to the higher sludge specific activity of 14.4 g·gVSS-1·d-1 and the salinity inhibition constant of 49.3 gL-1. Read- and genome-resolved metagenomics together indicated that a higher level of tryptophan/tyrosine synthase gene (trpBD, tyrBC) and enrichment of the key gene hosts Rhodobacteraceae, Marinicella in R3, which was about 5.4-fold and 1.4-fold of that in control, could be the driving factors of rapid development of SAGS. Furthermore, the augmented salt-tolerant potential in R3 could result from that R1 was dominated by Rhodospirillaceae, Bacteroidales, which carried more trehalose synthase gene (otsB, treS), while the dominant members Rhodobacteraceae, Marinicella in R3 were main contributors to the glycine betaine synthase gene (ectC, betB, gbsA). This study could provide deeper insights into the rapid development and improved salt-tolerant potential of SAGS via bioaugmentation of intertidal sludge, which could promote the application of hypersaline wastewater treatment.

Sampling can be faster than optimization.

Optimization algorithms and Monte Carlo sampling algorithms have provided the computational foundations for the rapid growth in applications of statistical machine learning in recent years. There is, however, limited theoretical understanding of the relationships between these 2 kinds of methodology, and limited understanding of relative strengths and weaknesses. Moreover, existing results have been obtained primarily in the setting of convex functions (for optimization) and log-concave functions (for sampling). In this setting, where local properties determine global properties, optimization algorithms are unsurprisingly more efficient computationally than sampling algorithms. We instead examine a class of nonconvex objective functions that arise in mixture modeling and multistable systems. In this nonconvex setting, we find that the computational complexity of sampling algorithms scales linearly with the model dimension while that of optimization algorithms scales exponentially.

Machine learning can be used to distinguish protein families and generate new proteins belonging to those families.

Proteins are classified into families based on evolutionary relationships and common structure-function characteristics. Availability of large data sets of gene-derived protein sequences drives this classification. Sequence space is exponentially large, making it difficult to characterize family differences. In this work, we show that Machine Learning (ML) methods can be trained to distinguish between protein families. A number of supervised ML algorithms are explored to this end. The most accurate is a Long Short Term Memory (LSTM) classification method that accounts for the sequence context of the amino acids. Sequences for a number of protein families where there are sufficient data to be used in ML are studied. By splitting the data into training and testing sets, we find that this LSTM classifier can be trained to successfully classify the test sequences for all pairs of the families. Also investigated is whether the addition of structural information increases the accuracy of the binary comparisons. It does, but because there is much less available structural than sequence information, the quality of the training degrades. Another variety of LSTM, LSTM_wordGen, a context-dependent word generation algorithm, is used to generate new protein sequences based on seed sequences for the families considered here. Using the original sequences as training data and the generated sequences as test data, the LSTM classification method classifies the generated sequences almost as accurately as the true family members do. Thus, in principle, we have generated new members of these protein families.

[Spectroscopic Studies on the Host Plant Selection Mechanism of Trabala vishnou gigantina Yang (Lepidoptera: Lasiocampidae)].

Trabala vishnou gigantina Yang (Lepidoptera: Lasiocampidae) is a polyphagous forestry pest whose periodic breaking out results in great economic damage including total crop failure to forestry and fruit production in China. In this study, in order to improve the understanding of the host plant selection mechanism of T. vishnou gigantina larvae, locust, caragana, willow, poplar, apricot and sea-buckthorn were used as potential host plants for the test. Two-way choice experiment method was used to study the T. vishnou gigantina Yang feeding preferences of the six kinds of plants. Moreover, the chemical component and physical structure of six plants were analyzed with Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD). Among the six plants, T. vishnou gigantina larvae showed a strong preference for sea-buckthorn, followed by, apricot, willow, poplar, locust, and caragana. The FTIR analysis displayed that those six plants presented similar characteristic on absorption peak position, peak amount, and shape. The targets (1 154/1 733, 1 154/898) by FTIR showed that lipids and polysaccharide were major nutriments to affect the host plant selection of T. vishnou gigantina larvae. The XRD results showed that crystallinity index (CrI) also could affect the host plant selection of T. vishnou gigantina larvae. In this research, spectroscopy technology was firstly applied to the study of interactive relationship between insect and host, which would blaze a trail for intensive study of host plant selection mechanism of insect at molecular level.

Effect of Four Types of Chemical Pretreatment on Enzymatic Hydrolysis by SEM, XRD and FTIR Analysis.

Catalpa sawdust was respectively pretreated by NaOH, Ca(OH)2, H2SO4 and HCl solution, and the enzymatic hydrolysis of catalpa sawdust was significantly enhanced by alkaline pretreatments. In order to investigate the mechanisms of pretreatment of catalpa sawdust, the characteristics of catalpa sawdust before and after pretreatments were analyzed by scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. It was found that the surface of catalpa sawdust was disrupted by four kinds of chemical pretreatment, and the pretreatment with Ca(OH)2 solution resulted in the most serious damage. The XRD results showed that part of amorphous regions was damaged by alkaline pretreatments, which led to a relative increase of crystallinity Index (CrI) of catalpa sawdust; while the CrI of catalpa sawdust was insignificantly influenced by acid pretreatments. The FTIR analysis displayed that the molecular structures of hemicellulose and lignin of catalpa sawdust were damaged in different degrees by four types of pretreatment. The significant improvement of enzymatic hydrolysis of catalpa sawdust after alkaline pretreatment might be attributed to the effective delignification of alkaline.

The first description of the male Euophrys atrata and E. bulbus from southern China (Araneae: Salticidae).

The males of Euophrys atrata Song & Chai,1992 and Euophrys bulbus Bao & Peng, 2002 are described and illustrated for the first time from southern China.

Cornifronus gen. nov., a new dark sac spider genus from China (Araneae: Trachelidae).

A new genus of the spider family Trachelidae L. Koch, 1872, Cornifronus gen. nov. from China is described, as well as one new species, C. simplex sp. nov. (), known only from Hainan and Yunnan Province.

Carbon-based photoelectrochemical sensors: recent developments and future prospects.

Owing to the considerable potential of photoelectrochemical (PEC) sensors, they have gained significant attention in the analysis of biological, environmental, and food markers. However, the limited charge mass transfer efficiency and rapid recombination of electron hole pairs have become obstacles in the development of PEC sensors. In this case, considering the unique advantages of carbon-based materials, they can be used as photosensitizers, supporting materials and conductive substrates and coupled with semiconductors to prepare composite materials, solving the above problems. In addition, there are many types of carbon materials, which can have semiconductor properties and form heterojunctions after coupling with semiconductors, effectively promoting the separation of electron hole pairs. Herein, we aimed to provide a comprehensive analysis of reports on carbon-based PEC sensors by introducing their research and application status and discussing future development trends in this field. In particular, the types and performance improvement strategies of carbon-based electrodes and the working principles of carbon-based PEC sensors are explained. Furthermore, the applications of carbon-based photoelectric sensors in environmental monitoring, biomedicine, and food detection are highlighted. Finally, the current limitations in the research on carbon-based PEC sensors are emphasized and the need to enhance the sensitivity and selectivity through material modification, structural design, improved device performance, and other strategies are emphasized.

KHK-A promotes fructose-dependent colorectal cancer liver metastasis by facilitating the phosphorylation and translocation of PKM2.

Excessive fructose diet is closely associated with colorectal cancer (CRC) progression. Nevertheless, fructose's specific function and precise mechanism in colorectal cancer liver metastasis (CRLM) is rarely known. Here, this study reported that the fructose absorbed by primary colorectal cancer could accelerate CRLM, and the expression of KHK-A, not KHK-C, in liver metastasis was higher than in paired primary tumors. Furthermore, KHK-A facilitated fructose-dependent CRLM in vitro and in vivo by phosphorylating PKM2 at Ser37. PKM2 phosphorylated by KHK-A inhibited its tetramer formation and pyruvic acid kinase activity but promoted the nuclear accumulation of PKM2. EMT and aerobic glycolysis activated by nuclear PKM2 enhance CRC cells' migration ability and anoikis resistance during CRLM progression. TEPP-46 treatment, targeting the phosphorylation of PKM2, inhibited the pro-metastatic effect of KHK-A. Besides, c-myc activated by nuclear PKM2 promotes alternative splicing of KHK-A, forming a positive feedback loop.

The Uridylyl Transferase TUT7-Mediated Accumulation of Exosomal miR-1246 Reprograms TAMs to Support CRC Progression.

Tumor-associated macrophages (TAMs) play a crucial role in promoting tumor growth and dissemination, motivating a search for key targets to interfere with the activation of TAMs or reprogram TAMs into the tumor-suppressive type. To gain insight into the mechanisms of macrophage polarization, a designed co-culture system is established, allowing for the education of macrophages in a manner that closely mimics the intricacies of TAMs in the tumor immune microenvironment (TIME). Through database mining, exosomal miR-1246 is identified and is then validated. Exosomal miR-1246-driven polarization of TAMs disrupts the infiltration and function of CD8+ T cells. Mechanically, the amassment of exosomal miR-1246 stems from TUT7-mediated degradation of small noncoding RNA, a process stabilized by SNRPB, but not the precursor of miR-1246. Moreover, an Exo-motif is present in the exosomal miR-1246 sequence, enabling it to bind with the exosomal sorting protein hnRNPA2B1. RNA-seq analysis reveals that exogenous miR-1246 modulates the polarization of TAMs at a post-transcriptional level, emphasizing the pivotal role of the NLRP3 in macrophage polarization. In conclusion, the findings underscore the importance of exosomal miR-1246 as a trigger of macrophage reprogramming and uncover a novel mechanism for its enhanced presence in the TIME.

CFD of the diffusion movement and concentration distribution of culinary particles in the respiratory zone of restaurant diners.

Released aerosol particles during restaurant culinary activity affect diners' health. The air conditioning system is crucial for regulating indoor air quality. However, its improper air distribution increases the individuals' exposure to particle pollution. This study investigates restaurants employing side-up airflow during summer with numerous heat sources and examines the culinary particle diffusion in the diners' respiratory zone under the combined influence of air conditioning cold jet air supply and culinary heat source heat plume. It elucidates the change rule of the concentration distribution of culinary particles under the combined action of these two heterogeneous airflows. This study investigated the movement and concentration distribution of indoor particle by numerical simulation under various air supply velocities, culinary heat source strengths and positions and tuyere opening modes. In restaurants with culinary sources, the thermal buoyancy by the heat plume causes particles to rise. However, the drag force exerted by the cold air supply jet impedes the particles' upward motion. The particle concentration distribution is significantly influenced by both the air supply velocity and the relative positioning of the heat source and the tuyere. Particle concentration increases by 27.13 % in the respiratory zone when the air supply jet trajectory is above the pollution emission source than below. Therefore, lowered air supply velocity is ideal with increased horizontal distance between the emission source and the tuyere under the condition of comfort satisfaction. This scenario mitigates the downward movement exerted by the jet on the particles. The drag force is increased with the air supply velocity increasing from 2.5 m/s to 4.0 m/s. Particle concentration is raised to 41.38 % in the respiratory zone. The drag force by the cold jet on the particles is also heightened with the bilateral tuyere than its single-side counterpart which increases particle concentration maximum by 40.30 % in the respiratory zone.

Three new Spinirta species from China (Araneae, Corinnidae), with the first description of the male of S. leigongshanensis Jin & Zhang, 2020.

Three new species of the genus Spinirta Jin & Zhang, 2020 are described from China: S. shenwushanensis sp. nov., S. lanceola sp. nov. and S. caudata sp. nov.. Additionally, the male of S. leigongshanensis Jin & Zhang, 2020 is described for the first time. Finally, we provide an updated distribution map of the genus.

UBE2J1 inhibits colorectal cancer progression by promoting ubiquitination and degradation of RPS3.

Ubiquitin-conjugating enzyme E2 J1 (UBE2J1) has been proven to participate in the ubiquitination of multiple substrate proteins. However, the underlying mechanisms of UBE2J1 as a ubiquitin-conjugating enzyme participating in cancer development and progression remain largely unknown. Here, we identified that UBE2J1 is downregulated in colorectal cancer (CRC) tissues and cell lines which are mediated by DNA hypermethylation of its promoter, and decreased UBE2J1 is associated with poor prognosis. Functionally, UBE2J1 serving as a suppressor gene inhibits the proliferation and metastasis of CRC cells. Mechanistically, UBE2J1-TRIM25, forming an E2-E3 complex, physically interacts with and targets RPS3 for ubiquitination and degradation at the K214 residue. The downregulated RPS3 caused by UBE2J1 overexpression restrains NF-κB translocation into the nucleus and therefore inactivates the NF-κB signaling pathway. Our study revealed a novel role of UBE2J1-mediated RPS3 poly-ubiquitination and degradation in disrupting the NF-κB signaling pathway, which may serve as a novel and promising biomarker and therapeutic target for CRC.

Construction of a novel mRNAsi-related risk model for predicting prognosis and immunotherapy response in osteosarcoma.

Background: Targeting cancer stem cells (CSC) may represent a future therapeutic direction for osteosarcoma (OS), which mainly relies on the identification of CSC markers. This study aimed to classify OS based on messenger ribonucleic acid (mRNA) stemness indices (mRNAsi) and construct a mRNAsi-related risk model to predict the prognosis of OS. Methods: The one-class logistic regression (OCLR) algorithm was applied to the RNA- sequencing (seq) data of human embryonic stem cells (hESC) and induced pluripotent stem cell (iPSC) lines to calculate mRNAsi. Weighted gene co-expression network analysis (WGCNA) was performed on data obtained from the TARGET database to screen the mRNAsi-related genes. Univariate Cox regression analysis was implemented to screen mRNAsi-related genes with prognostic significance for consensus clustering of OS. The least absolute shrinkage and selection operator (LASSO) and COX regression analysis were conducted to construct a risk model based on mRNAsi-related genes. Results: Six gene modules were identified in the TARGET database. The yellow module showed the strongest negative correlation with mRNAsi and the strongest significant positive correlation with the immune score and stromal score. OS was divided into three molecular subtypes with significant survival differences based on 73 mRNAsi-related genes with prognostic value for OS. The survival rate was ranked as C3 < C1 < C2 from low to high. The levels of immune components in C2 was significantly higher than those in C1 and C3. HSD11B2, GBP1, RNF130, APBB1IP, and NPC2 in the yellow module were used as variables for building the mRNAsi-related risk model. The survival rate of the high-risk group (as defined by this model) was significantly higher than that of the low-risk group, and it had significant survival prediction ability in 28 types of cancer. In addition, the mRNAsi-related risk model was superior to the Tumor Immune Dysfunction and Exclusion (TIDE) model in predicting the prognosis and immunotherapy response in all three immunotherapy cohorts. Conclusions: This study classified OS and constructed a mRNAsi-related risk model based on mRNAsi-related genes, which provides a potential tool for more accurate risk stratification of OS and prediction of immunotherapy response.

Low intensity focused ultrasound: a new prospect for the treatment of Parkinson's disease.

Background: As a chronic and progressive neurodegenerative disease, Parkinson's disease (PD) still lacks effective and safe targeted drug therapy. Low-intensity focused ultrasound (LIFU), a new method to stimulate the brain and open the blood-brain barrier (BBB), has been widely concerned by PD researchers due to its non-invasive characteristics.Methods: PubMed was searched for the past 10 years using the terms 'focused ultrasound', 'transcranial ultrasound', 'pulse ultrasound', and 'Parkinson's disease'. Relevant citations were selected from the authors' references. After excluding articles describing high-intensity focused ultrasound or non-Parkinson's disease applications, we found more than 100 full-text analyses for pooled analysis.Results: Current preclinical studies have shown that LIFU could improve PD motor symptoms by regulating microglia activation, increasing neurotrophic factors, reducing oxidative stress, and promoting nerve repair and regeneration, while LIFU combined with microbubbles (MBs) can promote drugs to cross the BBB, which may become a new direction of PD treatment. Therefore, finding an efficient drug carrier system is the top priority of applying LIFU with MBs to deliver drugs.Conclusions: This article aims to review neuro-modulatory effect of LIFU and the possible biophysical mechanism in the treatment of PD, summarize the latest progress in delivering vehicles with MBs, and discuss its advantages and limitations.

Neuro-modulatory effects of LIFU at the cellular or molecular level.Opening the BBB through the combination of LIFU and MBs.Biophysical mechanism of LIFU.

High-Performance Quasi-Solid-State Lithium-Sulfur Battery with a Controllably Solidified Cathode-Electrolyte Interface.

Lithium-sulfur batteries are considered a promising "beyond Li-ion" energy storage technology. Currently, the practical realization of Li-S batteries is plagued by rapid electrochemical failure of S cathodes due to aggravated polysulfide dissolution and shuttle in the conventional liquid ether-based electrolytes. A gel polymer electrolyte obtained by in situ polymerization of liquid electrolyte solvent at the cathode-electrolyte interface has been proven an effective strategy to prevent polysulfide shuttle. However, notably reduced polysulfide solubility in the gel electrolyte leads to enrichment of poorly conductive sulfide species, which hinders charge migration across the interface and therefore accounts for retarded polysulfide conversion and a low capacity/energy output of batteries. Here, we show that thioacetamide, as a cathode additive, inhibits interfacial polymerization of ether molecules while assisting dissolution of polysulfides and Li2S at the cathode/electrolyte interface. In this way, a layer of liquid, sulfide-soluble electrolyte is preserved between the highly gelled electrolyte and the S particle surface, avoiding interfacial sulfide accumulation and improving polysulfide conversion kinetics. A Li-S battery with the controllably solidified interface demonstrates, without adding other performance-boosting agents or catalysts, a high reversible capacity, a long cycle life, and a favorable rate performance, showing promises for the next-generation storage applications.