Development of High-Capacity Anodes for Advanced Li-/Na- Ion Rechargeable Batteries

Since the introduction of the first commercial lithium ion batteries (LIBs) by Sony in 1991, LIBs have dominated the rechargeable energy storage market owing to their high energy density, good cycling stability, and long cycle life. By 2020, the global LIBs market revenue is projected to reach $76 billion dollars, a five-fold increase from 2013. To better accommodate the rising demand, research efforts are intensifying to develop next-generation LIB chemistries (i.e., silicon anodes and sulfur cathodes) that offer much higher energy densities. At the same time, concerns over lithium shortage have prompted research into sodium ion batteries (SIBs), a potentially cheaper and more abundant supplementary system to LIBs. My research has been dedicated to solving challenges presented in these new systems, particularly in regards to the anode materials. The conventional LIB anode, graphite, has a theoretical specific capacity of 372 mAh/g based on lithium intercalation reaction. Its practical specific capacity is already reaching this limit, leaving little room for improvement. Alternative anode materials based on alloying and conversion mechanisms have the potentials to achieve two- to ten-fold capacity increase. These materials however are plagued with technical hurdles that limit their immediate commercialization. For alloying materials such as silicon, volumetric expansion (up to 370%) upon lithiation destabilizes the solid electrolyte interphase (SEI) and electrode structure, leading to rapid capacity fade. To that end, I synthesized a carbon/silicon composite anode with built-in porosity in the carbon to accommodate volume expansion of silicon nanoparticles hence greatly improving the cycling stability. I also studied Fe2O3-based conversion anode and identified an interesting high-rate activation process that leads to significant capacity gains (greater than theoretical value). Taking inspiration from these studies, I designed and synthesized a series of iron oxide-silicon nanocomposites that yield substantial capacity improvement in comparison to the silicon control anode. I also carried out detailed characterization and electrochemical evaluation to reveal the synergetic interactions between iron oxide and silicon. Key findings from this study could provide important design guidelines for future development of similar conversion-alloying material (CAM) anodes. For the development of SIBs, the lack of suitable anode materials have greatly impeded its commercialization. The most promising anode material is hard carbon which could be derived from the pyrolysis of biomasses and petroleum coke. Challenges in production scalability, particle morphology control, and reduction of early-cycle irreversible capacity losses are frequently encountered during the early developmental stage. In collaboration with industrial partners, I evaluated the suitability of using carbon microsheets as a scalable SIB anode. While good cycling stability is demonstrated over 300 cycles, large early-cycle capacity losses severely limit energy density and reversible capacity of the cell. This motivated me to develop a pre-sodiation technology that could potentially be applied to all SIB anodes to reduce early-cycle losses. I employed pulse ultrasonication technique to synthesize sodium metal powders as the pre-sodiation agent. Subsequent half- and full-cell study confirms that sodium powders can effectively improve Coulombic efficiency, reversible capacity, and energy density

Modeling nitrogen loadings from agricultural soils in southwest China with modified DNDC

Degradation of water quality has been widely observed in China, and loadings of nitrogen (N) and other nutrients from agricultural systems play a key role in the water contamination. Process‐based biogeochemical models have been applied to quantify nutrient loading from nonpoint sources at the watershed scale. However, this effort is often hindered by the fact that few existing biogeochemical models of nutrient cycling are able to simulate the two‐dimensional soil hydrology. To overcome this challenge, we launched a new attempt to incorporate two fundamental hydrologic features, the Soil Conservation Service curve and the Modified Universal Soil Loss Equation functions, into a biogeochemistry model, Denitrification‐Decomposition (DNDC). These two features have been widely utilized to quantify surface runoff and soil erosion in a suite of hydrologic models. We incorporated these features in the DNDC model to allow the biogeochemical and hydrologic processes to exchange data at a daily time step. By including the new features, DNDC gained the additional ability to simulate both horizontal and vertical movements of water and nutrients. The revised DNDC was tested against data sets observed in a small watershed dominated by farmlands in a mountainous area of southwest China. The modeled surface runoff flow, subsurface drainage flow, sediment yield, and N loading were in agreement with observations. To further observe the behaviors of the new model, we conducted a sensitivity test with varied climate, soil, and management conditions. The results indicated that precipitation was the most sensitive factor determining the rate of N loading from the tested site. A Monte Carlo test was conducted to quantify the potential uncertainty derived by variations in four selected input parameters. This study demonstrates that it is feasible and effective to use enhanced biogeochemical models such as DNDC for quantifying N loadings by incorporating basic hydrological features into the model framework

Distribution Shift Matters for Knowledge Distillation with Webly Collected Images

Knowledge distillation aims to learn a lightweight student network from a pre-trained teacher network. In practice, existing knowledge distillation methods are usually infeasible when the original training data is unavailable due to some privacy issues and data management considerations. Therefore, data-free knowledge distillation approaches proposed to collect training instances from the Internet. However, most of them have ignored the common distribution shift between the instances from original training data and webly collected data, affecting the reliability of the trained student network. To solve this problem, we propose a novel method dubbed ``Knowledge Distillation between Different Distributions" (KD$^{3}$), which consists of three components. Specifically, we first dynamically select useful training instances from the webly collected data according to the combined predictions of teacher network and student network. Subsequently, we align both the weighted features and classifier parameters of the two networks for knowledge memorization. Meanwhile, we also build a new contrastive learning block called MixDistribution to generate perturbed data with a new distribution for instance alignment, so that the student network can further learn a distribution-invariant representation. Intensive experiments on various benchmark datasets demonstrate that our proposed KD$^{3}$ can outperform the state-of-the-art data-free knowledge distillation approaches

5-[2-(4-Acetyl­oxyphen­yl)ethen­yl]benzene-1,3-diyl diacetate

The title compound, C20H18O6, was prepared from resveratrol {systematic name: 5-[(E)-2-(4-hy­droxy­phen­yl)ethen­yl]ben­z­ene-1,3-diol}, which can be isolated from grapes, through triacetyl­ation with using acetic anhydride in pyridine. The two benzene rings are approximately coplanar, making a dihedral angle of 6.64 (14)°, and the three acet­oxy group are located on the same side of the plane. The skeleton of the compound resembles a table with three legs. In the crystal, mol­ecules are linked via C—H⋯O interactions, forming inversion dimers. These dimers are further linked via C—H⋯O interactions, forming a three-dimensional structure

Microtubule actin cross-linking factor 1, a novel target in glioblastoma

Genetic heterogeneity is recognized as a major contributing factor of glioblastoma resistance to clinical treatment modalities and consequently low overall survival rates. This genetic diversity results in variations in protein expression, both intratumorally and between individual glioblastoma patients. In this regard, the spectraplakin protein, microtubule actin cross-linking factor 1 (MACF1), was examined in glioblastoma. An expression analysis of MACF1 in various types of brain tumor tissue revealed that MACF1 was predominately present in grade III-IV astroctyomas and grade IV glioblastoma, but not in normal brain tissue, normal human astrocytes and lower grade brain tumors. Subsequent genetic inhibition experiments showed that suppression of MACF1 selectively inhibited glioblastoma cell proliferation and migration in cell lines established from patient derived xenograft mouse models and immortalized glioblastoma cell lines that were associated with downregulation of the Wnt-signaling mediators, Axin1 and β-catenin. Additionally, concomitant MACF1 silencing with the chemotherapeutic agent temozolomide (TMZ) used for the clinical treatment of glioblastomas cooperatively reduced the proliferative capacity of glioblastoma cells. In conclusion, the present study represents the first investigation on the functional role of MACF1 in tumor cell biology, as well as demonstrates its potential as a unique biomarker that can be targeted synergistically with TMZ as part of a combinatorial therapeutic approach for the treatment of genetically multifarious glioblastomas

Skyrmion-Bubble Bundles in an X-type Sr2Co2Fe28O46 Hexaferrite above Room Temperature

Magnetic skyrmions are spin swirls that possess topological nontriviality and are considered particle-like entities. They are distinguished by an integer topological charge Q. The presence of skyrmion bundles provides an opportunity to explore the range of values for Q, which is crucial for the advancement of topological spintronic devices with multi-Q properties. In this study, we present a new material candidate, Sr2Co2Fe28O46 hexaferrite of the X-type, which hosts small dipolar skyrmions at room temperature and above. By exploiting reversed magnetic fields from metastable skyrmion bubbles at zero fields, we can incorporate skyrmion-bubble bundles with different interior skyrmion/bubble numbers, topological charges, and morphologies at room temperature. Our experimental findings are consistently supported by micromagnetic simulations. Our results highlight the versatility of topological spin textures in centrosymmetric uniaxial magnets, thereby paving the way for the development of room-temperature topological spintronic devices with multi-Q characteristics.Comment: https://doi.org/10.1002/adma.20230611

Direct Distillation between Different Domains

Knowledge Distillation (KD) aims to learn a compact student network using knowledge from a large pre-trained teacher network, where both networks are trained on data from the same distribution. However, in practical applications, the student network may be required to perform in a new scenario (i.e., the target domain), which usually exhibits significant differences from the known scenario of the teacher network (i.e., the source domain). The traditional domain adaptation techniques can be integrated with KD in a two-stage process to bridge the domain gap, but the ultimate reliability of two-stage approaches tends to be limited due to the high computational consumption and the additional errors accumulated from both stages. To solve this problem, we propose a new one-stage method dubbed ``Direct Distillation between Different Domains" (4Ds). We first design a learnable adapter based on the Fourier transform to separate the domain-invariant knowledge from the domain-specific knowledge. Then, we build a fusion-activation mechanism to transfer the valuable domain-invariant knowledge to the student network, while simultaneously encouraging the adapter within the teacher network to learn the domain-specific knowledge of the target data. As a result, the teacher network can effectively transfer categorical knowledge that aligns with the target domain of the student network. Intensive experiments on various benchmark datasets demonstrate that our proposed 4Ds method successfully produces reliable student networks and outperforms state-of-the-art approaches

Apolipoprotein A-I levels in the survival of patients with colorectal cancer: a retrospective study

BackgroundAbnormal lipid levels have been associated with cancer incidence and progression. However, limited studies have investigated the relationship between apolipoprotein A-I (ApoA-I) and colorectal cancer (CRC). This study assessed the significance of ApoA-I levels in progression-free survival (PFS) and overall survival (OS) of patients with CRC.MethodsSurvival curves were compared using Kaplan–Meier analysis, while the predictive values of various lipid indicators in CRC prognosis were evaluated based on receiver operating characteristic curves. The factors influencing PFS and OS in patients with CRC were analyzed using Cox proportional hazards regression models. Finally, the relationship between ApoA-I level and disease recurrence was investigated through logistic regression analysis. The optimal Apo-I level was determined through maximally selected rank statistics.ResultsUsing the optimal ApoA-I cutoff value (0.9 g/L), the 1,270 patients with CRC were categorized into low (< 0.9 g/L, 275 cases) and high (≥0.9 g/L, 995 cases) ApoA-I groups. Compared with other lipid indicators, ApoA-I demonstrated superior predictive accuracy. The high ApoA-I group exhibited significantly higher survival rates than the low ApoA-I group (PFS, 64.8% vs. 45.2%, P < 0.001; OS, 66.1% vs. 48.6%, P < 0.001). Each one-standard-deviation increase in ApoA-I level was related to a 12.0% decrease in PFS risk (hazard ratio [HR] 0.880; 95% confidence interval [CI], 0.801–0.968; P = 0.009) and an 11.2% decrease in OS risk (HR 0.888; 95%CI, 0.806–0.978; P = 0.015). Logistic regression analysis revealed that patients with low ApoA-I had a 32.5% increased risk of disease recurrence (odds ratio [OR] 0.675; 95%CI, 0.481–0.946; P = 0.0225) compared with those with high ApoA-I. PFS/OS nomograms based on ApoA-I demonstrated excellent prognostic prediction accuracy.ConclusionsSerum ApoA-I level may be a valuable and non-invasive tool for predicting PFS and OS in patients with CRC

PGAGP: Predicting pathogenic genes based on adaptive network embedding algorithm

The study of disease-gene associations is an important topic in the field of computational biology. The accumulation of massive amounts of biomedical data provides new possibilities for exploring potential relations between diseases and genes through computational strategy, but how to extract valuable information from the data to predict pathogenic genes accurately and rapidly is currently a challenging and meaningful task. Therefore, we present a novel computational method called PGAGP for inferring potential pathogenic genes based on an adaptive network embedding algorithm. The PGAGP algorithm is to first extract initial features of nodes from a heterogeneous network of diseases and genes efficiently and effectively by Gaussian random projection and then optimize the features of nodes by an adaptive refining process. These low-dimensional features are used to improve the disease-gene heterogenous network, and we apply network propagation to the improved heterogenous network to predict pathogenic genes more effectively. By a series of experiments, we study the effect of PGAGP’s parameters and integrated strategies on predictive performance and confirm that PGAGP is better than the state-of-the-art algorithms. Case studies show that many of the predicted candidate genes for specific diseases have been implied to be related to these diseases by literature verification and enrichment analysis, which further verifies the effectiveness of PGAGP. Overall, this work provides a useful solution for mining disease-gene heterogeneous network to predict pathogenic genes more effectively

Synergy of Pd atoms and oxygen vacancies on In₂O₃ for methane conversion under visible light

Methane (CH4) oxidation to high value chemicals under mild conditions through photocatalysis is a sustainable and appealing pathway, nevertheless confronting the critical issues regarding both conversion and selectivity. Herein, under visible irradiation (420 nm), the synergy of palladium (Pd) atom cocatalyst and oxygen vacancies (OVs) on In2O3 nanorods enables superior photocatalytic CH4 activation by O2. The optimized catalyst reaches ca. 100 μmol h-1 of C1 oxygenates, with a selectivity of primary products (CH3OH and CH3OOH) up to 82.5%. Mechanism investigation elucidates that such superior photocatalysis is induced by the dedicated function of Pd single atoms and oxygen vacancies on boosting hole and electron transfer, respectively. O2 is proven to be the only oxygen source for CH3OH production, while H2O acts as the promoter for efficient CH4 activation through ·OH production and facilitates product desorption as indicated by DFT modeling. This work thus provides new understandings on simultaneous regulation of both activity and selectivity by the synergy of single atom cocatalysts and oxygen vacancies