Revealing Na+-coordination Induced Failure Mechanism of Metal Sulfide Anode for Sodium Ion Batteries.

Metal sulfide (MS) is regarded as a promising candidate of the anode materials for sodium-ion battery (SIB) with ideal capacity and low cost, yet still suffers from the inferior cycling stability and voltage degradation. Herein, the coordination relationship between the discharge product Na2S with the Na+ (NaPF6) in the electrolyte, is revealed as the root cause for the cycling failure of MS. Na+-coordination effect assistants the dissolution of Na2S, further delocalizing Na2S from the reaction interface under the function of electric field, which leads to the solo oxidation of the discharge product element metal without the participation of Na2S. Besides, the higher highest occupied molecular orbital of Na2S suggest the facilitated Na2S solo oxidation to produce sodium polysulfides (NaPSs). Based on these, lowering the Na+ concentration of the electrolyte is proposed as a potential improvement strategy to change the coordination environment of Na2S, suppressing the side reactions of the solo-oxidation of element metal and Na2S. Consequently, the enhanced conversion reaction reversibility and prolonged cycle life are achieved. This work renders in-depth perception of failure mechanism and inspiration for realizing advanced conversion-type anode.

Dose-Dependent Effect on Plant Growth of Exposure to Metal-Organic Framework MIL-101(Cr).

With the increasing use of metal-organic frameworks (MOFs), they will inevitably enter the environment intentionally or unintentionally. However, the effects of MOFs on plant growth are poorly understood. Here, we investigated the effects of exposure of the rhizosphere to MOFs on plant growth. MIL-101(Cr) was selected as a research model due to its commercial availability and wide use. Soybean plants at the two-leaf stage were subjected to various durations (1-7 days) and concentrations (0-1000 mg/L) of exposure in hydroculture with a control group treated with ultrapure water. We found that MIL-101(Cr) had a positive effect on soybean growth at a lower dose (i.e., 200 mg/L); however, at higher doses (i.e., 500 and 1000 mg/L), it exhibited significant toxicity to plant growth, which is evidenced by leaf damage. To investigate the mechanism of this effect, we used Cr as an indicator to quantify, track, and image MIL-101(Cr) in the plant with laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Results indicated that MIL-101(Cr) primarily accumulated in the cortex of roots (up to 40 times higher than that in stems), with limited translocation to stems and negligible presence in leaves and cotyledons. In addition, metabolomic analysis of soybeans indicated that low-dose MIL-101(Cr) could increase the sucrose content of soybean roots to promote plant growth, while a high dose could induce lipid oxidation in roots. This study provides valuable insights into the ecological toxicology of MOFs and underscores the importance of assessing their environmental impact for sustainable agricultural practices.

A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science.

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm-"ChatGPT + ML + Environment", providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of "secondary training" for future application of "ChatGPT + ML + Environment".

Mn-Based Mullites for Environmental and Energy Applications.

Mn-based mullite oxides AMn2O5 (A = lanthanide, Y, Bi) is a novel type of ternary catalyst in terms of their electronic and geometric structures. The coexistence of pyramid Mn3+-O and octahedral Mn4+-O makes the d-orbital selectively active toward various catalytic reactions. The alternative edge- and corner-sharing stacking configuration constructs the confined active sites and abundant active oxygen species. As a result, they tend to show superior catalytic behaviors and thus gain great attention in environmental treatment and energy conversion and storage. In environmental applications, Mn-based mullites have been demonstrated to be highly active toward low-temperature oxidization of CO, NO, volatile organic compounds (VOCs), etc. Recent research further shows that mullites decompose O3 and ozonize VOCs from -20 °C to room temperature. Moreover, mullites enhance oxygen reduction reactions (ORR) and sulfur reduction reactions (SRR), critical kinetic steps in air-battery and Li-S batteries, respectively. Their distinctive structures also facilitate applications in gas-sensitive sensing, ionic conduction, high mobility dielectrics, oxygen storage, piezoelectricity, dehydration, H2O2 decomposition, and beyond. A comprehensive review from basic physicochemical properties to application certainly not only gains a full picture of mullite oxides but also provides new insights into designing heterogeneous catalysts.

Expanding gain bandwidth using ion-hybridized fiber for kHz-linewidth single-frequency fiber lasers at S-, C-, and L-bands: design and performance evaluation.

Single-frequency fiber lasers at S-, C-, and L-bands play a crucial role in various applications such as optical network expansion, high-precision metrology, coherent lidar, and atomic physics. However, compared to the C-band, the S- and L-bands have wavelength deviations and suffer from excited-state absorption, which limits the output performance. To address this issue, a strategy called ion hybridization has been proposed to increase the differences in site locations of rare earth (RE) ions in the laser matrix, thereby achieving a broader gain bandwidth. This strategy has been applied to an Er3+/Yb3+ co-doped modified phosphate fiber (EYMPF), resulting in gain coefficients per unit length greater than 2 dB/cm at S-, C-, and L-bands. To demonstrate its capabilities, several centimeter-long EYMPFs have been used to generate single-frequency laser outputs at S-, C- and L-bands with kHz-linewidths, high signal-to-noise ratios (>70 dB), and low relative intensity noise (<-130 dB/Hz) in a compact short linear-cavity configuration.

PAD4 controls tumor immunity via restraining the MHC class II machinery in macrophages.

Tumor-associated macrophages (TAMs) shape tumor immunity and therapeutic efficacy. However, it is poorly understood whether and how post-translational modifications (PTMs) intrinsically affect the phenotype and function of TAMs. Here, we reveal that peptidylarginine deiminase 4 (PAD4) exhibits the highest expression among common PTM enzymes in TAMs and negatively correlates with the clinical response to immune checkpoint blockade. Genetic and pharmacological inhibition of PAD4 in macrophages prevents tumor progression in tumor-bearing mouse models, accompanied by an increase in macrophage major histocompatibility complex (MHC) class II expression and T cell effector function. Mechanistically, PAD4 citrullinates STAT1 at arginine 121, thereby promoting the interaction between STAT1 and protein inhibitor of activated STAT1 (PIAS1), and the loss of PAD4 abolishes this interaction, ablating the inhibitory role of PIAS1 in the expression of MHC class II machinery in macrophages and enhancing T cell activation. Thus, the PAD4-STAT1-PIAS1 axis is an immune restriction mechanism in macrophages and may serve as a cancer immunotherapy target.

The generation of glioma organoids and the comparison of two culture methods.

BACKGROUND: The intra- and inter-tumoral heterogeneity of gliomas and the complex tumor microenvironment make accurate treatment of gliomas challenging. At present, research on gliomas mainly relies on cell lines, stem cell tumor spheres, and xenotransplantation models. The similarity between traditional tumor models and patients with glioma is very low. AIMS: In this study, we aimed to address the limitations of traditional tumor models by generating patient-derived glioma organoids using two methods that summarized the cell diversity, histological features, gene expression, and mutant profiles of their respective parent tumors and assess the feasibility of organoids for personalized treatment. MATERIALS AND METHODS: We compared the organoids generated using two methods through growth analysis, immunohistological analysis, genetic testing, and the establishment of xenograft models. RESULTS: Both types of organoids exhibited rapid infiltration when transplanted into the brains of adult immunodeficient mice. However, organoids formed using the microtumor method demonstrated more similar cellular characteristics and tissue structures to the parent tumors. Furthermore, the microtumor method allowed for faster culture times and more convenient operational procedures compared to the Matrigel method. DISCUSSION: Patient-derived glioma organoids, especially those generated through the microtumor method, present a promising avenue for personalized treatment strategies. Their capacity to faithfully mimic the cellular and molecular characteristics of gliomas provides a valuable platform for elucidating tumor biology and evaluating therapeutic modalities. CONCLUSION: The success rates of the Matrigel and microtumor methods were 45.5% and 60.5%, respectively. The microtumor method had a higher success rate, shorter establishment time, more convenient passage and cryopreservation methods, better simulation of the cellular and histological characteristics of the parent tumor, and a high genetic guarantee.

KCNA1 promotes the growth and invasion of glioblastoma cells through ferroptosis inhibition via upregulating SLC7A11.

BACKGROUND: The high invasiveness and infiltrative nature of Glioblastoma (GBM) pose significant challenges for surgical removal. This study aimed to investigate the role of KCNA1 in GBM progression. METHODS: CCK8, colony formation assay, scratch assay, transwell assay, and 3D tumor spheroid invasion assays were to determine how KCNA1 affects the growth and invasion of GBM cells. Subsequently, to confirm the impact of KCNA1 in ferroptosis, western blot, transmission electron microscopy and flow cytometry were conducted. To ascertain the impact of KCNA1 in vivo, patient-derived orthotopic xenograft models were established. RESULTS: In functional assays, KCNA1 promotes the growth and invasion of GBM cells. Besides, KCNA1 can increase the expression of SLC7A11 and protect cells from ferroptosis. The vivo experiments demonstrated that knocking down KCNA1 inhibited the growth and infiltration of primary tumors in mice and extended survival time. CONCLUSION: Therefore, our research suggests that KCNA1 may promote tumor growth and invasion by upregulating the expression of SLC7A11 and inhibiting ferroptosis, making it a promising therapeutic target for GBM.

Surface reconstruction of pyrite-type transition metal sulfides during oxygen evolution reaction.

Reconstruction universally occurs over non-layered transition metal sulfides (TMSs) during oxygen evolution reaction (OER), leading to the formation of active species metal (oxy)hydroxide and thus significantly influences the OER performance. However, the reconstruction process and underlying mechanism quantitatively remain largely unexplored. Herein, we proposed an electrochemical reaction mechanism, namely sulfide oxidation reaction (SOR), to elucidate the reconstruction process of pyrite-type TMSs. Based on this mechanism, we evaluated the reconstruction capability of NiS2 doped with transition metals V, Cr, Mn, Fe, Co, Cu, Mo, Ru, Rh, and Ir within different doped systems. Two key descriptors were thus proposed to describe the reconstruction abilities of TMSs: USOR (the theoretical electric potential of SOR) and ΔU (the difference between the theoretical electric potential of SOR and OER), representing the initiation electric potential of reconstruction and the intrinsic reconstruction abilities of TMSs, respectively. Our finding shows that a lower USOR readily initiate reconstruction at a lower potential and a larger ΔU indicating a poorer reconstruction ability of the catalyst during OER. Furthermore, Fe-doped CoS2 was used to validate the rationality of our proposed descriptors, being consistent with the experiment findings. Our work provides a new perspective on understanding the reconstruction mechanism and quantifying the reconstruction of TMSs.

Regulating Electron Metal-Support Interaction to Suppress N2O Formation in the Selective Catalytic Oxidation of Ammonia.

N2O is a common byproduct in the selective catalytic oxidation of ammonia, and its generation often needs to be inhibited due to its strong greenhouse effect. In this paper, using Ag/ZSO-Y as a model catalyst, the N2O selectivity was reduced by 30% through modulation of the electron metal-support interaction. The results demonstrate that the work function of the support can be regulated by the content of the doping element. As the Zr content increases in SnO2, the work function of the support decreases. Moreover, there is a positive correlation between the charge transfer amount and the work function of the support. A series of in situ DRIFTS and density functional theory calculations revealed that the -NO and -N reactions are the primary pathways for N2O formation. By adjustment of the work function of the support through varying the Zr doping level, the electronic structure of Ag NPs was further tuned, resulting in an increased reaction energy barrier for -NO and -N reactions, effectively suppressing N2O formation.

Geochemical Characteristics of the Middle Jurassic Coal-Bearing Mudstones in the Dameigou Area (Qaidam Basin, NW China): Implications for Paleoclimate, Paleoenvironment, and Organic Matter Accumulation.

The Qaidam Basin is a prominent oil and gas exploration and production base of NW China's Jurassic coal-bearing strata. Coal-bearing mudstones are important source rocks for unconventional reservoirs and can record valuable paleoenvironment and paleoclimate information. Here, geochemical analysis including total organic carbon (TOC), total sulfur, organic carbon isotopic composition, rock pyrolysis, X-ray diffraction, and major and trace elements were carried out on mudstone samples from the Middle Jurassic coal-bearing strata of the Dameigou section in the Qaidam Basin to reveal the paleoclimatic and paleoenvironmental conditions during the deposition of the strata and their controls on organic matter accumulation. Results show that the Middle Jurassic Dameigou and Shimengou formations include three significant stages based on their average TOC values of (3.32%, Stage I; 0.87%, Stage II; and 4.42%, Stage III) from the bottom to the top. The organic matter in mudstones in Stages I and II are mainly derived from terrestrial higher plants, while the organic matter has mixed sources of higher plant debris and lower aquatic organisms in Stage III. Paleoclimate parameters indicate that the mudstones in Stage I were deposited under humid and warm conditions, while the climate in Stage II changed to semiarid and warm conditions before turning dry and hot in Stage III. The varying paleoenvironmental characteristics under different paleoclimatic conditions have also been reconstructed. Our results suggest that the accumulation of organic matter in Stages I and II was primarily controlled by redox conditions, while paleoproductivity is the major controlling factor for organic matter accumulation in Stage III.

Optimized carbonization of coffee shell via response surface methodology: A circular economy approach for environmental remediation.

The disposal of coffee shell waste on farmland, is a common practice that can causing the environmental and waste valuable resources. Carbonization has been identified as an effective method for transforming coffee shells into useful products that mitigate environmental pollution. Through the response surface methodology, the carbonization conditions of the coffee shells were optimized and its potential as a biochar-based slow-release urea fertilizer was explored. Experiments were conducted on coffee shell performance under varying carbonization conditions such as temperature (600-1000 °C), time (1-5 h), and heating rate (5-20 °C/min). The results indicated that the ideal urea adsorption was 56.3 mg/g, achieved under carbonization conditions of 2.83 h, 809 °C, and 15.3 °C/min. The optimal nutrient release rate within seven days was 45.4% under carbonization conditions of 3.19 h, 813 °C, and 15.0 °C/min. The infrared spectroscopy analysis indicates that carbonization conditions influenced the absorption peak intensity of coffee shell biochar, while the functional group types remain unchanged. The biochar exhibits diverse functional groups and abundant pores, making it a promising candidate for use as a biochar-based fertilizer material. Overall, the findings demonstrate an effective waste management approach that significantly reduces environmental pollutants while remediating pollution.

The male's scent triggered a neural response in females despite ambiguous behavioral response in Asian house rats.

Pheromones and olfactory communication play vital roles in sex recognition and mate choice in rodents. Asian house rats (Rattus tanezumi) (RT) often startle easily, making behavioral measurements difficult to carry out accurately in the laboratory. Here, the behavioral and olfactory preferences of the female RT between males and females were not observed using a conventional two-choice device; we then explored the neuro-immunohistochemical evidence in the brains of RT females. We found that male urine elicited significantly higher c-fos expression in the accessory olfactory system and sex-related brain regions in females than female urine did. On the other hand, the differences of volatile compounds and major urinary proteins (MUPs) in both voided urine and preputial glands (PGs) of the RT were detected using gas chromatography-mass spectrometer, sodium dodecyl sulfate-polyacrylamide gel electrophoresis, isoelectric focusing electrophoresis, and liquid chromatography-electrospray ionization mass spectrometry. We found that PG-derived 1-(4,5-dihydro-2-thiazolyl)-ethanone and total MUPs were more abundant in males versus females, suggesting these sexually divergent components might activate the female's accessory olfactory system. In conclusion, the neuro-immunohistochemical evidence indicated that potential sex pheromones might exist in RT; however, the strength of the chemical signal might be too weak to cause behavioral responses in females.

Chronic Ambient Ozone Exposure Aggravates Autism-Like Symptoms in a Susceptible Mouse Model.

Although there is evidence that exposure to ground-level ozone (O3) may cause an increased risk of neurological disorders (e.g., autistic spectrum disorder), low-dose chronic ozone exposure and its adverse effects on the nervous system have not been fully understood. Here, we evaluated the potential neurotoxic effects of long-term exposure to environmentally relevant O3 concentration (200 µg/m3 via a whole-body inhalation system, 12 h/day for 5 days/week) using a susceptible mouse model of autism induced by valproic acid. Various indicators of oxidative stress, mitochondria, and synapse in the brain tissues were then measured to determine the overall damage of O3 to the mouse brain. The results showed an aggravated risk of autism in mice offspring, which was embodied in decreased antioxidant contents, disturbed energy generation in mitochondria, as well as reduced expressions of protein kinase Mζ (PKMζ) and synaptic proteins [e.g., Synapsin 1 (SYN 1), postsynaptic density protein-95 (PSD-95)]. Overall, our study indicates that prenatal exposure to environmentally relevant O3 may exacerbate the symptoms of autism, shedding light on possible molecular mechanisms and providing valuable insights into the pathogenesis of autism, especially concerning low-dose levels of those pollutants.

Machine learning-aided metallomic profiling in serum and urine of thyroid cancer patients and its environmental implications.

The incidence rate of thyroid cancer has been growing worldwide. Thyroid health is closely related with multiple trace metals, and the nutrients are essential in maintaining thyroid function while the contaminants can disturb thyroid morphology and homeostasis. In this study, we conducted metallomic analysis in thyroid cancer patients (n = 40) and control subjects (n = 40) recruited in Shenzhen, China with a high incidence of thyroid cancer. We found significant alterations in serumal and urinary metallomic profiling (including Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Cd, I, Ba, Tl, and Pb) and elemental correlative patterns between thyroid cancer patients and controls. Additionally, we also measured the serum Cu isotopic composition and found a multifaceted disturbance in Cu metabolism in thyroid disease patients. Based on the metallome variations, we built and assessed the thyroid cancer-predictive performance of seven machine learning algorithms. Among them, the Random Forest model performed the best with the accuracy of 1.000, 0.858, and 0.813 on the training, 5-fold cross-validation, and test set, respectively. The high performance of machine learning has demonstrated the great promise of metallomic analysis in the identification of thyroid cancer. Then, the Shapley Additive exPlanations approach was used to further interpret the variable contributions of the model and it showed that serum Pb contributed the most in the identification process. To the best of our knowledge, this is the first study that combines machine learning and metallome data for cancer identification, and it supports the indication of environmental heavy metal-related thyroid cancer etiology.

Determinants of switching behavior to wear helmets when riding e-bikes, a two-step SEM-ANFIS approach.

E-bikes have become one of China's most popular travel modes. The authorities have issued helmet-wearing regulations to increase wearing rates to protect e-bike riders' safety, but the effect is unsatisfactory. To reveal the factors influencing the helmet-wearing behavior of e-bike riders, this study constructed a theoretical Push-Pull-Mooring (PPM) model to analyze the factor's relationship from the perspective of travel behavior switching. A two-step SEM-ANFIS method is proposed to test relationships, rank importance and analyze the combined effect of psychological variables. The Partial Least Squares Structural Equation Model (PLS-SEM) was used to obtain the significant influencing factors. The Adaptive Network-based Fuzzy Inference System (ANFIS), a nonlinear approach, was applied to analyze the importance of the significant influencing factors and draw refined conclusions and suggestions from the analysis of the combined effects. The PPM model we constructed has a good model fit and high model predictive validity (GOF = 0.381, R2 = 0.442). We found that three significant factors tested by PLS-SEM, perceived legal norms (ß = 0.234, p < 0.001), perceived inconvenience (ß = -0.117, p < 0.001) and conformity tendency (ß = 0.241, p < 0.05), are the most important factors in the effects of push, mooring and pull. The results also demonstrated that legal norm is the most important factor but has less effect on people with low perceived vulnerability, and low subjective norms will make people with high conformity tendency to follow the crowd blindly. This study could contribute to developing refined interventions to improve the helmet-wearing rate effectively.

Catalytic Ozonation of Polluter Benzene from -20 to >50 °C with High Conversion Efficiency and Selectivity on Mullite YMn2O5.

Catalytic decomposition of aromatic polluters at room temperature represents a green route for air purification but is currently challenged by the difficulty of generating reactive oxygen species (ROS) on catalysts. Herein, we develop a mullite catalyst YMn2O5 (YMO) with dual active sites of Mn3+ and Mn4+ and use ozone to produce a highly reactive O* upon YMO. Such a strong oxidant species on YMO shows complete removal of benzene from -20 to >50 °C with a high COx selectivity (>90%) through the generated reactive species O* on the catalyst surface (60 000 mL g-1 h-1). Although the accumulation of water and intermediates gradually lowers the reaction rate after 8 h at 25 °C, a simple treatment by ozone purging or drying in the ambient environment regenerates the catalyst. Importantly, when the temperature increases to 50 °C, the catalytic performance remains 100% conversion without any degradation for 30 h. Experiments and theoretical calculations show that such a superior performance stems from the unique coordination environment, which ensures high generation of ROS and adsorption of aromatics. Mullite's catalytic ozonation degradation of total volatile organic compounds (TVOC) is applied in a home-developed air cleaner, resulting in high efficiency of benzene removal. This work provides insights into the design of catalysts to decompose highly stable organic polluters.

Metallomic profiling and natural copper isotopic signatures of childhood autism in serum and red blood cells.

Excessive exposure to metals directly threatens human health, including neurodeve lopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, leaving great harms to children themselves, their families, and even society. In view of this, it is critical to develop reliable biomarkers for ASD in early childhood. Here we used inductively coupled plasma mass spectrometry (ICP-MS) to identify the abnormalities in ASD-associated metal elements in children blood. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was applied to detect isotopic differences in copper (Cu) for further assessment on account of its core role in the brain. We also developed a machine learning classification method for unknown samples based on a support vector machine (SVM) algorithm. The results indicated significant differences in the blood metallome (chromium (Cr), manganese (Mn), cobalt (Co), magnesium (Mg), and arsenic (As)) between cases and controls, and a significantly lower Zn/Cu ratio was observed in the ASD cases. Interestingly, we found a strong association of serum copper isotopic composition (δ65Cu) with autistic serum. SVM was successfully applied to discriminate cases and controls based on the two-dimensional Cu signatures (Cu concentration and δ65Cu) with a high accuracy (94.4%). Overall, our findings revealed a new biomarker for potential early diagnosis and screening of ASD, and the significant alterations in the blood metallome also helped to understand the potential pathogenesis of ASD in terms of metallomics.

Teacher and School Concerns and Actions on Elementary School Children Digital Safety.

Elementary school children are spending more time using digital technologies. Teachers and schools are concerned about the student's digital safety. We interviewed ten elementary school teachers virtually to understand their concerns and understand the actions they take regarding elementary school children's digital safety. Using thematic analysis, we identified themes of concerns and actions of teachers and schools on elementary school children's digital safety. Some digital safety concerns discussed by the teachers included content-related concerns such as accessing inappropriate content, contact-related concerns, inappropriate contact with others online, sharing personal information, lack of understanding of danger, conduct-related concerns regarding cyberbullying and digital footprint, contract-related concerns such as digital security and privacy, and home-related concerns. Teachers and schools have taken several actions to address these concerns. Some of the digital safety actions included security measures and limits, monitoring student activities, providing education on digital safety, and support from guidance counselors. This study has implications for elementary school educators, administrators, parents, and students on the safe use of digital technologies. Supplementary Information: The online version contains supplementary material available at 10.1007/s11528-022-00803-z.