Total embedding distributions of Ringel ladders

The total embedding distributions of a graph is consisted of the orientable embeddings and non- orientable embeddings and have been know for few classes of graphs. The genus distribution of Ringel ladders is determined in [Discrete Mathematics 216 (2000) 235-252] by E.H. Tesar. In this paper, the explicit formula for non-orientable embeddings of Ringel ladders is obtained

Engineering two-dimensional metal oxides and chalcogenides for enhanced electro- and photocatalysis

Two-dimensional (2D) metal oxides and chalcogenides (MOs & MCs) have been regarded as a new class of promising electro- and photocatalysts for many important chemical reactions such as hydrogen evolution reaction, CO2 reduction reaction and N2 reduction reaction in virtue of their outstanding physicochemical properties. However, pristine 2D MOs & MCs generally show the relatively poor catalytic performances due to the low electrical conductivity, few active sites and fast charge recombination. Therefore, considerable efforts have been devoted to engineering 2D MOs & MCs by rational structural design and chemical modification to further improve the catalytic activities. Herein, we comprehensively review the recent advances for engineering technologies of 2D MOs & MCs, which are mainly focused on the intercalation, doping, defects creation, facet design and compositing with functional materials. Meanwhile, the relationship between morphological, physicochemical, electronic, and optical properties of 2D MOs & MCs and their electro- and photocatalytic performances is also systematically discussed. Finally, we further give the prospect and challenge of the field and possible future research directions, aiming to inspire more research for achieving high-performance 2D MOs & MCs catalysts in energy storage and conversion fields

Physisorption-based charge transfer in two-dimensional SnS2 for selective and reversible NO2 gas sensing

Nitrogen dioxide (NO2) is a gas species that plays an important role in certain industrial, farming, and healthcare sectors. However, there are still significant challenges for NO2 sensing at low detection limits, especially in the presence of other interfering gases. The NO2 selectivity of current gas-sensing technologies is significantly traded-off with their sensitivity and reversibility as well as fabrication and operating costs. In this work, we present an important progress for selective and reversible NO2 sensing by demonstrating an economical sensing platform based on the charge transfer between physisorbed NO2 gas molecules and two-dimensional (2D) tin disulfide (SnS2) flakes at low operating temperatures. The device shows high sensitivity and superior selectivity to NO2 at operating temperatures of less than 160 °C, which are well below those of chemisorptive and ion conductive NO2 sensors with much poorer selectivity. At the same time, excellent reversibility of the sensor is demonstrated, which has rarely been observed in other 2D material counterparts. Such impressive features originate from the planar morphology of 2D SnS2 as well as unique physical affinity and favorable electronic band positions of this material that facilitate the NO2 physisorption and charge transfer at parts per billion levels. The 2D SnS2-based sensor provides a real solution for low-cost and selective NO2 gas sensing

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies

Engineering two-dimensional metal oxides via surface functionalization for biological applications

Two-dimensional (2D) metal oxides (MOs) have attracted a considerable amount of attention for various biological applications due to their unique physicochemcial properties such as high photothermal response, temperature superconductivity, photoluminescence, flexibility, unique catalytic capability, plasmonic tunability and relatively low toxicity. However, the sophisticated physiological environments in biosystems stimulate various explorations of surface functionalization to improve the dispersity, stability and biocompatibility of 2D MOs. Moreover, 2D MOs exhibit remarkably tuneable properties via creating oxygen vacancies or doping, which endow 2D MOs with additional capabilities in biological applications. The large surface to volume ratio inherent in these materials also allows easy functionalization and maximal interaction with the external environment. Much work has been done in tailoring 2D MOs through physical/chemical functionalization for use in a diverse range of biomedical applications such as biosensors, bioimaging, drug/gene delivery carriers or even as therapeutic agents. In this review, current progress on 2D MOs functionalized for various biological applications will be presented. Additional relevant issues concerning the research challenges, technology limitations, and future trends have also been discussed

Piezoelectric Responses of Mechanically Exfoliated Two-Dimensional SnS2 Nanosheets

The emergence of piezoelectric properties in two-dimensional (2D) layered transition metal dichalcogenides (TMDs) has triggered the intensive research on using low dimensional materials for conversion of mechanical stimuli into electrical signals or vice versa. While the bulk intrinsically presents no piezoelectric property, the origin of the piezoelectric responses in their 2D thin planes is ascribed to the loss of centrosymmetry. There are also other categories of 2D layered materials such as post-transition metal dichalcogenides (PTMDs) that might be of interests, which have been confirmed theoretically and are yet to be fully explored experimentally. In this work, we investigate the thickness-dependent piezoelectric responses of 2D tin disulfide (SnS2) nanosheets as a representative of layered PTMDs. The results indicate that the 2D SnS2 nanosheets with a thickness of ∼4 nm present an effective out-of-plane piezoelectric response of 2 ± 0.22 pm/V. Furthermore, the thickness dependence of the piezoelectric behavior at a resonant frequency shows that the piezoelectric coefficient decreases with increasing the thickness of 2D SnS2 nanosheets. Additionally, in reference to periodically poled lithium niobate piezoelectric crystal, the measured effective lateral piezoelectric coefficients at different voltages range from 0.61 to 1.55 pm/V with the average value at ∼1 pm/V. This study expands candidates for new piezoelectric materials in the 2D domain with comparable vertical and lateral coefficients, potentially opening a broader horizon for integration into sensors, actuators, and micro- and nanoelectromechanical systems

Coupling UAV Hyperspectral and LiDAR Data for Mangrove Classification Using XGBoost in China’s Pinglu Canal Estuary

The fine classification of mangroves plays a crucial role in enhancing our understanding of their structural and functional aspects which has significant implications for biodiversity conservation, carbon sequestration, water quality enhancement, and sustainable development. Accurate classification aids in effective mangrove management, protection, and preservation of coastal ecosystems. Previous studies predominantly relied on passive optical remote sensing images as data sources for mangrove classification, often overlooking the intricate vertical structural complexities of mangrove species. In this study, we address this limitation by incorporating unmanned aerial vehicle-LiDAR (UAV-LiDAR) point cloud 3D data with UAV hyperspectral imagery to perform multivariate classification of mangrove species. Five distinct variable scenarios were employed: band characteristics (S1), vegetation index (S2), texture measures (S3), fused hyperspectral characteristics (S4), and a canopy height model (CHM) combined with UAV hyperspectral characteristics and LiDAR point cloud data (S5). To execute this classification task, an extreme gradient boosting (XGBoost) machine learning algorithm was employed. Our investigation focused on the estuary of the Pinglu Canal, situated within the Maowei Sea of the Beibu Gulf in China. By comparing the classification outcomes of the five variable scenarios, we assessed the unique contributions of each variable to the accurate classification of mangrove species. The findings underscore several key points: (1) The fusion of multiple features in the image scenario led to a higher overall accuracy (OA) compared to models that employed individual features. Specifically, scenario S4 achieved an OA of 88.48% and scenario S5 exhibited an even more impressive OA of 96.78%. These figures surpassed those of the individual feature models where the results were S1 (83.35%), S2 (83.55%), and S3 (71.28%). (2) Combining UAV hyperspectral and LiDAR-derived CHM data yielded improved accuracy in mangrove species classification. This fusion ultimately resulted in an OA of 96.78% and kappa coefficient of 95.96%. (3) Notably, the incorporation of data from individual bands and vegetation indices into texture measures can enhance the accuracy of mangrove species classification. The approach employed in this study—a combination of the XGBoost algorithm and the integration of UAV hyperspectral and CHM features from LiDAR point cloud data—proved to be highly effective and exhibited strong performance in classifying mangrove species. These findings lay a robust foundation for future research efforts focused on mangrove ecosystem services and ecological restoration of mangrove forests

Synergetic coupling of Pd nanoparticles and amorphous MoSₓ toward highly efficient electrocatalytic hydrogen evolution reactions

Noble metal palladium (Pd) has been widely used in hydrogen-related catalytic reactions. However, its performances toward hydrogen evolution reactions (HER) are intrinsically restricted due to a strong bonding of Pd–H thus make the hydrogen desorption difficult. In this work, being as an electron–cocatalyst, Pd nanoparticles are anchored on our well-established amorphous MoSx/TiO2 nanotube arrays (TNAs) electrocatalyst system through an electrochemical deposition technique. The unique electronic structure in the S-vacancies and/or unsaturated S atoms of MoSx significantly weaken the Pd–H bonding in the electrocatalytic process, facilitating the hydrogen desorption process. In the meantime, conductivity of MoSx/TNAs is largely improved due to incorporation of Pd nanoparticles into the system, which enables the charge transfer from electrode to active site of MoSx more efficient. The synergetic coupling of Pd and MoSx/TNAs result in a superior electrocatalytic activity, achieving an onset overpotential of −29 mV, overpotentials of −64 and −88 mV at −10 and −20 mA cm−2, which are equivalent to that from Pt catalysts

Liquid metal/metal oxide frameworks with incorporated Ga2O3 for photocatalysis

Solvothermally synthesized Ga2O3 nanoparticles are incorporated into liquid metal/metal oxide (LM/MO) frameworks in order to form enhanced photocatalytic systems. The LM/MO frameworks, both with and without incorporated Ga2O3 nanoparticles, show photocatalytic activitydue to a plasmonic effect where performance is related to the loading of Ga2O3 nanoparticles. Optimum photocatalytic efficiency is obtained with 1 wt% incorporation of Ga2O3 nanoparticles. This can be attributed to the sub-bandgap states of LM/MO frameworks, contributing to pseudo-ohmic contacts which reduce the free carrier injection barrier to Ga2O3

Stable nanoporous Sn/SnO2 composites for efficient electroreduction of CO2 to formate over wide potential range

Seeking for an efficient and stable electrocatalyst in a wide potential range is vital for the electrocatalytic reduction of CO2 into high-value added liquid fuels. Herein, the nanoporous Sn/SnO2 (np-Sn/SnO2) composites with high mesoporosity are fabricated through a two-step dealloying strategy. At all the applied potentials, the as-prepared np-Sn/SnO2 composites show obviously higher Faradaic efficiency of formate relative to porous Sn structures. More importantly, the np-Sn/SnO2 composites exhibit high FEHCOO− of >70% at a wide potential range from −0.8 to −1.4 V vs. RHE. In addition, np-Sn/SnO2 composites possess an excellent long-term stability over 58 h at −0.8 V vs. RHE. As compared to the porous Sn structures, the superiority of np-Sn/SnO2 composites toward electroreduction of CO2 to formate could be mainly attributed to their unique mesoporous structures with high-density grain boundaries and large surface area