Far-Infrared Therapy Based on Graphene Ameliorates High-Fat Diet-Induced Anxiety-Like Behavior in Obese Mice via Alleviating Intestinal Barrier Damage and Neuroinflammation.

The consumption of a high-fat diet (HFD) has been implicated in the etiology of obesity and various neuropsychiatric disturbances, including anxiety and depression. Compelling evidence suggests that far-infrared ray (FIR) possesses beneficial effects on emotional disorders. However, the efficacy of FIR therapy in addressing HFD-induced anxiety and the underlying mechanisms remain to be elucidated. Here, we postulate that FIR emitted from a graphene-based therapeutic device may mitigate HFD-induced anxiety behaviors. The graphene-FIR modify the gut microbiota in HFD-mice, particularly by an enriched abundance of beneficial bacteria Clostridiaceae and Erysipelotrichaceae, coupled with a diminution of harmful bacteria Lachnospiraceae, Anaerovoracaceae, Holdemania and Marvinbryantia. Graphene-FIR also improved intestinal barrier function, as evidenced by the augmented expression of the tight junction protein occludin and G protein-coupled receptor 43 (GPR43). In serum level, we observed the decreased free fatty acids (FFA), lipopolysaccharides (LPS), diamine oxidase (DAO) and D-lactate, and increased the glucagon-like peptide-2 (GLP-2) levels in graphene-FIR mice. Simultaneously, inflammatory cytokines IL-6, IL-1ß, and TNF-α manifested a decrease subsequent to graphene-FIR treatment in both peripheral and central system. Notably, graphene-FIR inhibited over expression of astrocytes and microglia. We further noticed that the elevated the BDNF and decreased TLR4 and NF-κB expression in graphene-FIR group. Overall, our study reveals that graphene-FIR rescued HFD-induced anxiety via improving the intestine permeability and the integrity of blood-brain barrier, and reduced inflammatory response by down regulating TLR4/NF-κB inflammatory pathway.

Early-season and refined mapping of winter wheat based on phenology algorithms - a case of Shandong, China.

Winter wheat is one of the major food crops in China, and timely and effective early-season identification of winter wheat is crucial for crop yield estimation and food security. However, traditional winter wheat mapping is based on post-season identification, which has a lag and relies heavily on sample data. Early-season identification of winter wheat faces the main difficulties of weak remote sensing response of the vegetation signal at the early growth stage, difficulty of acquiring sample data on winter wheat in the current season in real time, interference of crops in the same period, and limited image resolution. In this study, an early-season refined mapping method with winter wheat phenology information as priori knowledge is developed based on the Google Earth Engine cloud platform by using Sentinel-2 time series data as the main data source; these data are automated and highly interpretable. The normalized differential phenology index (NDPI) is adopted to enhance the weak vegetation signal at the early growth stage of winter wheat, and two winter wheat phenology feature enhancement indices based on NDPI, namely, wheat phenology differential index (WPDI) and normalized differential wheat phenology index (NDWPI) are developed. To address the issue of " different objects with the same spectra characteristics" between winter wheat and garlic, a plastic mulched index (PMI) is established through quantitative spectral analysis based on the differences in early planting patterns between winter wheat and garlic. The identification accuracy of the method is 82.64% and 88.76% in the early overwintering and regreening periods, respectively, These results were consistent with official statistics (R2 = 0.96 and 0.98, respectively). Generalization analysis demonstrated the spatiotemporal transferability of the method across different years and regions. In conclusion, the proposed methodology can obtain highly precise spatial distribution and planting area information of winter wheat 4_6 months before harvest. It provides theoretical and methodological guidance for early crop identification and has good scientific research and application value.

Optimized monophasic pulses with equivalent electric field for rapid-rate transcranial magnetic stimulation.

Objective.Transcranial magnetic stimulation (TMS) with monophasic pulses achieves greater changes in neuronal excitability but requires higher energy and generates more coil heating than TMS with biphasic pulses, and this limits the use of monophasic pulses in rapid-rate protocols. We sought to design a stimulation waveform that retains the characteristics of monophasic TMS but significantly reduces coil heating, thereby enabling higher pulse rates and increased neuromodulation effectiveness.Approach.A two-step optimization method was developed that uses the temporal relationship between the electric field (E-field) and coil current waveforms. The model-free optimization step reduced the ohmic losses of the coil current and constrained the error of the E-field waveform compared to a template monophasic pulse, with pulse duration as a second constraint. The second, amplitude adjustment step scaled the candidate waveforms based on simulated neural activation to account for differences in stimulation thresholds. The optimized waveforms were implemented to validate the changes in coil heating.Main results.Depending on the pulse duration and E-field matching constraints, the optimized waveforms produced 12%-75% less heating than the original monophasic pulse. The reduction in coil heating was robust across a range of neural models. The changes in the measured ohmic losses of the optimized pulses compared to the original pulse agreed with numeric predictions.Significance.The first step of the optimization approach was independent of any potentially inaccurate or incorrect model and exhibited robust performance by avoiding the highly nonlinear behavior of neural responses, whereas neural simulations were only run once for amplitude scaling in the second step. This significantly reduced computational cost compared to iterative methods using large populations of candidate solutions and more importantly reduced the sensitivity to the choice of neural model. The reduced coil heating and power losses of the optimized pulses can enable rapid-rate monophasic TMS protocols.

Chelated Ion-Exchange Strategy toward BiOCl Mesoporous Single-Crystalline Nanosheets for Boosting Photocatalytic Selective Aromatic Alcohols Oxidation.

The photoresponse and photocatalytic efficiency of bismuth oxychloride (BiOCl) are greatly limited by rapid recombination of photogenerated carriers. The construction of porous single-crystal BiOCl photocatalyst can effectively alleviate this issue and provide accessible active sites. Herein, a facile chelated ion-exchange strategy is developed to synthesize BiOCl mesoporous single-crystalline nanosheets (BiOCl MSCN) using acetic acid and ammonia solution respectively as chelating agent and ionization promoter. The strong chelation between acetate ions and Bi3+ ions introduces acetate ions into the precipitated product to exchange with Cl- ions, resulting in large lattice mismatch, strain release, and formation of void-like mesopores. The prepared BiOCl MSCN photocatalyst exhibits excellent catalytic performance with 99% conversion and 98% selectivity for oxidation of benzyl alcohol to benzaldehyde and superior general adaptability for various aromatic alcohols. The theoretical calculations and characterizations confirm that the superior performance is mainly attributed to the abundant oxygen vacancies, plenty of accessible adsorption/active sites and fast charge transport path without grain boundaries.

Enhanced Metal-Semiconductor Interaction for Photocatalytic Hydrogen-Evolution Reaction.

The selective immobilization of noble metals right at the place where photogenerated electrons migrate through the photodeposition approach is a unique strategy to load cocatalysts on semiconductors for solar hydrogen production. However, a poor metal-semiconductor interaction is often formed, which not only hinders the interfacial charge transfer, but also results in the easy aggregation and shedding of cocatalysts during photocatalytic reactions. Herein, it is demonstrated that the photodeposited ultrafine metals, such as nanosized Au, can be well stabilized on TiO2 nanocrystallines without sintering by employing a sacrificial carbon coating annealing strategy to strengthen the metal-support interaction. Benefiting from the improved interfacial contact between Au and TiO2 for fast charge transfer and the well-preserved size-dependent catalytic behavior of Au nanoparticles toward hydrogen evolution reaction, the annealed Au/TiO2 exhibits a significant enhanced activity toward photocatalytic H2 production with good durability.

Atomistic Observation of Temperature-Dependent Defect Evolution within Sub-stoichiometric WO3-x Catalysts.

Tunable crystalline defects endow WO3-x catalysts with extended functionalities for a broad range of photo- and electric-related applications. However, direct visualization of the defect structures and their evolution mechanism is lacking. Herein, aberration-corrected and in situ transmission electron microscopy was complemented by theoretical calculations to investigate the effect of temperature on the defect evolution behavior during hydrogenation treatment. Low processing temperature (100-300 °C) leads to the occurrence of randomly distributed oxygen vacancies within WO3-x nanosheets. At higher temperatures, oxygen vacancies become highly mobile and aggregate into stacking faults. Planar defects are prone to nucleate at the surface and develop in a zigzag form at 400 °C, while treating at 500 °C promotes the growth of {200}-type stacking faults. Our work clearly establishes that the atomic configuration of the defects in WO3-x samples could be manipulated by regulating the hydrogenation temperature. This study not only expands our understanding of the structure-function relationships of sub-stoichiometric tungsten oxides but also unlocks their full potential as advanced catalysts by tuning stoichiometry in a controlled manner.

Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction.

Tight oil fields are affected by factors such as geology, technology, and development, so it is difficult to directly obtain an accurate recovery rate. The accurate prediction of the recovery rate is very important for measuring reservoir development effects and dynamic analysis. Traditional tight oil recovery predictions are obtained by conventional formula calculations and curve fitting, which are less applicable and very different from actual conditions. Machine learning can make accurate predictions based on a large amount of data, so it is used to predict the recovery rate of tight oil reservoirs. The recovery rate of 200 wells in M tight oil reservoirs ranges widely between 8.8 and 27.6%, with more than 14 factors affecting the recovery rate, and the overall declining rule is not clear. Therefore, this research combines the production data of horizontal wells with random forest, support vector regression (SVR), and other methods, establishing recovery prediction models to gain more accurate recovery predictions. First, the Pearson correlation coefficient and the random forest (RF) machine learning method are used to measure and calculate the degree of nonlinear influence of factors on oil well recovery. Second, SVR and optimization of support vector regression by particle swarm (PSO-SVR) recovery prediction models are developed and tested, with 75% of the data being used to train SVR and PSO-SVR recovery prediction models and 25% to verify the model. Third, the accuracy of the results of these two SVR oil recovery prediction models is compared, suggesting that when the data are scarce, the optimized model is more accurate than the unoptimized one by 10.85%. Thus, this model can assure a relatively more accurate prediction of oil recovery. Machine learning recovery prediction, being more accurate and applicable, enables the data of factors such as construction and production systems to be optimized in the future, enhancing the oil recovery rate.

On-Surface Polymerization of In-Plane Highly Ordered Carbon Nitride Nanosheets toward Photocatalytic Mineralization of Mercaptan Gas.

2D carbon nitride nanosheets have attracted ever-increasing interest in photocatalysis due to their unique structural advantages. However, the nanosheets synthesized by the traditional methods, such as post oxidation and liquid exfoliation, have suffered from in-plane disorder with abundant structural defects, which seriously counteracts their structural benefits for photocatalysis. Herein, it is demonstrated that polymer carbon nitride nanosheets with in-plane highly ordered structure (PCNNs-IHO) can be successfully prepared by on-surface polymerization of melamine on NaCl crystal surface at elevated temperatures. The NaCl crystals with relative high surface energy not only facilitate the adsorption and activation of melamine to undergo condensation reaction, but also function as unique substrates to orientate the assembly of 2D nanosheet structure. In addition, NaCl also acts as a reactant to provide Na+ doping into carbon nitride matrix, affording PCNNs-IHO with robust structural base sites. Benefiting from this structural basicity, PCNNs-IHO exhibits superior photocatalytic performance toward CH3 SH mineralization under visible light irradiation.

Efficient degradation of tetracycline hydrochloride by photocatalytic ozonation over Bi2WO6.

Photocatalytic ozonation technique for wastewater treatment has received much attention for their efficient capability in the mineralization of persistent organic pollutants. In this study, nanostructured Bi2WO6 was prepared by hydrothermal method and applied in the photocatalytic ozonation process for tetracycline hydrochloride (TCH) degradation under simulated solar light irradiation. Bi2WO6 triggered an effective synergy between photocatalysis and ozonation, and it showed a good activity and adaptability in the degradation of organic compounds. Besides, the influence of experimental factors on the total organic carbon removal (including catalyst dosage, ozone concentration, initial pH, reaction temperature and coexisting ions) was also investigated comprehensively. Spin-trapping electron paramagnetic resonance measurements and quenching experiments demonstrated that O2-, OH, 1O2 and h+ contributed to TCH degradation. The possible degradation pathways of TCH were proposed by identifying the intermediates with liquid chromatography-mass spectroscopy.

Molten salt assisted assembly growth of atomically thin boron carbon nitride nanosheets for photocatalytic H2 evolution.

Atomic-level boron carbon nitride nanosheets (BCNNS) have been prepared by a molten salt assisted assembly growth strategy, which effectively promotes the solvation of precursors, minimizes the surface energy and prevents the aggregation of layers. The as-synthesized BCNNS have atomic layered thickness and large lateral size, and show enhanced visible light H2 evolution activity compared to bulk BCN.

Titania-Samarium-Manganese Composite Oxide for the Low-Temperature Selective Catalytic Reduction of NO with NH3.

A novel Ti-doped Sm-Mn mixed oxide (TiSmMnOx) was first designed for the selective catalytic reduction (SCR) of NOx with NH3 at a low temperature. The TiSmMnOx catalyst exhibited a superior catalytic performance, in which NOx conversion higher than 80% and N2 selectivity above 90% could be achieved in a wide-operating temperature window (60-225 °C). Specially, the catalyst also showed high durability against the large space velocity and excellent SO2/H2O resistance. Ti incorporation can efficiently inhibit MnOx crystallization and tune the MnOx phase during calcination at a high temperature. Subsequently, a high specific surface area as well as an increased amount of acid sites on the TiSmMnOx catalysts were produced. Further, the reducibility of the Sm-doped MnOx catalyst was modulated, facilitating NO oxidation and inhibiting NH3 nonselective oxidation. Consequently, a superior SCR activity was achieved at a low temperature and the operating temperature window of the TiSmMnOx catalyst was significantly widened. These findings may provide new insights into the reasonable design and development of the new non-vanadium catalysts with a high NH3-SCR activity for industrial application.

Capturing Maize Stand Heterogeneity Across Yield-Stability Zones Using Unmanned Aerial Vehicles (UAV).

Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts. Processing techniques applied on images captured by unmanned aerial vehicles (UAVs) has been used successfully to identify crop rows and estimate stand density and, most recently, to estimate plant-to-plant interval distance. Here, we further test and apply an image processing algorithm on UAV images collected from yield-stability zones in a commercial crop field. Our objective was to implement the algorithm to compare variation of plant-spacing intervals to test whether yield differences within these zones are related to differences in crop stand characteristics. Our analysis indicates that the algorithm can be reliably used to estimate plant counts (precision >95% and recall >97%) and plant distance interval (R2 ~0.9 and relative error <10%). Analysis of the collected data indicated that plant spacing variability differences were small among plots with large yield differences, suggesting that it was not a major cause of yield variability across zones with distinct yield history. This analysis provides an example of how plant-detection algorithms can be applied to improve the understanding of patterns of spatial and temporal yield variability.

Taming the stability of Pd active phases through a compartmentalizing strategy toward nanostructured catalyst supports.

The design and synthesis of robust sintering-resistant nanocatalysts for high-temperature oxidation reactions is ubiquitous in many industrial catalytic processes and still a big challenge in implementing nanostructured metal catalyst systems. Herein, we demonstrate a strategy for designing robust nanocatalysts through a sintering-resistant support via compartmentalization. Ultrafine palladium active phases can be highly dispersed and thermally stabilized by nanosheet-assembled γ-Al2O3 (NA-Al2O3) architectures. The NA-Al2O3 architectures with unique flowerlike morphologies not only efficiently suppress the lamellar aggregation and irreversible phase transformation of γ-Al2O3 nanosheets at elevated temperatures to avoid the sintering and encapsulation of metal phases, but also exhibit significant structural advantages for heterogeneous reactions, such as fast mass transport and easy access to active sites. This is a facile stabilization strategy that can be further extended to improve the thermal stability of other Al2O3-supported nanocatalysts for industrial catalytic applications, in particular for those involving high-temperature reactions.

Yield stability analysis reveals sources of large-scale nitrogen loss from the US Midwest.

Loss of reactive nitrogen (N) from agricultural fields in the U.S. Midwest is a principal cause of the persistent hypoxic zone in the Gulf of Mexico. We used eight years of high resolution satellite imagery, field boundaries, crop data layers, and yield stability classes to estimate the proportion of N fertilizer removed in harvest (NUE) versus left as surplus N in 8 million corn (Zea mays) fields at subfield resolutions of 30 × 30 m (0.09 ha) across 30 million ha of 10 Midwest states. On average, 26% of subfields in the region could be classified as stable low yield, 28% as unstable (low yield some years, high others), and 46% as stable high yield. NUE varied from 48% in stable low yield areas to 88% in stable high yield areas. We estimate regional average N losses of 1.12 (0.64-1.67) Tg N y-1 from stable and unstable low yield areas, corresponding to USD 485 (267-702) million dollars of fertilizer value, 79 (45-113) TJ of energy, and greenhouse gas emissions of 6.8 (3.4-10.1) MMT CO2 equivalents. Matching N fertilizer rates to crop yield stability classes could reduce regional reactive N losses substantially with no impact on crop yields, thereby enhancing the sustainability of corn-based cropping systems.

Mapping Tobacco Fields Using UAV RGB Images.

Tobacco planting information is an important part of tobacco production management. Unmanned aerial vehicle (UAV) remote sensing systems have become a popular topic worldwide because they are mobile, rapid and economic. In this paper, an automatic identification method for tobacco fields based on UAV images is developed by combining supervised classifications with image morphological operations, and this method was used in the Yunnan Province, which is the top province for tobacco planting in China. The results show that the produce accuracy, user accuracy, and overall accuracy of tobacco field identification using the method proposed in this paper are 92.59%, 96.61% and 95.93%, respectively. The method proposed in this paper has the advantages of automation, flow process, high accuracy and easy operation, but the ground sampling distance (GSD) of the UAV image has an effect on the accuracy of the proposed method. When the image GSD was reduced to 1 m, the overall accuracy decreased by approximately 10%. To solve this problem, we further introduced the convolution method into the proposed method, which can ensure the recognition accuracy of tobacco field is above 90% when GSD is less than or equal to 1 m. Some other potential improvements of methods for mapping tobacco fields were also discussed in this paper.

Bioinspired cobalt cubanes with tunable redox potentials for photocatalytic water oxidation and CO2 reduction.

The development of efficient, robust and earth-abundant catalysts for photocatalytic conversions has been the Achilles' heel of solar energy utilization. Here, we report on a chemical approach based on ligand designed architectures to fabricate unique structural molecular catalysts coupled with appropriate light harvesters (e.g., carbon nitride and Ru(bpy)32+) for photoredox reactions. The "Co4O4" cubane complex Co4O4(CO2Me)4(RNC5H4)4 (R = CN, Br, H, Me, OMe), serves as a molecular catalyst for the efficient and stable photocatalytic water oxidation and CO2 reduction. A comprehensive structure-function analysis emerged herein, highlights the regulation of electronic characteristics for a molecular catalyst by selective ligand modification. This work demonstrates a modulation method for fabricating effective, stable and earth-abundant molecular catalysts, which might facilitate further innovation in the function-led design and synthesis of cubane clusters for photoredox reactions.

Total Oxidation of Propane over a Ru/CeO2 Catalyst at Low Temperature.

Ruthenium (Ru) nanoparticles (∼3 nm) with mass loading ranging from 1.5 to 3.2 wt % are supported on a reducible substrate, cerium dioxide (CeO2, the resultant sample is called Ru/CeO2), for application in the catalytic combustion of propane. Because of the unique electronic configuration of CeO2, a strong metal-support interaction is generated between the Ru nanoparticles and CeO2 to stabilize Ru nanoparticles for oxidation reactions well. In addition, the CeO2 host with high oxygen storage capacity can provide an abundance of active oxygen for redox reactions and thus greatly increases the rates of oxidation reactions or even modifies the redox steps. As a result of such advantages, a remarkably high performance in the total oxidation of propane at low temperature is achieved on Ru/CeO2. This work exemplifies a promising strategy for developing robust supported catalysts for short-chain volatile organic compound removal.

Estimating plant distance in maize using Unmanned Aerial Vehicle (UAV).

Distance between rows and plants are essential parameters that affect the final grain yield in row crops. This paper presents the results of research intended to develop a novel method to quantify the distance between maize plants at field scale using an Unmanned Aerial Vehicle (UAV). Using this method, we can recognize maize plants as objects and calculate the distance between plants. We initially developed our method by training an algorithm in an indoor facility with plastic corn plants. Then, the method was scaled up and tested in a farmer's field with maize plant spacing that exhibited natural variation. The results of this study demonstrate that it is possible to precisely quantify the distance between maize plants. We found that accuracy of the measurement of the distance between maize plants depended on the height above ground level at which UAV imagery was taken. This study provides an innovative approach to quantify plant-to-plant variability and, thereby final crop yield estimates.

Drought evaluation using the GRACE terrestrial water storage deficit over the Yangtze River Basin, China.

Droughts are some of the worst natural disasters that bring significant water shortages, economic losses, and adverse social consequences. Gravity Recovery and Climate Experiment (GRACE) satellite data are widely used to characterize and evaluate droughts. In this work, we evaluate drought situations in the Yangtze River Basin (YRB) using the GRACE Texas Center for Space Research (CSR) mascon (mass concentration) data from 2003 to 2015. Drought events are identified by water storage deficits (WSDs) derived from GRACE data, while the drought severity evaluation is based on the water storage deficit index (WSDI), standardized WSD time series, and total water storage deficit (TWSD). The WSDI is subsequently compared with the Palmer drought severity index (PDSI), standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized runoff index (SRI). The results indicate the YRB experienced increased wetness during the study period, with WSD values increasing at a rate of 5.20mm/year. Eight drought events are identified, and three major droughts occurred in 2004, 2006, and 2011, with WSDIs of -2.05, -2.38, and -1.30 and TWSDs of -620.96mm, -616.81mm, and -192.44mm, respectively. Our findings suggest that GRACE CSR mascon data can be used effectively to assess drought features in the YRB and that the WSDI facilitates robust and reliable characterization of droughts over large-scale areas.

Fibers with Hyper-Crosslinked Functional Porous Frameworks.

The incorporation of robust porous frameworks into polymer fibers with handleable morphologies and flexible chemical compositions exhibits significant advantages for device fabrication in a wide range of applications. However, the soft linear polymeric chains of the fibers make the generation of nanopores extremely challenging. Herein, a facile synthetic strategy based on a combination of functional monomer grafting and hyper-crosslinking technology is developed for the porous engineering of polymeric fibers. In this methodology, the nanoporous framework originating from the hyper-crosslinking of aromatic monomers is covalently grafted onto fibers, which is beneficial to retaining their unique fiber morphology and to preserving their excellent mechanical properties. Moreover, this promising protocol can be further extended to the porous functionalization of polymeric matrices with diverse morphologies for target-specific applications.