Biological oyster shell waste enhances polyphenylene sulfide composites and endows them with antibacterial properties

To date,there is no research that deals with biological waste as fillers in polyphenylene sulfide(PPS).In this study,oyster shells were recycled and treated to prepare thermally-treated oyster shells(TOS),which were used as PPS fillers to make new bio-based antibacterial composite materials.The effect of varying the content of TOS was studied by means of structure and performance characterization.PPS/TOS composites were demonstrated to have an antibacterial effect on the growth of E coli and S.aureus.Qualitative analysis showed that when the TOS content was≥30%and 40%,the composite materials had an apparent inhibition zone.Quantitative analysis showed that the antibacterial activity increased with the TOS content.Fourier transform infrared spectroscopy indicated the formation of hydrogen bonds between the molecular chains of TOS and PPS and the occurrence of a coordination reaction.At 10%TOS,the composite tensile strength reached a maximum value of 72.5 MPa,which is 9.65%higher than that of pure PPS.The trend of bending properties is the same as that of tensile properties,showing that the maximum property was reached for the composite with 10%TOS.At the same time,the crystallinity and contact angle were the highest,and the permeability coefficient was the lowest.The fatigue test results indicated that for the composite with 10%TOS,the tensile strength was 23%lower than static tensile strength,and the yield strength was 10%lower than the static yield strength.The results of the study showed that TOS not only could reduce the cost of PPS,but also could impart antibacterial properties and enhance the mechanical and,barrier properties,the thermostability,as well as the crystallinity.

A Model Average Algorithm for Housing Price Forecast with Evaluation Interpretation

In the field of computer research,the increase of data in result of societal progress has been remarkable,and the management of this data and the analysis of linked businesses have grown in popularity.There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices.Using regression methods in machine learning to segment the data set,examine the major factors affecting it,and forecast home prices is the most popular method for examining pricing information.It is challenging to generate precise forecasts since many of the regression models currently being utilized in research are unable to efficiently collect data on the distinctive elements that correlate y with a high degree of house price movement.In today’s forecasting studies,ensemble learning is a very prevalent and well-liked study methodology.The regression integration computation of large housing datasets can use a lot of computer resources as well as computation time,and ensemble learning uses more resources and calls for more machine support in integrating diverse models.The Average Model suggested in this paper uses the concept of fusion to produce integrated analysis findings from several models,combining the best benefits of separate models.The Average Model has a strong applicability in the field of regression prediction and significantly increases computational efficiency.The technique is also easier to replicate and very effective in regression investigations.Before using regression processing techniques,this work creates an average of different regression models using the AM(Average Model)algorithm in a novel way.By evaluating essential models with 90%accuracy,this technique significantly increases the accuracy of house price predictions.The experimental results show that the AM algorithm proposed in this paper has lower prediction error than other comparison algorithms,and the prediction accuracy is greatly improved compared with other algorithms,and has a good experimental effect in house price prediction.

A flow cytometer based protocol for quantitative analysis of bloom-forming cyanobacteria(Microcystis) in lake sediments

A quantitative protocol for the rapid analysis of Microcystis cells and colonies in lake sediment was developed using a modified flow cytometer, the CytoSense. For cell enumeration, diluted sediment samples containing Microcystis were processed with sonication to disintegrate colonies into single cells. An optimized procedure suggested that 5 mg dw （dry weight）/mL dilution combined with 200 W x 2 min sonication yielded the highest counting efficiency. Under the optimized determination conditions, the quantification limit of this protocol was 3.3x104 cells/g dw. For colony analysis, Microcystis were isolated from the sediment by filtration. Colony lengths measured by flow cytometry were similar to those measured by microscopy for the size range of one single cell to almost 400 ~tm in length. Moreover, the relationship between colony size and cell number was determined for three Microcystis species, including Microcystisflos-aquae, M. aeruginosa and M. wessenbergii. Regression formulas were used to calculate the cell numbers in different- sized colonies. The developed protocol was applied to field sediment samples from Lake Taihu. The results indicated the potential and applicability of flow cytometry as a tool for the rapid analysis of benthic Microcystis. This study provided a new capability for the high frequency monitoring of benthic overwintering and population dynamics of this bloom-forming cyanobacterium.

Efficient photocatalytic hydrogen evolution coupled with benzaldehyde production over 0D Cd_(0.5)Zn_(0.5)S/2D Ti_(3)C2 Schottky heterojunction

Converting water into hydrogen fuel and oxidizing benzyl alcohol to benzaldehyde simultaneously under visible light illumination is of great significance,but the fast recombination of photogenerated carriers in photocatalysts seriously decreases the conversion efficiency.Herein,a novel dual-functional 0D Cd_(0.5)Zn_(0.5)S/2D Ti_(3)C2 hybrid was fabricated by a solvothermally in-situ generated assembling method.The Cd_(0.5)Zn_(0.5)S nano-spheres with a fluffy surface completely and uniformly covered the ultrathin Ti_(3)C2 nanosheets,leading to the increased Schottky barrier(SB)sites due to a large contact area,which could accelerate the electron–hole separation and improve the light utilization.The optimized Cd_(0.5)Zn_(0.5)S/Ti_(3)C2 hybrid simultaneously presents a hydrogen evolution rate of 5.3 mmol/(g·h)and a benzaldehyde production rate of 29.3 mmol/(g·h),which are~3.2 and 2 times higher than those of pristine Cd_(0.5)Zn_(0.5)S,respectively.Both the multiple experimental measurements and the density functional theory(DFT)calculations further demonstrate the tight connection between Cd_(0.5)Zn_(0.5)S and Ti_(3)C2,formation of Schottky junction,and efficient photogenerated electron–hole separation.This paper suggests a dual-functional composite catalyst for photocatalytic hydrogen evolution and benzaldehyde production,and provides a new strategy for preventing the photogenerated electrons and holes from recombining by constructing a 0D/2D heterojunction with increased SB sites.

A new localization theory of adaptive machining of near-net-shape blades

In the fabrication of aero-engine blades,a great deal is gained when massive material removal is avoided at the end of the process,and as little as possible material is left on the blade billet.Due to the uncertainty of pre-process,the billet shapes are inconsistent.Sometimes,the near-net-shape billet doesn’t cover the blade design surface to be cut.Therefore,blade localization is necessary for these billets before the machining.In conventional localization methods,the design surface’s location focused on guaranteeing enough material to be cut.However,because the to-becut surface is in near-net and free-form shape,it is difficult to find a valid localized surface model to generate the tool path.Different from the localized surface is taken as rigid in previous investigation,it is allowed to deviate from the design surface no more than the tolerance band.In term of this principle,the tolerance band is utilized to promote localization ability.A series of optimization models with different priorities is established to avoid the abandonment expensive blade billet.Finally,with the experiments performed on the near-net-shape blades,the blade localization theory and the promotion of localization ability are verified.

Searches for Dark Matter via Mono-W Production in Inert Doublet Model at the LHC

The Inert Doublet Model(IDM) is one of the many beyond Standard Model scenarios with an extended scalar sector, which provide a suitable dark matter particle candidate. Dark matter associated visible particle production at high energy colliders provides a unique way to determine the microscopic properties of the dark matter particle. In this paper, we investigate that the mono-W + missing transverse energy production at the Large Hadron Collider(LHC),where W boson decay to a lepton and a neutrino. We perform the analysis for the signal of mono-W production in the IDM and the Standard Model(SM) backgrounds, and the optimized criteria employing suitable cuts are chosen in kinematic variables to maximize signal significance. We also investigate the discovery potential in several benchmark scenarios at the 14 TeV LHC. When the light Z_2 odd scalar higgs of mass is about 65 GeV, charged Higgs is in the mass range from 120 GeV to 250 GeV, it provides the best possibility with a signal significance of about 3σ at an integrated luminosity of about 3000 fb^(-1).