Search

• A robust model was developed and validated for evaluation of sorghum quality. • Isolated forest and CARS-SPA were conducive to the optimization of the model. • The average CRR of the model in identifying sorghum varieties was greater than 91%. • The deviation of the predicted mixing ratio of adulterated samples was less than 4%. This study combined hyperspectral imaging (HSI) and deep forest (DF) to develop a reliable model for conducting a rapid and nondestructive determination of sorghum purity. Isolated forest (IF) algorithm and principal component analysis (PCA) were used to remove the abnormal data of sorghum grains. Competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA) were combined and used to extract the characteristic wavelengths. Gray-level co-occurrence matrix (GLCM) was used to extract the textural features. DF models were established based on the different types of data. Specifically, the DF models established using the characteristic spectra produced the best recognition results: the average correct recognition rate (CRR) of the models was greater than 91%. In addition, the average CRR of validation set Ⅰ was 88.89%. These results show that a combination of HSI and DF could be used for the rapid and nondestructive determination of sorghum purity. [ABSTRACT FROM AUTHOR]

Abstract: Due to the aggregation or restacking of graphene and the weak interactions between pristine graphene and polymeric matrices, a method to chemically functionalize graphene nanosheets by silylation was developed in this study. The silanized graphene oxide sheets were reduced to produce well water-soluble graphene derivatives after graphene oxide sheets silanized with 3-Methacryloxypropyltrimethoxysilane (MPS). The MPS-graphene oxide (MPS-GO) and MPS-reduced graphene oxide (MPS-RGO) could be redispersed in polar solvents, which could facilitate their incorporation into poly(methyl methacrylate) (PMMA) through in situ copolymerization method. The mechanical properties of the nanocomposites were studied after fabrication of the graphene/PMMA film through hot press process. And the MPS-RGO/PMMA composites with 0.5wt.% of MPS-RGO incorporation lead to a ∼44% increases in tensile strength than that of the reduced graphene oxide (RGO)/PMMA composites. [Copyright &y& Elsevier]

Transition metal-doped graphene single-atom catalysts exhibit great application prospects for catalyzing hydrogen evolution applications. However, the precise timing and control of the activity of central atoms is a key problem to ensure the further development of catalysts. Revealing the correlation between the catalytic activity of the central atom and the coordination environment (atomic species, coordination number, and geometric configuration) is regarded as quite crucial for solving this key problem. Along these lines, in this work, Fe as the central atom and N, S, and P doped porous graphene (DPG) as the matrix material were used to systematically study the correlation between the coordination environment and the stability, catalytic activity of the catalyst. From the acquired results, it was demonstrated that: (1) The binding strength between the Fe atom and the N-atom DPG is the highest, and the anchoring strength of the Fe atom on two-element doping doped graphene is relatively low. Under the tetrad coordination condition, the catalyst doped with S and P atoms showed excellent performance in hydrogen evolution and water activation. A moderate correlation between the hydrogen evolution performance (ΔGH∗, E A , and ΔGOH∗) and d -band electronic properties (d -CU and d -SE) under single element doping (four and three coordination) was also detected. In addition, a certain correlation between the Fe atom outer-shell electron (3d 4s) and the hydrogen evolution performance was found. A weak correlation between the electronic properties of the double element doped graphene and the hydrogen evolution performance was also confirmed from our simulations. The results are of great significance for the regulation of the single-atom catalyst activity. [Display omitted] • The anchoring strength of the S or P doped PG to Fe atom is lower than N-doped. • The S/P doped PG with tetrad coordination showed excellent activity catalysts. • The catalytic activity showed moderate correlation with d band electronic properties. [ABSTRACT FROM AUTHOR]

• CF was used to predict the contents of amylose and amylopectin in sorghum. • PCA-SPA was used to extract characteristic wavelengths corresponding to amylose and amylopectin. • The average RPD values of the CF models were both greater than 4.7. • Accurate recognition for the varieties of sorghum grains was realized. • This study provides a new method for the starch content detection of cereals. The contents of amylose and amylopectin in sorghum directly affects the quality and yield of liquor. Hyperspectral imaging (HSI) is an emerging technology widely applied in the content analysis of food ingredients. In this study, the effects of different preprocessing methods on visible-light and near-infrared spectral data were analyzed, and the prediction accuracies of these spectral data were compared. Principal components analysis (PCA) and successive projections algorithm (SPA) were combined to extract the characteristic wavelengths. Using both the full and characteristic wavelengths, partial least square regression (PLSR) and cascade forest (CF) models were developed to predict the contents of amylose and amylopectin in different varieties of sorghum. The average RPD values of the CF models established by the characteristic wavelengths were 4.7622 and 5.5889, respectively. These results corroborated the utility of HSI in achieving the rapid and nondestructive prediction of amylose and amylopectin contents in different varieties of sorghum. [ABSTRACT FROM AUTHOR]

Scorodites are commonly used for arsenic immobilization, and it is also the main component of arsenic bearing tailings. Alkali-activated geopolymers are commonly used to landfill arsenic-bearing minerals. However, there no previous studies have explored the interaction between geopolymer molecules and the surface of scorodite. In this paper, Si(OH) 4 as a monomer molecule of geopolymer, the mechanism of adsorption and 'ion exchange' between Si(OH) 4 molecule and the surface of scorodite during alkali-activation is studied. Results show that the Fe-terminated scorodite (010) surface has high stability. Si(OH) 4 are more easily adsorbed on the hollow site of an Fe-terminated scorodite (010) surface, which is described as chemisorption. Compared with Si(OH) 4 , NaOH is easier to adsorb on an Fe-terminated scorodite (010) surface. The co-adsorption of NaOH and Si(OH) 4 on the Fe-terminated scorodite (010) surface was studied, and also belongs to chemical adsorption. When the hydroxyl binds to the As atom, the adsorbed Si(OH) 4 is more likely to undergo an 'ion exchange' reaction with the surface, and the reaction is barrierless. The intermediate As(OH) 4 produced by the 'ion exchange' reaction can be deprotonated to form an arsenate molecule, which can occur spontaneously. This work reveals that the interaction mechanism of geopolymer molecules on surface of scorodite. [Display omitted] • The stability of different surfaces of scorodites was studied. • The adsorption of geopolymer Si(OH) 4 molecules and NaOH on the surface was studied, and the co-adsorption of the two was studied. • The replacement reaction between the surface adsorbed molecules and the surface atoms was studied. • The ion exchange reaction between the coating material and the scorodites was demonstrated. [ABSTRACT FROM AUTHOR]

• First time HSI was used to detect adulterated sorghum. • Watershed algorithm could effectively segment sorghum grains. • PCA and CA could effectively find abnormal samples in a single sorghum variety. • PLS-DA model could rapidly and nondestructively detect sorghum adulteration. • The distribution map of the sorghum was obtained through visualization. This paper proposes a sorghum adulteration detection model using hyperspectral imaging technology (HSI), image processing technology, and multivariate analysis technology. The model used a watershed algorithm to extract hyperspectral data from sorghum grains. Principal component analysis (PCA) and clustering analysis (CA) were used to remove abnormal samples of sorghum. Partial least squares discriminant analysis (PLS-DA) was used to identify the variety of sample, and a sorghum distribution map and adulteration ratios were obtained by marking varieties with different colors. This paper presents, for the first time, HSI use for identification of adulteration in sorghum using PCA and CA. Accuracy of the model identification for the validation set reached 96%, and for the adulterated samples reached 91%, and comprehensive accuracy of the model could reach more than 90%. These results show that the model can rapidly and nondestructively detect sorghum adulteration. [ABSTRACT FROM AUTHOR]

In this study, arsenopyrite leaching test was conducted by sodium hydroxide solution with pH = 10, 12, and 13.5. The electrochemical oxidation behavior of arsenopyrite in sodium hydroxide solution at pH = 10, 12, and 13.5 was analyzed by cyclic voltammetry and electrochemical impedance spectroscopy. The elemental composition and chemical state of arsenopyrite after surface oxidation was investigated by X-ray photoelectron spectroscopy. The hydroxylation and electronic configuration of surface atoms of arsenopyrite were calculated by density functional theory. The concentration of Fe 3+ in the solution was found to increase by one order of magnitude with increasing pH of the sodium hydroxide solution. The total As content in the solution also increases with increase in pH value as a result of sharp increase in the ratio of As (Ⅲ)/ As (Ⅴ). In sodium hydroxide solution with pH = 10, the surface of arsenopyrite undergoes two oxidation stages. At pH = 12 and 13.5, the second stage (oxidation of the S-rich zone) disappears. The hydroxylation of Fe and As atoms on the arsenopyrite surface is the first step in the oxidation process. As atoms are then oxidized to As (Ⅲ) and enter the solution while Fe and S atoms form Fe(OH) 3 and S-rich zone covering the arsenopyrite surface. Finally, As (Ⅲ) in the solution is oxidized to As (Ⅴ), and the S-rich zone is oxidized to SO 4 2−. The oxidation dissolution rate of arsenopyrite increases sharply with increasing pH. The research results have important theoretical significance for the backfilling process of tailings containing arsenopyrite. • The oxidation behavior of arsenopyrite in alkaline solution was studied. • The oxide layer Fe(OH) 3 does not inhibit the further oxidation of arsenopyrite. • The hydroxylation sequence of the atoms on arsenopyrite surface was studied. [ABSTRACT FROM AUTHOR]

[Display omitted] • The chemical and geometric environment of each atom on arsenopyrite (0 0 1) surface was analyzed. • Physisorption of Si(OH) 4 on surface of arsenopyrite (0 0 1). • Physisorption and chemisorption of Al(OH) 4 exist simultaneously on surface of arsenopyrite (0 0 1). As a common arsenic-bearing mineral, arsenopyrite is highly abundant in most nonferrous metal tailings. Therefore, arsenic is leached into the environment from the stacking of tailings by rainwater and natural weathering. Using a geopolymer method to backfill tailings is a promising method to prevent leaching of arsenic from these tailings. Whether a geopolymer can effectively seal arsenopyrite is the key to preventing arsenic from entering the environment. Using Si(OH) 4 and Al(OH) 4 as geopolymer monomers, the adsorption energies, geometries, charge transfer, and density of states (DOS) of Si(OH) 4 and Al(OH) 4 on arsenopyrite crystal (0 0 1) surface were studied. The results indicated that the adsorption sites of Si(OH) 4 were mainly H atoms, mostly by means of physical adsorption. Si(OH) 4 was more easily adsorbed onto the S2 atom site. The Al(OH) 4 molecule used the H atom and O atoms as the adsorption sites. The H atom as the adsorption site was characterized as physical adsorption, whereas the O atom as the adsorption site was characterized as chemical adsorption. Based on the DOS and population analysis results, there was a strong tendency for orbital hybridization and bonding between the oxygen atoms in Al(OH) 4 molecules and the As and Fe atoms on the (0 0 1) surface. Compared to Si(OH) 4 , Al(OH) 4 had a stronger affinity for the crystal surface. These results suggest that it is theoretically feasible to use a geopolymer method to seal and backfill arsenopyrite. [ABSTRACT FROM AUTHOR]

Wheat is the main raw material for brewing Chinese liquor, and differences in the wheat varieties and mixing ratio will affect its quality and flavor. In this study, hyperspectral imaging (HSI) was combined with ensemble learning models for the classification and determination of the mixing ratio of wheat. The spectral information and textural and shape features of each wheat grain were respectively extracted. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to remove abnormal data, and Savitzky-Golay combined multiplicative scatter correction (SG-MSC) was used to pre-process the spectra of the wheat samples. The characteristic wavelengths were then extracted using the competitive adaptive reweighted sampling (CARS) algorithm, and the classification effects of BP-Adaboost models were compared when using feature spectral data, image features, and fusion data as the input. The recognition effects and visualization of the validation set proved the optimal classification of feature spectral data fused with shape features; the average accuracy was 92.29% and the maximum deviation range of mixing ratio prediction was 5%. With the addition of wheat classification categories, this method still achieved excellent results. The results prove the feasibility of using fusion data with HSI combined with ensemble learning models for the classification and mixing ratio detection of wheat. • Classification and determination of the adulteration ratio of wheat by fusing HSI spectral and image features. • An ensemble learning model was developed and validated for identification of wheat varieties. • The average ACC of the model in identifying 3 varieties of wheat grains was greater than 92%. • The deviation of the predicted mixing ratio of adulterated samples was less than 5%. [ABSTRACT FROM AUTHOR]

Improving volumetric capacitance of supercapacitor is important and challenging for practical application. Here, porous graphene oxide (PGO) is prepared through an efficient method at room temperature after etched by Zn(CH 3 COO) 2 in several minutes. The obtained densely packed porous graphene film (PGF) was constructed by a template-assisted method. And the perforated PGF electrode material exhibits a high volumetric capacitance (C V ) of 318.8 F cm −3 in 6.0 M KOH electrolyte at the current density of 1 A g −1 . In addition, it exhibited excellent cycling stability, that is 88% retention of capacitance after 10000 charge/discharge cycles in the current density of 5 A g −1 . Furthermore, it demonstrates a capacitance of 219.0 F cm −3 (1 A g −1 ) and a cycling stability of 113% (10000 charge/discharge cycles at 5 A g −1 ) after assembled into symmetric supercapacitor. So the PGF is promising to be applied in the high performance energy storage devices with lighter quality and smaller volume. [ABSTRACT FROM AUTHOR]

• First-time fusion of spectral and color features of HSI to predict the total acid content (TAC) and reducing sugar content (RSC) of fermented grains. • CARS-SPA and RC-SPA were conductive to the optimization of the detection model. • For TAC and RSC, the RPD value of the model was greater than 19. • This study provides a new method for the rapid and accurate detection of total acid and reducing sugar contents in fermented grains. Total acid content (TAC) and reducing sugar content (RSC) are important evaluation indicators for the quality of fermented grains. In this study, the TAC and RSC of fermented grains were quantified using hyperspectral imaging (HSI). Two combined algorithms were used to extract the characteristic wavelengths of TAC and RSC. Nine color features of fermented grains were extracted based on H, S and V color channels. Multivariate analytical models were developed to predict TAC and RSC using full wavelengths, characteristic wavelengths, color features and fused data, respectively. The CF model established based on characteristic wavelengths extracted by CARS-SPA showed the best results in predicting TAC. Meanwhile, the PSO-SVR model built using fused data was the best model for predicting RSC. The visualization of the TAC and RSC was achieved using the optimal models. These results show that HSI can achieve non-destructive detection and visualization of TAC and RSC in fermented grains. [ABSTRACT FROM AUTHOR]

• N, S co-doped graphene could effectively improve the structural stability of NCS. • NCS-G 3 electrode exhibits a specific capacitance of 1145 F g−1. • The asymmetric supercapacitor device demonstrates the high energy density of 33.8 Wh kg−1. • The asymmetric supercapacitor exhibits good cycling performance. With high electrochemical activity and good energy storage performance, bimetallic pseudocapacitive materials of Ni-Co sulfides have been extensively and deeply researched. However, the low electrical conductivity, irreversible reduction reaction and structural instability significantly limit the practical application of bimetallic NiCo 2 S 4. In this work, NiCo 2 S 4 @N, S co-doped graphene (NCS-G) composite material with high electrochemical performance is prepared by an efficient chemical precipitation assisted hydrothermal method. As the ideal skeleton and excellent conductive network, N, S co-doped graphene could effectively improve the structural stability of the as-prepared NCS-G, thus increasing the utilization and efficient electron transfer of NCS. Notably, the NiCo 2 S 4 could not only provide numerous reaction sites, but also avoid the re-stacking of graphene sheets. Owing to the above merits, the NCS-G 3 electrode exhibits a maximum specific capacitance of 1145 F g−1 (at 0.5 A g−1). The rate performance of the electrode retains 91.7% at the current density of 10 A g−1 compared with that of 0.5 A g−1. Furthermore, consisting of the graphene anode and the NCS-G 3 cathode, the asymmetric SC device demonstrates the outstanding energy density of 33.8 Wh kg−1 at the power density of 799.8 W kg−1 and good cycling performance of 74.5% after 5000 cycles at 2 A g−1. The synthesis strategy exploited in this work endows the electrode with marvelous rate performance, providing a new pathway to engineer the promising electrode material and manufacture high-performance energy storage devices. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: The current energy crisis greatly affects worldwide economic development. Therefore, identifying for new energy resources is critically important. In this study, we introduce a potential biodiesel source: Chrysomya megacephala (Fabricius) larvae (CML), which are proliferative and can be fed with a variety of low cost materials, such as manure, wheat bran, rotted meat and decayed vegetation. The potential of C. megacephala (Fabricius) larvae oil (CMLO) for biodiesel applications was explored. Oil was extracted from the CML raised by feeding on restaurant garbage for five days. The oil content obtained from the dehydrated CML ranged from 24.40% to 26.29% since restaurant garbage varies in composition day to day. The acid value of the CMLO was tested to be 1.10mgKOH/g. Four factors were subsequently considered to optimize the transesterification of CMLO to biodiesel. The optimized conditions included a 6:1 methanol to oil molar ratio, 1.6% KOH catalyst, a reaction temperature of 55°C and a reaction time of 30min. Under these conditions, the maximum yield of fatty acid methyl esters (FAME) from CMLO was 87.71%. Finally, properties of the FAME were within the specifications of ASTM D6751 and EN 14214 biodiesel standards. Therefore, we concluded that C. megacephala (Fabricius) larvae represent a potential alternative feedstock for biodiesel production. [Copyright &y& Elsevier]

• The TGOP-Na is synthesized based on an efficient chemical route. • The TGOP-Na electrode delivers a high volumetric capacitance of 469 F cm−3. • The TGOP-Na based supercapacitor exhibits a high volumetric energy density of 24.2 Wh L−1. • The supercapacitor can operate stably over 10,000 cycles with below 12% capacitance fade. With the rapid development of mobile electronics and electric vehicles, supercapacitors need to store as much energy as possible in relatively limited space. For aqueous-electrolyte supercapacitor, it is particularly important to design the electrode materials with high particle density, appropriate pore size distribution and doping degrees. In this work, we report an efficient approach to simultaneous increase the particle density and doping level of graphene-based electrode material. Through the interaction of oxygen functionalized graphene with NaOH solution, the electrostatic adsorption of sodium ions and oxygen-containing functional groups increases the density of the material while the reaction of hydroxide ions with oxygen-containing functional groups can increase the concentration of oxygen-containing functional groups of graphene. The as-prepared graphene displays both high particle density and pseudocapacitance which contributed from oxygen-containing functional groups. The graphene-based electrode delivers high volumetric capacitance of 469 F cm−3 as well as high rate capability (77% capacitance retention at 30 A g−1). Also, the assembled supercapacitor shows the high volumetric energy density of 24.2 Wh L−1, which is higher than most of the carbon materials in aqueous electrolyte. Therefore, this work provides a method to prepare high density and functionalized graphene, which has potential application in energy storage devices. [ABSTRACT FROM AUTHOR]