Composition Of Different Types Research Articles

Elevated temperature magnetic microstructures and demagnetization mechanism for grain boundary diffused dual-main-phase (Nd, Ce)-Fe-B magnets

The combination of dual-main-phase (DMP) (Nd, Ce)-Fe-B magnets and grain boundary diffusion process (GBDP) is currently a research topic for obtaining high-cost performance materials in rare earth permanent magnet fields. The novel structural features of GBDP (Nd, Ce)-Fe-B magnets give a version of different domain reversal processes from those of non-diffused magnets. In this work, the in-situ magnetic domain evolution of the DMP magnets was observed at elevated temperatures, and the temperature demagnetization and coercivity mechanism of the GBDP dual-main-phase (Nd, Ce)-Fe-B magnets are discussed. The results show that the shell composition of different types of grains in DMP magnets is similar, while the magnetic microstructure results indicate the Ce-rich grains tend to demagnetize first. Dy-rich shell with a high anisotropic field caused by GBDP leads to an increase in the nucleation field, which enhances the coercivity. It is found that much more grains exhibit single domain characteristics in the remanent state for GBDP dual-main-phase (Nd, Ce)-Fe-B magnets. In addition, the grains that undergo demagnetization first are Ce-rich or Nd-rich grains, which is different from that of non-diffused magnets. These results were not found in previous studies but can be intuitively characterized from the perspective of magnetic domains in this work, providing a new perspective and understanding of the performance improvement of magnetic materials.

Critical review of the role of ash content and composition in biomass pyrolysis

In the face of environmental challenges (e.g., dramatically increasing greenhouse gas emissions and climate change), it is utmost of importance to sustainable energy systems. Biomass consisting of agricultural and forest waste, municipal solid waste, and aquatics, has been identified as alternative and promising fuel sources. Thermochemical conversion approaches like pyrolysis can turn various types of biomass into three valuable product streams, namely, bio-oil, biochar, and syngas. To date, past review articles have considered the major operating parameters of kinetics, chemistry, and the application of pyrolysis products. However, ash content is one of the key biomass components that lacks investigation on its influence during biomass pyrolysis with respect to products yield and properties. This review article examines: i) the ash content and composition in different types of biomass; ii) effects of ash content on catalytic pathway and biomass thermal degradation; iii) ash related problems in the thermal degradation of biomass; and iv) available deashing techniques for biomass. The review aims to provide new understandings and insights regarding the effects of ash content and composition on biomass pyrolysis.

Quality analysis and antioxidant activity of different types of tea powder

As a natural product with health benefits, tea powder (TP) is widely used in food processing field. In order to explore sensory evaluation, quality components and antioxidant activities among different types of TP, matcha TP, Huangjinya green TP, oolong TP, black TP and Pu-erh TP were used as the research objects. Our results showed that total scores of sensory evaluation of unfermented TPs were higher than those of fermented TPs, whilst the colour of those unfermented TPs was more bright. Furthermore, taste attributes showed that sweetness was conducive to the formation of the taste of black TP, and aftertaste-bitterness was beneficial to matcha and Pu-erh TP. Umami was suggested as a vital contributing factor to the mellow taste of Huangjinya green TP, while sourness and bitterness were not propitious to form the taste quality of tea powder. The non-volatile quality components analysis revealed that tea powders with more fermentation usually contained fewer amino acids and catechins, but glutamate and gamma-aminobutyric acid were higher in black TP than in oolong TP. Theanine, glutamic acid and arginine were the main iconic components affecting the amino acid composition of different types of tea powder in our study. Meanwhile, the contents of ester catechins (ECG, EGC, EGCG) in black TP and Pu-erh TP were significantly lower than those in green TP and oolong TP. In addition, the free radical scavenging ability of unfermented TPs were stronger than that of fermented TPs. Therefore, to maximize the health benefits and sensory attributes of different types of TP, the use of unfermented tea powder is recommended. However, different manufacturing process of tea powder can enrich the taste of tea foods, thus giving people more choices.Graphical

Application of near-infrared hyperspectral imaging for determination of cheese chemical composition

Cheeses vary in chemical composition as they have different applications according to the technological features; some hard cheeses are consumed fresh while melting is a desirable characteristic for others. The conventional reference methods for determination of cheese composition and quality are accurate, but include physical-chemical analyses that are time-consuming and require use of chemical reagents. The objective of this work was to apply near-infrared hyperspectral imaging (NIR-HSI) as a fast method for prediction and visualization of spatial distribution of chemical composition of different types of cheeses (mozzarela, requeijão do norte, minas meia cura, coalho, prato). Moisture, fat, protein, and free oil were measured, and used as reference for prediction models, while spectral information extracted from images in the range of 928–2524 nm were used as predictors. Partial Least Squares (PLS) regression models were built with full spectra and using only few selected regions with higher importance, which were applied to the images to build chemical maps and visualize the spatial variation of the cheese composition. The regression models were able to predict moisture (R2P = 0.92, RMSEP = 1.1%); fat (R2P = 0.84, RMSEP = 1.5%); protein (R2P = 0.80, RMSEP = 1.8%); and free oil (R2P =0.64, RMSEP = 0.5%) for an external set of samples, while requiring a small number of latent variables (LVs) (between 3 and 4). The chemical maps allowed visualization of the spatial distribution for each attribute, demonstrating that the composition may vary within sample. The study demonstrated the potential of NIR-HSI for the determination of cheese composition and possible application to cheese quality control.

Differential analysis of volatiles in five types of mosquito-repellent products by chemometrics combined with headspace GC-Orbitrap HRMS nontargeted detection

This paper reports a method for the differential analysis of volatile chemical components in five novel types of mosquito-repellent products based on chemometrics combined with headspace gas chromatography-Orbitrap high-resolution mass spectrometry (HS-GC-Orbitrap HRMS) nontargeted screening. A total of 358 unknown substances were detected in 30 samples under specific headspace conditions. Through principal component analysis and orthogonal partial least-squares discriminant analysis, 36 significantly different substances with variable importance in the projection values greater than 1 were further screened, and these substances were accurately identified by GC-Orbitrap HRMS. Most substances were found for the first time in mosquito-repellent products. The clustered heat map, Venn diagram and peak area histogram showed that the mosquito-repellent products had similar volatile composition, and the volatile species and content of different types of mosquito-repellent products significantly varied. Substances, such as eucalyptol, d-limonene, α-pinene, β-pinene, dl-menthol and methyl salicylate, may be the main sources of odour in mosquito-repellent products. This work explored the characteristic volatile components in mosquito-repellent products and comparatively analysed the chemical composition of different types of products. It can be generalised to consumer products as a case study and has positive implications for promoting product quality and safety and improving production processes.

Oxidative stress and energy metabolic response of Isochrysis galbana induced by different types of pristine and aging microplastics and their leachates

The aging process and leachate composition of different types of MPs (PS, PS-NH2, PS-COOH and PMMA) with a particle size of 1.0 μm were characterized, and marine microalgae Isochrysis galbana OA3011(I. galbana) was used as test organism to investigate the 96 h toxic effects of MPs before and after aging as well as leachate exposure. Except for polymethyl methacrylate (PMMA), all other tested microplastics showed significant aggregation in seawater, which increased with the presence of surface amino and carboxyl groups, in addition, the increase in polymer dispersibility index (PDI) values after aging reflected more severe aggregation. Fourier transform infrared spectrometer (FTIR) showed that the surface amino groups were shed during the aging of PS-NH2, which can likewise be demonstrated by the change in surface electric potential from positive to negative before and after aging. PMMA, due to the addition of plasticizers (HEHP and DIBP detected in high concentration) and its own structure, has stronger resistance to aging than the other three microplastics, and no significant aging phenomenon occurs. As for I. galbana, growth inhibition, oxidative stress and energy metabolism were tested after exposure to different microplastics and their leachate. It was found that high concentrations of A-PS had a greater negative impact on I. galbana, while the toxic effects of PS-NH2 and PS-COOH on I. galbana behaved in a diametrically opposite way before and after aging compared to PS with the inhibitory effect decreasing after aging, which was caused by the shedding of surface groups. As for PMMA, the differences in the toxic effects on microalgae before and after aging were not significant. The inhibitory effect of low concentrations of PAEs (Phthalate acid esters) in the leachate of PS-COOH on I. galbana was not significant, and the stronger inhibitory effect of 4 d L-PS-NH2 was presumed to be the shedding of positively charged groups.

Study On the Composition of Glass Products Based on Soft Voting Model and Grey Correlation Analysis

The inspection and identification of ancient glass composition has always been an important research content, this paper on the ancient glass as a medium, for its chemical composition, weathering characteristics and classification rules and other aspects of the study, the establishment of a soft voting integrated learning model to random forest algorithm, SVM algorithms and GBDT algorithms as a base classifier, the use of soft voting on the base classifier for the classification of the results of the integration of the learning, and then to the The chemical composition of unknown artefacts is analysed and classified, and the classification accuracy of soft voting is as high as 96.6%, and the coverage rate of ROC curve reaches 99.1%, which indicates that it is reasonable and effective to solve the problem with the integrated learning model. Finally, by changing the number of decision trees, the classification accuracy rate is found to be unchanged, which verifies that the sensitivity of this model is good. In addition, this paper establishes a grey correlation analysis model to generate grey correlation coefficient matrix heat map for the chemical composition of different categories of glass artifacts, the horizontal coordinate of the heat map is the parent sequence, the vertical coordinate is the subsequence, according to the heat map of the correlation between the chemical composition of different types of glass artifacts to summarize and analyze, and in order to validate the reasonableness of the model, but also its Spearman correlation analysis, the results of the analysis of the two models are similar, so it can be judged that the application of the model is reasonable and effective. The results of the two models are similar, so it can be judged that the results of the grey correlation analysis model applied are reasonable. Finally, a detailed comparison of the chemical composition correlations of the major categories and subcategories is carried out, which is a differential analysis of the correlations of different categories of chemical compositions.

Features of damage to the leather substitute when fired from the “FORT 12R” and “AE 790G1” pistols from different distances

Annotation. Examination of damage caused by firearms is always a difficult challenge for a forensic scientist. The reason for this is the unpredictability of the interaction of the trace-receiving surface with the ammunition, additional factors of the shot and residual components of the shot, which in turn is caused by the specific design of different types of firearms and the composition of different types of ammunition, which is also combined with various factors affecting pre-wound ballistics (weather factors, etc.), wound ballistics (different types of human body tissue, different materials of obstacles, their presence, etc.). All these factors require detailed study. The purpose of the study is to compare the features of damage to the leatherette as a complex “clothing + non-biological human body simulator” when shot from different distances using the “Fort 12R” and “AE 790G” pistols. The experimental study was conducted by manufacturing ballistic gelatin blocks, which were covered with leatherette and fired at close range, 25 and 50 cm using “Fort 12R” and “AE 790G1” pistols. The analysis of all fired samples of leatherette revealed the formation of a minus-tissue type defect, with the formation of a round (mostly at close range for both pistols) or oval shape (at 25 and 50 cm for both pistols). Gap formation is characteristic of point-blank shots for both pistols and the Fort 12R at 25 cm. All shot distances and both pistols are characterized by the deposition of residual shot components in the form of half-burnt or unburned powder particles. The dimensions of the defect are approximately the same, but when using Fort 12R, the formation of larger defects was recorded at all shot distances. Thus, during the visual examination, when comparing the samples of leatherette from all the studied distances, significant differences were found between all the studied groups, in particular, between the respective groups of pistols, which makes it possible to identify the pistol already during the visual examination of the sample.

Classification of ancient glassware based on K-means and decision trees

In this paper, the relation between the chemical composition of different types of glass objects was analyzed, and the differences between the chemical compositions were used to classify the glass objects, mainly by K-means, decision tree, and Euclidean distance analysis. For the classification of glass artifacts, this article analyzed the chemical composition of different types of glass artifacts and the changes of chemical composition before and after weathering, and then obtained the differences of chemical composition of different types of glass artifacts to obtain the classification law. The model was tested for reasonableness by giving the classification criteria, and the sensitivity of the model was determined by the change in the number of classification species K.The sensitivity of the model was analyzed by calculating the Euclidean distances between the object and the cluster centers, and the variance of the model by calculating the Euclidean distances between the object and the cluster centers before and after the weathering of the glass objects.

Differentiation between copal and amber by their structure and thermal behaviour

The relationships between the polymerization related to structure and the composition of different types of natural resins were determined. Analyses were carried out by Fourier transform infrared spectroscopy (FTIR), differential thermal analysis–thermogravimetry (DTA-TG) and hot stage microscopy (HSM). Copal specimens were collected from the Mai-Ndombe Lake, Democratic Republic of Congo, and amber pieces that came from Bitterfeld, Germany, and from Kaliningrad, Russia. FTIR spectra of copal show a vibrational band at 1643 cm−1 (C=O stretching) attributed to communic acids, while amber shows a band at 1735 cm−1 associated with ester-group vibrations and a shoulder at about 3340 cm−1, suggesting partial oxidization. DTA shows the main exothermic peak, related to the combustion, at 546–552 °C in amber and at 518 °C in copal. The derivative thermogravimetry (DTG) peaks vary in the different resin types; in amber, they occur at 333–335, 401–404 and 548–555 °C and are related to mass losses of 31, 26 and 39 mass%, respectively; copal peaks are at 394 and 507 °C, with mass losses of 71 and 27 mass%, respectively. In copal, hot stage microscopy (HSM) shows the start of sintering at 131 °C, followed by an expansion produced by the material decomposition and the generation of gases that cannot be released because of the material plastic behaviour. Finally, the increase in pressure produces an explosion that results in a lower viscosity of the liquid, which at this point can no longer support the internal pressure of gases. In amber samples, a smaller decrease in viscosity is observed and the start of sintering occurs at 150 °C with no significant change in their morphology.

A comparative study of oil, protein, and fatty acid content of 12 cultivars of peanut (Arachis hypogaea L.) grown from two regions in Shandong province, China

AbstractPeanut is a primary oil crop that is cultivated on a large scale in Shandong province, China. This study evaluated the oil, protein, and fatty acid compositions of different types of peanut cultivars growing in different ecological regions (Yantai and Heze) in Shandong province from 2019 to 2020. The results showed that the oil content ranged from 38.83% to 56.48%, and the protein content ranged from 21.80% to 31.65% among the studied sites. The oleic acid content and ratio of oleic acid to linoleic acid (O/L) ranged from 39.19% to 52.58% and from 0.92 to 1.77, respectively. Accessions TY107, TY109, and HS‐D‐1 were identified as the most promising oil varieties; HY20 and HY6314 were identified as optimal protein varieties; and HY9311, HY9310, and LH09‐2 were identified as promising varieties for oleic acid production. A significant and positive relationship between seed oil content and accumulated growing degree days (GDD) and insolation duration demonstrated that environments with high temperatures and a long insolation duration promoted oil synthesis. However, a positive relationship was observed only between GDD and protein content. Based on the meteorological conditions of Shandong province, the southwest (such as Heze) is an optimal region for cultivating peanuts with high oil content; the east (Yantai) is most suitable for high‐protein peanut production. The results indicate that the growing conditions should carefully be considered for sustainable and healthy development of the peanut industry as climate variables will likely influence seed composition, thus affecting the suitability for various end uses.

Influence of smoke suppressant on the smoke inhibition effect and properties of different types of asphalt

The current understanding of the influence of smoke suppressants on the smoke inhibition effect and working performance of different types of asphalt is limited. In this study, portable gas detector (PGD) tests were used to investigate the influence of the smoke suppressant on different types of asphalt and the smoke purification effect. Subsequently, gas chromatography-mass spectrometry (GC-MS) was used to analyze the influence of the smoke suppressant on the volatile organic compounds (VOCs) in different types of asphalt fume, so as to compare and verify the PGD tests results. Finally, scanning electron microscopy and energy dispersive spectrometry were used to observe and analyze the influence of the smoke suppressant on the microstructure and element composition of different types of asphalt. The results show that the smoke suppressant can purify various harmful gases volatilized from different types of asphalt, but the effect and the optimal dosage of smoke suppressant are slightly different. The purification rate of the smoke suppressant for SO2 or H2S in the fumes of various asphalts can reach >70% and the NO and VOC purification rates can reach >40%. The influence of the smoke suppressant on the VOC purification rate of different asphalts detected through the GC-MS and PGD tests is similar. The smoke suppressant has little effect on the asphalt surface microstructure and on the changes in the element content in asphalt, indicating that it has no effect on the main properties of the asphalt itself. This study is helpful to further understand the relationship between smoke suppressants and the smoke inhibition effect and the working performance of different types of asphalt. Additionally, it provides basis and guidance for the development of low-smoke asphalt with a better emission reduction effect, which has a certain guiding and application significance.

Composition analysis and identification of Chinese glass artifacts

Ancient glass is susceptible to weathering by the burial environment, so it is important to promote the study of weathering and erosion of artifacts so as to make targeted conservation measures. In this paper, the chemical composition of ancient glass artifacts, based on logistic regression to obtain statistical patterns of chemical composition of different types of glass artifacts, and the use of hierarchical clustering method to identify unknown types of glass artifacts. Finding the chemical composition patterns of different categories of glass artifacts is an important reference for studying their raw material sources and facilitating comparative studies of artifacts.

K-Means++ Clustering Algorithm in Categorization of Glass Cultural Relics

We used statistical methods to study the classification of high-potassium glass and lead–barium glass and analyzed the correlation between the chemical composition of different types of glass samples. We investigated the categorization methodology of glass cultural relics, conducted a principal component analysis on the chemical composition data of the glass, and developed a case-specific clustering algorithm (K-Means++) to further categorize the glass cultural relics. K-Means++ was developed to reduce the sensitivity of a traditional K-Means clustering algorithm, by choosing the next clustering center with probability inversely proportional to the distance from the current clustering center. Then we verified the validity of the six subcategories we defined by inertia and silhouette score and evaluated the sensitivity of the clustering algorithm. We obtained a robustness ratio that maintained over 0.9 in the random noise test and a silhouette score of 0.525 in the clustering, which illustrated significant divergence among different clusters and showed the result is reasonable. With our proposed algorithm and classification result, a more comprehensive understanding of glass relics can be gained.

Research on glass classification based on linear regression model

This paper studies the rule of chemical composition of ancient glass products and the identification of glass types. Through Spearman correlation analysis, the establishment of multiple linear regression model and Fisher linear discriminant model, the relationship and classification rule of chemical composition of different types of glass are determined, and the linear regression model of chemical composition content before and after weathering and the identification method of glass types are obtained. The results show that the mathematical model proposed in this paper can effectively identify glass types and provide guidance for the identification of glass components.

Mineral composition of alfalfa, alfalfa-cereal, and cereal feed agrophytocenoses biomass

The relevance of the results of these studies lies in the fact that until recently, changes in the mineral composition of different types of meadow agrophytocenoses under the influence of symbiotic alfalfa and mineral nitrogen in the content of macro- and microelements, heavy metals, and nitrates in feed biomass have not yet been sufficiently investigated, which hinders the development and implementation of feed production findings in agricultural production. The purpose of the study is to establish changes in the accumulation of various meadow agrophytocenoses in dry feed biomass on various agricultural zones of mineral elements during cultivation on dark grey soils of the Right-Bank Forest-Steppe. During this study, field and laboratory methods were used, and an analytical method was used to determine the mineral composition of dry feed biomass. The results of studies on the accumulation of alfalfa, alfalfa-cereals, and cereal stands in in dry feed mass on various backgrounds of fertilisation and liming of raw ash, macro- and microelements, heavy metals, and nitrate nitrogen are presented. It is shown that under the action of symbiotic nitrogen in alfalfa and alfalfa cereal stands, compared with cereals in the dry mass of feed, raw ash accumulates more macroelements, trace elements, and heavy metals by 0.6-0.8%, the ratio of calcium to phosphorus increases by 0.11-0.26, and potassium to the sum of calcium and magnesium decreases by 0.11-0.26. Under the action of mineral nitrogen in the case of the introduction of N90 in the cereal stand, the content of nitrates increases from 0.02 to 0.04%, zinc – from 9.1-9.2 to 12.4-12.6 or by 3.2-3.5 mg/kg, copper – from 3.4-3.7 to 5.3-5.5 mg/kg, as well as manganese, lead, nickel, cadmium in the dry mass of the feed of the cereal stand. It is proved that the content of macro- and microelements in the dry mass of feed corresponds to zootechnical standards for feeding cattle, and nitrates and heavy metals do not exceed the maximum permissible concentrations. The results of these studies can be used in the development of recommendations on technologies for growing perennial cereals for feed purposes and in the educational process

Влияние химического состава пористой матрицы и атмосферы спекания на люминесцентные свойства висмутсодержащих композиционных материалов

The spectral-optical and luminescence properties of bismuth-containing composite materials based on matrices of high-silica porous glasses are investigated. Luminescence spectra, luminescence excitation spectra, infrared transmission spectra (8000–4000 cm-1) depending on the composition of different types of matrices and sintering atmosphere (nitrogen, argon) of bismuth-containing composite materials were examined. It was found that the samples of bismuth-containing composite materials are characterized by UV (λem = 350 nm), blue-green (λem = 410–550 nm) and orange-red (λem = 600–725 nm) luminescence due to the presence of various bismuth active centers. The analysis of the spectra obtained by near-infrared spectroscopy demonstrates the formation of Bi2+ dimers of bismuth and bismuth active centers associated with silicon.

Influence of the chemical composition of the porous matrix and sintering atmosphere on the luminescent properties of bismuth-containing composites

The spectral-optical and luminescence properties of bismuth-containing composite materials based on matrices of high-silica porous glasses are investigated. Luminescence spectra, luminescence excitation spectra, infrared transmission spectra (8000-4000 cm-1) depending on the composition of different types of matrices and sintering atmosphere (nitrogen, argon) of bismuth-containing composite materials were examined. It was found that the samples of bismuth-containing composite materials are characterized by UV (λem=350 nm), blue-green (λem=410-550 nm) and orange-red (λem=600-725 nm) luminescence due to the presence of various bismuth active centers. The analysis of the spectra obtained by near-infrared spectroscopy demonstrates the formation of Bi2+ dimers of bismuth and bismuth active centers associated with silicon. Keywords: Bismuth-containing composite materials, high-silica porous glass, near-infrared spectroscopy, luminescence, bismuth active centers.

Study of relic classification based on neural network and K mean classification

Over time, its own chemical elements will change in response to environmental changes, commonly known as weathering, which will lead to changes in the chemical composition ratios and affect the judgment of the class. This paper focuses on the chemical composition of different types of glass. By comparing the chemical indicators of each type, an identification model of the cultural relic category is established to predict the chemical content of the weathering process. To solve this, the neural network, chi-square test, and Spearman correlation analysis are used and the calculation results are given. According to the definition of sensitivity, after analysis, the accuracy was taken as the evaluation index, and the type of chemical composition was taken as the factor. K-value cluster analysis was performed for the type of chemical composition of the two glass categories in turn, and the accuracy was compared, and the accuracy of high potassium glass was close to 83%, and the accuracy of lead-barium glass was close to 69%, concluding that the type of chemical composition, which has little effect on the accuracy, has little sensitivity. In different categories, the variation of independent variables has a large impact on the accuracy and a large sensitivity. Finally, the advantages and disadvantages of the model are analyzed, and the model is extended to a larger domain.

Analysis of each components of glass samples based on the Spearman correlation coefficient model

In this paper, Spass is used to draw the scatter plot between the chemical component content, and then the Shapiro Verk test (Shapiro-Wilk test) is used to determine whether the sample follows a normal distribution. The Spearman correlation coefficient (Spearman correlation coefficient) model was then established to infer the correlation between the chemical components of the glass sample. At the same time, the chemical composition associations of different categories of glass were analyzed. This paper predicts the content of chemical composition of different types of glass based on rich data, refine, classification and use gradual regression model, establish logistic regression model and classify unknown samples; establish Spearman correlation coefficient model to obtain the correlation of different types of glass, and then analyze the difference of the correlation relations, making the results more significant and reliable.