Loading [Contrib]/a11y/accessibility-menu.js
Research article

Cold-curing mixtures based on biopolymer lignin complex for casting production in single and small-series conditions

  • Received: 22 April 2023 Revised: 17 June 2023 Accepted: 13 September 2023 Published: 27 September 2023
  • This study proves that lignin-based biopolymer materials can be employed as starting materials for the synthesis of novel casting binders that fulfill the current level of characteristics. The optimal concentration of the binder in the mixture was experimentally determined to be 5.8%–6.2%. It has been demonstrated in practice that the employment of ammonium salts as a technical lignosulfonate (TLS) modifier can result in the provision of cold (room temperature) curing of a mixture based on them. It was proposed to use as a technological additive that boosts the strength characteristics of a mixture of substances carboxymethyl cellulose (CMC). In a variety of adhesive materials, it is utilized as an active polymer base. The concentration limits for using CMC in the mixture are set at 0.15%–0.25%. To improve the moldability of the combination, it was suggested that kaolin clay be used as a plasticizing addition. The concentration limits for using a plasticizing additive are set at 3.5%–4.0%. The produced mixture was compared to the analog of the alpha-set method in a comparative analysis. It was discovered that the proposed composition is less expensive, more environmentally friendly, and enables the production of high-quality castings. In terms of physical, mechanical, and technological properties, the created composition of the cold curing mixture is not inferior to analogs from the alpha-set method. For the first time, a biopolymer-based binder system containing technical lignosulfonate with the addition of ammonium sulfate and carboxymethyl cellulose was used in the production of cast iron castings on the case of a cylinder casting weighing 18.3 kg from gray cast iron grade SCh20. Thus, it has been proved possible for the first time to replace phenol-based resin binders with products based on natural polymer combinations. For the first time, a cold-hardening mixture based on technological lignosulfonates has been developed without using hardeners made of very hazardous and cancer-causing hexavalent chromium compounds. But is achieved through a combination of specialized additives, including kaolin clay to ensure the mixture can be manufactured, ammonium sulfate to ensure the mixture cures, and carboxymethyl cellulose to enhance the strength properties of the binder composition. The study's importance stems from the substitution of biopolymer natural materials for costly and environmentally harmful binders based on phenolic resins. This development's execution serves as an illustration of how green technology can be used in the foundry sector. Reducing the amount of resin used in foundry manufacturing and substituting it with biopolymer binders based on technological lignosulfonates results in lower product costs as well as the preservation of the environment. Using lignin products judiciously can reduce environmental harm by using technical lignosulfonates, or compounds based on technical lignin. The combination is concentrated on businesses with single and small-scale manufacturing because it is presumable that this is merely the beginning of the investigation. This study confirms the viability of creating a cold-hardening combination based on technical lignosulfonates in practical applications and supports this with the castings produced, using the creation of a gray cast iron cylinder casting as an example.

    Citation: Falah Mustafa Al-Saraireh. Cold-curing mixtures based on biopolymer lignin complex for casting production in single and small-series conditions[J]. AIMS Materials Science, 2023, 10(5): 876-890. doi: 10.3934/matersci.2023047

    Related Papers:

    [1] Anna V. Babii, Anna L. Arkhipova, Irina N. Andreichenko, Artyom V. Brigida, Svetlana N. Kovalchuk . A TaqMan PCR assay for detection of DGAT1 K232A polymorphism in cattle. AIMS Agriculture and Food, 2018, 3(3): 306-312. doi: 10.3934/agrfood.2018.3.306
    [2] Svyatoslav Lebedev, Elena Sheida, Irina Vershinina, Victoria Grechkina, Ilmira Gubaidullina, Sergey Miroshnikov, Oksana Shoshina . Use of chromium nanoparticles as a protector of digestive enzymes and biochemical parameters for various sources of fat in the diet of calves. AIMS Agriculture and Food, 2021, 6(1): 14-31. doi: 10.3934/agrfood.2021002
    [3] Rinat R. Gadiev, Danis D. Khaziev, Chulpan R. Galina, Albert R. Farrakhov, Kamil D. Farhutdinov, Irina Yu. Dolmatova, Marina A. Kazanina, Gulnara F. Latypova . The use of chlorella in goose breeding. AIMS Agriculture and Food, 2019, 4(2): 349-361. doi: 10.3934/agrfood.2019.2.349
    [4] Gad G. Yousef, Allan F. Brown, Ivette Guzman, James R. Ballington, Mary A. Lila . Variations in chlorogenic acid levels in an expanded gene pool of blueberries. AIMS Agriculture and Food, 2016, 1(3): 357-368. doi: 10.3934/agrfood.2016.3.357
    [5] Marcelo Augusto de Carvalho, Cíntia Sorane Good Kitzberger, Altamara Viviane de Souza Sartori, Marta de Toledo Benassi, Maria Brígida dos Santos Scholz, Clandio Medeiros da Silva . Free choice profiling sensory analysis and principal component analysis as tools to support an apple breeding program. AIMS Agriculture and Food, 2020, 5(4): 769-784. doi: 10.3934/agrfood.2020.4.769
    [6] George K. Symeon, Ioannis A. Giantsis, Melpomeni Avdi . Effects of different reproduction management protocols on the reproduction efficiency of three indigenous Greek sheep breeds. AIMS Agriculture and Food, 2024, 9(2): 472-482. doi: 10.3934/agrfood.2024027
    [7] Martha Tampaki, Georgia Koutouzidou, Katerina Melfou, Athanasios Ragkos, Ioannis A. Giantsis . The contrasting mosaic of consumers' knowledge on local plant genetic resources sustainability vis a vis the unawareness for indigenous farm animal breeds. AIMS Agriculture and Food, 2024, 9(2): 645-665. doi: 10.3934/agrfood.2024035
    [8] Site Noorzuraini Abd Rahman, Rosimah Nulit, Faridah Qamaruz Zaman, Khairun Hisam Nasir, Mohd Hafiz Ibrahim, Mohd Ramdzan Othman, Nur Idayu Abd Rahim, Nor Sufiah Sebaweh . Profile of the grain physical traits and physicochemical properties of selected Malaysian rice landraces for future use in a breeding program. AIMS Agriculture and Food, 2024, 9(4): 934-958. doi: 10.3934/agrfood.2024051
    [9] Hamida Mahroug, Adra Mouellef, Hayat Bourekoua, Fairouz Djeghim, Haroun Chenchouni, Abdelkader Benbelkacem, Mohamed Hadef El Okki, Awatif Fetouhi, Nedjla Silini, Ana María Calderón de la Barca . Breadmaking and protein characteristics of wheat (Triticum aestivum L.) genotypes tolerant against drought and heat in Algeria. AIMS Agriculture and Food, 2024, 9(2): 531-550. doi: 10.3934/agrfood.2024030
    [10] Yusuff Oladosu, Mohd Y Rafii, Fatai Arolu, Suganya Murugesu, Samuel Chibuike Chukwu, Monsuru Adekunle Salisu, Ifeoluwa Kayode Fagbohun, Taoheed Kolawole Muftaudeen, Asma Ilyani Kadar . Genetic diversity and utilization of ginger (Zingiber officinale) for varietal improvement: A review. AIMS Agriculture and Food, 2024, 9(1): 183-208. doi: 10.3934/agrfood.2024011
  • This study proves that lignin-based biopolymer materials can be employed as starting materials for the synthesis of novel casting binders that fulfill the current level of characteristics. The optimal concentration of the binder in the mixture was experimentally determined to be 5.8%–6.2%. It has been demonstrated in practice that the employment of ammonium salts as a technical lignosulfonate (TLS) modifier can result in the provision of cold (room temperature) curing of a mixture based on them. It was proposed to use as a technological additive that boosts the strength characteristics of a mixture of substances carboxymethyl cellulose (CMC). In a variety of adhesive materials, it is utilized as an active polymer base. The concentration limits for using CMC in the mixture are set at 0.15%–0.25%. To improve the moldability of the combination, it was suggested that kaolin clay be used as a plasticizing addition. The concentration limits for using a plasticizing additive are set at 3.5%–4.0%. The produced mixture was compared to the analog of the alpha-set method in a comparative analysis. It was discovered that the proposed composition is less expensive, more environmentally friendly, and enables the production of high-quality castings. In terms of physical, mechanical, and technological properties, the created composition of the cold curing mixture is not inferior to analogs from the alpha-set method. For the first time, a biopolymer-based binder system containing technical lignosulfonate with the addition of ammonium sulfate and carboxymethyl cellulose was used in the production of cast iron castings on the case of a cylinder casting weighing 18.3 kg from gray cast iron grade SCh20. Thus, it has been proved possible for the first time to replace phenol-based resin binders with products based on natural polymer combinations. For the first time, a cold-hardening mixture based on technological lignosulfonates has been developed without using hardeners made of very hazardous and cancer-causing hexavalent chromium compounds. But is achieved through a combination of specialized additives, including kaolin clay to ensure the mixture can be manufactured, ammonium sulfate to ensure the mixture cures, and carboxymethyl cellulose to enhance the strength properties of the binder composition. The study's importance stems from the substitution of biopolymer natural materials for costly and environmentally harmful binders based on phenolic resins. This development's execution serves as an illustration of how green technology can be used in the foundry sector. Reducing the amount of resin used in foundry manufacturing and substituting it with biopolymer binders based on technological lignosulfonates results in lower product costs as well as the preservation of the environment. Using lignin products judiciously can reduce environmental harm by using technical lignosulfonates, or compounds based on technical lignin. The combination is concentrated on businesses with single and small-scale manufacturing because it is presumable that this is merely the beginning of the investigation. This study confirms the viability of creating a cold-hardening combination based on technical lignosulfonates in practical applications and supports this with the castings produced, using the creation of a gray cast iron cylinder casting as an example.



    Providing world’s population with food is one of the most acute issues these days. Due to climate change and depletion of natural resources food security keeps worsening worldwide [1,2]. Food security of every nation must touch upon different aspects of people’s life while consumption of organic products being the key point. Providing people with basic high-value protein products is one of the priority tasks of both livestock and processing industry. Animal breeding plays an important role in production of such important products as meat and milk. Sensible and balanced feeding of people depends, among other things, on the development and efficiency of animal breeding. In many countries, as well as in Russia, there is a tendency to reduce the livestock population. At the same time, providing people with beef is still one of the most difficult and important problems [3,4]. Increasing the production of high-quality meat should be carried out on the basis of all available reserves which include the increase in the livestock and poultry population, strengthening the fodder base, improving the quality of harvested feed, the use of resource-saving technologies, reducing production and labor costs, feed and funds per product unit [5,6,7,8,9,10,11,12,13,14].

    World experience shows that a significant increase in beef production can be achieved by means of intensification of livestock breeding. And first of all it can be done by creating conditions for increasing the level of animal productivity and maximum use of the genetic potential of livestock of both domestic and foreign selection [15]. In various regions of the Russian Federation the main amount of beef is produced by breeding dairy and combined breeds of cattle [16,17].

    Young animals in dairy cattle breeding are grown for meat using the technology of “indoors-outdoor feedlot”. First, it is necessary to create comfortable living conditions for calves in order to preserve their health and obtain high productivity. Secondly, older animals are able to adapt rather quickly to environmental factors. Finally, there is commercial importance as fattening young stock can be intensively grown at high rates of labor productivity and profitability. Also expenses for the construction of outdoor feedlots are relatively low and technical solutions are rather simple. However, the analysis of the feedlots activity shows that when rearing young animals for growing and fattening the productivity reduces sharply for a long period of time. This leads to irrational use of feed, reduced growth rate and, as a consequence, the loss of meat products. Therefore, technology of growing and fattening young animals requires further study in order to improve it.

    In this regard, the aim of the research was to assess both quantitative and qualitative indicators of meat productivity, depending on the use of different options for rearing and fattening of bull calves, namely indoors and on the outdoor feedlot in the conditions of the Republic of Bashkortostan.

    Ninety calves of 8-month age were selected on the principle of analogues to carry out a research and business experiment in “Akberdinskoye” Limited Liability Company of Iglinsky district of the Republic of Bashkortostan. The animals were divided into 6 groups. The first and the fourth groups were made of calves of black-and-white breed. The calves of Bestuzhev breed were in group two and five, and the 3rd and the 6th groups were made Simmental bull calves. Animals from groups 1, 2, 3 were kept on the outdoor feedlot. Calves from groups 4, 5, 6 were kept indoors. There was yard housing indoors. There were 15 animals having free access to self-filling drinking bowls and feeders in each room.

    The experience was conducted according to the requirements of animal breeding, health and sanitation.

    Animals were fed according to the diets made taking into account chemical composition of forages and their actual nutritional value according to recommendations [18].

    To assess meat productivity, control slaughter of 18-month-old bull calves (live weight being 457.8–511.9 kg) was carried out according to the “Guidelines for the evaluation of meat productivity and meat quality” [19].

    In order to study the morphological composition of meat carcass, the right half-carcasses were stripped. Then the content of flesh, bones and tendons in carcasses were determined. The carcass variety assortment was studied according to the classification of sausage production in which beef is divided into three classes: The highest grade is pure muscle tissue with no visible residues of other tissues and entities, in which fat content isn’t more than 6%; the Ist class has no more than 20% of fat; the IInd grade is all the other muscle tissue of the carcass, where there can be small veins, tendons and films.

    Physical and chemical parameters of meat were studied using generally accepted methods [20]: Determining moisture content in the samples by drying the sample to a constant weight at a temperature of 150 ± 2 ℃; protein content by the Kjeldahl method followed by photometry of the samples; fat content by extracting the dry weighing batch using the Soxhlet apparatus; ash content by burning in the muffle furnace.

    Most of the material obtained during the research was processed with “Statistica 10.0” (“Stat Soft Inc., ” USA) software package. The reliability was determined by the Student’s t-test.

    Morphological composition is an important indicator which determines the quality of meat carcass. At the same time, animal keeping and feeding technology influence the morphological composition of carcasses.

    The flesh part of the carcass is the most valuable in terms of nutritional value. It consists of muscle and fat tissue, and the greater their content, the higher the nutritional value of meat.

    The results of the studies prove that keeping conditions and genetic characteristics influence the morphological composition of the carcasses and the number of their individual parts.

    The data of Table 1 indicate that bull calves of Ⅳ-Ⅵ groups have the highest carcass flesh content. They significantly exceeded in weight their herd-mates by 6.3–16.1 kg (P > 0.05–P < 0.01) kept on the outdoor feedlot. It should be noted that among the studied breeds the carcasses of bull calves of Simmental breed had the largest weight of muscle tissue. In this indicator the calves of this breed surpassed their herd-mates of Black-and-white and Bestuzhev breeds by 12.0 kg (P < 0.01) and 6.6 kg (P < 0.05) when keeping animals on the outdoor feedlot and by 21.8 (P < 0.01) and 14.3 kg (P < 0.01) when keeping them indoors. A comparative analysis of the concentration of adipose tissue in carcasses, namely subcutaneous and intermuscular fat, revealed some differences in the nature of fat deposition depending on animal genotype. In particular, bull calves of Simmental breed kept indoors had 0.7–1.3 (P > 0.05–P > 0.05) kg of subcutaneous fat more and 0.3–0.9 kg (P > 0.05–P > 0.05) of intermuscular fat more in comparison with bull calves of Black-and-white and Bestuzhev breeds.

    Table 1.  Morphological composition of bull calf carcasses (m ± SE).
    Indicator Group (n = 3 in each group)
    Chilled carcass weight, kg 231.3 ± 2.16 240.2 ± 1.24 247.6 ± 1.15 239.2 ± 1.08 248.6 ± 0.75 265.9 ± 1.14
    Content in a carcass:
    Muscle tissue, kg 173.6 ± 1.26 179.0 ± 0.88 185.6 ± 1.95 179.9 ± 2.46 187.4 ± 1.18 201.7 ± 2.13
    % 75.06 ± 0.28 74.52 ± 0.46 74.96 ± 0.39 75.21 ± 0.21 75.38 ± 0.36 75.85 ± 0.28
    Adipose tissue (subcutaneous fat), kg 4.4 ± 0.09 4.8 ± 0.24 4.9 ± 0.19 4.6 ± 0.33 5.2 ± 0.18 5.9 ± 0.25
    % 1.90 ± 0.16 2.00 ± 0.08 1.98 ± 0.10 1.92 ± 0.07 2.09 ± 0.09 2.22 ± 0.11
    Intermuscular fat, kg 4.0 ± 0.54 4.6 ± 0.36 4.8 ± 0.45 4.6 ± 0.31 5.2 ± 0.12 5.5 ± 0.11
    % 1.73 1.92 1.94 1.92 2.09 2.07
    Bone tissue, kg 42.9 ± 0.86 44.6 ± 0.54 44.8 ± 0.72 43.2 ± 1.12 43.4 ± 1.42 44.8 ± 1.03
    % 18.54 ± 0.14 18.56 ± 0.06 18.09 ± 0.11 18.07 ± 0.12 17.46 ± 0.09 16.85 ± 0.13
    Tendons, kg 6.4 ± 0.34 7.2 ± 0.66 7.5 ± 0.52 6.9 ± 0.42 7.4 ± 0.27 8.0 ± 0.58
    % 2.77 ± 0.24 3.00 ± 0.14 3.03 ± 0.16 2.88 ± 0.09 2.98 ± 0.13 3.01 ± 0.11
    Fleshing index 4.2 4.2 4.4 4.4 4.6 4.8
    Flesh yield per 100 kg live weight, kg 39.6 40.0 40.3 39.8 40.6 41.2
    Ratio of edible and inedible parts of carcass, kg 3.7 3.6 3.7 3.8 3.9 4.1
    Note: In the outdoor feedlot: I—Black-and-white cattle; Ⅱ—Bestuzhev bull calves; Ⅲ—Simmental animals; kept indoors: Ⅳ—Black-and-white cattle; Ⅴ—Bestuzhev bull calves; Ⅵ—Simmental animals.

     | Show Table
    DownLoad: CSV

    The differences in the overall yield of bone tissue and tendons between the experimental groups were insignificant. At the same time, due to a higher carcass weight of animals of Bestuzhev and Simmental breeds, they surpassed their herd-mates of Black-and-white breed in absolute bone mass by 0.9–1.8%.

    An important indicator characterizing the meat quality of animals is fleshing index, which is determined by the flesh to bones ratio. The study results prove that the value of fleshing index of bull calf carcasses of Bestuzhev and Simmental breeds kept indoors is the highest and makes 4.6–4.8.

    The yield of flesh per 100 kg of pre-slaughter live weight was relatively high in all experimental groups. However, bull calves genotype as well as technology of their growing and fattening affected the yield of slaughter products. Thus, the yield of flesh per 100 kg of live weight of bull calves of Black-and-white breed kept on the outdoor feedlot was 39.6 kg, the flesh yield of Bestuzhev breed calves weighed 40.0 kg, the same indicator of Simmental breed calves was 40.3 kg; as for animals kept indoors, it was respectively 39.8; 40.6 and 41.2 kg which is higher by 0.7; 1.5 or 2.2 %.

    The flesh yield indicator affected the ratio of edible part weight to inedible part weight of the carcass, which was 3.7 for the bull calves of group Ⅰ; 3.6 for group Ⅱ; 3.7 for group Ⅲ; 3.8 for group Ⅳ; 3.9 for group Ⅴ and 4.1 for group Ⅵ.

    From the above material it can be concluded that meat quality of carcasses in bull calves of all groups was quite high, but the carcasses of bull calves of Black-and-white breed were more “bony”.

    It is well known that different anatomical parts of the carcass differ in a number of morphological parameters which affects their nutritional value, functional and technological properties and taste.

    In this respect the most valuable is the hip part. In our study the hip part made 35.5–36.3% of the total mass of the carcass. It is the yield of this cut that largely determines the quality of the carcass as a whole.

    The ratios of anatomical parts of bull calves of different breeds were studied. Different technologies were used during the period of growing and fattening of these bull calves. The intergroup differences in the intensity of their weight gain were defined (Table 2).

    Table 2.  The ratio of individual anatomical parts of the semi-carcasses (m ± SE).
    The anatomical part of the semi-carcass (n = 3 in each group)
    Neck Humeroscapular Back-rib Lumbar Hip
    Weight, kg % to weight Weight, kg % to weight Weight, kg % to weight Weight, kg % to weight Weight, kg % to weight
    11.3 9.8 20.2 17.4 31.9 27.6 10.4 9.0 41.8 36.2
    12.3 10.2 20.7 17.3 33.3 27.7 10.7 8.9 43.1 35.9
    12.4 10.0 21.7 17.5 34.0 27.5 11.8 9.5 43.9 35.5
    11.5 9.6 21.0 17.6 32.7 27.3 11.1 9.3 43.3 36.2
    12.3 9.9 21.8 17.5 33.8 27.2 11.4 9.2 45.0 36.2
    12.9 9.7 23.0 17.3 36.0 27.1 12.8 9.6 48.3 36.3
    Note: In the outdoor feedlot: I—Black-and-white cattle; Ⅱ—Bestuzhev bull calves; Ⅲ—Simmental animals; indoors: Ⅳ—Black-and-white cattle; Ⅴ—Bestuzhev bull calves; Ⅵ—Simmental animals.

     | Show Table
    DownLoad: CSV

    Thus, bull calves of Simmental breed had the advantage in absolute hip weight of the semi-carcass, regardless of the technology of their growing and fattening. At the same, they surpassed their herd-mates of Bestuzhev and Black-and-white breeds in hip weight by 0.8 kg (1.9%; P > 0.05) and 2.1 kg (5.0%; P > 0.05) kg when kept on outdoor feedlot and by 3.3 kg (7.3%; P > 0.05) and 5.0 kg (11.5%; P < 0.05) when kept indoors. There is some difference in weight of the same part of the semi-carcass of experimental animals, depending on their growing technology. The weight of this part of the semi-carcass of bull calves of Black-and-White breed is greater by 1.5 kg (3.6%), of Bestuzhev breed calves by 1.9 kg (4.4%), and of Simmental breed calves by 4.4 kg (10.0%) in comparison to young animals kept indoors.

    Regardless of keeping technology used during their growing and fattening period, Bull calves of Simmental breed surpassed the analogues of Black-and-White and Bestuzhev breeds in neck part weight by 1.1 and 1.4 and 0.1–0.6 kg, in a humeroscapular part weight by 1.5–2.0 and 1.0–1.2 kg; and in a back-rib part weight by 2.1 and 3.3 and 0.7–2.2 kg.

    Thus, the bull calves of Simmental breed had superiority over their herd-mates of Black-and-white and Bestuzhev breeds in the absolute weight of all natural anatomical parts of the half-carcass. The ratio of the carcasses cuts of experimental bull calves was determined by their breed and keeping technology.

    The established pattern had a positive effect on the increase in the yield of the edible part of the carcass by 1 kg of bones (Table 3).

    Table 3.  Flesh yield per 1 kg of bones when cutting semi-carcass into anatomical parts, kg (m ± SE).
    Group The anatomical part of the semi-carcass (n = 3 in each group)
    Neck Humeroscapular Back-rib Lumbar Hip
    4.40 3.89 3.35 6.28 4.47
    4.38 4.00 3.32 6.48 4.50
    4.64 4.11 3.31 6.69 4.60
    5.10 4.21 3.47 6.74 4.84
    5.22 4.15 3.56 6.48 4.83
    5.42 4.19 3.68 6.83 5.01
    Note: In the outdoor feedlot: I—Black-and-White cattle; Ⅱ—Bestuzhev bull calves; Ⅲ—Simmental animals; indoors: Ⅳ—Black-and-white cattle; Ⅴ—Bestuzhev bull calves; Ⅵ—Simmental animals.

     | Show Table
    DownLoad: CSV

    Data analysis shows that back-rib and humeroscapular parts of the half-carcass had the lowest flesh yield. The maximum flesh yield was revealed in the lumbar part in all studied groups.

    At the same time, Simmental bull calves grown on outdoor feedlot had superiority over their herd-mates of the Black-and-White and Bestuzhev breeds in a lumbar part yield by 0.41 kg (6.5%) and 0.21 kg (3.2%), and those grown indoors had superiority by 0.1 kg (1.3%) and 0.35 kg (5.4%), respectively.

    We get a fuller picture of meat qualities of an animal thanks to qualitative evaluation of carcass flesh depending on varieties and in accordance with sausage classification. Thus, taking into consideration the fact that variety and technological value of different carcass parts are not identical and depend on morphological structure, the ratio of muscle and fat tissue, fatness, age, breed and sex of an animal, the importance of this issue can not be overestimated. Further use of meat flesh by meat processing enterprises as well as the number and range of meat products are largely determined by its variety assortment.

    The data on the variety assortment of the carcass flesh of bull calves of different genotypes are presented in Table 4.

    Table 4.  Varietal assortment of the semi-carcass flesh of experimental young stock (m ± SE).
    Indicator Group (n = 3 in each group)
    Total flesh weight, kg 90.0 ± 2.19 93.8 ± 1.91 97.0 ± 2.25 94.6 ± 2.01 98.6 ± 2.11 106.0 ± 2.40
    Including the highest grade, kg 16.8 ± 0.54 18.9 ± 0.32 20.4 ± 0.44 18.0 ± 0.36 20.3 ± 0.34 22.9 ± 0.53
    % 18.67 20.15 21.03 19.03 20.59 21.60
    The first grade, kg 41.8 ± 1.11 44.3 ± 1.06 46.5 ± 1.20 44.0 ± 0.96 46.7 ± 1.34 50.8 ± 1.36
    % 46.43 47.23 46.40 46.51 47.36 47.93
    The second grade, kg 31.4 ± 0.46 30.6 ± 0.52 30.1 ± 0.59 32.6 ± 0.63 31.6 ± 0.43 32.3 ± 0.51
    % 34.90 32.62 31.03 34.46 32.05 30.47
    Note: In the outdoor feedlot: I—Black-and-White cattle; Ⅱ—Bestuzhev bull calves; Ⅲ—Simmental animals; indoors: Ⅳ—Black-and-white cattle; Ⅴ—Bestuzhev bull calves; Ⅵ—Simmental animals.

     | Show Table
    DownLoad: CSV

    The bull calves of Black-and-White breed kept on the outdoor feedlot were inferior to herd-mates of Bestuzhev and Simmental breeds in the absolute flesh weight of the highest grade by 12.5 and 21.4% (P < 0.05 and P < 0.05) and the first grade by 6, 0 and 11.2% (P > 0.05 and P > 0.05), respectively, and when kept indoors the difference in the flesh content of the highest grade was 12.8 and 27.2% (P < 0.05 and P < 0.01) and the first grade 6.1 and 15.5% (P > 0.05 and P < 0.05).

    The quality of the flesh got depends on both the number and ratio of its components and chemical composition. It is known that chemical composition of meat depends on various factors, the main of which are animals breed, sex, age, fatness, as well as their feeding and keeping conditions.

    Therefore, it is important to study chemical composition of meat as one of the important indicators characterizing meat products quality.

    The data of Table 5 show that the meat obtained from the bull calves kept indoors during the fattening period was characterized by the highest content of dry matter and fat. Their advantage in the content of dry matter in the average sample of minced meat over herd-mates kept on the outdoor feedlot was 2.07–2.21%. These differences are probably due to varying degrees of fat deposits in the body of experimental animals.

    Table 5.  Chemical composition of minced meat, % (m ± SE).
    Indicators The group of experimental bull calves (n = 3 in each group)
    Weight fraction, %
    Moisture 70.83 ± 0.90 70.51 ± 1.75 70.45 ± 0.85 68.62 ± 1.16 68.44 ± 1.17 68.26 ± 1.08
    Dry matter 29.17 ± 0.90 29.49 ± 1.75 29.55 ± 0.85 31.38 ± 1.16 31.56 ± 1.17 31.74 ± 1.08
    Including: protein 18.48 ± 0.94 18.52 ± 0.51 18.53 ± 0.48 18.26 ± 0.56 18.32 ± 1.51 18.46 ± 1.62
    Fat 9.78 ± 0.28 10.05 ± 0.42 10.11 ± 0.35 12.11 ± 0.21 12.33 ± 0.36 12.35 ± 0.29
    Ash 0.91 ± 0.03 0.92 ± 0.0.6 0.91 ± 0.02 0.91 ± 0.5 0.91 ± 0.03 0.93 ± 0.04
    Energy value, kJ 677.32 688.17 690.60 761.49 770.79 773.88

     | Show Table
    DownLoad: CSV

    In general, beef with good nutritional values was obtained from young animals of all experimental groups.

    The ratio of protein and fat in muscle tissue of experimental bull calves in group Ⅰ was 1:0.53, in group Ⅱ it was 1:0.54, in group Ⅲ—1:0.55, in group Ⅳ—1:0.66, in group Ⅴ—1:0.67 and in group Ⅵ it was 1:0.67. Animals of Bestuzhevskoaya and Simmental breeds had the best ratio of protein and fat in muscle tissue.

    Energy value of the average minced beef sample got from bull calves of groups Ⅰ, Ⅱ and Ⅲ was inferior to their herd-mates of groups Ⅳ, Ⅴ, and Ⅵ by 84.17 kJ, 82.62 kJ and 83.28 kJ, respectively.

    During the research it was found that the technological factor and genetic characteristics of animals had a significant impact on production efficiency. Keeping animals indoors helps to realize their inherent genetic potential of meat productivity. Quantitative and qualitative indicators of meat products prove a positive impact of keeping bull calves indoors. The obtained data are consistent with the data of other researchers and make it possible to assert that regardless of the breed the animals kept indoors have better indicators of meat productivity than animals kept on the outdoor feedlot [21,22,23,24].

    It should be noted that keeping bull calves indoors during their period of growing and fattening for meat helps to obtain beef with optimal characteristics. The ratio of carcass cuts of experimental bull calves was determined by their breed and keeping technology. The bull calf carcasses of Simmental breed were characterized by maximum meat yield of the highest grades. The bull calf carcasses of Bestuzhev breed occupied an intermediate position meat yield of the highest grades. In the bull calf semi-carcasses of Black-and-White breed there was the maximum meat yield of the second grade. This advantage in the value of the studied indicator over the herd-mates of Simmental and Bestuzhev breeds was 2.3 and 3.9% when keeping experimental animals both on outdoor feedlot and indoors, which is consistent with the [25].

    According to the analysis of the average carcass flesh samples, there are certain differences in chemical composition between groups. It can be explained by the fact that the process of nutrient enrichment in the body of bull calves of different genotypes kept under different conditions, took place differently. In general, the chemical composition of the flesh parts of the bull calf carcasses of all groups indicates a high nutritional value. The bull calves grown indoors had some advantage.

    Keeping bull calves indoors can improve the post-slaughter indicators of meat productivity and improve beef quality. The study of the morphological composition of carcasses showed that muscle tissue content in the carcasses of animals fed indoors was 179.9–201.7 kg, while those in the carcasses of young animals kept on the outdoor feedlot was 173.6–185.6 kg. There is a greater content of fat tissue in carcasses of bull calves grown and fed indoors. So the total amount of raw fat of bull calves kept indoors was 23.4–26.3 kg, of bull calves kept on the outdoor feedlot it was 20.7–23.7 kg. The bull calves of Simmental breed had superiority over their herd-mates of the Black-and-white and Bestuzhev breeds in the absolute weight of most anatomical parts of the semi-carcass. It is more preferable to fatten young animals of combined breeds for meat producing. Thus, growing and fattening the bull calves of Simmental breed indoors allowed to get the carcasses with the best morphological composition and variety assortment, as well as with the optimal ratio of protein and fat.

    The authors declare no conflict of interest.



    [1] B. Pang, L. Lee, Opinion mining and sentiment analysis, Trends Inf. Retr., 2 (2008), 1–135. DOI: 10.1561/1500000011 doi: 10.1561/1500000011
    [2] G. Vinodhini, R. Chandrasekaran, Sentiment analysis and opinion mining: a survey, Int. J., 2 (2012), 282–292. DOI: 10.1016/j.nlp.2022.100003 doi: 10.1016/j.nlp.2022.100003
    [3] M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, S. Manandhar, SemEval-2014 Task 4: Aspect based sentiment analysis, in Association for Computational Linguistics, (2014), 27–35. DOI: 10.3115/v1/S14-2004
    [4] M. Pontiki, D. Galanis, H. Papageorgiou, S. Manandhar, I. Androutsopoulos, Semeval-2015 task 12: Aspect based sentiment analysis, in Association for Computational Linguistics, (2015), 486–495. DOI: 10.18653/v1/S15-2082
    [5] M. Pontiki, D. Galanis, H. Papageorgiou, I. Androutsopoulos, S. Manandhar, M. AL-Smadi, et al. Semeval-2016 task 5: Aspect based sentiment analysis, in Association for Computational Linguistics, (2016), 19–30. DOI: 10.18653/v1/S16-1002
    [6] W. Zhang, X. Li, Y. Deng, L. Bing, W. Lam, A survey on aspect-based sentiment analysis: Tasks, methods, and challenges, IEEE Trans. Knowl. Data Eng., 2022. DOI: 10.1109/TKDE.2022.3230975
    [7] D. Tang, B. Qin, X. Feng, T. Liu, Effective LSTMs for target-dependent sentiment classification, preprint, arXiv: 151201100.
    [8] M. Yang, W. Tu, J. Wang, F. Xu, X. Chen, Attention based LSTM for target dependent sentiment classification, in Proceedings of the AAAI conference on artificial intelligence, 2017. DOI: 10.1609/aaai.v31i1.11061
    [9] Q. Liu, Y. Huang, Q. Yang, H. Peng, J. Wang, An attention-aware long short-term memory-like spiking neural model for sentiment analysis, Int. J. Neural Syst., (2023), 2350037–2350037. DOI: 10.1142/s0129065723500375 doi: 10.1142/s0129065723500375
    [10] Y. Huang, Q. Liu, H. Peng, J. Wang, Q. Yang, D. Orellana-Martín, Sentiment classification using bidirectional LSTM-SNP model and attention mechanism, Expert Syst. Appl., 221 (2023), 119730. DOI: 10.1016/j.eswa.2023.119730 doi: 10.1016/j.eswa.2023.119730
    [11] Y. Huang, H. Peng, Q. Liu, Q. Yang, J. Wang, D. Orellana-Martín, et al., Attention-enabled gated spiking neural P model for aspect-level sentiment classification, Neural Network, 157 (2023), 437–443. DOI: 10.1016/j.neunet.2022.11.006 doi: 10.1016/j.neunet.2022.11.006
    [12] Y. Kim, Convolutional neural networks for sentence classification, preprint, arXiv: 14085882.
    [13] D. Tang, B. Qin, T. Liu, Aspect level sentiment classification with deep memory network, preprint, arXiv: 160508900.
    [14] P. Lin, M. Yang, J. Lai. Deep mask memory network with semantic dependency and context moment for aspect level sentiment classification, in IJCAI, (2019), 5088–5094. DOI: 10.24963/ijcai.2019/707
    [15] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., Attention is all you need, in Advances in Neural Information Processing Systems, 30 (2017). DOI: 10.48550/arXiv.1706.03762
    [16] Z.-Y. Dou, Capturing user and product information for document level sentiment analysis with deep memory network, in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017. DOI: 10.18653/v1/D17-1054
    [17] K. Chakraborty, S. Bhattacharyya, R. Bag, A survey of sentiment analysis from social media data, IEEE Trans. Comput. Soc. Syst., 7 (2020), 450–464. DOI: 10.1109/TCSS.2019.2956957 doi: 10.1109/TCSS.2019.2956957
    [18] X. Zhu, Y. Zhu, L. Zhang, Y. Chen, A BERT-based multi-semantic learning model with aspect-aware enhancement for aspect polarity classification, Appl. Intell., 53 (2023), 4609–4623. DOI: 10.1007/s10489-022-03702-1 doi: 10.1007/s10489-022-03702-1
    [19] J. Devlin, M. W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, preprint, arXiv: 181004805.
    [20] N. Reimers, I. Gurevych, Sentence-bert: Sentence embeddings using siamese bert-networks, preprint, arXiv: 190810084.
    [21] L. Breiman, J. Friedman, C. J. Stone, R. A. Olshen, Classification and Regression Trees (CART), Biometrics, 1984 (1984). DOI: 10.2307/2530946
    [22] N. S. Altman, An introduction to kernel and nearest-neighbor nonparametric regression, Am. Stat., 46 (1992), 175–185. DOI: 10.1080/00031305.1992.10475879 doi: 10.1080/00031305.1992.10475879
    [23] I. Rish, An empirical study of the naive Bayes classifier, in IJCAI 2001 workshop on empirical methods in artificial intelligence, (2001), 41–46. DOI: 10.1109/CSCI46756.2018.00065
    [24] D. W. Hosmer Jr, S. Lemeshow, R. X. Sturdivant, Applied Logistic Regression, John Wiley & Sons, 2013. DOI: 10.1002/9781118548387
    [25] C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn., 20 (1995), 273–297.
    [26] L. Breiman, Random forests, Mach. Learn., 45 (2001), 5–32. DOI: 10.1023/A:1022627411411
    [27] N. S. Joshi, S. A. Itkat, A survey on feature level sentiment analysis, Int. J. Comput. Sci. Inf. Technol., 5 (2014), 5422–5425.
    [28] E. Cambria, B. White, Jumping NLP curves: A review of natural language processing research, IEEE Comput. Intell. Mag., 9 (2014), 48–57. DOI: 10.1109/MCI.2014.2307227 doi: 10.1109/MCI.2014.2307227
    [29] B. Zhang, X. Fu, C. Luo, Y. Ye, X. Li, L. Jing, Cross-domain aspect-based sentiment classification by exploiting domain-invariant semantic-primary feature, IEEE Trans. Affect. Comput., 2023 (2023), forthcoming. DOI: 10.1109/TAFFC.2023.3239540 doi: 10.1109/TAFFC.2023.3239540
    [30] H. Huang, B. Zhang, L. Jing, X. Fu, X. Chen, J. Shi, Logic tensor network with massive learned knowledge for aspect-based sentiment analysis, Knowl. Based Syst., 257 (2022), 109943. DOI: 10.1016/j.knosys.2022.109943 doi: 10.1016/j.knosys.2022.109943
    [31] X. Mei, Y. Zhou, C. Zhu, M. Wu, M. Li, S. Pan, A disentangled linguistic graph model for explainable aspect-based sentiment analysis, Knowl. Based Syst, 260 (2023), 110150. DOI: 10.1016/j.knosys.2022.110150 doi: 10.1016/j.knosys.2022.110150
    [32] B. Zhang, X. Huang, Z. Huang, H. Huang, B. Zhang, X. Fu, et al., Sentiment interpretable logic tensor network for aspect-term sentiment analysis, in Proceedings of the 29th International Conference on Computational Linguistics, (2022), 6705–6714.
    [33] B. Xu, X. Wang, B. Yang, Z. Kang, Target embedding and position attention with lstm for aspect based sentiment analysis, in Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence, (2020), 93–97. DOI: 10.1145/3395260.3395280
    [34] Y. Ma, H. Peng, E. Cambria, Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM, in Proceedings of the AAAI conference on artificial intelligence, (2018). DOI: 10.1609/aaai.v32i1.12048
    [35] L. Bao, P. Lambert, T. Badia, Attention and lexicon regularized LSTM for aspect-based sentiment analysis, in Proceedings of the 57th annual meeting of the association for computational linguistics: student research workshop, (2019), 253–259. DOI: 10.18653/v1/P19-2035
    [36] Y. Xing, C. Xiao, Y. Wu, Z. Ding, A convolutional neural network for aspect-level sentiment classification, Int. J. Pattern Recognit. Artif Intell., 33 (2019), 1959046. DOI: 10.18653/v1/2021.textgraphs-1.8 doi: 10.18653/v1/2021.textgraphs-1.8
    [37] X. Wang, F. Li, Z. Zhang, G. Xu, J. Zhang, X. Sun, A unified position-aware convolutional neural network for aspect based sentiment analysis, Neurocomputing, 450 (2021), 91–103. DOI: 10.1016/j.neucom.2021.03.092 doi: 10.1016/j.neucom.2021.03.092
    [38] C. Gan, L. Wang, Z. Zhang, Z. Wang, Sparse attention based separable dilated convolutional neural network for targeted sentiment analysis, Knowl. Based Syst., 188 (2020), 104827. DOI: 10.1016/j.knosys.2019.06.035 doi: 10.1016/j.knosys.2019.06.035
    [39] N. Zhao, H. Gao, X. Wen, H. Li, Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis, IEEE Access, 9 (2021), 15561–15569. DOI: 10.1109/ACCESS.2021.3052937 doi: 10.1109/ACCESS.2021.3052937
    [40] Y. Tay, L. A. Tuan, S. C. Hui, Dyadic memory networks for aspect-based sentiment analysis, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, (2017), 107–116. DOI: 10.1145/3132847.3132936
    [41] Y. Chen, T. Zhuang, K. Guo, Memory network with hierarchical multi-head attention for aspect-based sentiment analysis, Appl. Intell., 51 (2021), 4287–4304. DOI: 10.1007/s10489-020-02069-5 doi: 10.1007/s10489-020-02069-5
    [42] Y. Zhang, B. Xu, T. Zhao, Convolutional multi-head self-attention on memory for aspect sentiment classification, IEEE-CAA J. Automatica Sin., 7 (2020), 1038–1044. DOI: 10.1109/JAS.2020.1003243 doi: 10.1109/JAS.2020.1003243
    [43] Y. Song, J. Wang, T. Jiang, Z. Liu, Y. Rao, Attentional encoder network for targeted sentiment classification, preprint, arXiv: 190209314.
    [44] H. Yang, B. Zeng, J. Yang, Y. Song, R. Xu, A multi-task learning model for chinese-oriented aspect polarity classification and aspect term extraction, Neurocomputing, 419 (2021), 344–356. DOI: 10.1016/j.neucom.2020.08.001 doi: 10.1016/j.neucom.2020.08.001
    [45] A. Karimi, L. Rossi, A. Prati, Improving bert performance for aspect-based sentiment analysis, preprint, arXiv: 201011731.
    [46] A. Karimi, L. Rossi, A. Prati, Adversarial training for aspect-based sentiment analysis with bert, in 2020 25th International conference on pattern recognition (ICPR), (2021), 8797–8803. DOI: 10.1109/ICPR48806.2021.9412167
    [47] H. Peng, Y. Ma, Y. Li, E. Cambria, Learning multi-grained aspect target sequence for Chinese sentiment analysis, Knowl. Based Syst., 148 (2018), 167–176.
    [48] W. Che, Y. Zhao, H. Guo, Z. Su, T. Liu, Sentence compression for aspect-based sentiment analysis, IEEE-ACM Trans. Audio Speech Lang., 23 (2015), 2111–2124.
    [49] L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, K. Xu, Adaptive recursive neural network for target-dependent twitter sentiment classification, in Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: Short papers), (2014), 49–54.
    [50] B. Wang, W. Lu, Learning latent opinions for aspect-level sentiment classification, in Proceedings of the AAAI Conference on Artificial Intelligence, 2018.
    [51] H. T. Nguyen, M. Le Nguyen, Effective attention networks for aspect-level sentiment classification, in 2018 10th International Conference on Knowledge and Systems Engineering (KSE), (2018), 25–30. DOI: 10.1109/KSE.2018.8573324
    [52] D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, preprint, arXiv: 14126980.
    [53] Y. Wang, M. Huang, X. Zhu, L. Zhao, Attention-based LSTM for aspect-level sentiment classification, in Proceedings of the 2016 conference on empirical methods in natural language processing, (2016), 606–615. DOI: 10.18653/v1/D16-1058
    [54] D. Ma, S. Li, X. Zhang, H. Wang, Interactive attention networks for aspect-level sentiment classification, preprint, arXiv: 170900893.
    [55] H. Peng, L. Xu, L. Bing, F. Huang, W. Lu, L. Si, Knowing what, how and why: A near complete solution for aspect-based sentiment analysis, in Proceedings of the AAAI conference on artificial intelligence, (2020), 8600–8607. DOI: 10.1609/aaai.v34i05.6383
    [56] W. Song, Z. Wen, Z. Xiao, S. C. Park, Semantics perception and refinement network for aspect-based sentiment analysis, Knowl. Based Syst., 214 (2021), 106755.
    [57] L. Xu, L. Bing, W. Lu, F. Huang, Aspect sentiment classification with aspect-specific opinion spans, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), (2020), 3561–3567. DOI: 10.18653/v1/2020.emnlp-main.288
    [58] Q. Xu, L. Zhu, T. Dai, C. Yan, Aspect-based sentiment classification with multi-attention network, Neurocomputing, 388 (2020), 135–143. DOI: 10.1016/j.neucom.2020.01.024 doi: 10.1016/j.neucom.2020.01.024
    [59] B. Huang, J. Zhang, J. Ju, R. Guo, H. Fujita, J. Liu, CRF-GCN: An effective syntactic dependency model for aspect-level sentiment analysis, Knowl. Based Syst., 260 (2023), 110125. DOI: 10.1016/j.knosys.2022.110125 doi: 10.1016/j.knosys.2022.110125
    [60] B. Huang, R. Guo, Y. Zhu, Z. Fang, G. Zeng, J. Liu, et al., Aspect-level sentiment analysis with aspect-specific context position information, Knowl. Based Syst., 243 (2022), 108473. DOI:10.1016/j.knosys.2022.108473 doi: 10.1016/j.knosys.2022.108473
  • This article has been cited by:

    1. Vasily Prystupa, Olga Krotova, Diana Torosyan, Olga Sangadzhieva, Kermen Khalgaeva, 2023, Chapter 72, 978-3-031-21218-5, 646, 10.1007/978-3-031-21219-2_72
    2. Alexandra Marchenko, Elena Moskalenko, Elena Arakcheeva, Natalia Bychenko, 2022, Chapter 5, 978-3-030-91404-2, 35, 10.1007/978-3-030-91405-9_5
    3. ГОРЛОВ, И.Ф., СЛОЖЕНКИНА, М.И., НИКОЛАЕВ, Д.В., КНЯЖЕЧЕНКО, О.А., МОСОЛОВА, Д.А., ШАХБАЗОВА, О.П., РАДЖАБОВ, Р.Г., RELATIONSHIP OF MEAT PRODUCTIVITY AND PRESLAUGHTER WEIGHT OF BULLS DEPENDING ON THE INTENSITY OF GROWING, 2022, 00269034, 38, 10.33943/MMS.2022.30.13.007
    4. Anna Karamaeva, Sergey Karamaev, Nina Chupsheva, Roman Ershov, 2023, Chapter 347, 978-3-031-21431-8, 3140, 10.1007/978-3-031-21432-5_347
    5. Vasily Prystupa, Olga Krotova, Svetlana Yandyuk, Altana Ubushieva, Arslang Khakhlinov, 2023, Chapter 30, 978-3-031-21218-5, 290, 10.1007/978-3-031-21219-2_30
    6. Vasily Prystupa, Olga Krotova, Boris Ubushaev, Konstantin Savenkov, Natalia Moroz, Maria Savenkova, V.I. Pakhomov, A.N. Altybayev, M. Petković, T.A. Maltseva, Formation of meat productivity in descendants of Kalmyk breed improver bulls, 2024, 113, 2117-4458, 02022, 10.1051/bioconf/202411302022
    7. Vasily Prystupa, Olga Krotova, Konstantin Savenkov, Ruslan Azaev, Danzan Mashtykov, Nikita Boraev, M.-T. Liong, I.V. Tkacheva, Beef production in conditions of stable-pasture and industrial technologies in breeding farms, 2024, 84, 2117-4458, 01051, 10.1051/bioconf/20248401051
    8. I. P. Prokhorov, Yu. V. Shoshina, O. A. Kalmykova, V. N. Lukyanov, Conversion of protein and feed energy into food protein and meat fat in steers of Simmental breed, 2023, 20747454, 42, 10.33920/sel-03-2306-05
    9. Murat Ulimbashev, Irina Tletseruk, Oksana Krasnova, Zemfira Pskhatsieva, Nina Konik, O. Loretts, I. Donnik, Z. Abbas Rao, A. Ruchkin, V. Kukhar, The first results of the use of the gene pool of the Kalmyk breed on the brown stock of Brown Swiss cattle, 2024, 108, 2117-4458, 01012, 10.1051/bioconf/202410801012
    10. Natalia Gizatova, Albert Gizatov, Liliya Zubairova, Irina Mironova, Azat Nigmatyanov, Yuliya Chernyshenko, Alexey Pleshkov, Development of technology for the production of sausage produce using secondary collagen-containing raw materials, 2021, 10, 2182-1054, 282, 10.7455/ijfs/10.2.2021.a1
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1408) PDF downloads(102) Cited by(0)

Figures and Tables

Figures(5)  /  Tables(3)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog