Startseite Gray correlation analysis of factors influencing compressive strength and durability of nano-SiO2 and PVA fiber reinforced geopolymer mortar
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Gray correlation analysis of factors influencing compressive strength and durability of nano-SiO2 and PVA fiber reinforced geopolymer mortar

  • Peng Zhang , Xuemei Zhang , Yamin Zhang , Yuanxun Zheng EMAIL logo und Tingya Wang
Veröffentlicht/Copyright: 5. Dezember 2022
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Abstract

To investigate the mechanical properties and durability of nano-SiO2, polyvinyl alcohol (PVA) fiber-modified fly ash (FA), and metakaolin (MK)-based geopolymer mortar (FMGM), tests of compressive strength, electrical flux, freeze–thaw cycles, and sulfate dry and wet cycles were conducted. Based on the experimental results, combined with Dunn’s gray correlation theory analysis method, a mathematical analysis of the effect sensitivity of the contents of the four mixtures on the mechanical properties and durability of FMGM was carried out. The method of gray correlation analysis can solve the mathematical problem with partial unclear and uncertain information, and the method requires less data and less computation compared with other mathematical analysis method. The results showed that the gray correlation degree between the content of MK and the electric flux value is higher than the that of other comparison sequence and each reference sequence, while the gray correlation degree between the PVA fiber dosage and the loss rate of compressive strength is lower than that of other comparison sequence and each reference sequence. The influence of the four mixture contents on the compressive strength and mass loss rate was in the following decreasing order: MK and FA, PVA fiber, and nano-SiO2. In addition, the influence of the four material mixture contents on the electric flux value and compressive strength loss rate was consistent in the following decreasing order: MK and FA, nano-SiO2, and PVA fiber. Furthermore, the mechanical properties and durability reached the optimum when the PVA fiber content was 0.6% and the dosage of nano-SiO2 was 1.0%. The results of this study can provide a new method for the analysis and evaluation of mechanical properties and durability of nano-SiO2 and PVA fiber-reinforced FMGM in future.

1 Introduction

Since the reform and opening up, due to rapid economic development, China has emerged as a major energy-producing and consuming country, and its carbon emissions have been the highest in the world. Despite the global economy being affected by the epidemic in 2020, the total global carbon emissions remained high, reaching 32.28 billion tons, with China accounting for the highest share (30.7%). The cement industry is currently the second-largest industry in terms of carbon emissions, behind only the steel industry, and the production of cement mortar or concrete releases large amounts of carbon dioxide into the environment [1,2]. This is a serious violation of China’s policy of “double carbon” peaking by 2030, and carbon neutrality by 2060. It has been reported that each ton of cement production consumes nearly 100 kg of standard coal and emits approximately 1,000 kg of carbon dioxide; therefore, the massive use of cement will lead to increasingly serious problems of resource consumption, energy waste, and environmental pollution [3]. Traditional cement-based composites have disadvantages such as easy cracking, poor durability, low strength, and insufficient water retention [4,5]. Therefore, the development of a green and environmentally friendly cementitious material that can be used as an alternative to cement has become an urgent need [6,7].

Geopolymers are a class of new non-metallic material, proposed by Professor Davidovits in 1978, with inorganic SiO2 and AlO4 tetrahedra as the main composition. Geopolymers represent a new calcium-free aluminum-silica cementitious material with a three-dimensional shelf-like structure [8]. Compared with traditional silicate cement and concrete, a geopolymer has better workability, mechanical properties [9,10], and durability [11,12]. Geopolymers are environment friendly as their preparation process does not require the “two grinding and one burning” process [13], which saves a considerable amount of resources and energy and prevents NOX, SOX, and CO generation and substantial CO2 emissions [14]. In addition, geopolymer materials have advantages such as fast hardening, high early strength, low shrinkage, low permeability, high-temperature resistance, good thermal insulation, and adjustable thermal expansion coefficient [15]. Geopolymers have higher strength, lower water absorption, and lower porosity than ordinary silicate cement [16]. Chithambaram et al. [15] studied the thermodynamic properties of geopolymer mortar manufactured using slag powder instead of fly ash (FA) and found that geopolymer mortar exhibits a dense structure, early curing, and increased strength. Sreevidya et al. [17] studied the strength of FA geopolymer mortar and found that the highest 28 days compressive strength of the specimens was obtained when the exciter/FA mass ratio was 0.416. Bingol et al. [18] studied the durability of alkali-activated slag geopolymer mortar and found that the corrosion resistance of alkali-activated slag geopolymer mortar in corrosive media was significantly higher than that of cement mortar. Geopolymer mortars, given their excellent characteristics, have broad application prospects in the construction of roads and bridges, water conservancy, and other fields [19].

The drawbacks of brittleness, low toughness, and low deformation resistance exhibited by both geopolymer and cement mortars can be mitigated by adding certain high-performance fibers to geopolymer mortars [20]. The fiber materials [21] commonly used in mortars are polypropylene alcohol fibers [22,23], steel fibers [24,25], glass fibers [26], polyvinyl alcohol (PVA) fibers [27,28], etc. PVA fibers exhibit high tensile strength and modulus of elasticity, good adhesion with cementitious materials, good hydrophilicity, and nontoxicity. In addition, PVA fibers exhibit good acid and alkali resistance, which can improve the durability of the material [29]. Du and Li [30] showed that PVA fiber-reinforced high-strength concrete has excellent mechanical properties. Wang et al. [31] showed that the durability and ductility of rubber concrete improves with PVA-fiber reinforcement. PVA fiber incorporated into geopolymer mortars can improve the toughness of the material [32], changing the damage mode of the material from brittle to ductile [33]. Therefore, the use of PVA fibers in geopolymer mortars improves their properties, making them suitable for many applications [34].

Nanomaterials are favored by scholars all over the world because of their small-size effect, quantum effect, and interfacial effect, which can enhance traditional construction materials in terms of their structure and physicochemical properties. Nano-SiO2 has many advantages, such as being non-toxic, odorless, and non-polluting, in addition to having a small particle size. The application of nanomaterials in mortars or concretes can play the role of nano-filling and nano-enhancement, improve the performance of the interfacial transition zone in mortar, optimize the microstructure of mortar, and reduce the porosity while enhancing the impermeability [35], mechanical properties [36,37], and durability [38,39] of the matrix. With the maturity of nano-SiO2 modification technology for cement-based materials, many scholars have studied the properties of nano-SiO2 incorporated into geopolymer-based construction materials. Li [40] studied high-strength concrete with bulk FA mixed with nano-SiO2 and found that it exhibited increased compressive strength and improved pore distribution compared to concrete without nano-SiO2. Xiao et al. [41] studied the effect of nano-SiO2 on different microstructures and impermeability of graded concrete and found that the microstructure was the densest and the impermeability was the strongest when the Fuller exponent was 0.4–1.0. Related studies have shown that the incorporation of an appropriate quantity of nano-SiO2 in geopolymer mortar can improve its microstructure and durability properties such as frost resistance, permeability resistance, acid rain erosion line, and washout resistance, as well as increase the strength of the geopolymer mortar [42].

Environmental differences between regions and harsh environments have caused serious damage to exposed buildings, such as impact, abrasion, carbonization, freeze–thaw damage, and salt–freeze damage. These damages directly or indirectly cause cracks, corrosion, decline in load-bearing capacity and durability, and other effects. Offshore projects and structures are subjected to damage owing to harsh marine environment [43,44,45,46]. Many buildings have suffered freeze–thaw damage due to the existence of large cold zones, especially in the north [47]. The degradation of building materials due to freeze–thaw cycles is an important durability issue for cementitious materials and structures in cold regions [48]. Severely cold places have a high demand for geopolymer mortars as repair and reinforcement materials [49]. Owing to their excellent durability, geopolymer mortars are more energy efficient than cement-based materials, and the incorporation of nano-SiO2 and PVA fibers can enhance the toughness of the matrix and durability of the material [50,51,52,53]. Therefore, it is necessary to study the mechanical properties and durability of geopolymer mortars incorporated with nano-SiO2 and PVA fibers and reveal the mechanism of nano-SiO2 and PVA fibers that enhance the mechanical properties and durability of geopolymer mortars.

There are many ways to analyze the experimental data involving the mechanical properties and durability parameters of nano-SiO2, PVA fiber-reinforced FA, and metakaolin (MK)-based geopolymer mortar (FMGM). Most conventional methods of system analysis are traditional mathematical and statistical analyses, including regression analysis [54], analysis of variance [55], and principal component analysis [56]. Among these, regression analysis is the most widely used. Its types include linear, multi-factor, single-factor, stepwise, and non-stepwise. Regression is best suited in the case of a small number of variables, with a large amount of statistical data that exhibit typical distribution patterns, such as linear, exponential, logarithmic, and normal distributions. The results are not intuitive when the amount of information is insufficient. However, for experiments on the mechanical properties and durability of nano-SiO2 and PVA fiber-reinforced FMGM, there are certain limitations on the acquisition of experimental data. (1) It is difficult to obtain a large amount of valid data under exactly the same experimental conditions due to the influence of human and environmental factors. (2) It is difficult to maintain a certain distribution pattern of experimental data [57]. For such data, if traditional mathematical and statistical analysis methods are used, it is likely that the system is found unsolvable, or that the conclusion deviates from the facts and is misleading.

Gray correlation analysis is an important method of the gray system theory [58,59] that requires less data and computation [60,61,62]. Gray correlation analysis theory has been widely used in the evaluation and analysis of various properties of concrete [63,64,65,66]. Lai et al. [67] evaluated concrete structure crack patterns and found that the Mahalanobis distance and gray correlation analysis can effectively classify the dataset and identify the concrete resultant crack types. Arici and Kelestemur [68] used the Taguchi theory-based gray correlation analysis method to obtain the optimal parameter classes that satisfy the mortar performance. With the exploration of social development, the economy, and other abstract systems, gray correlation theory is gradually being improved and has been widely used in the analysis of water resources, the economy, agriculture, and other fields. Gray correlation theory has now become a new theoretical tool for the analysis, modeling, prediction, decision making, control, and transformation of objective systems. Gray correlation analysis can not only compensate for the shortcomings of conventional system analysis methods but also has the advantages of a simple calculation process, small calculation volume, and intuitive results that are suitable for practical engineering applications. Therefore, in this study, using the method of gray correlation analysis, comparison and reference sequences were established to calculate the weight ranking for the influence of the four mixture contents on the mechanical properties and durability evaluation indices of FMGM. The quantities of alkali exciter, FA, water reducer, MK, water, quartz sand, PVA fiber, and nano-SiO2 were used as the comparison sequence. The electric flux value, compressive strength loss rate, mass loss rate index under sulfate attack, and compressive strength of the material were used as the reference sequence. Based on the calculation results, the effect sensitivity of the contents of the four mixtures on the mechanical properties and durability of FMGM was analyzed.

2 Experimental procedure

2.1 Raw materials and mix design

In this study, a control variable method was chosen in the mix design, that is, the water–cement ratio, cement–sand ratio, water–glass modulus, and admixture were fixed, while the admixture of nano-SiO2 or PVA fiber was changed. The water–cement ratio (the ratio of water to cementitious material mass contained in the water and alkali exciter) was 0.65, and the cement–sand ratio was 1:1. FA, in the same amount, was used to replace 30% of the kaolinite mass, and the alkali exciter solution was prepared by mixing sodium hydroxide, water glass, and water. The content of Na2O is 8.2%, and the content of SiO2 is 26.1% in water glass. The water glass has the gravity of 1.38 g/cm3 and solid content of 34.4%. The initial modulus of the water glass was 3.2, which was adjusted to 1.3 by adding sodium hydroxide flakes, and then, the mass fraction of sodium oxide in the solution was adjusted to 15% by adding water. The concentration of the NaOH is 99.0%. The chemical and physical composition of kaolinite is listed in Tables 1 and 2. The primary FA used was produced by Datang Luoyang Thermal Power Co., Ltd. The main chemical and physical properties of the FA are listed in Tables 3 and 4. The quartz sand used was of extra-fine quality from the Gongyi Yuanheng Water Purification Material Plant, with a particle size range of 75–120 μm. The modulus of the sodium silicate solution used in the test was 3.2, the specific gravity was 1.38 g/cm3, the solid content was 34.3%, and the purity of sodium hydroxide was 99.0%. The PVA fibers were produced by Kuraray Co., Ltd. The apparent density of nano-SiO2 was 54 g/L, pH was 6.21, average particle size was 30 nm, heating reduction and cautery reduction were 1.0%, specific surface area was 200 m2/g, and content was 99.7%. The water-reducing agent had a water reduction rate of 21%, pH value of 4.52, fixed content of 24.56%, and density of 1.058 g/cm3. The volume doping of the PVA fibers used in this study was in the range of 0–1.2%, varied in increments of 0.2%. The nano-SiO2 contents were varied in the range of 0–2.5%, in increments of 0.5%. The nano-SiO2 content was determined as the ratio of the amount of nano-SiO2 to the amount of MK and FA.

Table 1

Chemical composition of MK

Chemical composition SiO2 Al2O3 Fe2O3 CaO + MgO K2O + Na2O
Content (%) 54 ± 2 43 ± 2 ≤1.3 ≤0.8 ≤0.7
Table 2

Physical properties of MK

Whiteness (%) Activity index (%) Availability of lime (mL/4N-HCl) Average particle size (µm) Ignition loss (%)
75 12 1,350 1.2 0.5
Table 3

Chemical compositions of FA

Chemical compositions (wt%) SiO2 Al2O3 Fe2O3 CaO + MgO SO3
FA 60.98 24.47 6.70 5.58 0.27
Table 4

Physical properties of FA

Water absorbing capacity (%) Standard consistency (%) Bulk density (g/cm3) Specific gravity (g/cm3)
105 47.1 0.77 2.16

2.2 Preparation of mixture

During the mixing procedure of geopolymer mortar, the precursor was first dry mixed with quartz sand for 2 min. The sand was spread on a plate and dried in an oven at 70°C for 24 h prior to mixing. After that, both PVA fiber and nano-SiO2 (NS) was added to the mixer in 2 batches, stirring for 2 min each time. Then, the mixed solution of alkaline solution and super plasticizer was added to the dry mixture. The prepared alkaline solution should be set for 1 day before use. After thoroughly mixing, the fresh mortar was molded. In this study, 22 groups of specimens with different nano-SiO2 and PVA fiber content were tested. The molded specimens were cured in ambient environment (about 28°C) for 24 h and then transferred to a curing room (20 ± 2°C, 95% humidity) for 28 days.

2.3 Experimental method

The durability of nano-SiO2 and PVA-fiber-reinforced FMGM was investigated, and the compressive strength was tested using a uniaxial compressive test. The electric flux was tested using a chloride ion electric flux test. The cathode was connected to a 3.0% NaCl solution, and the anode was connected to a 0.3 M NaOH solution. 60 V DC voltage was continuously supplied for 6 h, and the current was recorded every 30 min. The average value of triplicate samples was recorded as final. The apparent damage to the geopolymer mortar was observed by rapid freeze–thaw tests after 25 freeze–thaw cycles for each doping amount. After 26 days of standard curing, the samples were immersed in 15–20°C water for 2 days and then were surface dried. The single freezing–thawing time was 8 h (freezing for 4 h and thawing for 4 h). The temperature of sample was controlled at −17 and 8°C at the end of freezing and thawing, respectively. After 26 days of standard curing, the samples were put in an oven of 80 ± 5°C for 48 h and then were cooled to room temperature in a dry environment. The dried samples were placed at intervals in the testing machine. The 5% Na2SO4 was used as the erosion solution, and the solution was replaced by fresh solution every month. The compressive strength loss coefficient of the geopolymer mortar was tested for each admixture, and the quality change rate of the geopolymer mortar was tested after 90 dry and wet cycles of sulfate, via the sulfate dry and wet cycle tests. Therefore, in the gray correlation analysis, the compressive strength, electric flux value, mass loss rate, and compressive strength loss rate were selected as the reference sequence, and the ratio design of each material of the geopolymer mortar was used as the comparison sequence. However, because the dosage amounts of water, quartz sand, water glass, sodium hydroxide, and water-reducing agent were fixed values, only the dosages of partial kaolin, FA, PVA fiber, and nano-SiO2 were chosen as the comparison sequence.

2.4 The experimental result

The mix proportions of the nano-SiO2 and PVA fiber-reinforced FMGM are listed in Table 5. The values of each comparative sequence are listed in Table 6.

Table 5

Mixing proportions of nano-SiO2 and PVA fiber reinforced FMGM [69]&&&

Mix no. Water (kg/m3) MK (kg/m3) FA (kg/m3) Quartz sand (kg/m3) Water glass (kg/m3) NaOH (kg/m3) PVA fiber (%) NS (%) Water-reducing agents (kg/m3)
1 106.2 429.5 184.1 613.6 445.4 71 0 0 3.07
2 106.2 429.5 184.1 613.6 445.4 71 0.2 0 3.07
3 106.2 429.5 184.1 613.6 445.4 71 0.4 0 3.07
4 106.2 429.5 184.1 613.6 445.4 71 0.6 0 3.07
5 106.2 429.5 184.1 613.6 445.4 71 0.8 0 3.07
6 106.2 429.5 184.1 613.6 445.4 71 1.0 0 3.07
7 106.2 429.5 184.1 613.6 445.4 71 1.2 0 3.07
8 106.2 427.2 183.1 613.6 445.4 71 0 0.5 3.07
9 106.2 425.0 182.2 613.6 445.4 71 0 1.0 3.07
10 106.2 422.7 181.2 613.6 445.4 71 0 1.5 3.07
11 106.2 420.4 180.2 613.6 445.4 71 0 2.0 3.07
12 106.2 418.1 179.2 613.6 445.4 71 0 2.5 3.07
13 106.2 425.0 182.2 613.6 445.4 71 0.2 1.0 3.07
14 106.2 425.0 182.2 613.6 445.4 71 0.4 1.0 3.07
15 106.2 425.0 182.2 613.6 445.4 71 0.8 1.0 3.07
16 106.2 425.0 182.2 613.6 445.4 71 1.0 1.0 3.07
17 106.2 425.0 182.2 613.6 445.4 71 1.2 1.0 3.07
18 106.2 427.2 183.1 613.6 445.4 71 0.6 0.5 3.07
19 106.2 425.0 182.2 613.6 445.4 71 0.6 1.0 3.07
20 106.2 422.7 181.2 613.6 445.4 71 0.6 1.5 3.07
21 106.2 420.4 180.2 613.6 445.4 71 0.6 2.0 3.07
22 106.2 418.1 179.2 613.6 445.4 71 0.6 2.5 3.07
Table 6

Reference sequence of nano-SiO2 and PVA fiber reinforced FMGM

Mix no. Compressive strength (MPa) Electric flux values (C) Loss rate of compressive strength (%) Mass loss rate (%)
1 44.2 1426.31 18.8 −3.74
2 50.8 1294.38 17.7 2.75
3 55.3 1216.08 15.9 2.46
4 58.5 1185.84 14.7 1.81
5 60.3 1150.24 12.6 1.44
6 50.5 1158.52 10.1 1.64
7 48.1 1195.41 8.9 2.07
8 45.0 1220.82 17.1 −2.31
9 47.3 1185.06 15.0 −1.12
10 50.1 1121.13 12.4 1.6
11 48.8 1164.84 13.5 2.1
12 46.4 1190.52 15.7 1.9
13 53.9 1147.62 13.7 2.06
14 57.4 1107.48 11.1 1.47
15 62.4 1071.78 8.2 0.82
16 55.7 1076.94 6.8 0.99
17 54.1 1102.36 5.4 1.38
18 59.1 1157.88 11.2 1.67
19 61.1 1096.02 9.7 1.21
20 63.6 1055.16 7.5 0.92
21 62.3 1107.06 11.6 1.15
22 59.7 1166.98 14.4 1.49

3 Model establishment

3.1 Brief description of gray correlation theory

Gray correlation theory is a system science theory pioneered by a famous scholar in China. Professor Deng [70,71,72] proposed the gray correlation model and the concept of gray correlation degree analysis for each subsystem to establish a relationship between the system and each subsystem (influencing factors). However, statistical data in China are very limited, and some data are incomplete. Human influence factors are inevitable, so it is common for the data to have many inaccuracies. It may be difficult to find a suitable distribution pattern, making data processing and analysis difficult.

At present, the more commonly used methods are gray correlation, regression analysis, and so on. Although regression analysis can predict and work out the corresponding function, and carry out residual test on the results, it requires a large number of samples and is limited in some conditions, so it can only be used for prediction. Gray correlation analysis requires fewer samples, which can be used for prediction and analysis. Therefore, gray correlation analysis can compensate for the shortcomings of traditional mathematical and statistical methods and has become a widely used system analysis method. The specific steps for conducting the gray correlation analysis are explained below.

3.2 Establishment of the gray correlation model

3.2.1 Determination of the sequence of system analysis

The series for conducting system analysis include the parent series, which represents the system, and the sub-series, which represents the subsystem.

  1. Determination of the parent series. The parent series, also known as the main series, reference series, or parent indicator, is a data series that can reflect the characteristics of the system behavior and is denoted as X 0 .

    (1) X 0 = { x 0 ( 1 ) , x 0 ( 2 ) , , x 0 ( n ) } .

  2. Determination of the sub-series. Known as the correlation series, comparison series, or sub-indicator, it is a data series consisting of various factors that affect the behavior of the system and is denoted as X i , i = 1,2,3…n.

    (2) X 1 = { x 1 ( 1 ) , x 1 ( 2 ) , , x 1 ( n ) } X i = { x i ( 1 ) , x i ( 2 ) , , x i ( n ) } X m = { x m ( 1 ) , x m ( 2 ) , , x m ( n ) } .

  3. Determination of the systematic series for gray correlation analysis is as follows:

{ X 0 , X 1 , , X i , , X m } .

3.2.2 Preprocessing of variables

  1. The mean of each indicator is determined as follows:

(3) x i ¯ = 1 n ( x i ( 1 ) + x i ( 2 ) + + x i ( n ) ) .

2) Each element of the indicator is divided by the mean, and the series is normalized to obtain a new data column, x i ( k ) .

(4) x i ( k ) = x i ( k ) x i ¯ , k = 1 , 2 , , m ; i = 0 , 1 , n .

It is necessary to preprocess the variables and narrow the range of variables to simplify the calculation.

3.2.3 Calculation of the correlation coefficient

  1. The minimum and maximum differences between the two levels are calculated as follows:

    (5) a = min i min k x 0 ( k ) x i ( k ) , i = 0 , 1 , , n ,

    (6) b = min i max k x 0 ( k ) x i ( k ) , k = 1 , 2 , , m ,

    where a and b are the minimum and maximum differences between the two levels, respectively.

  2. The gray correlation coefficient of the evaluation object is calculated as follows:

(7) γ i ( x 0 ( k ) , x i ( k ) ) = a + ρ b x 0 ( k ) x i ( k ) + ρ b ,

where γ i ( k ) is the gray correlation coefficient between x 0 ( k ) and x i ( k ) ; ρ ( 0 , 1 ) , where ρ represents the resolution coefficient, often taken as 0.5, and its value is negatively correlated with the resolution ability, that is, the larger the resolution coefficient, the smaller the resolution ability of the evaluation scheme.

3.2.4 Calculation of gray correlation

The gray correlation coefficient between X 0 and X i ( i = 1 , 2 , n ) can be calculated as follows:

(8) η i ( X 0 , X i ) = 1 n k = 1 n γ i ( x 0 ( k ) , x i ( k ) ) , k = 1 , 2 , , n ,

where, η i ( X 0 , X i ) is the gray correlation coefficient between X 0 and X i ( i = 1 , 2 , n ) .

3.2.5 Gray relational decision

According to the above operation steps, the gray correlation degree of each subsequence with the parent sequence can be determined and then compared in terms of size. The greater the gray correlation, the greater is the influence on the parent index.

4 Model training and result analysis

In order to analyze the effects of different factors on the durability of the nano-SiO2 and PVA fiber reinforced FMGM, a gray correlation was performed with FA admixture, MK admixture, PVA fiber admixture, and SiO2 nanoparticle admixture as the comparison sequence and the mass loss rate, electric flux value, compressive strength loss rate, and compressive strength of the FMGM as the reference sequence.

The variables were first preprocessed, that is, the variables were made dimensionless, and the preprocessed results are listed in Table 7.

Table 7

Pre-processed data of each factor

Mix no. MK FA PVA fiber NS Compressive strength Electric flux values Loss rate of compressive strength Mass loss rate
1 1.0097 1.0096 0.0000 0.0000 0.8140 1.2258 1.5375 −3.4630
2 1.0097 1.0096 0.4074 0.0000 0.9355 1.1124 1.4476 2.5463
3 1.0097 1.0096 0.8148 0.0000 1.0184 1.0451 1.3304 2.2778
4 1.0097 1.0096 1.2222 0.0000 1.0773 1.0191 1.2022 1.6759
5 1.0097 1.0096 1.6296 0.0000 1.1105 0.9885 1.0305 1.3333
6 1.0097 1.0096 2.0370 0.0000 0.9300 0.9957 0.8260 1.5185
7 1.0097 1.0096 2.4444 0.0000 0.8858 1.0274 0.7279 1.9167
8 1.0043 1.0042 0.0000 0.5500 0.8287 1.0492 1.3985 −2.1389
9 0.9991 0.9992 0.0000 1.1000 0.8711 1.0185 1.2268 −1.037
10 0.9937 0.9937 0.0000 1.6500 0.9227 0.9635 1.0141 1.4815
11 0.9883 0.9883 0.0000 2.2000 0.8987 1.0011 1.1041 1.9444
12 0.9829 0.9828 0.0000 2.7500 0.8545 1.0232 1.2840 1.7593
13 0.9991 0.9992 0.4074 1.1000 0.9926 0.9863 1.1204 1.9074
14 0.9991 0.9992 0.8148 1.1000 1.0571 0.9518 0.9807 1.3611
15 0.9991 0.9992 1.6296 1.1000 1.1492 0.9211 0.6706 0.7593
16 0.9991 0.9992 2.0370 1.1000 1.0258 0.9256 0.5561 0.9167
17 0.9991 0.9992 2.4444 1.1000 0.9963 0.9474 0.4416 1.2778
18 1.0043 1.0042 1.2222 0.5500 1.0884 0.9951 0.9160 1.5463
19 0.9991 0.9992 1.2222 1.1000 1.1252 0.9419 0.7933 1.1204
20 0.9937 0.9937 1.2222 1.6500 1.1713 0.9068 0.6134 0.8519
21 0.9883 0.9883 1.2222 2.2000 1.1473 0.9514 0.9487 1.0648
22 0.9829 0.9828 1.2222 2.7500 1.0994 1.0029 0.9323 1.3769

The correlation coefficients of the FA admixture, MK admixture, PVA fiber admixture, nano-SiO2 admixture, mass loss rate, electric flux value, compressive strength loss rate, and compressive strength of nano-SiO2 and PVA fiber-reinforced FMGM were calculated and the results are displayed in Figures 14.

Figure 1 
               Correlation between various factors and compressive strength.
Figure 1

Correlation between various factors and compressive strength.

Figure 2 
               Correlation between each factor and electric flux value.
Figure 2

Correlation between each factor and electric flux value.

Figure 3 
               Correlation between various factors and loss rate of compressive strength.
Figure 3

Correlation between various factors and loss rate of compressive strength.

Figure 4 
               Correlation between each factor and quality loss rate.
Figure 4

Correlation between each factor and quality loss rate.

In Figures 14, most of the correlation curves of MK and FA are in a state of coincidence. By calculating the correlation coefficients of FA dosing, MK dosing, PVA fiber dosing, and nano-SiO2 dosing with the mass loss rate, electric flux value, compressive strength loss rate, and compressive strength of the nano-SiO2 and PVA fiber-reinforced FMGM under different ratio designs, it can be seen that the correlation coefficients of PVA fiber dosing first increased and then decreased when the nano-SiO2 dosage was kept constant using the control variable method. The correlation coefficient of the nano-SiO2 dosage also tends to increase and then decrease when the PVA fiber dosage is constant. At the same time, the correlation coefficient of PVA fiber doping is higher when the PVA fiber doping is 0.6%, and the correlation coefficient of nano-SiO2 doping is higher when the nano-SiO2 doping is 1.0%. From Tables 69, it can be seen that the correlation coefficients of nano-SiO2 and PVA fiber doping vary widely, and the mass loss rate, electric flux value, compressive strength loss rate, and compressive strength of nano-SiO2 and PVA fiber-reinforced FMGM are more sensitive to nano-SiO2 and PVA fiber doping. Analysis of the above data revealed that among these 22 sets of data, the correlation coefficients of both materials were higher when the nano-SiO2 admixture was 1.0% and the PVA fiber admixture was 0.6%. Therefore, the mass loss rate, electric flux value, compressive strength loss rate, and compressive strength of nano-SiO2 and PVA fiber-reinforced FMGM reached optimum values when the doping of PVA fibers was approximately 0.6% and the nano-SiO2 dosage was approximately 1.0%, that is, the mechanical properties and durability performance of this material were optimal.

Table 8

Gray correlation degree of each comparison sequence and each reference sequence

Indicators MK FA PVA fiber NS
Compressive strength 0.9117 0.9117 0.6592 0.6438
Electric flux values 0.9607 0.9606 0.5961 0.6010
Loss rate of compressive strength 0.8360 0.8360 0.5869 0.5932
Mass loss rate 0.7695 0.7695 0.7218 0.6983
Table 9

Correlation coefficient between each factor and quality loss rate

Mix no. MK FA PVA fiber NS
1 0.3364 0.3364 0.3960 0.3960
2 0.5981 0.5981 0.5158 0.4719
3 0.6440 0.6440 0.6110 0.4999
4 0.7775 0.7775 0.8389 0.5768
5 0.8815 0.8815 0.8911 0.6322
6 0.8221 0.8221 0.8192 0.6010
7 0.7179 0.7179 0.8164 0.5434
8 0.4195 0.4195 0.5158 0.4582
9 0.5282 0.5282 0.6894 0.5160
10 0.8284 0.8284 0.6070 0.9384
11 0.7069 0.7069 0.5398 0.9056
12 0.7491 0.7490 0.5648 0.6993
13 0.7176 0.7177 0.6040 0.7414
14 0.8685 0.8686 0.8110 0.9036
15 0.9114 0.9113 0.7264 0.8757
16 0.9755 0.9756 0.6723 0.9327
17 0.8973 0.8973 0.6632 0.9348
18 0.8122 0.8122 0.8814 0.6981
19 0.9572 0.9573 0.9652 1.0000
20 0.9489 0.9489 0.8657 0.7437
21 0.9757 0.9757 0.9428 0.6693
22 0.8571 0.8570 0.9428 0.6257

Finally, the gray correlation coefficients were averaged to determine the gray correlation between the four materials and the mass loss rate, electric flux value, compressive strength loss rate, and compressive strength of the nano-SiO2 and PVA fiber-reinforced FMGM, and the results are listed in Table 8.

According to the results in Table 6, the effect of amount of the four materials on the compressive strength and mass loss rate is in the following decreasing order: MK and FA, PVA fiber, and nano-SiO2. The effect of amount of doping of the four materials on the electric flux value and compressive strength loss rate is in the following decreasing order: MK and FA, nano-SiO2, and PVA fiber. For the compressive strength and mass loss rate, the correlations of all 4 materials were greater than 0.6. For the electric flux values, the correlations of MK, FA, and nano-SiO2 were greater than 0.6, while the correlations of PVA fiber were less than 0.6. For the compressive strength loss rate, only the correlations of the doping amounts of MK and FA were greater than 0.6; although the correlations of the 4 materials with each reference series were individually less than 0.6, they were close to 0.6. Therefore, the doping amounts of kaolin and FA, PVA fiber, and nano-SiO2 have important effects on the mass loss rate, electric flux value, compressive strength loss rate, and compressive strength of the nano-SiO2 and PVA fiber-reinforced FMGM. The results obtained in this study are consistent with the results of previous studies. Gray correlation analysis of (a) factors influencing the early frost resistance of structural concrete [48], (b) optimal mix ratio of multi-responsive recycled aggregate concrete [73], and (c) frost resistance index [74], produced similar results.

The results of the above analysis have a guiding effect on the ratio design of nano-SiO2 and PVA fiber-reinforced FMGM. It has been proven that the gray correlation analysis method, having been applied in the fields of social, agricultural, and education systems and water quality assessment, has important practical value. Due to the small amount of data in this study, there will be a large error if regression analysis is adopted. To improve its mechanical properties and durability performance, the amount of nano-SiO2 and PVA fiber admixture should be strictly controlled. When pre-configuring the geopolymer mortar, for optimal properties, the amount of PVA fiber admixture should be approximately 0.6%, and the amount of nano-SiO2 admixture should be approximately 1.0%. At the same time, nano-SiO2 should be taken with equal amounts of MK and FA according to the mass ratio of MK and FA.

5 Conclusion

In this study, the correlation coefficients of FA, MK, PVA fiber, and nano-SiO2 admixtures, with mass loss rate under sulfate attack, electric flux value, compressive strength loss rate, and compressive strength of nano-SiO2 and PVA fiber-reinforced FMGM were analyzed using gray correlation analysis. The degree of influence of the four materials on the compressive strength and durability of FMGM was determined as follows:

  1. The durability and compressive strength of nano-SiO2 and PVA fiber-reinforced FMGM are influenced by various factors with different sensitivities. The experimental results were validated using gray correlation analysis to clarify the relationships between the influencing factors. An important reference basis for the design and construction of nano-SiO2 and PVA fiber-reinforced FMGM was obtained from the results of the analysis.

  2. Using the gray system theory, the patterns of influence of the four materials’ doping on the compressive strength and mass loss rate was in the following decreasing order: MK and FA, PVA fiber, and nano-SiO2. The patterns of influence of the four materials’ doping on the electric flux value and compressive strength loss rate are in the following decreasing order: MK and FA, nano-SiO2, and PVA fiber. The results of this study are useful for guiding the preparation of geopolymer mortar.

  3. The doping of all four materials, i.e., MK and FA, PVA fiber, and nano-SiO2, has important effects on the compressive strength and durability of the FMGM. The durability and compressive strength of the FMGM were optimized when the optimal dosage of PVA fibers was approximately 0.6% and the nano-SiO2 dosage was approximately 1.0%. Therefore, to improve the mechanical properties and durability, the amount of FA and MK must be strictly controlled, with the amount of PVA fiber doping maintained at approximately 0.6% and the nano-SiO2 dosage at approximately 1.0%.

  1. Funding information: The authors would like to acknowledge the financial support received from Natural Science Foundation of Henan (Grant no. 212300410018), National Natural Science Foundation of China (Grant no. 51979251 and U2040224), Program for Innovative Research Team (in Science and Technology) in the University of Henan Province of China (Grant no. 20IRTSTHN009), and National Innovation and Entrepreneurship Training Program for College Students (Grant no. 202110459175).

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Conflict of interest: The authors state no conflict of interest.

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Received: 2022-04-23
Revised: 2022-06-02
Accepted: 2022-10-18
Published Online: 2022-12-05

© 2022 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  47. Dynamics of convective slippery constraints on hybrid radiative Sutterby nanofluid flow by Galerkin finite element simulation
  48. Preparation of vanadium by the magnesiothermic self-propagating reduction and process control
  49. Microstructure-dependent photoelectrocatalytic activity of heterogeneous ZnO–ZnS nanosheets
  50. Cytotoxic and pro-inflammatory effects of molybdenum and tungsten disulphide on human bronchial cells
  51. Improving recycled aggregate concrete by compression casting and nano-silica
  52. Chemically reactive Maxwell nanoliquid flow by a stretching surface in the frames of Newtonian heating, nonlinear convection and radiative flux: Nanopolymer flow processing simulation
  53. Nonlinear dynamic and crack behaviors of carbon nanotubes-reinforced composites with various geometries
  54. Biosynthesis of copper oxide nanoparticles and its therapeutic efficacy against colon cancer
  55. Synthesis and characterization of smart stimuli-responsive herbal drug-encapsulated nanoniosome particles for efficient treatment of breast cancer
  56. Homotopic simulation for heat transport phenomenon of the Burgers nanofluids flow over a stretching cylinder with thermal convective and zero mass flux conditions
  57. Incorporation of copper and strontium ions in TiO2 nanotubes via dopamine to enhance hemocompatibility and cytocompatibility
  58. Mechanical, thermal, and barrier properties of starch films incorporated with chitosan nanoparticles
  59. Mechanical properties and microstructure of nano-strengthened recycled aggregate concrete
  60. Glucose-responsive nanogels efficiently maintain the stability and activity of therapeutic enzymes
  61. Tunning matrix rheology and mechanical performance of ultra-high performance concrete using cellulose nanofibers
  62. Flexible MXene/copper/cellulose nanofiber heat spreader films with enhanced thermal conductivity
  63. Promoted charge separation and specific surface area via interlacing of N-doped titanium dioxide nanotubes on carbon nitride nanosheets for photocatalytic degradation of Rhodamine B
  64. Elucidating the role of silicon dioxide and titanium dioxide nanoparticles in mitigating the disease of the eggplant caused by Phomopsis vexans, Ralstonia solanacearum, and root-knot nematode Meloidogyne incognita
  65. An implication of magnetic dipole in Carreau Yasuda liquid influenced by engine oil using ternary hybrid nanomaterial
  66. Robust synthesis of a composite phase of copper vanadium oxide with enhanced performance for durable aqueous Zn-ion batteries
  67. Tunning self-assembled phases of bovine serum albumin via hydrothermal process to synthesize novel functional hydrogel for skin protection against UVB
  68. A comparative experimental study on damping properties of epoxy nanocomposite beams reinforced with carbon nanotubes and graphene nanoplatelets
  69. Lightweight and hydrophobic Ni/GO/PVA composite aerogels for ultrahigh performance electromagnetic interference shielding
  70. Research on the auxetic behavior and mechanical properties of periodically rotating graphene nanostructures
  71. Repairing performances of novel cement mortar modified with graphene oxide and polyacrylate polymer
  72. Closed-loop recycling and fabrication of hydrophilic CNT films with high performance
  73. Design of thin-film configuration of SnO2–Ag2O composites for NO2 gas-sensing applications
  74. Study on stress distribution of SiC/Al composites based on microstructure models with microns and nanoparticles
  75. PVDF green nanofibers as potential carriers for improving self-healing and mechanical properties of carbon fiber/epoxy prepregs
  76. Osteogenesis capability of three-dimensionally printed poly(lactic acid)-halloysite nanotube scaffolds containing strontium ranelate
  77. Silver nanoparticles induce mitochondria-dependent apoptosis and late non-canonical autophagy in HT-29 colon cancer cells
  78. Preparation and bonding mechanisms of polymer/metal hybrid composite by nano molding technology
  79. Damage self-sensing and strain monitoring of glass-reinforced epoxy composite impregnated with graphene nanoplatelet and multiwalled carbon nanotubes
  80. Thermal analysis characterisation of solar-powered ship using Oldroyd hybrid nanofluids in parabolic trough solar collector: An optimal thermal application
  81. Pyrene-functionalized halloysite nanotubes for simultaneously detecting and separating Hg(ii) in aqueous media: A comprehensive comparison on interparticle and intraparticle excimers
  82. Fabrication of self-assembly CNT flexible film and its piezoresistive sensing behaviors
  83. Thermal valuation and entropy inspection of second-grade nanoscale fluid flow over a stretching surface by applying Koo–Kleinstreuer–Li relation
  84. Mechanical properties and microstructure of nano-SiO2 and basalt-fiber-reinforced recycled aggregate concrete
  85. Characterization and tribology performance of polyaniline-coated nanodiamond lubricant additives
  86. Combined impact of Marangoni convection and thermophoretic particle deposition on chemically reactive transport of nanofluid flow over a stretching surface
  87. Spark plasma extrusion of binder free hydroxyapatite powder
  88. An investigation on thermo-mechanical performance of graphene-oxide-reinforced shape memory polymer
  89. Effect of nanoadditives on the novel leather fiber/recycled poly(ethylene-vinyl-acetate) polymer composites for multifunctional applications: Fabrication, characterizations, and multiobjective optimization using central composite design
  90. Design selection for a hemispherical dimple core sandwich panel using hybrid multi-criteria decision-making methods
  91. Improving tensile strength and impact toughness of plasticized poly(lactic acid) biocomposites by incorporating nanofibrillated cellulose
  92. Green synthesis of spinel copper ferrite (CuFe2O4) nanoparticles and their toxicity
  93. The effect of TaC and NbC hybrid and mono-nanoparticles on AA2024 nanocomposites: Microstructure, strengthening, and artificial aging
  94. Excited-state geometry relaxation of pyrene-modified cellulose nanocrystals under UV-light excitation for detecting Fe3+
  95. Effect of CNTs and MEA on the creep of face-slab concrete at an early age
  96. Effect of deformation conditions on compression phase transformation of AZ31
  97. Application of MXene as a new generation of highly conductive coating materials for electromembrane-surrounded solid-phase microextraction
  98. A comparative study of the elasto-plastic properties for ceramic nanocomposites filled by graphene or graphene oxide nanoplates
  99. Encapsulation strategies for improving the biological behavior of CdS@ZIF-8 nanocomposites
  100. Biosynthesis of ZnO NPs from pumpkin seeds’ extract and elucidation of its anticancer potential against breast cancer
  101. Preliminary trials of the gold nanoparticles conjugated chrysin: An assessment of anti-oxidant, anti-microbial, and in vitro cytotoxic activities of a nanoformulated flavonoid
  102. Effect of micron-scale pores increased by nano-SiO2 sol modification on the strength of cement mortar
  103. Fractional simulations for thermal flow of hybrid nanofluid with aluminum oxide and titanium oxide nanoparticles with water and blood base fluids
  104. The effect of graphene nano-powder on the viscosity of water: An experimental study and artificial neural network modeling
  105. Development of a novel heat- and shear-resistant nano-silica gelling agent
  106. Characterization, biocompatibility and in vivo of nominal MnO2-containing wollastonite glass-ceramic
  107. Entropy production simulation of second-grade magnetic nanomaterials flowing across an expanding surface with viscidness dissipative flux
  108. Enhancement in structural, morphological, and optical properties of copper oxide for optoelectronic device applications
  109. Aptamer-functionalized chitosan-coated gold nanoparticle complex as a suitable targeted drug carrier for improved breast cancer treatment
  110. Performance and overall evaluation of nano-alumina-modified asphalt mixture
  111. Analysis of pure nanofluid (GO/engine oil) and hybrid nanofluid (GO–Fe3O4/engine oil): Novel thermal and magnetic features
  112. Synthesis of Ag@AgCl modified anatase/rutile/brookite mixed phase TiO2 and their photocatalytic property
  113. Mechanisms and influential variables on the abrasion resistance hydraulic concrete
  114. Synergistic reinforcement mechanism of basalt fiber/cellulose nanocrystals/polypropylene composites
  115. Achieving excellent oxidation resistance and mechanical properties of TiB2–B4C/carbon aerogel composites by quick-gelation and mechanical mixing
  116. Microwave-assisted sol–gel template-free synthesis and characterization of silica nanoparticles obtained from South African coal fly ash
  117. Pulsed laser-assisted synthesis of nano nickel(ii) oxide-anchored graphitic carbon nitride: Characterizations and their potential antibacterial/anti-biofilm applications
  118. Effects of nano-ZrSi2 on thermal stability of phenolic resin and thermal reusability of quartz–phenolic composites
  119. Benzaldehyde derivatives on tin electroplating as corrosion resistance for fabricating copper circuit
  120. Mechanical and heat transfer properties of 4D-printed shape memory graphene oxide/epoxy acrylate composites
  121. Coupling the vanadium-induced amorphous/crystalline NiFe2O4 with phosphide heterojunction toward active oxygen evolution reaction catalysts
  122. Graphene-oxide-reinforced cement composites mechanical and microstructural characteristics at elevated temperatures
  123. Gray correlation analysis of factors influencing compressive strength and durability of nano-SiO2 and PVA fiber reinforced geopolymer mortar
  124. Preparation of layered gradient Cu–Cr–Ti alloy with excellent mechanical properties, thermal stability, and electrical conductivity
  125. Recovery of Cr from chrome-containing leather wastes to develop aluminum-based composite material along with Al2O3 ceramic particles: An ingenious approach
  126. Mechanisms of the improved stiffness of flexible polymers under impact loading
  127. Anticancer potential of gold nanoparticles (AuNPs) using a battery of in vitro tests
  128. Review Articles
  129. Proposed approaches for coronaviruses elimination from wastewater: Membrane techniques and nanotechnology solutions
  130. Application of Pickering emulsion in oil drilling and production
  131. The contribution of microfluidics to the fight against tuberculosis
  132. Graphene-based biosensors for disease theranostics: Development, applications, and recent advancements
  133. Synthesis and encapsulation of iron oxide nanorods for application in magnetic hyperthermia and photothermal therapy
  134. Contemporary nano-architectured drugs and leads for ανβ3 integrin-based chemotherapy: Rationale and retrospect
  135. State-of-the-art review of fabrication, application, and mechanical properties of functionally graded porous nanocomposite materials
  136. Insights on magnetic spinel ferrites for targeted drug delivery and hyperthermia applications
  137. A review on heterogeneous oxidation of acetaminophen based on micro and nanoparticles catalyzed by different activators
  138. Early diagnosis of lung cancer using magnetic nanoparticles-integrated systems
  139. Advances in ZnO: Manipulation of defects for enhancing their technological potentials
  140. Efficacious nanomedicine track toward combating COVID-19
  141. A review of the design, processes, and properties of Mg-based composites
  142. Green synthesis of nanoparticles for varied applications: Green renewable resources and energy-efficient synthetic routes
  143. Two-dimensional nanomaterial-based polymer composites: Fundamentals and applications
  144. Recent progress and challenges in plasmonic nanomaterials
  145. Apoptotic cell-derived micro/nanosized extracellular vesicles in tissue regeneration
  146. Electronic noses based on metal oxide nanowires: A review
  147. Framework materials for supercapacitors
  148. An overview on the reproductive toxicity of graphene derivatives: Highlighting the importance
  149. Antibacterial nanomaterials: Upcoming hope to overcome antibiotic resistance crisis
  150. Research progress of carbon materials in the field of three-dimensional printing polymer nanocomposites
  151. A review of atomic layer deposition modelling and simulation methodologies: Density functional theory and molecular dynamics
  152. Recent advances in the preparation of PVDF-based piezoelectric materials
  153. Recent developments in tensile properties of friction welding of carbon fiber-reinforced composite: A review
  154. Comprehensive review of the properties of fly ash-based geopolymer with additive of nano-SiO2
  155. Perspectives in biopolymer/graphene-based composite application: Advances, challenges, and recommendations
  156. Graphene-based nanocomposite using new modeling molecular dynamic simulations for proposed neutralizing mechanism and real-time sensing of COVID-19
  157. Nanotechnology application on bamboo materials: A review
  158. Recent developments and future perspectives of biorenewable nanocomposites for advanced applications
  159. Nanostructured lipid carrier system: A compendium of their formulation development approaches, optimization strategies by quality by design, and recent applications in drug delivery
  160. 3D printing customized design of human bone tissue implant and its application
  161. Design, preparation, and functionalization of nanobiomaterials for enhanced efficacy in current and future biomedical applications
  162. A brief review of nanoparticles-doped PEDOT:PSS nanocomposite for OLED and OPV
  163. Nanotechnology interventions as a putative tool for the treatment of dental afflictions
  164. Recent advancements in metal–organic frameworks integrating quantum dots (QDs@MOF) and their potential applications
  165. A focused review of short electrospun nanofiber preparation techniques for composite reinforcement
  166. Microstructural characteristics and nano-modification of interfacial transition zone in concrete: A review
  167. Latest developments in the upconversion nanotechnology for the rapid detection of food safety: A review
  168. Strategic applications of nano-fertilizers for sustainable agriculture: Benefits and bottlenecks
  169. Molecular dynamics application of cocrystal energetic materials: A review
  170. Synthesis and application of nanometer hydroxyapatite in biomedicine
  171. Cutting-edge development in waste-recycled nanomaterials for energy storage and conversion applications
  172. Biological applications of ternary quantum dots: A review
  173. Nanotherapeutics for hydrogen sulfide-involved treatment: An emerging approach for cancer therapy
  174. Application of antibacterial nanoparticles in orthodontic materials
  175. Effect of natural-based biological hydrogels combined with growth factors on skin wound healing
  176. Nanozymes – A route to overcome microbial resistance: A viewpoint
  177. Recent developments and applications of smart nanoparticles in biomedicine
  178. Contemporary review on carbon nanotube (CNT) composites and their impact on multifarious applications
  179. Interfacial interactions and reinforcing mechanisms of cellulose and chitin nanomaterials and starch derivatives for cement and concrete strength and durability enhancement: A review
  180. Diamond-like carbon films for tribological modification of rubber
  181. Layered double hydroxides (LDHs) modified cement-based materials: A systematic review
  182. Recent research progress and advanced applications of silica/polymer nanocomposites
  183. Modeling of supramolecular biopolymers: Leading the in silico revolution of tissue engineering and nanomedicine
  184. Recent advances in perovskites-based optoelectronics
  185. Biogenic synthesis of palladium nanoparticles: New production methods and applications
  186. A comprehensive review of nanofluids with fractional derivatives: Modeling and application
  187. Electrospinning of marine polysaccharides: Processing and chemical aspects, challenges, and future prospects
  188. Electrohydrodynamic printing for demanding devices: A review of processing and applications
  189. Rapid Communications
  190. Structural material with designed thermal twist for a simple actuation
  191. Recent advances in photothermal materials for solar-driven crude oil adsorption
Heruntergeladen am 24.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/ntrev-2022-0493/html
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