Thermal Conductivity of Compacted GO-GMZ Bentonite Used as Buffer Material for a High-Level Radioactive Waste Repository

Chen, Yong-Gui; Liu, Xue-Min; Mu, Xiang; Ye, Wei-Min; Cui, Yu-Jun; Chen, Bao; Wu, Dong-Bei

doi:https://doi.org/10.1155/2018/9530813

Advances in Civil Engineering

On this page

Abstract Introduction Materials Results and Discussion Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Special Issue

Geomaterials in Geotechnical Engineering

View this Special Issue

Research Article | Open Access

Volume 2018 | Article ID 9530813 | https://doi.org/10.1155/2018/9530813

Thermal Conductivity of Compacted GO-GMZ Bentonite Used as Buffer Material for a High-Level Radioactive Waste Repository

Yong-Gui Chen,¹Xue-Min Liu,¹Xiang Mu,¹Wei-Min Ye,¹Yu-Jun Cui,²Bao Chen,¹and Dong-Bei Wu³

Academic Editor: Filippo Ubertini

Received19 May 2018

Revised03 Jul 2018

Accepted19 Jul 2018

Published04 Sept 2018

Abstract

In China, Gaomiaozi (GMZ) bentonite serves as a feasible buffer material in the high-level radioactive waste (HLW) repository, while its thermal conductivity is seen as a crucial parameter for the safety running of the HLW disposal. Due to the tremendous amount of heat released by such waste, the thermal conductivity of the buffer material is a crucial parameter for the safety running of the high-level radioactive waste disposal. For the purpose of improving its thermal conductivity, this research used the graphene oxide (GO) to modify the pure bentonite and then the nanocarbon-based bentonite (GO-GMZ) was obtained chemically. The thermal conductivity of this modified soil has been measured and investigated under various conditions in this study: the GO content, dry density, and water content. Researches confirm that the thermal conductivity of the modified bentonite is codetermined by the three conditions mentioned above, namely, the value of GO content, dry density, and water content. Besides, the study proposes an improved geometric mean model based on the special condition to predict the thermal conductivity of the compacted specimen; moreover, the calculated values are also compared with the experimental data.

1. Introduction

Burying the high-level radioactive waste (HLW) in a deep geological disposal (800 m–1500 m underground) has been widely accepted as an approach for the permanent disposal of such waste generated from a nuclear reactor. The HLW repositories generally consist of a multibarrier system, in which the natural geological barrier and an engineered barrier system are included. The buffer material, as a pivotal part of the engineered barrier, plays a crucial role in keeping the chronic safety of the HLW repository, from which enough strength for construction and effective intercept for the radionuclide can be drawn [1]. Compacted bentonite has been valued as the feasible buffer material for the HLW repository, due to its excellent properties of swelling and sealing, the low permeability, and high retention capability of radionuclides [2, 3]. In China, a local bentonite named Gaomiaozi (GMZ) has been selected for this aim [4, 5].

As in the deep geological disposal, the buffer material can diffuse decay heat generated from the HLW to the host rock. Literatures show that the ambient temperature could rise to a peak of 90°C [6] and the maximum temperature grades could reach 24°C over a buffer material whose thickness is 35 cm [7]. According to the design criterion [8–10], the highest temperature in the engineered barrier system ought not to exceed 100°C because the smectite may undergo a mineralogical alteration exposed to alkaline/salt pore fluids at high temperature [11–14]. Both compacted bentonite and the host rock will undergo a decrease in the hydraulic, mechanical, and chemical performances because of these kinds of transformation under the high temperature which could threaten the safety of the HLW disposal [13, 15]. Therefore, it is of essence to study the thermal conductivity of the buffer material and to comprehend how it behaves under the given conditions in the disposal.

Over the past few decades, previous researches have been conducted on the thermal conductivity of many kinds of soil, and calculation models were developed to predict its thermal conductivity under different conditions [16–18]. Thermal conductivity is the basic property of a material to conduct heat which depends mainly upon its composition, pore fluid, density, environment temperature, and texture [19]. Coulon et al. [20] have measured this property of 18 smectite clays from 14 deposits, and based on these measurement results, they claimed that the value of the thermal conductivity was jointly influenced by soil parameters such as the water content, the dry density, the microstructure of clay samples, and the mineral composition of the soil as well. A large volume of measured data has been found in the works of Zhu et al. [21] on GMZ bentonite, and the similar conclusion has been drawn. However, the thermal conductivity of bentonite fails to meet the expected value even at high density and water content [22–25]. To improve the thermal performance of bentonite as the buffer material, some admixtures were added physically to modify its property. Using sand as a modifier was first reported in Japan [26]. Moreover, results from further more investigations confirmed that the thermal conductivity of the bentonite-sand mixture will increase as the sand content increases [27–30]. Nevertheless, the increasing degree becomes constant or even decreases with more increasing sand content [25, 31]. Because of the high thermal conductivity of graphite, Pacovsky [32] added graphite to Czech RMN bentonite to increase its thermal conductivity. Michael and Gunter [33] compared the improvement effect of sand to graphite and found that using sand as the only admixture is not enough to achieve suitable thermal conductivity while the addition of 15% graphite can make the thermal conductivity of the barrier approximately equal to that of the clay host rock. Nanocarbon material possesses high specific surface and strong interface effect which exerts extraordinary mechanics and thermal property compared to the macromaterial [34–36]; therefore, the graphene oxide was used as a proper modifier.

In China, a lot of studies about the basic physical and chemical properties, hydromechanical behaviour, and soil water retention characteristics (SWRC) of GMZ bentonite have been extensively researched during the past few years [37–44]. Studies on the thermal conductivity of pure bentonite and bentonite-sand mixture have also been carried out with varied sand content, dry density, and water content [45–47]. However, few researches consider graphite or other materials as a modifier to increase the thermal conductivity of bentonite [48].

In this study, graphene oxide (GO) was added to the GMZ bentonite, and the thermal conductivity of GO-GMZ bentonite was measured using the hot wire method. In this experiment, several factors including GO content, dry density, and water content of the modified soil were considered. At last, an empirical model was introduced to predict the thermal conductivity of compacted GO-GMZ bentonite in advance.

2. Materials and Method

2.1. Materials

The GMZ bentonite analysed with the X-ray diffraction in this test is sampled from Gaomiaozi township in Northern China. This light-grey powder consists of several minerals including 75.4% of montmorillonite, 11.7% of quartz, 7.3% of cristobalite, 4.3% of feldspar, 0.5% of calcite, and 0.8% kaolinite [38]. The other basic properties are listed in Table 1 [49].

The graphene oxide (GO) used in the present work is produced by Suzhou Tanfeng Graphene Technology Co., Ltd (Jiangsu Province, China). It is a drab powder material whose monolayer ratio is more than 99%. The structure schematics of graphite and graphene oxide are pictured in Figure 1, and its basic properties are listed in Table 2. Graphene oxide is an important and promising ramification of graphite which is obtained by treating graphite with strong oxidizers and is produced based on the improved Hummers method [50]. It has been proved that graphene oxide is nontoxic and biodegradable among all nanocarbon materials [51].

(a)

(b)

2.2. Bentonite Modification

In order to obtain GO-GMZ bentonite, firstly the GMZ bentonite was grafted using 3-aminopropyltriethoxysilane (APTES); then, the graft bentonite was modified by using 1-ethyl-3-(dimethylamino)propylcarbodiimide (EDC) and n-hydroxysuccinimide (NHS) as an activator. The modification procedure is showed in Figure 2. Quantities of 2.5 g GMZ bentonite, 50 mL APTES, and 200 mL absolute ethanol (with a purity of 99.7%) were poured into a 500 mL round bottom flask with three necks. The mixture was heated in a water bath at 80°C for 2 h. To keep it uniform, the mixture was intensively stirred (400 rad/min) in the process. The product was washed with absolute ethanol for six times and then dried in a vacuum oven at 60°C. The dried mass was pulverized and sieved (200 mm sieve). Quantities of 2 g powder, 2.5 g NHS, 2.5 g EDC, and 20 ml GO solution (10 mg/mL) were poured into a 50 mL beaker and stirred uniformly for 12 h at room temperature. After standing, centrifugation, freeze-drying, and sieving in a 2 mm mesh, the GO-GMZ bentonite was ready to use.

2.3. Specimen Preparation

To obtain the compacted GO-GMZ bentonite at a certain dry density, its powder was compressed statically in a steel mould using a 300 kN electronic universal testing machine (Model CSS-44000, produced by Changchun Research Institute for Mechanical Science) at a steady displacement rate of 0.2 mm/min. Besides, in order to make the dry density of the specimen longitudinal uniformly, the compression process was divided into four steps: (1) 1/3 soil powder of the total mass was compacted to obtain a specimen whose height is 40 mm; (2) the upper surface of the compacted specimen was carefully scratched with a thin steal stick; (3) the next 1/3 soil powder of the total mass was poured and compacted to form another soil segment; and (4) repeat steps 2 and 3 until the height of the compacted soil rises up to its target height (i.e., 120 mm). Once the dry density of bentonite reached the desirable value (1.7 Mg/m³, 1.8 Mg/m³, and 1.9 Mg/m³), the press on bentonite will stop and be kept still for 1 hour to avoid resilience. The sample is 50 mm in diameter and 120 mm high.

2.4. Measurement Method

2.4.1. Apparatus and Test Principle

Usually, two main kinds of laboratory techniques, steady-state methods [52, 53] and unsteady methods [54, 55], are utilized to measure the thermal conductivity of many kinds of materials. In this study, the thermoprobe method, as one of the unsteady methods, is adopted to measure the thermal conductivity of compacted GO-GMZ bentonite using a Decagon Device KD2 PRO thermal analyser (Figure 3). It is composed of a thermal needle probe, a cable, and a controller. Through monitoring heat dissipation of linear heat resource under specific voltage, the thermal conductivity of materials can be calculated and displayed on the controller’s screen (ASTM D 5334-00, 2000). After the insertion of the thermoprobe which is 1.28 mm in diameter and 120 mm long into the soil, the thermal conductivity can be obtained by heating the soil and monitoring temperature variation during the heat transfer.

2.4.2. Test Procedure

To ensure the smooth insertion of the thermoprobe, a hole sized 1.3 mm in diameter and 120 mm in depth is drilled into the middle of the specimen with a gimlet before testing. To avoid a small gap between the soil and thermoprobe which may influence the heat transfer, a thin layer of thermal grease was smeared on the surface of the thermoprobe. After that, it was inserted into the hole. Figure 3 shows the measuring operation and the KD2 analyser instrument. In the measurement, the controller was first balanced for 30 seconds, and the sample was then heated with the probe for another 30 seconds. The thermal conductivity was computed according to the cooling rate of the probe during the heat transmission. To ensure the accuracy of the experiment data, the testing values of repeated measurements were averaged as the final value.

3. Results and Discussion

3.1. Influence of GO Content on Thermal Conductivity

To figure out the enhancement of thermal conductivity of GO-GMZ bentonite, various GO contents (between 0 and 50% wt.) were used in the process of modification. The test results are illustrated in Figure 4, which shows a clear change of the thermal conductivity with different GO contents. According to the experimental data, the thermal conductivity shows apparent increases with greater dry density of GO content. Generally, the relation between the thermal conductivity and GO content can be formulated as a quadratic function with different dry densities: 1.7 Mg/m³, 1.8 Mg/m³, and 1.9 Mg/m³. As seen from the results, values of thermal conductivity range from 0.864 W/(mK) to 21.662 W/(mK) as GO content increases from 0% to 50% in given conditions. This attributes to the better heat conduction behaviour of GO whose thermal conductivity is 129 W/(mK), much higher than that value of the pure bentonite. The thermal conductivity-GO content quadratic relationship at different dry densities can be expressed as follows:where is the thermal conductivity (W/(mK)), is the GO content of the specimens, represents the dry density of compacted GO-GMZ bentonite (Mg/m³), and is the correlation coefficient. As can be seen from the equations, the second-order coefficient for each equation increases with the increase of dry density which maybe because of the increasing contact area between the bentonite and GO particles.

Though the measured data in this study are insufficient enough to draw a perfect relationship between several parameters, the work had been done in literatures and the good fitting result provides convincing evidence for the conclusion. Some researchers use graphite to improve the thermal conductivity of bentonite physically, and similar patterns can be found in Czech RMN bentonite [32], Montigel bentonite [33], and GMZ bentonite [48]. Numbers of experiment results of this study and other literatures have been illustrated in Figure 4. This figure shows the values of GO and graphite content range from 0% to 50%, while values of the dry density of different compacted bentonites range from 1.4 Mg/m³ to 1.9 Mg/m³ in different researches. After comparison, the thermal conductivity of bentonite-graphite mixture can range from 0.56 W/(mK) to 9.7 W/(mK) under different experiment conditions which is obviously lower than that of GO-GMZ bentonite. Probable reasons for this situation might be that (1) the thermal conductivity of GO nanoplatelets is higher than the bulk of graphite because of its single-layer structure [35, 56] and the increase in the interlayer coupling because of covalent interactions provided by the oxygen atoms and (2) the bentonite used in this study has been changed chemically with GO, leading to a better heat conduction behaviour. However, the relationship between the thermal conductivity of the mixture and the graphite content agrees with the modified bentonite: the higher the graphite content, the higher the value of thermal conductivity for the bentonite-graphite mixture. This maybe because the thermal conductivity of graphite is more than 10 times than that of pure bentonite [48]. Moreover, the thermal conductivity-graphite content shows similar quadratic trend for reported bentonite with GO-GMZ bentonite in this study for each dry density.

3.2. Influence of Dry Density on Thermal Conductivity

Measured values of thermal conductivity for highly compacted GO-GMZ bentonite with different dry densities (1.7 Mg/m³, 1.8 Mg/m³, and 1.9 Mg/m³) are presented in Figure 5. As shown, the effect of dry density can be plotted as a linear function when the GO content equals to 0%, 10%, 30%, and 50%, respectively. For the specimen with GO content of 0% (i.e., pure GMZ bentonite), the value of thermal conductivity increases from 0.86 W/(mK) to 1.10 W/(mK) with the increase of dry density. While for the specimen whose GO content is 50%, its thermal conductivity increases from 11.94 W/(mK) to 21.66 W/(mK) when water content equals to 10%. This phenomenon can be explained as the increasing contact area between the same and different particles (i.e., GO-GMZ bentonite and water particles) with the increase of dry density, leading to a better performance of heat transmission, namely, the thermal conductivity. The specific linear fitting functions and the calculated correlation coefficients of thermal conductivity-dry density are as follows:where the meaning of , , and in this equation are the same with (1). According to the equations above, the thermal conductivity of GO-GMZ bentonite has a good linear relationship with its dry density when the water content keeps invariable. This is due to the increase of the contact area between the particles [57].

Figure 5 also shows that the slope coefficient has a greater value at higher GO content. This means that the influence degree of dry density is greater when GO content is higher. This probably attributes to much higher thermal conductivity of graphene oxide than pure bentonite. Previous studies have confirmed that, for various bentonites, a linearity exists in thermal conductivity-dry density relationship, which includes MX-80 [58, 59], Kunigel [60], FEBEX [61], GMZ [46] and Kyeongju bentonite [62]. This relationship has also been found in the bentonite-sand mixture [46, 47]. A comparison of this study with those of the results in literatures has been illustrated in Figure 6. As shown, the dry density for different bentonites and bentonite-sand mixtures ranged from 1.5 Mg/m³ to 1.9 Mg/m³ and the water content from 0% to 17.4%. At these experimental conditions, the thermal conductivity of various pure bentonites is in a range of 0.30 W/(mK) to 1. 34 W/(mK). In addition, its value for the bentonite-sand mixture ranges from 0.89 W/(mK) to 1.33 W/(mK) when the sand content ranges from 10% to 50% and the dry density increases from 1.7 Mg/m³ to 1.9 Mg/m³. This is because the thermal conductivity of sand is higher than that of bentonite. However, the maximum thermal conductivity of compacted GO-GMZ bentonite is 21.66 W/(mK). Apparently, this value is much higher than that of bentonite-sand mixture. This maybe due to the better heat conduction behaviour of GO (129 W/(mK) than quartz sand (7.7 W/(mK)) [63]. In general, the thermal conductivity-dry density relationship for various bentonites has a similar trend with GO bentonite. The denser the compacted soil specimen, the higher the thermal conductivity value of bentonites. This is due to the increase of the interacting area between bentonite particles with increase in dry density. Nevertheless, the increasing rates are somewhat different for different bentonites, and this probably attributes to the differences in their mineralogical composition, texture, and water content [62, 64].

3.3. Influence of Water Content on Thermal Conductivity

The effect of water content on thermal conductivity is illustrated in Figure 7. As shown, the thermal conductivity and water content have a clear linear relationship with increasing water content when water content increases from 0% to 10% and GO content equals to 0%, 10%, 30%, and 50%, respectively. The minimum value of thermal conductivity is 0.37 W/(mK), and the maximum value is 15.52 W/(mK). This increase can be explained that the air in the void has been replaced by the water whose thermal conductivity is higher than air, leading to an additional increase of the thermal conductivity. The linear relationship can be expressed as follows:where is the water content. According to the equations above, the thermal conductivity of GO-GMZ bentonite has a good linear relationship with its water content. However, Lee et al. [62] reported that the thermal conductivity-water content relationship will become nonlinear when the water content is beyond 12%. Several studies used the sigmoidal-type equation to express this nonlinear relationship [61]:where represents the value of when , represents that value when (i.e., the specimen is saturated), is the corresponding saturation degree when the thermal conductivity of the specimen equals to the average of the two extreme values, and while A functions as an empirical parameter, is the corresponding saturation degree when the thermal conductivity of the specimen equals to the average of the two extreme value. In the equation above, the saturation degree () can be calculated by water content (), dry density (), and water density () as follows:

Figure 8 plots the fitting curves of thermal conductivity and saturation degree. The parameter values are best fitted and listed in Table 3. Theoretically, the fitted values of should be equal to the experimental values when saturation of the samples is 0. However, the fitted values are slightly higher when the GO content is equal or greater than 10%. This maybe due to the inhomogeneous distribution of the material (i.e., GO-GMZ bentonite), leading to a decrease in its thermal conductivity [65].

3.4. Model of Thermal Conductivity for GO-GMZ Bentonite

In the HLW repository, the heat transfer behaviour of highly compacted bentonite can influence the process of decay heat from the high-level radioactive to the host rock greatly. Some additives will be added to improve its thermal property due to its low thermal conductivity. Because of the inflowing groundwater coming from the host rock, the buffer material will become saturated gradually. In addition, its dry density also will change when the confining pressure changes. These processes can change its thermal property which will influence the safety of HLW disposal. Therefore, the prediction of thermal conductivity of bentonite is an important part of study for the buffer material. Numerous predicting models of thermal conductivity have been proposed in the past several decades. Among them, empirical models [66–68] and geometric mean model are widely used [16, 62, 69]. Empirical models can be adopted to build up the relationship of the thermal conductivity between various types of soils and their dry density and water content. However, none of them take into account mass additives into the soil. The geometric mean model was firstly presented by Woodside and Messmer [69]. Then, it was improved and successfully used by Lee et al. [62]. Based on the volumetric composition of sample-forming minerals, the geometric mean model can be presented as follows:where represents the product of . and are the thermal conductivity of each component and its volumetric proportion, respectively. Obviously, the sum of the volumetric proportion for each component equals to 1. j refers to the jth component, and there are n kinds of components in total.

According to the geometric mean model mentioned above, the present study proposes a new model to predict the thermal conductivity considering the influence of GO. The following prediction model is based on several assumptions: (1) bentonite is homogeneous; (2) GO disperses homogeneously in the soil; and (3) the voids in the compacted soil are full of air at first when the degree of saturation equals to 0, and it has been replaced by water with the increasing water content. To take all components into account, (6) can be rewritten aswhere present the thermal conductivity of bentonite, GO, water, and air, respectively, while are the volumetric proportions for bentonite, GO, water, and air, respectively; is its dry density; and are the particle densities of bentonite and graphene oxide, respectively; and is the saturation degree of compacted specimens. Each component of the compacted material would not have homogeneous distribution and its influence on the thermal conductivity is different. In consideration of all these facts, (7) can be modified and expressed as follows:where , , and are the influence factors which take into account the different influence degrees for various components. The values of are 129 W/(mK), 0.059 W/(mK), and 0.024 W/(mK) at 20°C, respectively. However, the mineral composition of the compacted specimen may differ from each other because of uneven sampling, leading to a varying value of . Therefore, its value is determined with other parameters including , , and with the multivariate regression analysis method. After regression, the modified model and the fitted values of parameters are presented as follows:

The correlation coefficient of (8) is 0.97. Values of and are higher than 1, respectively, while the value of is lower than 1, which indicate that the bentonite and GO present stronger influence to the thermal conductivity than water and air does.

The comparison of measured values and calculated thermal conductivity using the proposed constitutive model above is plotted in Figure 9. As shown in the figure, when the GO content is higher than 10%, the calculated values are approximately equal to the experimental value. This means that this modified model is proper to predict the thermal conductivity of GO-GMZ bentonite when the GO content is higher than 10%. However, the calculated values are apparently higher than the measured values when the GO content is lower than 10%. Considering such fact, there may cause a misleading in the thermal conductivity prediction when the additive’s content is low. Therefore, the practical application of the calculated thermal conductivity using the proposed constitutive model needs more consideration. In general, the modified geometric mean model proposed in this research can be adopted as a constitutive model to predict the thermal conductivity of the GO-GMZ bentonite, which has been considered as the feasible buffer material for an HLW repository in China.

Bentonite will undergo thermal, chemical, and mechanical effects when used in the HLW repository. Therefore, the durability of GO-GMZ bentonite is of concern. Generally, the structure of GO is stable chemically when there is no photocatalyst or peroxidase and its decomposition temperature is found to be above 100°C [70–72]. Some chemical bonds such as the amido bond form in this process, and they are stable thermally and chemically [73]. In addition, the size of the particles used and obtained in the modified process is too small (i.e., nanosized or even smaller) to be influenced by the mechanical pressure. Therefore, the structure of GO-GMZ will stay durable under swelling pressure or other mechanical forces.

4. Conclusions

This research measures the thermal conductivity of highly compacted GO-GMZ bentonite under different conditions like graphene oxide content, dry density, and water content with the thermoprobe method. The thermal conductivity of GO-GMZ bentonite increases evidently as values of GO content, dry density, and water content are fortified. The functional relationship between the thermal conductivity and GO content is quadratic. However, thermal conductivity-dry density relationship or thermal conductivity-water content relationship is linear within a certain range. Moreover, a modified geometric mean model is also proposed in the present study to predict the thermal conductivity of GO-GMZ bentonite. The comparison between experimental data and calculated values implies that this model is appropriate for such buffer material when the GO content exceeds beyond a certain range. Therefore, this proposed model can be applied to predict the thermal conductivity of the GO-GMZ bentonite as a constitutive model for a HLW repository in China. In addition, hydraulic conductivity is another crucial parameter for its safe application which will be studied in the further work.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors are grateful to the National Natural Science Foundation of China (41422207 and 41772279), China Atomic Energy Authority ([2011]1051), and Fundamental Research Funds for the Central Universities.

References

F. N. Hodges, J. H. Westsik, and L. A. Bray, “Development of a backfill for containment of high-level nuclear waste,” MRS Proceedings, vol. 11, p. 641, 1981.
View at: Publisher Site | Google Scholar
SNF Waste Management CO, “SKB91 final disposal of spent nuclear fuel: importance of the bedrock for safety,” pp. 92–120, SKB, Stockholm, Sweden, 1992, SKB Technical Report.
View at: Google Scholar
JNC, “JNC supporting report 2: repository design and engineering technology,” Japan Nuclear Cycle Development Institute, Ibaraki, Japan, 1999, JNC TN 1400 99–012.
View at: Google Scholar
G. Q. Xu, “Selection of buffer/backfill materials and their additives,” Cranium Geology, vol. 12, no. 4, pp. 238–244, 1996.
View at: Google Scholar
J. Wang, R. Su, W. M. Chen, Y. H. Guo, Z. J. Wen, and Y. M. Liu, “Deep geological disposal of high-level radioactive wastes in China,” Chinese Journal of Rock Mechanics and Engineering, vol. 25, no. 4, pp. 649–658, 2006.
View at: Google Scholar
B. Pastina and P. Hella, “Expected evolution of a spent nuclear fuel repository at Olkiluoto,” Posiva Oy, Olkiluoto, Finland, 2006, POSIVA Report 2006-05.
View at: Google Scholar
H. Hökmark and B. Fälth, “Thermal dimensioning of the deep repository,” SvenskKärnbränslehantering, Stockholm, Sweden, 2003, Technical Report, TR-03–09.
View at: Google Scholar
SKB, Final Storage of Spent Nuclear Fuel-KBS-3, Swedish Nuclear Fuel Supply Company, Stockholm, Sweden, 1983.
W. J. Cho, J. O. Lee, C. H. Kang, and K. S. Chun, “Basic physicochemical and mechanical properties of domestic bentonite for use as a buffer material in a high-level radioactive waste repository,” Journal of Korean Society, vol. 31, pp. 39–50, 1999.
View at: Google Scholar
P. Wersin, L. H. Johnson, and I. G. McKinley, “Performance of the bentonite barrier at temperatures beyond 100°C: a critical review,” Physics and Chemistry of the Earth Parts A/B/C, vol. 32, no. 8, pp. 780–788, 2007.
View at: Publisher Site | Google Scholar
D. D. Eberl, “The reaction of montmorillonite to mixed-layer clay: the effect of interlayer alkali and alkaline-earth cations,” Geochimica et Cosmochimica Acta, vol. 42, pp. 1–7, 1978.
View at: Publisher Site | Google Scholar
T. Sato, M. Kuroda, S. Yokoyama et al., “Dissolution kinetics of smectite under alkaline conditions,” in Proceedings of International Meeting on Clays in Natural and Engineering Barriers for Nuclear Waste Confinement, Tours, France, 2005.
View at: Google Scholar
J. O. Lee, I. M. Kang, and W. J. Cho, “Smectite alteration and its influence on the barrier properties of smectite clay for a repository,” Applied Clay Science, vol. 47, no. 1, pp. 99–104, 2010.
View at: Publisher Site | Google Scholar
W. M. Ye, Y. He, Y. G. Chen, B. Chen, and Y. J. Cui, “Thermochemical effects on the smectite alteration of GMZ bentonite for deep geological repository,” Environmental Earth Sciences, vol. 75, no. 10, p. 906, 2016.
View at: Publisher Site | Google Scholar
A. Inoue, M. Utada, H. Nagata, and T. Watanabe, “Chemical and morphological evidence for the conversion of smectite to illite,” Clay and Clay Minerals, vol. 35, no. 2, pp. 111–120, 1987.
View at: Publisher Site | Google Scholar
J. Cote and J. Konrad, “Generalized thermal conductivity model for soils and construction materials,” Canadian Geotechnical Journal, vol. 42, no. 2, pp. 443–458, 2005.
View at: Publisher Site | Google Scholar
N. H. Abu-Hamdeh and R. C. Reeder, “Soil thermal conductivity: effects of density, moisture, salt concentration, and organic matter,” Soil Science Society of America Journal, vol. 64, no. 4, pp. 1285–1290, 2000.
View at: Publisher Site | Google Scholar
B. Usowicz, J. Lipiec, and A. Ferrero, “A prediction of soil thermal conductivity based on penetration resistance and water content or air-filled porosity,” International Journal of Heat Mass Transfer, vol. 49, no. 25-26, pp. 5010–5017, 2006.
View at: Publisher Site | Google Scholar
Aurangzeb, L. A. Khan, and A. Maqsood, “Prediction of effective thermal conductivity of porous consolidated media as a function of temperature: a test example of limestones,” Journal of Physics D: Applied Physics, vol. 40, no. 16, pp. 4953–4958, 2007.
View at: Publisher Site | Google Scholar
H. Coulon, A. Lajudie, P. Debrabant et al., “Choice of French clays as engineered barrier components for waste disposal,” Materials Research Society Symposium Proceedings, vol. 84, pp. 813–824, 1987.
View at: Google Scholar
G. P. Zhu, Y. M. Liu, and T. A. Luo, “Study on heat conductivity of Gaomiaozi bentonite,” Resources Environment and Engineering, vol. 19, no. 3, pp. 220–222, 2005.
View at: Google Scholar
Y. J. Cui, N. Sultan, and P. Delage, “A thermomechanical model for saturated clays,” Canadian Geotechnical Journal, vol. 37, no. 3, pp. 607–620, 2000.
View at: Publisher Site | Google Scholar
E. E. Alonso, J. Alcoverro, F. Coste et al., “The FEBEX benchmark test: case definition and comparison of modelling approaches,” International Journal of Rock Mechanics and Mining Sciences, vol. 42, no. 5-6, pp. 611–638, 2005.
View at: Publisher Site | Google Scholar
X. Pintado and A. Lloret, “Thermohydraulic test on bentonite,” Journal of Thermal Analysis and Calorimetry, vol. 84, no. 2, pp. 325–330, 2006.
View at: Publisher Site | Google Scholar
L. Xu, W. M. Ye, B. Chen, Y. G. Chen, and Y. J. Cui, “Experimental investigations on thermo-hydro-mechanical properties of compacted GMZ01 bentonite-sand mixture using as buffer materials,” Engineering Geology, vol. 213, pp. 46–54, 2016.
View at: Publisher Site | Google Scholar
JNC, “H12 project to establish the scientific and technical basis for HLW disposal in Japan, supporting report 2: repository design and engineering technology,” Japan Nuclear Cycle Development Institute, Ibaraki, Japan, 2000, JNC-TN1410 2000-003.
View at: Google Scholar
D. A. Sun, H. B. Cu, and W. J. Sun, “Swelling of compacted sand-bentonite mixtures,” Applied Clay Science, vol. 43, no. 3-4, pp. 485–492, 2009.
View at: Publisher Site | Google Scholar
D. A. Sun, W. J. Sun, and L. Fang, “Swelling characteristics of Gaomiaozi bentonite and its prediction,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 6, no. 2, pp. 113–118, 2014.
View at: Publisher Site | Google Scholar
S. L. Cui, H. Y. Zhang, and M. Zhang, “Swelling characteristics of compacted GMZ bentonite-sand mixtures as a buffer/backfill material in China,” Engineering Geology, vol. 141-142, no. 3, pp. 65–73, 2012.
View at: Publisher Site | Google Scholar
M. Zhang, H. Y. Zhang, S. Y. Cui, L. Y. Jia, L. Zhou, and H. Chen, “Engineering properties of GMZ bentonite-sand as buffer/backfilling material for high-level waste disposal,” European Journal of Environmental and Civil Engineering, vol. 16, no. 10, pp. 1216–1237, 2012.
View at: Publisher Site | Google Scholar
Y. C. Zhuang, K. H. Xie, and Y. H. Sun, “Experimental study on thermal conductivity of mixed materials of sand and bentonite,” Rock and Soil Mechanics, vol. 26, no. 2, pp. 261–269, 2005, in Chinese.
View at: Google Scholar
J. Pacovsky, “Selected results from geotechnical research on bentonite,” in Proceedings of the 8th International Conference on Radioactive Waste Management and Environmental Remediation, Bruges, Belgium, 2001.
View at: Google Scholar
J. Michael and B. Gunter, “Influence of graphite and quartz addition on the thermo–physical properties of bentonite for sealing heat-generating radioactive waste,” Applied Clay Science, vol. 44, no. 3-4, pp. 206–210, 2009.
View at: Publisher Site | Google Scholar
C. Lee, X. Wei, J. W. Kysar, and J. Hone, “Measurement of the elastic properties and intrinsic strength of monolayer graphitic,” Science, vol. 321, no. 5887, pp. 385–388, 2008.
View at: Publisher Site | Google Scholar
A. A. Balandin, S. Ghosh, W. Bao et al., “Superior thermal conductivity of single-layer graphene,” Nano Letters, vol. 8, no. 3, pp. 902–907, 2008.
View at: Publisher Site | Google Scholar
M. D. Stoller, S. Park, Y. Zhu, J. An, and R. S. Ruoff, “Graphitic-based ultracapacitors,” Nano Letters, vol. 8, no. 10, p. 3498, 2008.
View at: Publisher Site | Google Scholar
Y. M. Liu and Z. R. Chen, “Bentonite from Gaomiaozi, inner Mongolia as an ideal buffer/backfilling material in handing highly radioactive wastes-a feasibility study,” Acta Mineralogica Sinica, vol. 21, no. 3, pp. 541–543, 2001.
View at: Google Scholar
Z. J. Wen, “Physical property of China’s buffer material for high-level radioactive waste repositories,” Chinese Journal of Rock Mechanics and Engineering, vol. 25, no. 4, pp. 794–800, 2006.
View at: Google Scholar
W. M. Ye, X. L. Lai, Y. Liu, Y. G. Chen, and Y. J. Cui, “Ageing effects on swelling behaviour of compacted GMZ01 bentonite,” Nuclear Engineering and Design, vol. 265, pp. 262–268, 2013.
View at: Publisher Site | Google Scholar
W. M. Ye, Z. J. Zheng, B. Chen, Y. G. Chen, Y. J. Cui, and J. Wang, “Effects of pH and temperature on the swelling pressure and hydraulic conductivity of compacted GMZ01 bentonite,” Applied Clay Science, vol. 101, pp. 192–198, 2014.
View at: Publisher Site | Google Scholar
D. A. Sun, J. Y. Zhang, J. R. Zhang, and L. Zhang, “Swelling characteristics of GMZ bentonite and its mixtures with sand,” Applied Clay Science, vol. 83-84, pp. 224–230, 2013.
View at: Publisher Site | Google Scholar
C. M. Zhu, W. M. Ye, Y. G. Chen, B. Chen, and Y. J. Cui, “Influence of salt solutions on the swelling pressure and hydraulic conductivity of compacted GMZ01 bentonite,” Engineering Geology, vol. 166, pp. 74–80, 2013.
View at: Publisher Site | Google Scholar
M. Wan, W. M. Ye, Y. G. Chen, Y. J. Cui, and J. Wang, “Influence of temperature on water retention properties of compacted GMZ01 bentonite,” Environmental Earth Science, vol. 73, no. 8, pp. 4053–4061, 2014.
View at: Publisher Site | Google Scholar
Y. G. Chen, C. M. Zhu, W. M. Ye, Y. J. Cui, and Q. Wang, “Swelling pressure and hydraulic conductivity of compacted GMZ01 bentonite under salinization-desalinization cycle conditions,” Applied Clay Science, vol. 114, pp. 454–460, 2015.
View at: Publisher Site | Google Scholar
Y. M. Liu, M. F. Cai, and J. Wang, “Thermal properties of buffer material for high-level radioactive waste disposal,” Chinese Journal of Rock Mechanics and Engineering, vol. 26, pp. 3891–3896, 2007.
View at: Google Scholar
W. M. Ye, Q. Wang, H. Pan, and B. Chen, “The thermal conductivity of high compacted GMZ01 bentonite,” Chinese Journal of Geotechnical Engineering, vol. 32, no. 6, pp. 821–826, 2010.
View at: Google Scholar
J. L. Xie, Y. M. Liu, and H. W. Zhou, “Mesurement of thermal conductivity of sand-bentointe mixtures,” in The 3rd Proceedings of Waste Underground Disposal, pp. 439–444, Beijing, China, 2010, in Chinese.
View at: Google Scholar
C. Ma, “Study on the thermal conductivity of crushed granite and bentonite mixture,” China University of Geosciences, Beijing, China, 2014, Master thesis.
View at: Google Scholar
L. X. Qian, “A fundamental study of GMZ bentonite as buffer malarial in deep geological disposal for high-level radioactive waste,” Tongji University, Shanghai, China, 2007, Ph.D. thesis.
View at: Google Scholar
M. Hirata, T. Gotou, S. Horiuchi, M. Fujiwara, and M. Ohba, “Thin-film particles of graphite oxide 1: high-yield synthesis and flexibility of the particles,” Carbon, vol. 42, no. 14, pp. 2929–2937, 2004.
View at: Publisher Site | Google Scholar
X. Zhang, J. Yin, C. Peng et al., “Distribution and biocompatibility studies of graphitic oxide in mice after intravenous administration,” Carbon, vol. 49, no. 3, pp. 986–995, 2011.
View at: Publisher Site | Google Scholar
L. Kubicar, V. Vretenar, V. Stofanik, and V. Bohac, “Hot-ball method for measuring thermal conductivity,” International Journal of Thermophysics, vol. 31, pp. 1904–1918, 2010.
View at: Publisher Site | Google Scholar
D. Kraemer and G. Chen, “A simple differential steady-state method to measure the thermal conductivity of solid bulk materials with high accuracy,” Review of Scientific Instruments, vol. 85, no. 2, pp. 1–7, 2014.
View at: Publisher Site | Google Scholar
N. H. Abu-Hamdeh, I. Khdair, and R. C. Reeder, “A comparison of two methods used to evaluate thermal conductivity for some soils,” International Journal of Heat Mass Transfer, vol. 44, no. 5, pp. 1073–1078, 2001.
View at: Publisher Site | Google Scholar
O. K. Nusier and N. H. Abu-Hamdeh, “Laboratory techniques to evaluate thermal conductivity for some soils,” Heat and Mass Transfer, vol. 39, no. 2, pp. 119–123, 2003.
View at: Publisher Site | Google Scholar
R. Prasher, “Graphene spreads the heat,” Science, vol. 328, no. 5975, pp. 185-186, 2010.
View at: Publisher Site | Google Scholar
P. N. Mishra, S. Surendran, V. K. Gadi, R. A. Joseph, and D. N. Arnepalli, “Generalized approach for determination of thermal conductivity of buffer materials,” Journal of Hazardous Toxic and Radioactive Waste, vol. 21, no. 4, pp. 172–178, 2017.
View at: Publisher Site | Google Scholar
G. Kahr and M. Muullervonmoos, Warmeleitfahigkeit Warmeleitfahigkeit von Bentonit MX80 und von Montigel nach der Heizdrahtmethode, NAGRA, Cheseaux-sur-Lausanne, Switzerland, 1982.
A. M. Tang and Y. J. Cui, “Effects of mineralogy on thermo-hydro-mechanical parameters of MX80 bentonite,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 2, no. 1, pp. 91–96, 2010.
View at: Google Scholar
H. Suzuki, M. Shibata, J. Yamagata, I. Hirose, and K. Terakado, “Physical and mechanical properties of bentonite (I),” PNC TN1410 92–52, 1992, in Japanese.
View at: Google Scholar
M. V. Villar, Thermo-Hydro-Mechanical Characterization of a Bentonite From Cabo de Gata, ENRESA Publication Tecnica, Barcelona, Spain, 2002.
J. O. Lee, H. Choi, and J. Y. Lee, “Thermal conductivity of compacted bentonite as a buffer material for a high-level radioactive waste repository,” Annals of Nuclear Energy, vol. 94, pp. 848–855, 2016.
View at: Publisher Site | Google Scholar
O. Johansen, “Thermal conductivity of soils,” Trondheim University, Trondheim, Norway, 1975, Ph.D. thesis.
View at: Google Scholar
I. Kukkonen and I. Suppala, “Measurement of thermal conductivity and diffusivity in situ: literature survey and theoretical modelling of measurements,” Journal of Molecular Structure, vol. 857, pp. 447–455, 1999.
View at: Google Scholar
P. G. Klemens, “Thermal conductivity of inhomogeneous materials,” International Journal of Thermophysics, vol. 10, no. 6, pp. 1213–1219, 1989.
View at: Publisher Site | Google Scholar
M. S. Kersten, “Laboratory research for the determination of the thermal properties of soils,” ACFEL Technical Report, vol. 23, pp. 153–182, 1949.
View at: Google Scholar
H. Kiyohashi and K. Banno, “Effective thermal conductivity of compact bentonite as a buffer material for high level radioactive waste,” High Temperatures-High Pressures, vol. 27-28, no. 6, pp. 653–663, 1995.
View at: Publisher Site | Google Scholar
W. J. Cho, J. O. Lee, and S. Kwon, “An empirical model for the thermal conductivity of compacted bentonite and a bentonite-sand mixture,” International Journal of Heat and Mass Transfer, vol. 47, no. 11, pp. 1385–1393, 2011.
View at: Publisher Site | Google Scholar
W. H. Woodside and J. H. Messmer, “Thermal conductivity of porous media I: uncosolidated sands & II: consolidated sands,” Journal of Applied Physics, vol. 32, no. 9, pp. 1688–1699, 1961.
View at: Publisher Site | Google Scholar
G. Williams, B. Seger, and P. V. Kamat, “Tio₂-graphene nanocomposites. UV-assisted photocatalytic reduction of graphene oxide,” ACS Nano, vol. 2, no. 7, pp. 1487–1491, 2008.
View at: Publisher Site | Google Scholar
F. Yin, D. Z. Chen, J. J. Liu, Q. Chen, H. X. Kong, and C. J. Wu, “Preparation technology and thermostability of graphite oxide,” Journal of Materials Science and Engineering, vol. 31, no. 3, pp. 328–335, 2013, in Chinese.
View at: Google Scholar
E. Siegfried, G. Stefan, and H. Andreas, “Investigation of the thermal stability of the carbon framework of graphene oxide,” Chemistry-A European Journal, vol. 20, no. 4, pp. 984–989, 2014.
View at: Publisher Site | Google Scholar
W. Liao, F. Wei, D. Liu, M. X. Qian, G. Yuan, and X. S. Zhao, “FTIR-ATR detection of proteins and small molecules through DNA conjugation,” Sensors and Actuators B: Chemical, 2006.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2018 Yong-Gui Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

2782

Downloads

1398

Citations