Citation: Carina Ladeira, Lenka Smajdova. The use of genotoxicity biomarkers in molecular epidemiology: applications in environmental, occupational and dietary studies[J]. AIMS Genetics, 2017, 4(3): 166-191. doi: 10.3934/genet.2017.3.166
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Regardless of long service life of civil engineering infrastructures, they cannot be considered as maintenance-free. These engineering structures are the most expensive investments and assets of any nation. Worldwide incidents of tragic failures of civil infrastructures remind that suitable measures are required to avoid sudden collapse of civil structures and associated loss of money and lives. Concrete is the most extensively used material in civil engineering structures. Due to some inherent drawbacks of concrete, these structures weaken with time. The weakening and failure of concrete structures occur mainly due to ageing of materials, aggressive environmental conditions, prolonged usage, overloading, difficulties involved in proper inspection methods, and lack of maintenance [1,2]. Within the microstructure of concrete, it contains numerous cracks in nano-scale. These cracks are formed during manufacturing or use. With time, nano-cracks join to form micro-cracks, which in turn, leads to formation of macro-cracks and failure of structures [3]. Through early detection of these inherent damages, sudden collapse and accidents can be avoided. Timely detection of damages and proper maintenance can greatly enhance the service life of concrete structures.
The process of monitoring of deformation and damage that occur within civil engineering structures is commonly known as Structural Health Monitoring (SHM) [1,2,4,5,6]. SHM is highly essential for important civil structures such as nuclear power plants, dams, bridges, high-rise buildings, and power utilities. An active monitoring system can, in real time and online, recognize different defects and monitor damage, strain, and temperatures so that the optimal maintenance of the structures can be undertaken to provide enough safety and life span [1,2]. In general, a typical SHM system consists of three major components: a sensor system, a data processing system (containing data procuring, storage and transmission systems), and an evaluation system (comprising information management and diagnostic algorithms). The primary step to set-up an SHM system is to use stable and reliable sensing tools or sensors) [1,2]. Different sensors such as fibre optic sensors, piezoelectrics, magnetostrictive sensors, self-sensing composite materials, etc. possess capabilities of sensing various physical and chemical parameters related to the health of civil structures [7,8,9,10,11].
Fibre optic sensors (FOS) are suitable for health monitoring of civil structure due to several reasons such as (a) due to their small size, they can easily embedded within civil structures without affecting their performance, (b) distributed sensing technology can be used to monitor civil structures at various locations, (c) electromagnetic interference does not have any effect on the sensing behavior, (d) can be used to monitor various parameters such as strain, displacement, vibration, cracks, corrosion, and chloride ion concentration, etc. [1,2,4]. However, optical fibres may be fragile and should be encapsulated within a protective material and also it is quite difficult to repair the damages. Many attempts have been made to incorporate FOS in pavements, bridges and buildings and field trials have been taken [12,13]. Figure 1 shows the use of Fibre Bragg Grating (FBG) sensor for monitoring of pavements [13].
Piezoelectric sensors also offer a number of advantages and are suitable for SHM of civil engineering structures. The advantageous features include their variety of sizes and possibility to incorporate them in very remote and inaccessible locations [1]. They can also be used to harvest energy from pavements due to the movement of the vehicles and generated pressure.
Self-diagnosing or self-sensing is the property by which a material can sense its own conditions such as stress, strain, damage, temperature, and so on. A self-sensing composite has the ability to sense its own deformation and damage and this ability makes them an excellent material for health monitoring of civil engineering structures. Strain and damage sensing in a composite material is usually achieved through detecting change in their electrical resistivity, i.e. self-sensing composite works based on piezoresistivity principle. One major advantage with the self-sensing composites is the possibility to achieve sensing as well as strengthening of civil structures simultaneously.
To achieve piezo-resistivity in a composite material, it should contain a conducting element. Different types of conducting components have been used in the existing self-sensing composite materials. Short and continuous carbon fibres (CFs), carbon particles as well as carbon nanomaterials such as carbon nanofibers (CNFs) and nanotubes (CNTs) have been utilized for this purpose [14,15,16,17], as shown in Figure 2.
These conducting components form a conducting electrical network within the composites. When the composites are subjected to deformation or damage, this conducting network is disturbed leading to a change in the electrical resistivity. Theconducting network and resulting change in resistivity are highly dependent on the type of conducting component, their amount as well as their distribution. One of the biggest advantages of self-sensing composites is their design flexibility. The type of response can be tailored easily through proper designing of the composite structure. As mentioned earlier, in civil infrastructures, composites are already in use as strengthening material. Therefore, these composites can also be designed as self-sensing so that they can perform both strengthening and health monitoring functions. This eliminates the need for incorporating sensors from outside for health monitoring of structures. Self-sensing composites can also be based on cementitious materials. Conducting fibres can be introduced directly within the structural elements to obtain the sensing behaviour. Alternatively, sensing composites can be developed by introducing conducting fibres or nanomaterials within polymers or cementitious materials and these sensors can be subsequently introduced within structural elements to perform health monitoring. Different types of self-sensing composites used for the health monitoring of civil structural elements are presented in Figure 3.
Usually, self-sensing performance of composite materials is quantified by measuring the fractional change in electrical resistance, which is expressed as follows [18]:
@Fractional \, change \, in \, resistance\left( {FCR} \right) = \frac{{({\rm{R}} - {R_{0)}}}}{{{R_0}}}@ | (1) |
Where R0 and R are the initial and final electrical resistances. Also, gauge factor is another frequently used parameter to quantify the self-sensing behavior of composites. Using the mechanical strain (@\varepsilon @), gauge factor can be calculated as follows [18]:
@Gauge \, Factor\left( {GF} \right) = \frac{{FCR}}{\varepsilon }*100@ | (2) |
Electrical resistance in a self-sensing composite can be measured in different ways as shown in Figure 4 [19].
If the current contacts are on the same surface in the plane of the composite (Figure 4a), the current penetration is in the surface region only. When the current contacts are on the opposite surfaces in the plane of composites, but not located directly opposite to each other, the current penetration can be oblique, as shown in Figure 4b. When the current contacts are on the edge of the composites or located in the holes that goes through the thickness of the composites, the current penetrates through the entire cross-section of the composites, as presented in Figure 4c and 4d. When resistance is measured in the plane of the composites in the direction parallel to the fibres, it represents mainly the fiber breakage. On the other hand, when the resistance is measured through the thickness direction it represents the delamination damage. The oblique resistance, however, represents both of these damages and therefore, oblique resistance measurement is the most suitable method of detecting damage in composites.
Carbon short fibres (CSF) are used as admixtures in cement mixture to improve mechanical performance and incorporate functionalities in to the cementitious materials. When dispersed within cement, short carbon fibres introduce piezo-resistivity and self-sensing property. A self-sensing CSF based cementitious composite has been reported by Wen and Chung [20]. CSFs (0.5 wt.%) with diameter and length of 15 µm and 5 mm were dispersed within Portland cement using a rotary mixer. The self-sensing behaviour of the produced samples was tested under cyclic compressive stress in both longitudinal and transverse directions. The test results demonstrated the damage sensing capability of CSF dispersed cement samples. The damage was sensed by the irreversible increase in resistivity of the specimens under compression. An irreversible increase in both longitudinal and transverse resistivity (Figure 5a and 5b) occurred due to major damages such as breakage of fibres
that bridged the micro-cracks. The major damage was sensed by the irreversible increase in the specimen resistivity in the range of 10 to 30%. On the contrary, smaller change in resistivity ranging from 1 to 7% indicated smaller damages in the structure.
Similarly, Wang et al. [21] developed an innovative CSF (5 mm) reinforced concrete beam for sensing of fatigue damage. In this reinforcedconcrete (RC) beam (shown schematically in Figure 6), CSF reinforced concrete (CFRC) was used as a layer for both self-sensing and strengthening purpose.
The trend of fractional resistance change with load under monotonic flexural loading for this type of CFRC is shown in Figure 7. At lower loads, the inherent flaws in the specimens slowly merged to develop new micro-cracks which continued to expand in a stable manner with increasing load. Consequently, due to disturbance in the conducting network, the electrical resistance also continued to increase in a stable manner due to these minor damages. However, when the load increased considerably to the failure load of the specimens, due to the formation of continuous cracks fractional resistance change increased sharply. Under cyclic loading, when the stress amplitude was lower (80% of first cracking stress), only slight damage occurred in the beam resulting in only 7% increase in the fractional resistance change in 50 cycles. On the other hand, when a stress amplitude of 80% of the ultimate stress was applied, the fractional resistance increased irreversibly with the loading cycles, reaching 179% during failure of the beam in 38 cycles, as shown in Figure 8. The irreversibly increased electrical resistance, which is called the “residual resistance”, increased with fatigue damage and therefore, can be a useful parameter to monitor fatigue damage in the RC beams. Damage monitoring in concrete structure using CSF has also been reported by Chen and Liu [22].
Recently, short carbon fibre reinforced polymeric composites have been developed for health monitoring of civil engineering elements [23]. For this purpose, short carbon fibres (1 mm and 3 mm lengths) at various weight % (0.5, 0.75, and 1.25) were dispersed in an unsaturated polyester resin through mechanical stirring. After curing, the short fibre dispersed composites showed excellent strain sensing behaviour, as shown in Figure 9. Chopped fibres with different lengths exhibited similar strain sensitivity which, however, enhanced with the decrease in their concentrations (0.5%). Gauge factor as high as about 36 was obtained with the optimized composites. Therefore, these short fibre dispersed composites can have good potential for strain sensing of civil engineering structures.
Polymeric composites of carbon fibres have been investigated extensively for strengthening as well as self-sensing of structural elements. Differentarrangements of carbon fibres such as unidirectional tows and textile fabrics (either woven or knitted) have been used for this purpose. Moreover, hybrid composites of carbon and other fibres (such as glass, aramid, etc.) have also been developed to improve the sensing as well as strengthening capability.
Carbon fibre reinforced polymer composites exhibit piezoresistive behaviour under different types of loading [24]. Unidirectional carbon fibre reinforced epoxy composites have been found to sense their own strain in the fibre direction. Upon tensile loading, the longitudinal electrical resistance decreases reversibly with strain and transverse electrical resistance increases [25]. The reason behind the change in electrical resistance with tensile strain is the change in electrical contacts due to change in the fibre alignment. Under tensile loading, the fibres become more aligned in the loading direction leading to increase in the electrical contacts and decrease in resistance. The alignment of the fibres in longitudinal direction, however, decreaseselectrical contacts in the transverse direction and consequently, increases the transverse electrical resistance. This type of continuous carbon fibre composites could provide gauge factor from −35.7 to −37.6 and from +34.2 to +48.7 in the longitudinal and transverse direction, respectively. Therefore, they can be highly useful for sensing application in civil engineering structures. Carbon fibre reinforced plastic laminates were also found effective in sensing delamination, cracks and different types of damages occurred within the composite structures [26,27,28,29,30].
However, continuous carbon fibre/epoxy composites have low ductility. Nowadays, civil engineers are looking for light weight ductile reinforcements which can replace steel to avoid its corrosion and other problems. For this purpose, hybrid composites have been designed and developed with tailorable mechanical properties. Hybrid composites can exhibit higher breaking strains and ductility due to the so called “pseudo-ductile” behaviour [31]. Therefore, hybrid composites of carbon with other fibres have been investigated for their strengthening as well as self-sensing behaviour [31,32].
Currently existing hybrid composites exhibited continuous strain monitoring capability and can also be used to generate alarm signal well before the breakage of the composites. Both pseudo-ductility and self-sensing behaviours are highly dependent on the properties of the constituent fibres, their proportion and arrangement within the composites. Properly designed carbon fibre-glass fibre (CF-GF) hybrid composites were found to generate vital alarm signal representing the damage occurred in their structure [33,34]. These composites were designed by incorporating an internal carbon fibre core wrapped externally by glass fibre bundle, as shown in Figure 10. These composites showed reliable sensing capability under both monotonic and cyclic loading conditions. A sharp rise in the electrical resistance during the breakage of carbon fibres can be considered as the alarm signal, as shown in Figure 11. It was observed that the load at which the alarm signal was obtained could be designed by changing the relative proportion of carbon and glass fibres. At higher carbon fibre (CF-2.4%, GF-49%), the alarm signal was obtained almost at the breaking load of composites and therefore, these composites were not able to produce warning signal well before the breakage of composites. However, when the carbon fibre was used at lower quantity (CF-0.6%, GF-48% or CF-0.2%, GF-48%), the sharp rise in the electrical resistance was achieved at much lower load than the breaking load, as shown in Figure 11. Therefore, these self-sensing composites can be a suitable candidate for health monitoring of civil engineering structures.
However, one of the major drawbacks of CF-GF self-sensing composites is their inability to detect early stage of damage. The change in electrical resistance at low strain (below 0.6%) was found to be only 1%. Good strain sensitivity at low strain through measurement of residual resistance could be obtained for CF-GF composites only in pre-stressed conditions [35]. To overcome this limitation, an innovative composite material with excellent low strain sensitivity has been developed. In this type of hybrid composites, carbon particles were used instead of carbon fibre in combination with glass fibres [36]. Figure 12 shows the schematic of these composites. A fractional change in resistance of 6.2% was obtained at 0.6% strain and therefore, these composites are able to detect early stage of damage. The higher resistance change achieved in this case was attributed to the significant change in the conducting network formed by the carbon particles even at low strain level.
Improved strain sensitivity at low strain was also obtained with recently developed carbon fibre reinforced braided composite rods (BCRs) [37,38]. In BCRs, carbon fibres were axially introduced and over-braided using polyester filaments. Carbon fibres were impregnated with a polymeric resin before introducing to the braiding process and the produced structures were cured subsequently to produce the composite rods (Figure 13). The uniqueness of this technique is that the braiding of polyester yarns introduces certain degree of misalignment to the axial carbon fibres. Therefore, the change in the alignment of axial fibres under loading conditions and the resulting change in the electrical contacts lead to substantial change in electrical resistance even at low strain levels. The extent of misalignment introduced in the carbon fibres can be controlled by adjusting the braiding process parameters (such as speed, tension, etc.). Similar to the other sensing composites, strain sensitivity was found better with lower carbon fibre % and the best self-sensing BCR provided a gauge factor of 23.4 at a flexural strain of 0.55%.
Recently, self-sensing concretes have been developed to detect their own strain and damage using continuous carbon fibre based materials. Smart concretes incorporating carbon fibre textiles (Figure 14a) showed the capability to effectively monitor their strain [18]. Very good correlation was observed between the readings obtained from the textile sensor and conventional strain gauges, as shown in Figure 14b. The difference between the two readings was lower than 5%. The carbon textile based smart concretes provided gauge factor of around 10 and therefore, these smart materials can be advantageously utilized in the construction of self-sensing civil engineering structures.
Hybrid carbon/glass fabric reinforced concrete beams are also able to detect strain and monitor its interaction with a wet environment [39]. An electromechanical sensing with a gauge factor in the order of 1 can be obtained. These smart concrete beams also show detectable correlation between electrical resistance with the load, displacement and strain responses. The wet environment can also be detected by a fractional resistance change in the order of 10−5, which can be detected effectively using the Wheatstone bridge principle.
CNF and CNT are nanostructures made of carbon atoms. CNF comprises of graphene layers arranged as stacks of cones, plates or cups to create cylindrical nanostructures, whereas CNT comprises of graphene layers wrapped into perfect cylinders. These nanostructures possess outstanding mechanical properties and excellent electrical and thermal conductivities [40]. These characteristics of carbon nanomaterials make them attractive engineering materials for construction applications.
Carbon nano materials can be used for strengthening as well as sensing in construction applications. These nanostructures form conducting networks within the matrix at nanoscale and any change in this network at nano or micro-scale leads to change in the electrical resistivity of the matrix. Consequently, CNT or CNF reinforced composites are able to detect nano and micro-scale damages present in their structure. In addition, changes in the electrical network at very low strain enables the detection of micro strains by these nanostructures.
Different types of mechanical sensors have been developed until today using CNT/CNF or other carbon nano particle (CnP) based composite materials for sensing stress, strain, pressure, and so on. Among them, a few have been demonstrated for civil engineering applications. Nanni et al. [41] developed hybrid self-sensing composite rods consisting of internal conductive core surrounded by an external insulating skin (Figure 15). The conductive core was made of glass fibres impregnated with CnP/epoxy mixture. This sensing part was shielded by an outer GFRP skin, both to increase mechanical performance and to assure electric isolation. The used CnPs were spherical in shape with an average diameter of 30 nm and 5% CnP was used to produce the hybrid composites with good electrical conductivity. Concrete elements incorporating these hybrid rods exhibited good sensing behaviour, as shown in Figure 16a. The discontinuity in the resistance variation curve at points
1, 2 and 3 represented various changes occurring in the concrete specimens such as initial concrete cracking (point 1), formation and propagation of additional cracks (point 2) and severe cracking and failure of concrete specimens (point 3), as shown in Figure 16b.
Self-sensing ability of CNF reinforced concrete has also been reported [42]. Under compressive strain, electrical resistance variation up to 80% wasobtained using the most conducting nanofibers at 1 vol.% concentration. The strain monitoring capability of CNF/concrete specimens was observed to be highly dependent on the type, conductivity and concentration of CNF and optimum conditions resulted in strain sensitivity suitable for practical applications.
Under reverse cyclic loading also, CNF reinforced concrete showed good strain and damage sensing ability [43]. At smaller strains, the peaks and valleys in the electrical resistance of CNF reinforced concrete matched well with that of the applied force and the strain in the concrete, as shown in Figure 17. However, when the strain became high and the specimen was severely damaged, no correlation was observed between the electrical resistance and strain/force and electrical resistance increased quite irreversibly. This change in electrical resistance pattern indicated the occurrence of damage in the concrete specimens.
Hybrid cement composites of CNTs and carbon fibres (Figure 18) also exhibit good sensing behaviour [44]. In these composites, 1 vol% of multi-walled CNTs was used in combination with 15 vol% of carbon fibres. Under cyclic compressive loading, the changes in electrical resistance could mimic both the changes in load and strain with high reliability. However, the response was nonlinear and rate dependant. Nevertheless, for a particular loading rate, the strain in the developed materials could be predicted from the fractional change in resistivity using a non-linear calibration curve. As compared to only carbon fibre sensor, hybrid sensor exhibited better results with good repeatability. This can be observed from the lower scatter of FCR values in case of hybrid sensors than the carbon fibre sensor, as presented in Figure 19.
Recently, CNT/cement composite sensors were developed and demonstrated their application in pavement monitoring [45]. For this purpose, carboxyl functionalized CNTs were dispersed within cement using ultrasonication process with help of a surfactant (sodium dodecyl benzene sulfonate). At 0.1% MWNT concentration, very good sensing behaviour was achieved, as shown in Figure 20. These CNT sensors were installed in the road for testing the pavement monitoring capability (Figure 21). Figure 22 shows the response of pre-cast and cast-in-place CNT sensors while a truck passes over the road and compares the response with that obtained in case of strain gauges. It can be observed that an abrupt change in voltage occurs when a wheel passes over the road and each wheel represents one signal peak. As compared to the signals obtained with the strain gauges, the CNT/cement sensors showed higher detection accuracy, as some signals were missed in case of the strain gauges.
Self-sensing hybrid polymeric composites have also been developed using CNT. These composites are commonly known as multi-scale composites as they are fabricated combining macro and nano scale reinforcements [46,47,48,49]. The conventional macro-scale reinforcements (such as glass, carbon, etc.) are used for the strengthening purpose, whereas CNTs are incorporated to achieve sensing behaviour. In addition, CNTs can also improve mechanical properties of these composites. As compared to other hybrid composites, one major advantage with CNT based multi-scale composites is that they can detect micro-scale damages in the composite structure [50]. This is possible as damages even in nano and micro-scales can alter the conducting network of CNTs, resulting in change of resistivity of the composites. Figure 23 shows the sensing behaviour of a CNT based multi-scale braided composites.
It can be observed that the change in the slope of the resistance curve represents different types of damages in the composites. The microscale damages such as transverse cracks or micro delamination starts at stage 2 and accumulates in stage 3, resulting in considerable increase in the electrical resistance change. In stage 4, the saturation of micro-damages occurs and they close due to Poisson’s contraction and jamming of yarns in stage 5.
Braided composites using continuous CNT yarns have also been developed for developing self-monitoring systems [51]. These advanced braided composites have huge potential for application in structural applications. Braided composites present tailorable mechanical properties and surface characteristics and have been demonstrated as very good strengthening materials of concrete or masonry structures [52,53,54,55]. Therefore, sensing braided composites can be extensively utilized for both strengthening and health monitoring of civil engineering structures.
In this paper, an overview of carbon composites developed for health monitoring of civil engineering structures is presented. Carbon materials in different forms such as short fibre, particle, tows, fabrics and nanomaterials have been extensively studied for developing health monitoring systems. They have been either directly incorporated within cementitious materials for developing smart concrete or have been incorporated within polymers to fabricate self-sensing composites. Self-sensing polymeric composites can be advantageously utilized for strengthening as well as health monitoring of civil structures. Hybrid composites of carbon with other fibres offer the possibility of achieving higher ductility and generating alarm signal well before the composite’s failure. Therefore, they are useful to avoid sudden collapse of structures. However, they are not capable of detecting early stage damages in the structures. The low strain sensitivity of carbon composites can be greatly enhanced by using carbon nanoparticles, nanofibers or nanotubes. Formation of conducting networks at nano-scale offer them the possibility to detect very low strain and micro-damages in the structures. Smart concretes incorporating carbon nanomaterials also exhibit very good sensing performance. However, although carbon based self-sensing materials offer huge possibility to develop effective health monitoring systems, there exists a few critical issues which need to be solved in near future. More research and developments are required to develop self-sensing systems which can identify the location of damage. Although carbon nanomaterials based self-sensing materials offer better sensing performance, they are expensive and have processing difficulties. Enough information is also not available in the existing literature on the effect of environmental and usage conditions on the self-sensing performance of the developed composites. Therefore, for practical application of carbon based SHM systems, further research work is extremely essential for overcoming the practical problems in implementing these systems and reducing the cost and improving affordability of these materials.
Authors declare that there is not conflict of interest.
[1] |
Boffetta P, Nyberg F (2003) Contribution of environmental factors to cancer risk. Br Med Bull 68: 71-94. doi: 10.1093/bmp/ldg023
![]() |
[2] | Cancer Research UK, Cancer risk in the workplace. Cancer Research UK, 2016. Available from: http://www.cancerresearchuk.org/about-cancer/causes-of-cancer/cancer-risks-in-the-workplace. |
[3] | Ladeira C, Gomes MC, Brito M (2014) Human nutrition, DNA damage and cancer: a review, In: Mutagenesis: Exploring Novel Genes and Pathways. Wageningen: Wageningen Academic Publishers, 73-104. |
[4] | Key JT, Schatzkin A, Willett CW, et al. (2004) Diet, nutrition and the prevention of cancer. Public Health Nutr 7: 187-200. |
[5] |
Perera FP, Weinstein IB (2000) Molecular epidemiology: recent advances and future directions. Carcinogenesis 21: 517-524. doi: 10.1093/carcin/21.3.517
![]() |
[6] | Portier CJ, Bell DA (1998) Genetic susceptibility: significance in risk assessment. Toxicol Lett 28: 185-189. |
[7] |
Vainio H (1998) Use of biomarkers-new frontiers in occupational toxicology and epidemiology. Toxicol Lett 102-103:581-589. doi: 10.1016/S0378-4274(98)00252-5
![]() |
[8] |
Bartsch H (2000) Studies on biomarkers in cancer etiology and prevention: a summary and challenge of 20 years of interdisciplinary research. Mutat Res, Rev Mutat Res 462: 255-279. doi: 10.1016/S1383-5742(00)00008-9
![]() |
[9] |
Dusinska M, Collins AR (2008) The comet assay in human biomonitoring: gene–environment interactions. Mutagenesis 23: 191-205. doi: 10.1093/mutage/gen007
![]() |
[10] |
Perera FP (1996) Molecular epidemiology: insights into cancer susceptibility, risk assessment, and prevention. J Natl Cancer Inst 88: 496-509. doi: 10.1093/jnci/88.8.496
![]() |
[11] |
Au WW (2007) Usefulness of biomarkers in population studies: From exposure to susceptibility and to prediction of cancer. Int J Hyg Environ Health 210: 239-246. doi: 10.1016/j.ijheh.2006.11.001
![]() |
[12] |
El-Zein R, Vral A, Etzel CJ, et al. (2011) Cytokinesis-blocked micronucleus assay and cancer risk assessment. Mutagenesis 26:101-106. doi: 10.1093/mutage/geq071
![]() |
[13] | Husgafvel-Pursiainen K (2002) Molecular biomarkers in studies on environmental cancer. J Epidemiol Community Health 56(10):730-1. |
[14] |
Perera FP (2000) Molecular epidemiology: On the path to prevention? J Natl Cancer Inst 92: 602-612. doi: 10.1093/jnci/92.8.602
![]() |
[15] | Goldstein B, Gibson J, Henderson R, et al. (1987) Biological markers in environmental health research. Environ Health Perspect 74: 3-9. |
[16] | Fergurson L (2008) Biomarkers as endpoints in intervention studies. In: Wild, C., Vineis, P., Garte, S. Author, Molecular Epidemiology of Chronic Diseases, West Sussex: John Wiley & Sons Ltd, 255-266. |
[17] |
Schulte P, Mazzuckelli LF (1991) Validation of biological markers for quantitative risk assessment. Environ Health Perspect 90: 239-246. doi: 10.2307/3430874
![]() |
[18] |
Davis CD, Milner JA (2007) Biomarkers for diet and cancer prevention research: potentials and challenges. Acta pharmacol Sin 28: 1262-1273. doi: 10.1111/j.1745-7254.2007.00678.x
![]() |
[19] | US Congress (1990) Genetic monitoring and screening in the workplace. Office of Technology Assessment. |
[20] |
Barrett JC, Vainio H, Peakall D, et al. (1997) 12th meeting of the scientific group on methodologies for the safety evaluation of chemicals: susceptibility to environmental hazards. Environ Health Perspect 105: 699-737. doi: 10.1289/ehp.97105s4699
![]() |
[21] | Ladeira C, Viegas S (2016) Human biomonitoring-An overview on biomarkers and their application in occupational and environmental health. Biomonitoring 3: 15-24. |
[22] |
Battershill JM, Burnett K, Bull S (2008) Factors affecting the incidence of genotoxicity biomarkers in peripheral blood lymphocytes: impact on design of biomonitoring studies. Mutagenesis 23: 423-437. doi: 10.1093/mutage/gen040
![]() |
[23] |
Knudsen LE, Hansen AM (2007) Biomarkers of intermediate endpoints in environmental and occupational health. Int J Hygiene Environ Health 210: 461-470. doi: 10.1016/j.ijheh.2007.01.015
![]() |
[24] | Cavallo D, Ursini CL, Rondinone B et al. (2009) Evaluation of a suitable DNA damage biomarker for human biomonitoring of exposed workers. Environmental and Molecular Mutagenesis 50 (9):781–790. |
[25] |
Fenech M, Crott J, Turner J, et al. (1999) Necrosis, apoptosis, cytostasis and DNA damage in human lymphocytes measured simultaneously within the cytokinesis-block micronucleus assay: description of the method and results for hydrogen peroxide. Mutagenesis 14:605-612. doi: 10.1093/mutage/14.6.605
![]() |
[26] |
Majer BJ, Laky B, Knasmüller S, et al. (2001) Use of the micronucleus assay with exfoliated epithelial cells as a biomarker for monitoring individuals at elevated risk of genetic damage and in chemoprevention trials. Mutat Res 489: 147-172. doi: 10.1016/S1383-5742(01)00068-0
![]() |
[27] |
Burgaz S, Erdem O, Cakmak G, et al. (2002) Cytogenetic analysis of buccal cells from shoe-workers and pathology and anatomy laboratory workers exposed to n-hexane, toluene, methyl ethyl ketone and formaldehyde. Biomarkers 7: 151-161. doi: 10.1080/13547500110113242
![]() |
[28] |
Proia NK (2006) Smoking and smokeless tobacco-associated human buccal cell mutations and their association with oral cancer-A Review. Cancer Epidemiol Biomarkers Prev 15: 1061-1077. doi: 10.1158/1055-9965.EPI-05-0983
![]() |
[29] |
Fenech M (2007) Cytokinesis-block micronucleus cytome assay. Nat Protoc 2: 1084-1104. doi: 10.1038/nprot.2007.77
![]() |
[30] |
Holland N, Bolognesi C, Kirschvolders M, et al. (2008) The micronucleus assay in human buccal cells as a tool for biomonitoring DNA damage: The HUMN project perspective on current status and knowledge gaps. Mutat Res 659: 93-108. doi: 10.1016/j.mrrev.2008.03.007
![]() |
[31] |
Thomas P, Fenech M (2011) Buccal micronucleus cytome assay. Methods Mol Biol 682: 235-248. doi: 10.1007/978-1-60327-409-8_17
![]() |
[32] | Cerqueira EMM, Meireles JRC (2012) The use of the micronucleus test to monitoring individuals at risk for oral cancer. In: The Research and Biology of Cancer, Hong Kong: Icon Press Ltd, 1-26. |
[33] |
Kashyap B, Reddy PS (2012) Micronuclei assay of exfoliated oral buccal cells: means to assess the nuclear abnormalities in different diseases. J Cancer Res Ther 8: 184-191. doi: 10.4103/0973-1482.98968
![]() |
[34] |
Göethel G, Brucker N, Moro AM, et al. (2014) Evaluation of genotoxicity in workers exposed to benzene and atmospheric pollutants. Mutat Res Genet Toxicol Environ Mutagen 770: 61-65. doi: 10.1016/j.mrgentox.2014.05.008
![]() |
[35] |
Fenech M (1997) The advantages and disadvantages of the cytokinesis-block micronucleus method. Mutat Res 392: 11-18. doi: 10.1016/S0165-1218(97)00041-4
![]() |
[36] |
Fenech M (2000) The in vitro micronucleus technique. Mutat Res 455: 81-95. doi: 10.1016/S0027-5107(00)00065-8
![]() |
[37] |
Fenech M, Crott JW (2002) Micronuclei, nucleoplasmic bridges and nuclear buds induced in folic acid deficient human lymphocytes-evidence for breakage-fusion-bridge cycles in the cytokinesis-block micronucleus assay. Mutat Res 504: 131-136. doi: 10.1016/S0027-5107(02)00086-6
![]() |
[38] |
Mateuca R, Lombaert N, Aka PV, et al. (2006) Chromosomal changes: induction, detection methods and applicability in human biomonitoring. Biochimie 88: 1515-1531. doi: 10.1016/j.biochi.2006.07.004
![]() |
[39] |
Fenech M (2006) Cytokinesis-block micronucleus assay evolves into a 'cytome' assay of chromosomal instability, mitotic dysfunction and cell death. Mutat Res 600: 58-66. doi: 10.1016/j.mrfmmm.2006.05.028
![]() |
[40] |
Fenech M, Kirsch-Volders M, Natarajan AT, et al. (2011) Molecular mechanisms of micronucleus, nucleoplasmic bridge and nuclear bud formation in mammalian and human cells. Mutagenesis 26: 125-132. doi: 10.1093/mutage/geq052
![]() |
[41] |
Speit G (2013) Does the recommended lymphocyte cytokinesis-block micronucleus assay forhuman biomonitoring actually detect DNA damage induced by occupational and environmental exposure to genotoxic chemicals? Mutagenesis 28: 375-380. doi: 10.1093/mutage/get026
![]() |
[42] | Moller P, Knudsen LE, Loft S, et al. (2000) The comet assay as a rapid test in biomonitoring occupational exposure to DNA-damaging agents and effect of confounding factors. Cancer Epidemiol Biomarkers Prev 9: 1005-1015. |
[43] | Collins A, Dusinska M (2009) Applications of the comet assay in human biomonitoring. In: Dhawan, A., Anderson, D., Author, The Comet Assay in Toxicology, Cambridge: Royal Society of Chemistry, 201-202. |
[44] |
Collins AR (2004) The comet assay for DNA damage and repair: principles, applications, and limitations. Molecular Biotechnol 26: 249-261. doi: 10.1385/MB:26:3:249
![]() |
[45] |
Collins AR (2009) Investigating oxidative DNA damage and its repair using the comet assay. Mutat Res 681: 24-32. doi: 10.1016/j.mrrev.2007.10.002
![]() |
[46] | Azqueta A (2009) Detection of oxidised DNA using DNA repair enzymes. In: Dhawan, A., Anderson, D., Author, The Comet Assay in Toxicology, Cambridge: Royal Society of Chemistry, 58-63. |
[47] |
Valverde M, Rojas E (2009) Environmental and occupational biomonitoring using the comet assay. Mutat Res 681: 93-109. doi: 10.1016/j.mrrev.2008.11.001
![]() |
[48] | Valverde M, Rojas E (2009) The comet assay in human biomonitoring. In: Dhawan, A., Anderson, D., Author, The Comet Assay in Toxicology. Cambridge: Royal Society of Chemistry, 227-251. |
[49] |
Digue L, Orsière T, De Méo M, et al. (1999) Evaluation of the genotoxic activity of paclitaxel by the in vitro micronucleus test in combination with fluorescent in situ hybridization of a DNA centromeric probe and the alkaline single cell gel electrophoresis technique (comet assay) in Human T-Lymphocytes. Environ Mol Mutagenesis 34: 269-278. doi: 10.1002/(SICI)1098-2280(1999)34:4<269::AID-EM7>3.0.CO;2-D
![]() |
[50] | Hoffmann H, Speit G (2005) Assessment of DNA damage in peripheral blood of heavy smokers with the comet assay and the micronucleus test Mutat Res 581: 105-114. |
[51] |
Vasquez MZ (2010) Combining the in vivo comet and micronucleus assays: a practical approach to genotoxicity testing and data interpretation. Mutagenesis 25: 187-199. doi: 10.1093/mutage/gep060
![]() |
[52] |
Minozzo R, Deimling LI, Santos-Mello R (2010) Cytokinesis-blocked micronucleus cytome and comet assays in peripheral blood lymphocytes of workers exposed to lead considering folate and vitamin B12 status. Mutat Res/Genet Toxicol Environ Mutagen 697: 24-32. doi: 10.1016/j.mrgentox.2010.01.009
![]() |
[53] |
Olden K, Guthrie J (2001) Genomics: implications for toxicology. Mutat Res 473: 3-10. doi: 10.1016/S0027-5107(00)00161-5
![]() |
[54] |
Toscano WA, Oehlke KP (2005) Systems biology: new approaches to old environmental health problems. Int J Environ Res Public Health 2: 4-9. doi: 10.3390/ijerph2005010004
![]() |
[55] |
Sexton K, Needham L, Pirkle J (2004) Human biomonitoring of environmental chemicals. Am Sci 92: 38. doi: 10.1511/2004.45.921
![]() |
[56] |
Angerer J, Ewers U, Wilhelm M (2007) Human biomonitoring: state of the art. Int J Hygiene Environ Health 210: 201-228. doi: 10.1016/j.ijheh.2007.01.024
![]() |
[57] |
Au WW, Cajas-Salazar N, Salama S (1998) Factors contributing to discrepancies in population monitoring studies. Mutat Res, Fundam Mol Mech Mutagen 400: 467-478. doi: 10.1016/S0027-5107(98)00058-X
![]() |
[58] |
Ataseven N, Yüzbaşıoğlu D, Keskin AÇ, et al. (2016) Genotoxicity of monosodium glutamate. Food Chem Toxicol 91: 8-18. doi: 10.1016/j.fct.2016.02.021
![]() |
[59] |
Geras'kin SA, Kimb JK, Oudalova AA (2005) Bio-monitoring the genotoxicity of populations of Scots pine in the vicinity of a radioactive waste storage facility. Mutat Res 583: 55-66. doi: 10.1016/j.mrgentox.2005.02.003
![]() |
[60] | Choi J, Morck TA, Joas A, et al. (2015) Major national human biomonitoring programs in chemical exposure assessment. Environ Sci 2: 782-802. |
[61] |
Dagnino A, Bo T, Copetta A, et al. (2013) Development and application of an innovative expert decision support system to manage sediments and to assess environmental risk in freshwater ecosystems. Environ Int 60: 171-182. doi: 10.1016/j.envint.2013.08.011
![]() |
[62] |
Maffei F, Hrelia P, Angelini S, et al. (2005) Effects of environmental benzene: Micronucleus frequencies and haematological values in traffic police working in an urban area. Mutat Res 583: 1-11. doi: 10.1016/j.mrgentox.2005.01.011
![]() |
[63] |
Kim K-H, Jahan SA, Kabir E (2013) A review of airborne polycyclic aromatic hydrocarbons (PAHs) and their human health effects. Environ Inter 60: 71-80. doi: 10.1016/j.envint.2013.07.019
![]() |
[64] |
Song XF, Chen ZY, Zang ZJ (2013) Investigation of polycyclic aromatic hydrocarbon level in blood and semen quality for residents in Pearl River Delta Region in China. Environ Int 60: 97-105. doi: 10.1016/j.envint.2013.08.003
![]() |
[65] |
Grawe ́ J, Biko J, Lorenz R, et al. (2005) Evaluation of the reticulocyte micronucleus assay in patients treated with radioiodine for thyroid cancer. Mutat Res 583: 12-25. doi: 10.1016/j.mrgentox.2005.01.010
![]() |
[66] |
Harvey JS, Lyons BP, Page TS, et al. (1999) An assessment of the genotoxic impact of the Sea Empress oil spill by the measurement of DNA adduct levels in selected invertebrate and vertebrate species. Mutat Res 441: 103-114. doi: 10.1016/S1383-5718(99)00037-6
![]() |
[67] | Ceretti E, Feretti D, Viola GC, et al. (2014) DNA damage in buccal mucosa cells of pre-school children exposed to high levels of urban air pollutants. PLoS One 2: 1-9. |
[68] |
Demircigil GÇ, Erdem O, Gaga EO, et al. (2014) Cytogenetic biomonitoring of primary school children exposed to air pollutants: micronuclei analysis of buccal epithelial cells.Environ Sci Pollut Res Int 21: 1197-1207. doi: 10.1007/s11356-013-2001-6
![]() |
[69] | Marcon A, Fracasso ME, Marchetti P, et al. (2014) Outdoor formaldehyde and NO2 exposures and markers of genotoxicity in children living near chipboard industries. Environ Health Perspect 122: 639-645. |
[70] | Coelho P, García-Lestón J, Costa S, et al. (2013) Genotoxic effect of exposure to metal(loid)s. A molecular epidemiology survey of populations living and working in Panasqueira mine area, Portugal. Environ Int 60: 163-170. |
[71] | Mesic A, Nefic H (2015) Assessment of the genotoxicity and cytotoxicity in environmentally exposed human populations to heavy metals using the cytokinesis-block micronucleus cytome assay. Environ Toxicol 30: 1331-1342. |
[72] |
Kligerman AD, Erexson GL (1999) An evaluation of the feasibility of using cytogenetic damage as a biomarker for alachlor exposure. Mutat Res 441: 95-101. doi: 10.1016/S1383-5718(99)00031-5
![]() |
[73] | Banerjee S, Singh NN, Sreedhar G, et al. (2016) Analysis of the genotoxic effects of mobile phone radiation using buccal micronucleus assay: A comparative evaluation. J Clin Diagn Res 10: 82-85. |
[74] |
Pastor S, Creus A, Parrón T, et al. (2003) Biomonitoring of four European populations occupationally exposed to pesticides: use of micronuclei as biomarkers. Mutagenesis 18: 249-258. doi: 10.1093/mutage/18.3.249
![]() |
[75] | Bernardi N, Gentile N, Mañas F, et al. (2015) Assessment of the level of damage to the genetic material of children exposed to pesticides in the province of Córdoba. Arch Argent Pediatr 113: 126-131 |
[76] |
De Coster S, Koppen G, Bracke M, et al. (2008) Pollutant effects on genotoxic parameters and tumor-associated protein levels in adults: A cross sectional study. Environ Health 7: 26. doi: 10.1186/1476-069X-7-26
![]() |
[77] |
Rodríguez TG, Aldeco RG, Alvarez HB, et al. (2008) Genotoxicity in child populations exposed to polycyclic aromatic hydrocarbons (PAHs) in the air from Tabasco, Mexico. Int J Environ Res Public Health 5: 349-355. doi: 10.3390/ijerph5050349
![]() |
[78] |
Mielzyńska D, Siwińska E, Kapka L, et al. (2006) The influence of environmental exposure to complex mixtures including PAHs and lead on genotoxic effects in children living in Upper Silesia, Poland. Mutagenesis 21: 295-304. doi: 10.1093/mutage/gel037
![]() |
[79] | Nagya K, Rácz G, Matsumotoa T, et al. (2014) Evaluation of the genotoxicity of the pyrethroid insecticide Phenothrin. Mutat Res, Genet Toxicol Environ Mutagen 770: 1-5. |
[80] |
Moro AM, Brucker N, Charão M, et al. (2012) Evaluation of genotoxicity and oxidative damage in painters exposed to low levels of toluene. Mutat Res, Genet Toxicol Environ Mutagen 746: 42-48. doi: 10.1016/j.mrgentox.2012.02.007
![]() |
[81] |
Moro AM, Charãoa MF, Brucker N, et al. (2013) Genotoxicity and oxidative stress in gasoline station attendants. Mutat Res, Genet Toxicol Environ Mutagen 754: 63-70. doi: 10.1016/j.mrgentox.2013.04.008
![]() |
[82] | Marco P, Priestly B, Buckett K (1998) Carcinogen risk assessment. Can we harmonise? Toxicol Lett 102-103: 241-246. |
[83] | International Labour Organization (ILO). Chemical Exposure Limits. ILO 2011. Available from: http://www.ilo.org/safework/info/publications/WCMS_151534/lang--en/index.htm . |
[84] |
Ladeira C, Viegas S, Pádua M, et al. (2014) Assessment of genotoxic effects in nurses handling cytostatic drugs. J Toxicol Environ Health 77: 879-887. doi: 10.1080/15287394.2014.910158
![]() |
[85] |
Martino-Roth MG, Viégas J, Amaral M, et al. (2002) Evaluation of genotoxicity through micronuclei test in workers of car and battery repair garages. Genet Mol Biol 25: 495-500. doi: 10.1590/S1415-47572002000400021
![]() |
[86] | Ladeira C, Viegas S, Carolino E, et al. (2011) Genotoxicity biomarkers in occupational exposure to formaldehyde-The case of histopathology laboratories. Mutatn Res, Genet Toxicol Environ Mutagen 721: 115-120. |
[87] |
Grover P, Rekhadevi PV, Danadevi K, et al. (2010) Genotoxicity evaluation in workers occupationally exposed to lead. Int J Hygiene Environ Health 213: 99-106. doi: 10.1016/j.ijheh.2010.01.005
![]() |
[88] |
Danadevi K, Rozati R, Banu BS, et al. (2004) Genotoxic evaluation of welders occupationally exposed to chromium and nickel using the comet and micronucleus assays. Mutagenesis 19: 35-41. doi: 10.1093/mutage/geh001
![]() |
[89] |
Calvert GM, Talaska G, Mueller CA, et al. (1998) Genotoxicity in workers exposed to methyl bromide. Mutat Res, Genet Toxicol Environ Mutagen 417: 115-128. doi: 10.1016/S1383-5718(98)00105-3
![]() |
[90] |
Heuser VD, Andrade MV, Silva J, et al. (2005) Comparison of genetic damage in Brazilian footwear-workers exposed to solvent-based or water-based adhesive. Mutat Res 583: 85-94. doi: 10.1016/j.mrgentox.2005.03.002
![]() |
[91] |
Silveira HC, Schmidt-Carrijo M, Seidel EH, et al. (2013) Emissions generated by sugarcane burning promote genotoxicity in rural workers: A case study in Barretos, Brazil. Environ Health 12: 87. doi: 10.1186/1476-069X-12-87
![]() |
[92] | Pitarque M, Vaglenov A, Nosko M, et al. (1999) Evaluation of DNA damage by the comet assay in shoe workers exposed to toluene and other organic solvents. Mutat Res 44: 115-127. |
[93] | Strickland PT, Groopman JD (1995) Biomarkers for assessing environmental exposure to carcinogens in the diet. Am J Clin Nutr 61: 710-720. |
[94] | Sutandyo N (2010) Nutritional carcinogenesis. Acta Med Indones 42: 36-42. |
[95] |
Anand P, Kunnumakkara AB, Kunnumakara AB, et al. (2008) Cancer is a preventable disease that requires major lifestyle changes. Pharm Res 25: 2097-2116. doi: 10.1007/s11095-008-9661-9
![]() |
[96] | Willett W, Giovannucci E, et al. (2006) Epidemiology of diet and cancer risk. In: Shils, M.E., Shike, M. Author, Modern Nutrition in Health and Disease, Philadelphia: Lippincot Williams and Wilkins, 1627. |
[97] | The American Cancer Society, Food additives, safety, and organic foods. The American Cancer Society medical and editorial content team, 2012. Available from: https://www.cancer.org/healthy/eat-healthy-get-active/acs-guidelines-nutrition-physical-activity-cancer-prevention/food-additives.html . |
[98] | Swaroop VR, Dinesh RD, Vijayakumar T (2011) Genotoxicity of synthetic food colorants. J Food Sci Eng 1: 128-134. |
[99] |
Duarte-Salles T, Mendez MA, Meltzer HM, et al. (2013) Dietary benzo(a)pyrene intake during pregnancy and birth weight: Associations modified by vitamin C intakes in the Norwegian mother and child cohort study (MoBa). Environ Int 60: 217-223. doi: 10.1016/j.envint.2013.08.016
![]() |
[100] |
Papadopoulou E, Caspersen IH, Kvalem HE (2013) Maternal dietary intake of dioxins and polychlorinated biphenyls and birth size in the Norwegian mother and child cohort study (MoBa). Environ Int 60: 209-216. doi: 10.1016/j.envint.2013.08.017
![]() |
[101] | Banerjee M, Banerjee N, Bhattacharjee P, et al. (2013) High arsenic in rice is associated with elevated genotoxic effects in humans. Sci Rep 3: 1-8. |
[102] | Klarić MS, Darabos D, Rozgaj R, et al. (2010) Beauvericin and ochratoxin A genotoxicity evaluated using the alkaline comet assay: Single and combined genotoxic action. Arch Toxicol 84: 641-650. |
[103] |
Yılmaz S, Ünal F, Yüzbaşıoğlu D (2009) The in vitro genotoxicity of benzoic acid in human peripheral blood lymphocytes. Cytotechnology 60: 55-61. doi: 10.1007/s10616-009-9214-z
![]() |
[104] |
Mamur S, Yüzbaşıoğlu D, Unal F, et al. (2012) Genotoxicity of food preservative sodium sorbate in human lymphocytes in vitro. Cytotechnology 64: 553-562. doi: 10.1007/s10616-012-9434-5
![]() |
[105] | Kus E, Eroglu HE (2015) Genotoxic and cytotoxic effects of sunset yellow and brilliant blue, colorant food additives, on human blood lymphocytes, Pak J Pharm Sci 28: 227-230. |
[106] |
Spitz MR, Bondy ML (2010) The evolving discipline of molecular epidemiology of cancer. Carcinogenesis 31: 127-134. doi: 10.1093/carcin/bgp246
![]() |
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36. | Mohammadmahdi Abedi, Omid Hassanshahi, Joaquim A.O. Barros, António Gomes Correia, Raul Fangueiro, Three-dimensional braided composites as innovative smart structural reinforcements, 2022, 297, 02638223, 115912, 10.1016/j.compstruct.2022.115912 | |
37. | Amir A. E. Elseady, Ivan Lee, Yan Zhuge, Xing Ma, Christopher W. K. Chow, Nima Gorjian, Piezoresistivity and AC Impedance Spectroscopy of Cement-Based Sensors: Basic Concepts, Interpretation, and Perspective, 2023, 16, 1996-1944, 768, 10.3390/ma16020768 | |
38. | Mohammadmahdi Abedi, Raul Fangueiro, António Gomes Correia, Effects of multiscale carbon-based conductive fillers on the performances of a self-sensing cementitious geocomposite, 2021, 43, 23527102, 103171, 10.1016/j.jobe.2021.103171 | |
39. | Zere Bekzhanova, Shazim Ali Memon, Jong Ryeol Kim, Self-Sensing Cementitious Composites: Review and Perspective, 2021, 11, 2079-4991, 2355, 10.3390/nano11092355 | |
40. | Xiaoyan Huang, Lu Han, Xiao Yang, Zhiwen Huang, Jun Hu, Qi Li, Jinliang He, Smart dielectric materials for next-generation electrical insulation, 2022, 1, 2771-9197, 19, 10.23919/IEN.2022.0007 | |
41. | Akbar Shojaei, Samaneh Salkhi Khasraghi, 2021, 9780128205129, 307, 10.1016/B978-0-12-820512-9.00015-0 | |
42. | Namgyu Kim, Jong-Jae Lee, Noncontact stress measurement technique for concrete structure using photoluminescence piezospectroscopy, 2021, 11, 2190-5452, 1189, 10.1007/s13349-021-00501-z | |
43. | Andrea Meoni, Antonella D’Alessandro, Massimo Mancinelli, Filippo Ubertini, A Multichannel Strain Measurement Technique for Nanomodified Smart Cement-Based Sensors in Reinforced Concrete Structures, 2021, 21, 1424-8220, 5633, 10.3390/s21165633 | |
44. | D. D. L. Chung, A review to elucidate the multi-faceted science of the electrical-resistance-based strain/temperature/damage self-sensing in continuous carbon fiber polymer-matrix structural composites, 2023, 58, 0022-2461, 483, 10.1007/s10853-022-08106-7 | |
45. | Giacomo Selleri, Francesco Mongioì, Emanuele Maccaferri, Riccardo D’Anniballe, Laura Mazzocchetti, Raffaella Carloni, Davide Fabiani, Andrea Zucchelli, Tommaso Maria Brugo, Self-Sensing Soft Skin Based on Piezoelectric Nanofibers, 2023, 15, 2073-4360, 280, 10.3390/polym15020280 | |
46. | Chuanyi Ma, Xue Xin, Ning Zhang, Jianjiang Wang, Chuan Wang, Ming Liang, Yunfeng Zhang, Zhanyong Yao, Encapsulation for Sensing Element and Its Application in Asphalt Road Monitoring, 2023, 13, 2079-6412, 390, 10.3390/coatings13020390 | |
47. | Giacomo Selleri, Maria Elena Gino, Tommaso Maria Brugo, Riccardo D'Anniballe, Johnnidel Tabucol, Maria Letizia Focarete, Raffaella Carloni, Davide Fabiani, Andrea Zucchelli, Self-sensing composite material based on piezoelectric nanofibers, 2022, 219, 02641275, 110787, 10.1016/j.matdes.2022.110787 | |
48. | Nazire Deniz Yilmaz, 2022, 9781119654780, 1, 10.1002/9781119654872.ch1 | |
49. | Shama Parveen, Bruno Vilela, Olinda Lagido, Sohel Rana, Raul Fangueiro, Development of Multi-Scale Carbon Nanofiber and Nanotube-Based Cementitious Composites for Reliable Sensing of Tensile Stresses, 2021, 12, 2079-4991, 74, 10.3390/nano12010074 | |
50. | A. Meoni, C. Fabiani, A. D’Alessandro, A.L. Pisello, F. Ubertini, Strain-sensing smart bricks under dynamic environmental conditions: Experimental investigation and new modeling, 2022, 336, 09500618, 127375, 10.1016/j.conbuildmat.2022.127375 | |
51. | Tangfeng Feng, Yunfei Wang, Junjie Yang, Yunlong Li, Peng Xu, Huan Wang, Hua-Xin Peng, Faxiang Qin, Real-time self-monitoring and smart bend recognizing of fiber-reinforced polymer composites enabled by embedded magnetic fibers, 2023, 232, 02663538, 109869, 10.1016/j.compscitech.2022.109869 | |
52. | Krzysztof Lalik, Mateusz Kozek, Ireneusz Dominik, Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect, 2021, 14, 1996-1944, 4116, 10.3390/ma14154116 | |
53. | Mohammadmahdi Abedi, António Gomes Correia, Raul Fangueiro, Geotechnical and piezoresistivity properties of sustainable cementitious stabilized sand reinforced with recycled fibres, 2021, 6, 2666691X, 100096, 10.1016/j.treng.2021.100096 | |
54. | Zhuang Tian, Shaoqi Li, Yancheng Li, Aligning conductive particles using magnetic field for enhanced piezoresistivity of cementitious composites, 2021, 313, 09500618, 125582, 10.1016/j.conbuildmat.2021.125582 | |
55. | Yunlong Zhang, Jianxin Wang, Jing Wang, Xuesong Qian, Preparation, mechanics and self-sensing performance of sprayed reactive powder concrete, 2022, 12, 2045-2322, 10.1038/s41598-022-11836-y | |
56. | Rajani Kant Rao, Saptarshi Sasmal, 2022, Chapter 17, 978-981-16-9092-1, 203, 10.1007/978-981-16-9093-8_17 | |
57. | Minxiao Lin, Shijun Guo, Shun He, Wenhao Li, Daqing Yang, Structure health monitoring of a composite wing based on flight load and strain data using deep learning method, 2022, 286, 02638223, 115305, 10.1016/j.compstruct.2022.115305 | |
58. | Cecílie Mizerová, Ivo Kusák, Pavel Rovnaník, Patrik Bayer, Conductive Metakaolin Geopolymer with Steel Microfibres, 2021, 321, 1662-9779, 59, 10.4028/www.scientific.net/SSP.321.59 | |
59. | Sahar Hassani, Mohsen Mousavi, Amir H. Gandomi, Structural Health Monitoring in Composite Structures: A Comprehensive Review, 2021, 22, 1424-8220, 153, 10.3390/s22010153 | |
60. | Xin Qian, Heng Yang, Jialai Wang, Yi Fang, Mengxiao Li, Eco-friendly treatment of carbon nanofibers in cementitious materials for better performance, 2022, 16, 22145095, e01126, 10.1016/j.cscm.2022.e01126 | |
61. | Anthony Palumbo, Eui-Hyeok Yang, 2022, 9780128234426, 361, 10.1016/B978-0-12-823442-6.00008-8 | |
62. | Maria Elena Gino, Giacomo Selleri, Davide Cocchi, Tommaso Maria Brugo, Nicola Testoni, Luca De Marchi, Andrea Zucchelli, Davide Fabiani, Maria Letizia Focarete, On the design of a piezoelectric self-sensing smart composite laminate, 2022, 219, 02641275, 110783, 10.1016/j.matdes.2022.110783 | |
63. | Gouri Sankar Das, Vijayendra Kumar Tripathi, Jaya Dwivedi, Lokesh Kumar Jangir, Kumud Malika Tripathi, Nanocarbon-based sensors for the structural health monitoring of smart biocomposites, 2024, 16, 2040-3364, 1490, 10.1039/D3NR05522A | |
64. | Xinyue Wang, Siqi Ding, Yi-Qing Ni, Liqing Zhang, Sufen Dong, Baoguo Han, Intrinsic self-sensing concrete to energize infrastructure intelligence and resilience: A review, 2024, 3, 27729915, 100094, 10.1016/j.iintel.2024.100094 | |
65. | Liangsheng Qiu, Siqi Ding, Danna Wang, Baoguo Han, Self-sensing GFRP-reinforced concrete beams containing carbon nanotube-nano carbon black composite fillers, 2023, 34, 0957-0233, 084003, 10.1088/1361-6501/accc20 | |
66. | Ayushi Thakur, Ruchira Srivastava, Preeti Singh Bahadur, Ajay Rana, 2024, chapter 8, 9798369343975, 249, 10.4018/979-8-3693-4397-5.ch008 | |
67. | Mohammadmahdi Abedi, Raul Fangueiro, António Gomes Correia, Javad Shayanfar, Smart Geosynthetics and Prospects for Civil Infrastructure Monitoring: A Comprehensive and Critical Review, 2023, 15, 2071-1050, 9258, 10.3390/su15129258 | |
68. | Self-Sensing Potential of Metashale Geopolymer Mortars with Carbon Fiber/Graphite Powder Admixtures, 2024, 14, 2226-809X, 423, 10.46604/ijeti.2024.13570 | |
69. | M. Rama, J.S. Sudarsan, N. Sunmathi, S. Nithiyanantham, Behavioral assessment of intrinsically formed smart concrete using steel fibre and carbon black composite, 2024, 10, 24058440, e26948, 10.1016/j.heliyon.2024.e26948 | |
70. | António Gomes Correia, Mohammad Jawed Roshan, Self-sensing cementitious geocomposites in rail track substructures, 2024, 46, 22143912, 101260, 10.1016/j.trgeo.2024.101260 | |
71. | Vo Minh Chi, Nguyen Lan, Nguyen Minh Hai, Nguyen Van Huong, Compression self-sensibility of the concrete using high content carbon black with various measurement conditions, 2023, 1289, 1757-8981, 012033, 10.1088/1757-899X/1289/1/012033 | |
72. | Said Quqa, Sijia Li, Yening Shu, Luca Landi, Kenneth J Loh, Crack identification using smart paint and machine learning, 2024, 23, 1475-9217, 248, 10.1177/14759217231167823 | |
73. | Anthony Palumbo, Zheqi Li, Eui-Hyeok Yang, Trends on Carbon Nanotube-Based Flexible and Wearable Sensors via Electrochemical and Mechanical Stimuli: A Review, 2022, 22, 1530-437X, 20102, 10.1109/JSEN.2022.3198847 | |
74. | Qi Cui, Zhen-gang Feng, Ruoting Shen, Xiangnan Li, Zhuang Wang, Dongdong Yao, Xinjun Li, Piezoresistive response of self-sensing asphalt concrete containing carbon fiber, 2024, 426, 09500618, 136121, 10.1016/j.conbuildmat.2024.136121 | |
75. | Hashim Hassan, William A Crossley, Tyler N Tallman, Hybrid optimization schemes for solving the piezoresistive inversion problem in self-sensing materials, 2024, 33, 0964-1726, 065033, 10.1088/1361-665X/ad49ec | |
76. | Ziyan Hang, Zhi Ni, Jinlong Yang, Yucheng Fan, Chuang Feng, Shuguang Wang, Nonlinear Vibration of FG-GNPRC Dielectric Beam with Kelvin–Voigt Damping in Thermal Environment, 2024, 24, 0219-4554, 10.1142/S021945542450130X | |
77. | Ely Leburu, Yuting Qiao, Yanshen Wang, Jiakuan Yang, Sha Liang, Wenbo Yu, Shushan Yuan, Huabo Duan, Liang Huang, Jingping Hu, Huijie Hou, Flexible electronics for heavy metal ion detection in water: a comprehensive review, 2024, 26, 1387-2176, 10.1007/s10544-024-00710-5 | |
78. | Yifei Gong, Zhiyu Xie, Guanhao Chen, Dawei Zhang, Influence of Cementitious Material Infiltration on Piezoresistive Effect of Carbon Fiber Bundle, 2023, 35, 0899-1561, 10.1061/JMCEE7.MTENG-14701 | |
79. | Omolayo M. Ikumapayi, Temitayo S. Ogedengbe, Sunday A. Afolalu, Adebayo T. Ogundipe, Emeka S. Nnochiri, 2024, 3007, 0094-243X, 100010, 10.1063/5.0197101 | |
80. | Francisco Alfonso Alvarez del Castillo Manzanos, Robert R. Hughes, Anthony J. Croxford, Passive Wireless Mechanical Overload Sensing: Proof of Concept Using Agarose Hydrogels, 2023, 72, 0018-9456, 1, 10.1109/TIM.2023.3291741 | |
81. | Michela Rossi, Dionysios Bournas, Structural Health Monitoring and Management of Cultural Heritage Structures: A State-of-the-Art Review, 2023, 13, 2076-3417, 6450, 10.3390/app13116450 | |
82. | Hao Chen, Inge Hoff, Gang Liu, Xuemei Zhang, Diego Maria Barbieri, Fusong Wang, Jianan Liu, Development of finite element model based on indirect tensile test for various asphalt mixtures, 2023, 394, 09500618, 132085, 10.1016/j.conbuildmat.2023.132085 | |
83. | Abdalaziz Al-Maeeni, Mikhail Lazarev, Nikita Kazeev, Kostya S Novoselov, Andrey Ustyuzhanin, Review on automated 2D material design, 2024, 11, 2053-1583, 032002, 10.1088/2053-1583/ad4661 | |
84. | Omar Shabbir Ahmed, Abdul Aabid, Jaffar Syed Mohamed Ali, Meftah Hrairi, Norfazrina Mohd Yatim, Progresses and Challenges of Composite Laminates in Thin-Walled Structures: A Systematic Review, 2023, 8, 2470-1343, 30824, 10.1021/acsomega.3c03695 | |
85. | Rajani Kant Rao, S. Gautham, Saptarshi Sasmal, A Comprehensive Review on Carbon Nanotubes Based Smart Nanocomposites Sensors for Various Novel Sensing Applications, 2024, 64, 1558-3724, 575, 10.1080/15583724.2024.2308889 | |
86. | Md. Zobair Al Mahmud, Moyeen Khan, Md. Faysal Ahamed Dewan Refati, Md Hosne Mobarak, Md. Abdullah Al Shafi, Nayem Hossain, A. K. M. Foysal Ahmed, Advances and Significances of Nanoparticles as Concrete Additives: A Comprehensive Review, 2024, 14, 1793-9844, 10.1142/S179398442440004X | |
87. | Zhizhong Deng, Wengui Li, Wenkui Dong, Zhihui Sun, Jayantha Kodikara, Daichao Sheng, Multifunctional asphalt concrete pavement toward smart transport infrastructure: Design, performance and perspective, 2023, 265, 13598368, 110937, 10.1016/j.compositesb.2023.110937 | |
88. | Zhuang Wang, Zhen-gang Feng, Qi Cui, Genmiao Guang, Xinjun Li, Evaluation of piezoresistive response and mechanical performance of self-sensing asphalt concrete mixed with different lengths of carbon fiber, 2025, 462, 09500618, 139942, 10.1016/j.conbuildmat.2025.139942 | |
89. | Xue Xin, Junyao Hui, Lin Chen, Ming Liang, Zhanyong Yao, Monitoring the Internal Conditions of Road Structures by Smart Sensing and In Situ Monitoring Technology: A Review, 2025, 15, 2076-3417, 3945, 10.3390/app15073945 | |
90. | Manish Baboo Agarwal, Manu Mehrotra, Manish Kumar Panday, Pankaj Mittal, Vinay Kumar Jadon, Seema Agarwal, 2025, Chapter 4, 978-3-031-77295-5, 69, 10.1007/978-3-031-77296-2_4 | |
91. | Shu‐Yang Wang, Gui‐Hua Xie, Hong‐Yun Xia, Shuai Xu, Zi‐Han Lin, Shi‐Quan Li, A Review on Resistance‐Based Self‐Sensing of Carbon Fiber‐Reinforced Polymer Subjected to Loads, 2025, 1438-1656, 10.1002/adem.202500244 | |
92. | Xue Xin, Xingchi Zhao, Jing Gao, Zhanyong Yao, Yunzhen Li, Mechanism of Strain-Resistance Response of CNT/Polymer Composite Materials for Pavement Strain Self-Sensing Based on the Molecular Dynamics Simulation Method, 2025, 17, 2073-4360, 1427, 10.3390/polym17111427 |