Mechanical properties and piezoresistive performances of intrinsic graphene nanoplate/cement-based sensors subjected to impact load

Dong, Wenkui; Li, Wengui; Guo, Yipu; Wang, Kejin; Sheng, Daichao

doi:10.1016/j.conbuildmat.2022.126978

Cited by 42 publications

(8 citation statements)

References 87 publications

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“…Besides, GNP with concentration in the range of 0.025%-0.10% has zero impact on the flexural strength. [74][75][76][77][78][79] Furthermore, the sensor mortar with high dosage (2%) of GNP has a higher impact load resistance under ultimate loading conditions. 75 Moreover, increasing the dosage of GNP increases the hardness of the cement composite and decreases the abrasion over the cement composites by 71%.…”

Section: Sequel Of Graphenementioning

confidence: 99%

“…[74][75][76][77][78][79] Furthermore, the sensor mortar with high dosage (2%) of GNP has a higher impact load resistance under ultimate loading conditions. 75 Moreover, increasing the dosage of GNP increases the hardness of the cement composite and decreases the abrasion over the cement composites by 71%. 80 It also reduces the porosity which in turn enhances the microstructural properties.…”

Section: Sequel Of Graphenementioning

confidence: 99%

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Smart cement‐sensor composite: The evolution of nanomaterial in developing sensor for structural integrity

Kanagasundaram

Elavenil

2023

Structural Concrete

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The integration of self‐sensing ability and different functional properties of nanomaterials into construction materials leads to smart and multifunctional cementitious sensor composites. Significant evolution of the Structural Health Monitoring (SHM) has helped to elevate the life span and mitigate damages of the structure. Developing smart cementitious sensor composite with electrical properties as a parent element and embedding it in the structural components have proved to be advantageous in terms of evaluating the damages, monitoring the periodic health and life assessment of the structures. In this review, the copious development processes and various methods of designing smart‐sensing cementitious composites have been scrutinized. This work recapitulates the principle of self‐sensing technique, typical functional fillers, trends of percolation threshold, dispersing material, dispersion technology, and also describes the required sensing ability of the sensors using the conduction theory. Subsequently application of smart cement‐sensors in structural components for SHM is discussed. In addition, this review article provides comprehensive information on the effects of nano materials and their dispersion in the development for smart and multifaceted cementitious sensor composites with enhanced sensitivity parameters and economic benefits that are suitable for future prospects.

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Section: Sequel Of Graphenementioning

confidence: 99%

Section: Sequel Of Graphenementioning

confidence: 99%

Smart cement‐sensor composite: The evolution of nanomaterial in developing sensor for structural integrity

Kanagasundaram

Elavenil

2023

Structural Concrete

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“…Indeed, a combination of CNTs (one-dimensional) and GNPs (two-dimensional) that have different geometric shapes can act as nanoreinforcement through the bridging of nanoscale cracks and/or their deviation and branches [52]. Crack bridging, deflection, and branching are known to be important sources of strengthening and toughening in CNM-reinforced composites, especially in those with brittle matrix [53][54][55][56][57][58]. In CNM-reinforced composites, crack bridging is typically assured by CNTs, while GNPs often increase the energy required to open nanocracks by deviating their paths and, consequently, increase the total fracture surface and prevent their degeneration into microcracks (Fig.…”

Section: Cnms-reinforced Bcrsmentioning

confidence: 99%

Three-dimensional braided composites as innovative smart structural reinforcements

Abedi

Hassanshahi²,

Barros³

et al. 2022

Composite Structures

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“…20−23 Voice signals contain rich character information on humans and are easy to use, so people can use voice signals to make and realize accurate commands. At present, the main sensors currently used for speech recognition are capacitive sensors, 24,25 piezoresistive sensors, 26,27 piezoelectric sensors, 28−30 and triboelectric sensors. 31−33 However, some components in capacitive and piezoresistive sensors, such as conventional commercial microphones, rely on battery charging and are vulnerable to environmental noise.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, there are two main methods of human–machine interaction: voice–machine interaction − and gesture–machine interaction. − Voice signals contain rich character information on humans and are easy to use, so people can use voice signals to make and realize accurate commands. At present, the main sensors currently used for speech recognition are capacitive sensors, , piezoresistive sensors, , piezoelectric sensors, − and triboelectric sensors. − However, some components in capacitive and piezoresistive sensors, such as conventional commercial microphones, rely on battery charging and are vulnerable to environmental noise. On the other hand, piezoelectric sensors have complex structures, troublesome manufacturing process and short working frequency ranges.…”

Section: Introductionmentioning

confidence: 99%

Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

Luo

Yang

et al. 2023

ACS Appl. Mater. Interfaces

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With the development of science and technology, human− machine interaction has brought great benefits to the society. Here, we design a voice and gesture signal translator (VGST), which can translate natural actions into electrical signals and realize efficient communication in human−machine interface. By spraying silk protein on the copper of the device, the VGST can achieve improved output and a wide frequency response of 20−2000 Hz with a high sensitivity of 167 mV/dB, and the resolution of frequency detection can reach 0.1 Hz. By designing its internal structure, its resonant frequency and output voltage can be adjusted. The VGST can be used as a high-fidelity platform to effectively recover recorded music and can also be combined with machine learning algorithms to realize the function of speech recognition with a high accuracy rate of 97%. It also has good antinoise performance to recognize speech correctly even in noisy environments. Meanwhile, in gesture recognition, the triboelectric translator is able to recognize simple hand gestures and to judge the distance between hand and the VGST based on the principle of electrostatic induction. This work demonstrates that triboelectric nanogenerator (TENG) technology can have great application prospects and significant advantages in human−machine interaction and high-fidelity platforms.

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Mechanical properties and piezoresistive performances of intrinsic graphene nanoplate/cement-based sensors subjected to impact load

Cited by 42 publications

References 87 publications

Smart cement‐sensor composite: The evolution of nanomaterial in developing sensor for structural integrity

Smart cement‐sensor composite: The evolution of nanomaterial in developing sensor for structural integrity

Three-dimensional braided composites as innovative smart structural reinforcements

Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

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