laser scribing, the GO film is quite flat and dense. After the laser scribing, a graphene film of 10 μm thickness is obtained. It is noticed that there is an almost 10-fold thickness increase for the LSG compared to the original GO film. **Figure 11d** shows the I-V curves of the film before and after the laser scribing. The resistance of the GO film is 580 MΩ. However, when the GO film is reduced to the LSG, the small resistance can be obtained as 8.2 kΩ, which is an almost five orders of magnitude reduction.

Graphene sound source devices can be packaged into traditional earphones. After the laser scribing of the wafer-scale graphene patterns, these patterns could be cut into individual graphene earphones. **Figure 12a** shows the graphene earphone with

establish electrical input. The graphene earphones are finally connected to the electrical wiring of a commercial earphone casing using copper wires, as shown in **Figure 12b**. The structure of the graphene earphone is made up of the top cap, the graphene sheets, the Ag electrodes, the PET, and the bottom cap, as shown in **Figure 12c**. The packaged graphene earphones for human use is shown in **Figure 12d**. Compared with the traditional earphones, the distinctive feature of graphene device is significantly thinner than a voice coil. In order to obtain a sufficiently high SPL and equal-frequency playback sound, the periphery circuit is successfully designed. The schematic diagram of the circuit is shown in **Figure 13**. It is worth mentioning that the input sound frequency is doubled due to the thermoacoustic effect, and this needs to be compensated during actual testing, as described ahead. The drive circuit uses a USB port to apply power to the circuit for amplifying the AC signal and also to apply an up to 15 V DC bias to the graphene earphone. In this way, the graphene earphone can

*The demonstration of graphene earphone [13]. (a) Graphene earphone in hand. (b) View of the graphene earphone in a commercial earphone casing. (c) Exploded view of a packaged graphene earphone. (d) A pair of*

. Silver paste is applied on both sides of the graphene film to

#### *Graphene Acoustic Devices DOI: http://dx.doi.org/10.5772/intechopen.81603*

*Graphene and Its Derivatives - Synthesis and Applications*

SP and the working mechanism is thermoacoustic effect.

where ks and Cp,s are the thermal conductivity and heat capacity of the paper substrate, respectively; ω is the angular frequency of sound. The average temperature is linearly related to the input power, and the theoretical curve is in good agreement with the experimental results. Combined with the test results of **Figure 8c**, the SP increases with the input power, which indicates that the Joule heating is related to the

**4. Graphene earphones: entertainment for both humans and animals**

Laser scribing can be used to prepare graphene due to its advantage of low cost, high speed and no transfer. The earphone based on laser scribed graphene (LSG) can cover audible range and ultrasonic range. Animal hearing is more sensitive to the sound of the ultrasound band than audible domain. Therefore, the graphene earphones can be applied not only to humans, but also to animals. This section will introduce laser direct writing to prepare graphene earphones. The advantage of fabricating graphene earphones by using the laser scribing technology is that the large scale array can be prepared without the mask. **Figure 11a** shows the preparation process of graphene earphones. The graphene oxide (GO) solution can be directly coated on PET substrates. Then the GO film can be reduced to graphene under 788 nm laser irradiation by using the DVD light engraving machine. It is noted that the preparation of the wafer-scale precise graphene earphones requires only 25 minutes by using laser scribing technology. **Figure 11b** shows the wafer-scale flexible graphene earphones on the PET substrate. The inset in **Figure 11b** shows the graphene earphones at 1 cm2 dimensions. The SEM image of graphene sheets is shown in **Figure 11c**. Before the

**78**

**Figure 11.**

*and after laser scribing.*

*Laser scribing technology for flexible graphene earphone fabrication [13]. (a) Process flow of the graphene earphone fabrication; (b) wafer-scale flexible graphene earphones. The inset shows an optical microscope image of the LSG earphone; (c) SEM image of the LSG and GO in false color; (d) electrical properties before*  laser scribing, the GO film is quite flat and dense. After the laser scribing, a graphene film of 10 μm thickness is obtained. It is noticed that there is an almost 10-fold thickness increase for the LSG compared to the original GO film. **Figure 11d** shows the I-V curves of the film before and after the laser scribing. The resistance of the GO film is 580 MΩ. However, when the GO film is reduced to the LSG, the small resistance can be obtained as 8.2 kΩ, which is an almost five orders of magnitude reduction.

Graphene sound source devices can be packaged into traditional earphones. After the laser scribing of the wafer-scale graphene patterns, these patterns could be cut into individual graphene earphones. **Figure 12a** shows the graphene earphone with an area of 10 × 10 mm2 . Silver paste is applied on both sides of the graphene film to establish electrical input. The graphene earphones are finally connected to the electrical wiring of a commercial earphone casing using copper wires, as shown in **Figure 12b**. The structure of the graphene earphone is made up of the top cap, the graphene sheets, the Ag electrodes, the PET, and the bottom cap, as shown in **Figure 12c**. The packaged graphene earphones for human use is shown in **Figure 12d**. Compared with the traditional earphones, the distinctive feature of graphene device is significantly thinner than a voice coil. In order to obtain a sufficiently high SPL and equal-frequency playback sound, the periphery circuit is successfully designed. The schematic diagram of the circuit is shown in **Figure 13**. It is worth mentioning that the input sound frequency is doubled due to the thermoacoustic effect, and this needs to be compensated during actual testing, as described ahead. The drive circuit uses a USB port to apply power to the circuit for amplifying the AC signal and also to apply an up to 15 V DC bias to the graphene earphone. In this way, the graphene earphone can connect to a laptop for playing music.

#### **Figure 12.**

*The demonstration of graphene earphone [13]. (a) Graphene earphone in hand. (b) View of the graphene earphone in a commercial earphone casing. (c) Exploded view of a packaged graphene earphone. (d) A pair of graphene earphones in its final packaged form.*

**Figure 13.** *Graphene earphone drive circuit structure diagram [9].*

**Figure 14.**

*Sound pressure and frequency characteristics of the graphene earphone [13]. (a) Experimental setup for the graphene earphone. (b) Sound pressure level (SPL) curves of a graphene earphone compared with a commercial earphone.*

**Figure 14a** shows the graphene earphones acoustic test platform. **Figure 14b** shows the acoustic spectrum of the device. The graphene earphone is placed 1 cm away from the standard microphone. It can be seen that the graphene earphone has a flatter acoustic spectrum output than commercial earphone. Meanwhile, the sound intensity of the graphene earphone remains relatively stable. The graphene earphone has a fluctuation of 10 dB, which is much lower than the normal commercial earphone with the fluctuation of 30 dB. Especially, the spectrum can not only cover the 20 Hz to 20 kHz, but also cover the ultrasonic frequency band of 20–50 kHz. Animals are more sensitive to ultrasonic frequency than audible range, and graphene earphone has flat acoustic output in ultrasonic band. Therefore, graphene earphones can be used to train and control animal behaviors.

Finally, an application of the graphene earphone is demonstrated. The graphene earphone is fixed on the steel ring so that a dog can wear it, as shown in **Figure 15a** and **b**. The response test process is divided into the following sections. First, the dog needs to be trained so that it can recognize the "stand up" command when hearing the 35 kHz sound wave. The Chinese audio frequency of "stand up" was recorded first. Then, the audio was mixed with 35 kHz sine wave by computer software. The sampling frequency was 88.2 kHz, which was more than twice that of 35 kHz to ensure that the sampling was not distorted. The pure human voice was played to the

**81**

integration.

**Figure 15.**

*Graphene Acoustic Devices*

*DOI: http://dx.doi.org/10.5772/intechopen.81603*

dog, and the dog will stand up, and then it will be mixed with 35 kHz sound waves. The dog will be given a food reward every time he stands up successfully. And then it went on to increase the proportion of 35 kHz sound waves, and to reduce the proportion of human voice. Until the final 35 kHz sound waves was 100%, the dog was able to stand up to prove that it was successful in establishing a 35 kHz sound wave to the dog and the "stand up" command. **Figure 15c** shows a demonstration of controlling dog behavior through graphene earphones. The initial state of the dog is sitting, and when the dog hears the 35 kHz sound from the graphene earphone, it will stand up,

*Animal responding to ultrasound signals through graphene earphone [13]. (a) Graphene earphone for dogs. (b) Subject wearing the graphene earphone. (c) Wearing a graphene earphone for the dog to establish a 35 kHz sound wave and a "stand up" command to reflect the training process. The dog is initially sitting down. After*

which indicates that the graphene earphone can control the dog's behavior.

**laser-induced graphene**

*receiving a familiar 35 kHz signal, it stands up.*

ity and biocompatibility is of great significance.

**5. An intelligent artificial throat with sound-sensing ability based on**

The graphene sound source device based on thermoacoustic effect is introduced. Based on its piezoresistive effect, graphene can also be used for throat detection, which can be used as a good sound receiver. Therefore, based on thermoacoustic effect and piezoresistive effect, graphene is expected to realize transceiver

At the present stage, devices with integrated sound and transceiver function are usually called ultrasonic transducers, which can only work in the ultrasonic frequency band or under water [14]. At the same time, these substrates are hard, not flexible and biocompatible, and not suitable for wearable applications. Therefore, studying the audible frequency band, the sound-flexible device with good flexibil*Graphene Acoustic Devices DOI: http://dx.doi.org/10.5772/intechopen.81603*

#### **Figure 15.**

*Graphene and Its Derivatives - Synthesis and Applications*

*Graphene earphone drive circuit structure diagram [9].*

*Sound pressure and frequency characteristics of the graphene earphone [13]. (a) Experimental setup for the graphene earphone. (b) Sound pressure level (SPL) curves of a graphene earphone compared with a*

**Figure 14a** shows the graphene earphones acoustic test platform. **Figure 14b** shows the acoustic spectrum of the device. The graphene earphone is placed 1 cm away from the standard microphone. It can be seen that the graphene earphone has a flatter acoustic spectrum output than commercial earphone. Meanwhile, the sound intensity of the graphene earphone remains relatively stable. The graphene earphone has a fluctuation of 10 dB, which is much lower than the normal commercial earphone with the fluctuation of 30 dB. Especially, the spectrum can not only cover the 20 Hz to 20 kHz, but also cover the ultrasonic frequency band of 20–50 kHz. Animals are more sensitive to ultrasonic frequency than audible range, and graphene earphone has flat acoustic output in ultrasonic band. Therefore, graphene earphones can be used to train and control animal behaviors. Finally, an application of the graphene earphone is demonstrated. The graphene earphone is fixed on the steel ring so that a dog can wear it, as shown in **Figure 15a** and **b**. The response test process is divided into the following sections. First, the dog needs to be trained so that it can recognize the "stand up" command when hearing the 35 kHz sound wave. The Chinese audio frequency of "stand up" was recorded first. Then, the audio was mixed with 35 kHz sine wave by computer software. The sampling frequency was 88.2 kHz, which was more than twice that of 35 kHz to ensure that the sampling was not distorted. The pure human voice was played to the

**80**

**Figure 14.**

**Figure 13.**

*commercial earphone.*

*Animal responding to ultrasound signals through graphene earphone [13]. (a) Graphene earphone for dogs. (b) Subject wearing the graphene earphone. (c) Wearing a graphene earphone for the dog to establish a 35 kHz sound wave and a "stand up" command to reflect the training process. The dog is initially sitting down. After receiving a familiar 35 kHz signal, it stands up.*

dog, and the dog will stand up, and then it will be mixed with 35 kHz sound waves. The dog will be given a food reward every time he stands up successfully. And then it went on to increase the proportion of 35 kHz sound waves, and to reduce the proportion of human voice. Until the final 35 kHz sound waves was 100%, the dog was able to stand up to prove that it was successful in establishing a 35 kHz sound wave to the dog and the "stand up" command. **Figure 15c** shows a demonstration of controlling dog behavior through graphene earphones. The initial state of the dog is sitting, and when the dog hears the 35 kHz sound from the graphene earphone, it will stand up, which indicates that the graphene earphone can control the dog's behavior.

#### **5. An intelligent artificial throat with sound-sensing ability based on laser-induced graphene**

The graphene sound source device based on thermoacoustic effect is introduced. Based on its piezoresistive effect, graphene can also be used for throat detection, which can be used as a good sound receiver. Therefore, based on thermoacoustic effect and piezoresistive effect, graphene is expected to realize transceiver integration.

At the present stage, devices with integrated sound and transceiver function are usually called ultrasonic transducers, which can only work in the ultrasonic frequency band or under water [14]. At the same time, these substrates are hard, not flexible and biocompatible, and not suitable for wearable applications. Therefore, studying the audible frequency band, the sound-flexible device with good flexibility and biocompatibility is of great significance.

Graphene force acoustic devices are mainly faced with two problems: (1) Sound receiving device based on graphene piezoresistive effect cannot be used as sound emitting device because these devices are wrapped in a polymer and the heat cannot be released into the air [15]. Or the device has a higher resistance, resulting in poor thermoacoustic effects [16]. (2) Sounding devices based on the thermoacoustic effect of graphene are also not suitable as sound receivers. Because these device are fabricated by using single layer or few layer graphene [17]. These kinds of graphenes are easily broken, which cause the devices damage. Graphene devices based on conventional processes and methods are difficult to simultaneously receive sound and emit sound using thermoacoustic effects and piezoresistive effects. Therefore, it is necessary to explore the realization of graphene transceiver sound integration based on new materials and new technology.

The method of laser scribed graphene (LSG) is described above. However, graphene reduced by this method cannot be simultaneously transmitted and received sound due to packaging reasons. In 2014, Lin et al. proposed a method for preparing porous structure graphene by laser reduction of polyamide material (PI) [18]. This method can be completed in a single step process without the need to drop coating GO, and the prepared graphene has a porous structure and a good piezoresistive effect. Because graphene materials have good thermal properties, they can make sound based on thermoacoustic effect. Therefore, the porous graphene based on laser reduction can be used to fabricate the flexible force acoustic device to realize sound transmission and reception integration.

This chapter introduces the process of preparing porous graphene by 450 nm wavelength laser. The porous graphene has the ability to integrate sound transmission and reception based on thermoacoustic effect and piezoresistive effect. Hence, a new intelligent artificial throat was prepared based on porous graphene. This device can be attached to the throat to sense the vibration mode of the throat of the deaf mute, and emit a preset sound when a specific throat vibration mode is detected. Therefore, the artificial throat can assist the deaf and mute to achieve sound, which will have potential application in biomedical, acoustic and other fields.

Laser direct writing technology promotes the fast growth of porous, and a lowcost and portable laser platform is chosen. **Figure 16a** is a schematic diagram of a laser processing platform. The 450 nm laser can directly reduce the yellow PI to the porous graphene. Then, the artificial throat based on the porous graphene has been integrated to achieve the functions of emitting and detecting sounds, as shown in **Figure 16b**. **Figure 16c** shows the working mechanism of the artificial throat. When AC voltage is applied to the device, the periodic joule heat will cause air to expand, thus producing sound waves. When a low bias voltage is applied to the device, the vibration of the throat leads to the change of the resistance of the device, resulting in current fluctuations. Therefore, this device can act as both sound sources and detectors. A coughs, buzzing or screams can cause throats to vibrate, which can be detected by LSG artificial throats, and then LSG artificial throats can produce controllable sounds. Therefore, LSG artificial throat can achieve the conversion from meaningless sound to controllable and pre-designed sound. **Figure 16d** shows the image of LSG at laser power ranging from 20 to 350 mW. It can be seen that there is no obvious LSG at the bottom when the power is 20 mW. The second black part and the fourth black part from the bottom, which are produced at the power of 125 and 290 mW, respectively, are chosen to show the SEM image, as shown in **Figure 16e**–**j**. It can be seen that regular ridge lines are formed from top to bottom along the laser scanning trajectory. The line width is approximately 100 μm, which is similar to the focused spot size of the laser. As the laser power increases, the morphological differences are significant. A polygonal porous carbon film appears at the power of 125 mW. However, when the power is 290 mW, more porous irregular structures can be produced. This is mainly

**83**

*Graphene Acoustic Devices*

*DOI: http://dx.doi.org/10.5772/intechopen.81603*

production and discharge of gases.

**Figure 16.**

the device performance in 3 hours.

The area of the LSG is around 1 × 2 cm2

because the high power will lead to a sharp rise of the PI local temperature, thus breaking the C–O, C═O and N–C bonds, and causing the venting of some carbonaceous and nitric gases. Therefore, the porous structure of sample is formed due to the

*(a) One-step fabrication process of LSG. (b) LSG has the ability of emitting and detecting sound in one device. (c) The artificial throat can detect the movement of throat and generate controllable sound, respectively. (d) Six LSG samples produced by 450 nm laser with different power ranging from 20 to 350 mW. (e) The morphology of LSG sample produced at 290 mW under scanning electron microscopy, scale bar, 150 μm. (f) The morphology of LSG sample produced at 290 mW under high magnification, scale bar, 5 μm. (g) Cross-sectional view of LSG sample produced at 290 mW, scale bar, 12.5 μm. (h) The morphology of LSG sample produced at 125 mW under scanning electron microscopy, Scale bar, 150 μm. (i) The morphology of LSG sample produced at 125 mW under high magnification, Scale bar, 5 μm. (j) Cross-sectional view of LSG sample produced at 125 mW, scale bar, 12.5 μm [19].*

Four samples of LSG artificial throats are produced at different laser powers.

350 mW, respectively. The average thickness of LSG is about 8, 22, 38 and 60 μm, corresponding to the laser power. **Figure 17a** is a test diagram of the device. The distance between the sample and standard microphone is 2.5 cm. **Figure 17b** shows the relationship of the LSG (produced under 125 mW) between input power and SP. The result indicates that the SP increases linearly with increasing input power, and a higher SP can be obtained at 20 kHz. **Figure 17c** shows the spectrum response of the LSG produced by different power. The input power is normalized to 1 W. It can be observed that the SPL gradually decreases with the increase of power. **Figure 17d** shows the comparison of theoretical curve and the experimental results. The experimental analysis matches well with the theory model. **Figure 17e** shows the experimental data and theoretical curve as a function of the thickness of the LSG under the frequency of 10 and 20 kHz. The SP is inversely proportional to the thickness of the sounding unit and the theoretical curve matches well with the experimental results. **Figure 17f** shows the stability of LSG. It can be seen that there are no signs of degradation or changes of SPL in

. The laser powers are 125, 200, 290 and

*Graphene Acoustic Devices DOI: http://dx.doi.org/10.5772/intechopen.81603*

#### **Figure 16.**

*Graphene and Its Derivatives - Synthesis and Applications*

based on new materials and new technology.

sound transmission and reception integration.

Graphene force acoustic devices are mainly faced with two problems: (1) Sound receiving device based on graphene piezoresistive effect cannot be used as sound emitting device because these devices are wrapped in a polymer and the heat cannot be released into the air [15]. Or the device has a higher resistance, resulting in poor thermoacoustic effects [16]. (2) Sounding devices based on the thermoacoustic effect of graphene are also not suitable as sound receivers. Because these device are fabricated by using single layer or few layer graphene [17]. These kinds of graphenes are easily broken, which cause the devices damage. Graphene devices based on conventional processes and methods are difficult to simultaneously receive sound and emit sound using thermoacoustic effects and piezoresistive effects. Therefore, it is necessary to explore the realization of graphene transceiver sound integration

The method of laser scribed graphene (LSG) is described above. However, graphene reduced by this method cannot be simultaneously transmitted and received sound due to packaging reasons. In 2014, Lin et al. proposed a method for preparing porous structure graphene by laser reduction of polyamide material (PI) [18]. This method can be completed in a single step process without the need to drop coating GO, and the prepared graphene has a porous structure and a good piezoresistive effect. Because graphene materials have good thermal properties, they can make sound based on thermoacoustic effect. Therefore, the porous graphene based on laser reduction can be used to fabricate the flexible force acoustic device to realize

This chapter introduces the process of preparing porous graphene by 450 nm wavelength laser. The porous graphene has the ability to integrate sound transmission and reception based on thermoacoustic effect and piezoresistive effect. Hence, a new intelligent artificial throat was prepared based on porous graphene. This device can be attached to the throat to sense the vibration mode of the throat of the deaf mute, and emit a preset sound when a specific throat vibration mode is detected. Therefore, the artificial throat can assist the deaf and mute to achieve sound, which

Laser direct writing technology promotes the fast growth of porous, and a lowcost and portable laser platform is chosen. **Figure 16a** is a schematic diagram of a laser processing platform. The 450 nm laser can directly reduce the yellow PI to the porous graphene. Then, the artificial throat based on the porous graphene has been integrated to achieve the functions of emitting and detecting sounds, as shown in **Figure 16b**. **Figure 16c** shows the working mechanism of the artificial throat. When AC voltage is applied to the device, the periodic joule heat will cause air to expand, thus producing sound waves. When a low bias voltage is applied to the device, the vibration of the throat leads to the change of the resistance of the device, resulting in current fluctuations. Therefore, this device can act as both sound sources and detectors. A coughs, buzzing or screams can cause throats to vibrate, which can be detected by LSG artificial throats, and then LSG artificial throats can produce controllable sounds. Therefore, LSG artificial throat can achieve the conversion from meaningless sound to controllable and pre-designed sound. **Figure 16d** shows the image of LSG at laser power ranging from 20 to 350 mW. It can be seen that there is no obvious LSG at the bottom when the power is 20 mW. The second black part and the fourth black part from the bottom, which are produced at the power of 125 and 290 mW, respectively, are chosen to show the SEM image, as shown in **Figure 16e**–**j**. It can be seen that regular ridge lines are formed from top to bottom along the laser scanning trajectory. The line width is approximately 100 μm, which is similar to the focused spot size of the laser. As the laser power increases, the morphological differences are significant. A polygonal porous carbon film appears at the power of 125 mW. However, when the power is 290 mW, more porous irregular structures can be produced. This is mainly

will have potential application in biomedical, acoustic and other fields.

**82**

*(a) One-step fabrication process of LSG. (b) LSG has the ability of emitting and detecting sound in one device. (c) The artificial throat can detect the movement of throat and generate controllable sound, respectively. (d) Six LSG samples produced by 450 nm laser with different power ranging from 20 to 350 mW. (e) The morphology of LSG sample produced at 290 mW under scanning electron microscopy, scale bar, 150 μm. (f) The morphology of LSG sample produced at 290 mW under high magnification, scale bar, 5 μm. (g) Cross-sectional view of LSG sample produced at 290 mW, scale bar, 12.5 μm. (h) The morphology of LSG sample produced at 125 mW under scanning electron microscopy, Scale bar, 150 μm. (i) The morphology of LSG sample produced at 125 mW under high magnification, Scale bar, 5 μm. (j) Cross-sectional view of LSG sample produced at 125 mW, scale bar, 12.5 μm [19].*

because the high power will lead to a sharp rise of the PI local temperature, thus breaking the C–O, C═O and N–C bonds, and causing the venting of some carbonaceous and nitric gases. Therefore, the porous structure of sample is formed due to the production and discharge of gases.

Four samples of LSG artificial throats are produced at different laser powers. The area of the LSG is around 1 × 2 cm2 . The laser powers are 125, 200, 290 and 350 mW, respectively. The average thickness of LSG is about 8, 22, 38 and 60 μm, corresponding to the laser power. **Figure 17a** is a test diagram of the device. The distance between the sample and standard microphone is 2.5 cm. **Figure 17b** shows the relationship of the LSG (produced under 125 mW) between input power and SP. The result indicates that the SP increases linearly with increasing input power, and a higher SP can be obtained at 20 kHz. **Figure 17c** shows the spectrum response of the LSG produced by different power. The input power is normalized to 1 W. It can be observed that the SPL gradually decreases with the increase of power. **Figure 17d** shows the comparison of theoretical curve and the experimental results. The experimental analysis matches well with the theory model. **Figure 17e** shows the experimental data and theoretical curve as a function of the thickness of the LSG under the frequency of 10 and 20 kHz. The SP is inversely proportional to the thickness of the sounding unit and the theoretical curve matches well with the experimental results. **Figure 17f** shows the stability of LSG. It can be seen that there are no signs of degradation or changes of SPL in the device performance in 3 hours.

#### **Figure 17.**

*(a) The LSG is clamped under a commercial microphone to test the performance of emitting sound, scale bar, 1 cm. (b) The plot of the SP versus the input power at 10 and 20 kHz. (c) The output SPL versus the frequency of LSG generated by the laser with different power. (d) The SPL versus the frequency showing that the model agrees well with experimental results. (e) The plot of the SP versus the thickness of LSG at 10 and 20 kHz. (f) The stability of output SPL over time [19].*

#### **Figure 18.**

*Responses towards different audios from a loudspeaker [19]. The LSG is placed 3 cm away from the loudspeaker. The orange insets above indicate the sound wave profiles of the original audios. Relative resistance changes show almost synchronous response to profiles of the original audios when the loudspeaker plays the audio of (a) firecrackers, (b) a cow, (c) a piano, (d) a helicopter, (e) a bird and (f) a drum.*

Except for emitting sound, the LSG artificial throat also has excellent responses when detecting sound. The 25 μm-thick PI can be chosen to produce the LSG due to obvious resistance change. The sample is fixed and the loudspeaker is placed 3 cm

**85**

**Figure 19.**

*Graphene Acoustic Devices*

amplitude of the signal.

*DOI: http://dx.doi.org/10.5772/intechopen.81603*

away from the artificial throat. The six kinds of audios including firecracker, cow, piano, helicopter, bird and drum are performed. **Figure 18** shows the resistance response of the device at six kinds of audios. Although the sampling frequency of this artificial throat is 100 Hz, which is far lower than the frequency of sound, it can be still noticed that the responses of the transducer are well synchronous to these original audio signals. Especially, the characteristic peaks are retained and reflected with high fidelity. Besides, the volume of loudspeaker has a great effect on the

After identifying some kinds of audio clearly, LSG artificial throat is used to detect the vibration of throat cords. As shown in **Figure 19a**, the tester performs coughing, snoring and screaming twice in succession, and then the tester swallows and nods twice. After two consecutive tests, the test results are reproducible. In addition, swallowing and nodding can also cause muscle movement, which can also lead to changes in resistance. Fortunately, the waveforms of these muscle movements also have identifiable features. Different movement has its unique characteristic waveform as shown in **Figure 19a**, thus, the useful waveforms can be gotten by relying on the pattern recognition and machine learning. Interference from other activities can be identified and eliminated by multiple trainings in advance. Then, the tester makes the hums with four different tones as shown in **Figure 19b**,

*Responses towards different kinds of throat vibrations. (a) The LSG's resistance changes towards the throat vibrations of the tester who makes two successive coughs, hums, screams, swallowing and nods. (b) The LSG's resistance change caused by four different kinds of hum tones and the hum tone 2 is same with the hum in (a). (c) The relative resistance change of LSG increases with the increase of the sound intensities of the hum [19].*

#### *Graphene Acoustic Devices DOI: http://dx.doi.org/10.5772/intechopen.81603*

*Graphene and Its Derivatives - Synthesis and Applications*

**84**

**Figure 18.**

**Figure 17.**

*The stability of output SPL over time [19].*

Except for emitting sound, the LSG artificial throat also has excellent responses when detecting sound. The 25 μm-thick PI can be chosen to produce the LSG due to obvious resistance change. The sample is fixed and the loudspeaker is placed 3 cm

*loudspeaker. The orange insets above indicate the sound wave profiles of the original audios. Relative resistance changes show almost synchronous response to profiles of the original audios when the loudspeaker plays the*

*(a) The LSG is clamped under a commercial microphone to test the performance of emitting sound, scale bar, 1 cm. (b) The plot of the SP versus the input power at 10 and 20 kHz. (c) The output SPL versus the frequency of LSG generated by the laser with different power. (d) The SPL versus the frequency showing that the model agrees well with experimental results. (e) The plot of the SP versus the thickness of LSG at 10 and 20 kHz. (f)*

*Responses towards different audios from a loudspeaker [19]. The LSG is placed 3 cm away from the*

*audio of (a) firecrackers, (b) a cow, (c) a piano, (d) a helicopter, (e) a bird and (f) a drum.*

away from the artificial throat. The six kinds of audios including firecracker, cow, piano, helicopter, bird and drum are performed. **Figure 18** shows the resistance response of the device at six kinds of audios. Although the sampling frequency of this artificial throat is 100 Hz, which is far lower than the frequency of sound, it can be still noticed that the responses of the transducer are well synchronous to these original audio signals. Especially, the characteristic peaks are retained and reflected with high fidelity. Besides, the volume of loudspeaker has a great effect on the amplitude of the signal.

After identifying some kinds of audio clearly, LSG artificial throat is used to detect the vibration of throat cords. As shown in **Figure 19a**, the tester performs coughing, snoring and screaming twice in succession, and then the tester swallows and nods twice. After two consecutive tests, the test results are reproducible. In addition, swallowing and nodding can also cause muscle movement, which can also lead to changes in resistance. Fortunately, the waveforms of these muscle movements also have identifiable features. Different movement has its unique characteristic waveform as shown in **Figure 19a**, thus, the useful waveforms can be gotten by relying on the pattern recognition and machine learning. Interference from other activities can be identified and eliminated by multiple trainings in advance. Then, the tester makes the hums with four different tones as shown in **Figure 19b**,

#### **Figure 19.**

*Responses towards different kinds of throat vibrations. (a) The LSG's resistance changes towards the throat vibrations of the tester who makes two successive coughs, hums, screams, swallowing and nods. (b) The LSG's resistance change caused by four different kinds of hum tones and the hum tone 2 is same with the hum in (a). (c) The relative resistance change of LSG increases with the increase of the sound intensities of the hum [19].*

it can be seen that different tones have different responses, increasing the diversity of dumb people's "language". Especially, the hum tone 2 is same with the hum in **Figure 19a**. Furthermore, as shown in **Figure 19c**, the resistance increases as the sound intensity increases, which is due to the increase in mechanical vibration of the throat.

#### **6. Conclusion**

Thermoacoustic device for the current low reliability, high performance and poor driving voltage problem, Ren's group successfully proposed and implemented graphene thermoacoustic devices with high performance, high reliability and low driving voltage. Besides, these devices have the advantages of low driving voltage, soft, transparent, thin thickness and wide band sound output, especially the extremely flat sound output in the ultrasonic frequency band. Exploring the application of graphene in the field of acoustics is the first time in the world and obtains the following three research results:

#### **Acknowledgements**

This work was supported by the National Key R&D Program (2016YFA0200400), National Natural Science Foundation (61434001, 61574083, 61874065,<PHONE_NUMBER>2), and National Basic Research Program (2015CB352101) of China. He Tian thanks for the support from Young Elite Scientists Sponsorship Program by CAST (2018QNRC001). The authors are also thankful for the support of the Research Fund from Beijing Innovation Center for Future Chip, Beijing Natural Science Foundation (4184091), and Shenzhen Science and Technology Program (JCYJ20150831192224146).

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**Author details**

†

and Tian-Ling Ren1,2\*

and<EMAIL_ADDRESS>
, Guang-Yang Gou1,2†

(BNRist), Tsinghua University, Beijing, China

† These authors contributed equally to this work.

1 Institute of Microelectronics, Tsinghua University, Beijing, China

2 Beijing National Research Center for Information Science and Technology

He Tian1,2\*

provided the original work is properly cited.

*Graphene Acoustic Devices*

*DOI: http://dx.doi.org/10.5772/intechopen.81603*

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
, Fan Wu1,2, Lu-Qi Tao1,2, Yi Yang1,2\*

*Graphene Acoustic Devices DOI: http://dx.doi.org/10.5772/intechopen.81603*

*Graphene and Its Derivatives - Synthesis and Applications*

obtains the following three research results:

tion of throat cords with a fine repetition.

the throat.

**6. Conclusion**

value.

animal behavior.

**Acknowledgements**

it can be seen that different tones have different responses, increasing the diversity of dumb people's "language". Especially, the hum tone 2 is same with the hum in **Figure 19a**. Furthermore, as shown in **Figure 19c**, the resistance increases as the sound intensity increases, which is due to the increase in mechanical vibration of

Thermoacoustic device for the current low reliability, high performance and poor driving voltage problem, Ren's group successfully proposed and implemented graphene thermoacoustic devices with high performance, high reliability and low driving voltage. Besides, these devices have the advantages of low driving voltage, soft, transparent, thin thickness and wide band sound output, especially the extremely flat sound output in the ultrasonic frequency band. Exploring the application of graphene in the field of acoustics is the first time in the world and

1.A multilayer graphene sound source device is proposed and realized. The sound output performance from 1 to 50 kHz is obtained. It is observed that there is a flat sound spectrum in the range of 20–50 kHz. The performance of multilayer graphene sound source device with different thickness is compared. It was found that graphene with thinner thickness had a higher SPL

2.A low cost graphene earphones at wafer level are realized by laser scribing. Graphene earphone has a wider and flatter acoustic spectrum output than commercial earphones. In addition, graphene earphone achieves control of

3.A wearable artificial throat that is manufactured in one step based on LSG has been implemented. The LSG device achieves the functional integration of emitting and detecting sound due to its excellent thermoacoustic and piezoresistive properties. The LSG artificial throat has a relatively broad frequency spectrum because of resonance-free oscillations of the sound sources. Besides, as a sound detector, the LSG artificial throat can capture the mechanical vibra-

This work was supported by the National Key R&D Program (2016YFA0200400), National Natural Science Foundation (61434001, 61574083, 61874065,<PHONE_NUMBER>2), and National Basic Research Program (2015CB352101) of China. He Tian thanks for the support from Young Elite Scientists Sponsorship Program by CAST (2018QNRC001). The authors are also thankful for the support of the Research Fund from Beijing Innovation Center for Future Chip, Beijing Natural Science Foundation (4184091), and Shenzhen Science and Technology Program (JCYJ20150831192224146).

**86**

#### **Author details**

He Tian1,2\* † , Guang-Yang Gou1,2† , Fan Wu1,2, Lu-Qi Tao1,2, Yi Yang1,2\* and Tian-Ling Ren1,2\*

1 Institute of Microelectronics, Tsinghua University, Beijing, China

2 Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China

\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>and<EMAIL_ADDRESS>
† These authors contributed equally to this work.

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **References**

[1] Novoselov KS, Geim AK, Morozov SV, et al. Electric field effect in atomically thin carbon films. Science. 2004;**306**:666-669

[2] Ho KI, Boutchich M, Su CY, et al. A self-aligned high-mobility graphene transistor: Decoupling the channel with fluorographene to reduce scattering. Advanced Materials. 2015;**27**:6519-6525

[3] Li D, Chen M, Zong Q, et al. Floatinggate manipulated graphene-black phosphorus heterojunction for nonvolatile ambipolar schottky junction memories, memory inverter circuits, and logic rectifiers. Nano Letters. 2017;**17**:6353-6359

[4] Sarker BK, Cazalas E, Chung TF, et al. Position-dependent and millimetre-range photodetection in phototransistors with micrometrescale graphene on SiC. Nature Nanotechnology. 2017;**12**:668-674

[5] SMM Z, Holt M, Sadeghi MM, et al. 3D integrated monolayer graphene-Si CMOS RF gas sensor platform. npj 2D Materials and Applications. 2017;**1**:36

[6] Arnold HD, Crandall IB. The thermophone as a precision source of sound. Physical Review. 1917;**10**:22

[7] Shinoda H, Nakajima T, Ueno K, et al. Thermally induced ultrasonic emission from porous silicon. Nature. 1999;**400**:853-855

[8] Xiao L, Chen Z, Feng C, et al. Flexible, stretchable, transparent carbon nanotube thin film loudspeakers. Nano Letters. 2008;**8**:4539-4545

[9] Tian H. Graphene-Based Novel Micro/Nano Devices. Beijing: Tsinghua University; 2015

[10] Tian H, Ren TL, Xie D, et al. Graphene-on-paper sound source devices. ACS Nano. 2011;**5**:4878-4885 [11] Blackstock DT. Fundamentals of Physical Acoustics. Vol. 465. New York: John Wiley and Sons, Ltd; 2000. p. 440

[12] Vesterinen V, Niskanen AO, Hassel J, Helisto P. Fundamental efficiency of nanothermophones: Modeling and experiments. Nano Letters. 2010;**10**:5020-5024

[13] Tian H, Li C, Mohammad MA, et al. Graphene earphones: Entertainment for both humans and animals. ACS Nano. 2014;**8**:5883-5890

[14] Meeks SW, Timme RW. Rare earth iron magnetostrictive underwater sound transducer. The Journal of the Acoustical Society of America. 1977;**62**:1158-1164

[15] Park JJ, Hyun WJ, Mun SC, et al. Highly stretchable and wearable graphene strain sensors with controllable sensitivity for human motion monitoring. ACS Applied Materials & Interfaces. 2015;**7**:6317-6324

[16] Cheng Y, Wang R, Sun J, et al. A stretchable and highly sensitive graphene-based fiber for sensing tensile strain, bending, and torsion. Advanced Materials. 2015;**27**:7365-7371

[17] Tian H, Xie D, Yang Y, et al. Singlelayer graphene sound-emitting devices: Experiments and modeling. Nanoscale. 2012;**4**:2272-2277

[18] Lin J, Peng Z, Liu Y, et al. Laserinduced porous graphene films from commercial polymers. Nature Communications. 2014;**5**:5714

[19] Tao LQ, Tian H, Liu Y, et al. An intelligent artificial throat with soundsensing ability based on laser induced graphene. Nature Communications. 2017;**8**:14579

**88**

*Graphene and Its Derivatives - Synthesis and Applications*

[11] Blackstock DT. Fundamentals of Physical Acoustics. Vol. 465. New York: John Wiley and Sons, Ltd; 2000. p. 440

[12] Vesterinen V, Niskanen AO, Hassel J, Helisto P. Fundamental efficiency of nanothermophones: Modeling and experiments. Nano Letters.

[13] Tian H, Li C, Mohammad MA, et al. Graphene earphones: Entertainment for both humans and animals. ACS Nano.

[14] Meeks SW, Timme RW. Rare earth iron magnetostrictive underwater sound transducer. The Journal of the Acoustical Society of America.

[15] Park JJ, Hyun WJ, Mun SC, et al. Highly stretchable and wearable graphene strain sensors with controllable sensitivity for human motion monitoring. ACS Applied Materials & Interfaces. 2015;**7**:6317-6324

[16] Cheng Y, Wang R, Sun J, et al. A stretchable and highly sensitive graphene-based fiber for sensing tensile strain, bending, and torsion. Advanced

[17] Tian H, Xie D, Yang Y, et al. Singlelayer graphene sound-emitting devices: Experiments and modeling. Nanoscale.

[18] Lin J, Peng Z, Liu Y, et al. Laserinduced porous graphene films from commercial polymers. Nature Communications. 2014;**5**:5714

[19] Tao LQ, Tian H, Liu Y, et al. An intelligent artificial throat with soundsensing ability based on laser induced graphene. Nature Communications.

Materials. 2015;**27**:7365-7371

2012;**4**:2272-2277

2017;**8**:14579

2010;**10**:5020-5024

2014;**8**:5883-5890

1977;**62**:1158-1164

[1] Novoselov KS, Geim AK, Morozov SV, et al. Electric field effect in atomically thin carbon films. Science.

[2] Ho KI, Boutchich M, Su CY, et al. A self-aligned high-mobility graphene transistor: Decoupling the channel with fluorographene to reduce scattering. Advanced Materials. 2015;**27**:6519-6525

[3] Li D, Chen M, Zong Q, et al. Floatinggate manipulated graphene-black

phosphorus heterojunction for nonvolatile ambipolar schottky junction memories, memory inverter circuits, and logic rectifiers. Nano Letters. 2017;**17**:6353-6359

[4] Sarker BK, Cazalas E, Chung TF, et al. Position-dependent and millimetre-range photodetection in phototransistors with micrometrescale graphene on SiC. Nature Nanotechnology. 2017;**12**:668-674

[5] SMM Z, Holt M, Sadeghi MM, et al. 3D integrated monolayer graphene-Si CMOS RF gas sensor platform. npj 2D Materials and Applications. 2017;**1**:36

[6] Arnold HD, Crandall IB. The thermophone as a precision source of sound. Physical Review. 1917;**10**:22

[7] Shinoda H, Nakajima T, Ueno K, et al. Thermally induced ultrasonic emission from porous silicon. Nature.

[8] Xiao L, Chen Z, Feng C, et al. Flexible, stretchable, transparent carbon nanotube thin film loudspeakers.

Nano Letters. 2008;**8**:4539-4545

[9] Tian H. Graphene-Based Novel Micro/Nano Devices. Beijing: Tsinghua

[10] Tian H, Ren TL, Xie D, et al. Graphene-on-paper sound source devices. ACS Nano. 2011;**5**:4878-4885

1999;**400**:853-855

University; 2015

2004;**306**:666-669

**References**

### *Edited by Ishaq Ahmad and Fabian I. Ezema*

Graphene and its derivatives are potential nanomaterials currently being widely investigated for diverse applications due to its exceptional mechanical, electrical, physical, and chemical properties. Examples of the applications include drug delivery, shape memory polymers, gene delivery, biosensor, tissue engineering, flexible electronic devices, antibacterial composites, photovoltaic devices, and physical sensors. Its excellent properties can be used to develop smart nanomaterials with enhanced properties for various advanced applications. There is no doubt that graphene-based nanomaterials are helping to develop next generation technologies with enhancing properties to change people's lifestyles. This book provides an overview of recent research and development of synthesis of graphene and its applications.

Published in London, UK © 2019 IntechOpen © iLexx / iStock

Graphene and Its Derivatives - Synthesis and Applications

Graphene and Its Derivatives

Synthesis and Applications

*Edited by Ishaq Ahmad and Fabian I. Ezema*


*Article* **Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning**

**Vahid Nasir 1 , Hamidreza Fathi 2 , Arezoo Fallah 2,\*, Siavash Kazemirad 2,\*, Farrokh Sassani 1 and Petar Antov 3,\***

**Abstract:** Color parameters were used in this study to develop a machine learning model for predicting the mechanical properties of artificially weathered fir, alder, oak, and poplar wood. A CIELAB color measuring system was employed to study the color changes in wood samples. The color parameters were fed into a decision tree model for predicting the MOE and MOR values of the wood samples. The results indicated a reduction in the mechanical properties of the samples, where fir and alder were the most and least degraded wood under weathering conditions, respectively. The mechanical degradation was correlated with the color change, where the most resistant wood to color change exhibited less reduction in the mechanical properties. The predictive machine learning model estimated the MOE and MOR values with a maximum R2 of 0.87 and 0.88, respectively. Thus, variations in the color parameters of wood can be considered informative features linked to the mechanical properties of small-sized and clear wood. Further research could study the effectiveness of the model when analyzing large-sized timber.

**Keywords:** wood characterization; mechanical properties; photodegradation; artificial weathering; color change; ultraviolet radiation; machine learning

#### **1. Introduction**

Nondestructive evaluation (NDE) of wood is crucial for monitoring purposes and timber grading [1,2], especially when the wood is used in load-carrying applications. The characterization of the mechanical properties of wood, including the modulus of elasticity (MOE) and modulus of rupture (MOR), can be performed using tensile, compression, and bending static tests [3]. However, these methods are costly and time-consuming, and are not suitable for in situ characterization and monitoring purposes. While visual strength grading is being practiced in some industrial applications [4], fast and reliable assessment of timber and wood-based materials that accounts for material variability and anisotropic properties and natural defects requires the application of NDE methods.

Near-infrared (NIR) spectroscopy is one of the most commonly used NDE methods for wood characterization. NIR spectroscopy is sensitive to changes in the chemical composition of wood [5] and has been used for wood classification and the estimation of different wood properties [6–10]. Wave propagation-based methods have also been employed to estimate timber MOE [11,12]. For example, a wave is generated in wood using an impact or piezoelectric actuators, respectively, in the stress wave or ultrasonic wave methods. The propagated wave velocity, and consequently the wood dynamic MOE, are typically calculated in wave propagation techniques using the time-of-flight method. The prediction of the mechanical properties [13,14] and the detection of internal

**Citation:** Nasir, V.; Fathi, H.; Fallah, A.; Kazemirad, S.; Sassani, F.; Antov, P. Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning. *Materials* **2021**, *14*, 6314. https:// doi.org/10.3390/ma14216314

Academic Editors: Dimitris S Argyropoulos and Tomasz Sadowski

Received: 8 September 2021 Accepted: 18 October 2021 Published: 22 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

check formation [15,16] in weathered thermally modified timber have been performed using the ultrasonic wave method. The physical and mechanical properties of thermally modified wood have also been predicted using the stress wave method [17]. Recently, the elastic and viscoelastic properties of wood have been characterized using the Lamb wave propagation method [18,19]. Another readily available NDE method for wood classification and characterization is related to measuring the surface color of wood.

The surface color is affected when the wood is used in applications that cause changes to its chemical composition, such as through thermal treatment, or ultraviolet (UV) or laser irradiation [20,21]. The color change has been shown to be a quality indicator for thermally modified timber (TMT) [22,23]. It has been reported that color change can be linked to the intensity of thermal treatment [24–29] and the mechanical properties of TMT [30]. The correlation between the color change and the pressure treatment of wood has also been reported in the literature [31]. Color measurement has also been employed for the classification [32] and characterization [33,34] of thermally modified timber. The color is also an important feature to be studied during the wood weathering since the weathering of wood causes photodegradation. The CIELAB color measuring system has been widely employed to study the color change in wood under UV radiation [35–42]. The color was considered as an informative parameter to monitor the wood photodegradation [43]. Additionally, the infrared spectroscopy analysis of wood has been used to assess the change in wood chemical composition under weathering [44–48]. The critical role of color change during wood weathering may suggest further investigations to see if the color change can be used as a quality control tool for monitoring the in-service weathered wood. One of these monitoring tasks is to assess the feasibility of using the color change for predicting the MOE and MOR of weathered wood.

The mechanical degradation of wood under weathering depends on different factors. These factors include but are not limited to the type of wood, weathering condition (UV radiation, temperature, humidity, rain, etc.), thickness of wood under weathering, and conditioning parameters. It has been reported that UV and solar irradiation result in the degradation of the mechanical properties in thin wood strips [49–51]. Accelerated UV exposure can result in a 20–40% drop in the strength of wood strips [52]. The mechanical degradation in wood-based composites under weathering conditions with a panel thickness of 8 mm and 12.5 mm has also been reported in the literature [53,54]. Weathering has also been reported to cause a reduction in the impact bending properties of spruce, fir, and oak wood with a board thickness of 20 mm [55]. The impact of weathering on the mechanical properties of full-size timber has also been investigated [56]. van Blokland et al. [14] studied the impact of natural weathering on the mechanical properties of Norway spruce timber. The timbers were conditioned after the weathering and mechanically tested. They reported that the bending strength of the control and thermally treated spruce were reduced after weathering by 6% and 9%, respectively. A 4% reduction in the MOE of both types of timbers was also reported.

Apart from the size of the wood cross-section, the type of wood species, weathering situations, and conditioning also impact the mechanical properties of weathered wood. Tomak et al. [57] showed that the reduction in the mechanical properties under weathering is significantly dependent on the type of wood species. They reported that while the MOR of Ash wood decreased by 18% after 24 months of weathering, Iroko wood experienced a 40% MOR reduction in the same time period. The mechanical degradation of softwood and hardwood under weathering can also be significantly different [57]. The impact of weathering is greatly affected by the environmental conditions. In the case of artificial weathering, for example, the impact of UV radiation is affected by the moisture content (MC), relative humidity (RH), and temperature conditions. Timar et al. [36] reported that the combination of UV radiation and temperature could cause lignin degradation, while exposure of wood to temperature alone did not affect the lignin. Persze and Tolvaj [58] also reported that the photodegradation of wood is affected by temperature. Therefore, while UV radiation alone is mainly known to be a phenomenon causing surface damage,

its impact on the wood can be aggravated when combined with harsh environmental conditions. To study the effect of UV radiation, the exposed samples are conditioned after the weathering test. This is useful to understand the mechanism of damage caused; however, some researchers did not condition their samples after the weathering and before mechanical testing to simulate the practical situations [56]. For example, Boonstra et al. [56] showed that, compared to the conditioned samples after weathering, the weathered samples that were directly tested without conditioning showed higher reduction in the MOE and MOR. While such an approach assesses the combined effect of different parameters (UV radiation, temperature, MC change, etc.) on the mechanical properties of wood under weathering, this is more aligned with real situations, where in situ monitoring of in-service weathered wood structures is intended.

The above discussion shows that it is hard to generalize the mechanical behavior of wood under weathering conditions, since it depends on many factors. The mechanical behavior of the weathered wood depends on the size of wood cross-section, type of wood species, and range and intensity of environmental parameters such as the temperature and conditioning situations. Therefore, finding an NDE method for monitoring the mechanical behavior of weathered wood becomes a crucial quality control task. The MOE and MOR of defect-free wood subjected to UV radiation were recently estimated using the Lamb wave propagation method [59,60]. Yet, the color parameters of wood have not been used to make an intelligent monitoring model to predict the MOE and MOR of degraded wood under weathering. The use of color parameters to monitor the mechanical properties of photodegraded wood may offer a fast, cost-effective, and reliable complement to wave propagation-based methods.

The aim of this research work was to link the color change of weathered wood to its mechanical properties and develop a machine learning-based model for monitoring the MOE and MOR of weathered wood. The weathering condition consisted of UV radiation at 40 ◦C. This is a very common temperature in regions with hot climate, such as Iran. The weathered samples were not conditioned after the weathering to better simulate the practical conditions. Thus, the weathering impacted the samples not only due to UV radiation, but also as a result of the temperature conditions. Since the objective of this study was to develop an NDE tool for the in situ monitoring of weathered wood and the evaluation of the combined effect of all parameters involved during the weathering on its mechanical properties, finding the UV penetration depth or separating the share of different playing factors on the mechanical degradation of wood was not within the scope of this study.

#### **2. Materials and Methods**

#### *2.1. Sample Preparation*

In this study, twenty samples of poplar (*Populus euroamerican*), alder (*Alnus glutinosa*), oak (*Quercus spp*.), and fir (*Abies alba*) were prepared, resulting in a total of eighty samples. The samples were clear, with no types of defects such as knots. The dimensions of the flatsawn samples in the radial, tangential, and longitudinal directions were 20 mm × 20 mm × 300 mm, respectively, according to ISO-13061-3 and -4 standard methods [61,62]. The samples were divided into groups of four specimens from each wood species. One group was considered as the control and was not exposed to artificial weathering. Other groups were exposed to weathering for different time periods. Considerations were made to ensure that there was no significant variation of wood initial properties between the different groups according to the procedure explained in [60] based on guided wave propagation method. Figure 1 illustrates the experimental procedure employed in this study.

**Figure 1.** The experimental procedure employed in this study.

#### *2.2. Weathering Test*

In the present study, the wood samples were placed in a weathering chamber. Weathering experiments were conducted immediately after the sample preparation. Samples of each group from each wood species were exposed to UV radiation for 24, 100, 150, and 240 h, respectively. The chamber had two lamps (OSRAM HQE-40 Hg, Munich, Germany) with a length, diameter, and a spectrum of 90 mm, 10 mm, and 240–570 nm, respectively, with a radiated power in the wavelength range of 315–400 nm. The wood samples were placed at a distance of 500 mm from the UV light source. Before the exposure to UV radiation, the samples were placed in a conditioning room with a relative humidity of 65% and a temperature of 20 ◦C to reach the equilibrium moisture content of 12% (standard deviation = 0.33). During the experiment, the humidity and temperature inside the chamber were controlled to be 55–60% and 40 ◦C, respectively. One of the longitudinal-tangential (L-T) surfaces of the wood samples was subjected to the UV radiation and then the color measurements and mechanical bending tests were immediately conducted without any further conditioning. As already explained in the Introduction section, such a method can better simulate the practical conditions, especially during the in situ monitoring tasks. The weathering conditions can degrade wood through both the UV effect and the temperature condition resulting in a change in the MC. This study does not focus on the pure effect of UV radiation on the degradation of wood samples. Mechanisms of degradation under weathering, especially UV radiation, has been discussed in the literature [36,49,58,63–65]. Thus, instead of analyzing the role of different governing factors in wood degradation, this study aims to evaluate the feasibility of developing monitoring models for predicting the mechanical degradation of weathered wood.

#### *2.3. Measurement of Color Parameters*

The color of the wood samples was evaluated using the CIELAB color measuring system. The color measurement was performed on all wood samples before and after the weathering experiment. The color measurement was performed on three locations in the center and close to the ends of the samples (30 mm away). The color measurement was carried out on the degraded surface of the wood samples and performed using a spectrophotometer (Model #CM-2600d, Konica Minolta Inc., Tokyo, Japan) with a D65 illuminant, a 10◦ standard observer, and a sensor head of 6-mm (ASTM D2244-16 standard [66]). Once the color coordinates (*L*, *a*, and *b*) were measured before and after the weathering tests, the difference in the lightness (∆L) and the chromatic coordinates (∆*b* and

∆*a*) were calculated. The total color change (∆*E*) was obtained for each wood sample using the following equation:

$$
\Delta E = \left[ (\Delta L)^2 + (\Delta a)^2 + (\Delta b)^2 \right]^{1/2} \tag{1}
$$

The color measurement was also performed on the samples that were not placed into the weathering chamber.

#### *2.4. Mechanical Properties*

The three-point bending tests were performed using a STM-1 50 testing machine (Santam Engineering Desgin Co. Ltd., Tehran, Iran). The crosshead speed was set to 1 mm/min (ISO 13061-3 and -4 [61,62]). The bending tests were performed on the longitudinaltangential surface of the samples where the degraded plane was placed under tension. The MOE and MOR of the wood samples were calculated based on the ISO 13061-3 and -4 from the mechanical bending tests.

#### *2.5. MC Measurements*

The measurement of MC and density after weathering was done based on the ISO-13061-1 and -2 standard methods [67,68] from the cookie samples (50 mm × 20 mm × 20 mm [L, T, R]) cut from the vicinity of the wood samples. The average MC of the samples was about 12% before the weathering experiment. For this purpose, separate cookies with the same size were prepared before the preparation of the final weathering samples (20 mm × 20 mm × 300 mm). The samples prepared for MC measurements were weighed (*m*1) and then placed in an oven at the temperature of 101 ◦C for 24 h. The oven-dried weights (*m*2) of the samples were then measured and the MC was calculated via:

$$W\left(\%\right) = \frac{m\_1 - m\_2}{m\_2} \times 100\tag{2}$$

where *W*, *m*2, and *m*1 are the MC, the dry mass and the wet mass of the wood samples, respectively.

#### *2.6. Statistical Analysis*

The data of color change were analyzed with a one-way ANOVA in Minitab 19 (AppOnFly s.r.o., Prague, Czech Republic). For each wood species, the significance between the color changes of the samples weathered at different time periods was analyzed using Tukey's comparison test. Having a low number of replications is a limitation of such a study. However, it should be noted that the main aim of this research was to monitor the weathered wood samples through machine learning modeling, which does not necessarily depend on the requirements for statistical analysis.

#### *2.7. Machine Learning Analysis*

Figure 2 illustrates the flowchart of the adopted methodology in this study to estimate the mechanical properties of weathered wood through machine learning (ML) modeling. Different ML models and artificial neural networks (ANNs) have been employed for damage/defect detection [69,70], prediction of the material's properties [71,72], and process condition monitoring [73–76]. Decision tree regression modeling was used in this study for the prediction of the mechanical properties of wood samples. While ANNs are challenging to interpret, the outputs of decision tree models can be comprehended easier. Furthermore, the significance of predictor variable can be identified in the model, which helps to study the relationships between the features and take care of the redundant features with relatively lower importance [77]. The current study dealt with a small dataset; however, decision tree modeling has been successfully employed in the literature to analyze small datasets [15,59,78] when predicting the mechanical properties or checks formation in weathered wood samples. The classification and regression trees (CART) algorithm [79] was used in this study. The decision tree development process included tree growing and pruning phases. In the tree growing phase, the node splitting criteria were based on the highest contribution to lowering the least squared error during the model training. The optimal tree was chosen in the pruning phase as the smallest tree that has an R2 within one standard error of the tree representing the highest R2 obtained during the model validation. The 5-fold cross-validation method was employed in this study. The details of the developed decision tree model were similar to those chosen in [59] and based on the discussion provided in [80].

**Figure 2.** Flowchart of the proposed methodology to monitor the mechanical properties of the weathered wood by color measurement.

The contribution of each feature (either as a primary splitters or surrogate one) in improving the performance of the model was studied based on minimizing the least squared error. The most important feature causes the highest improvement to the model and the other features are relatively ranked, accordingly. Additionally, clustering analysis was employed to observe the level of similarity and find the common characteristics between the color parameters, MOE and MOR, using agglomerative hierarchical clustering as explained by Fathi et al. [81].

#### **3. Results**

#### *3.1. Mechanical Degradation under Artificial Weathering*

The MC and density of the wood samples decreased under weathering. Following 240 h of exposure, fir, alder, oak, and poplar wood experienced a 4.9%, 3.2%, 4.7%, and 8.1% reduction in the density, respectively. The initial MC of fir (12.1%), alder (11.6%), oak (12.4%), and poplar (12.1%) were also reduced to 6.3%, 5.3%, 5.8%, 5.3%, respectively, after 240 h of weathering. Ouadou et al. [49] reported that 120 h of weathering resulted in the mass loss due to the reduction of the MC. Figure 3 shows the impact of weathering on the MOE, MOR, and deflection to failure of different wood samples. It shows that the MOE and MOR decreased with increasing weathering duration. Overall, alder was shown to be the most resilient wood with an 11.5% and 11% reduction in its MOE and MOR after 240 h of weathering. Fir also exhibited the highest degradation, with a 21.5% and 17.5% reduction in its MOE and MOR after 240 h of weathering. The mechanical degradation under ultraviolet conditions was discussed to affect the microstructure of wood and its effect on the chemical composition and results in degradation of lignin [49]. Additionally, Persze and Tolvaj [58] explained that the higher temperatures increase the wood degradation under ultraviolet conditions. The mass loss of wood may also occur during the weathering [49]. Boonstra et al. [56] reported that wood specimens conditioned after weathering showed lower reduction in the mechanical properties. Thus, the mechanical degradation observed in Figure 3 is affected by the combined impact of the UV radiation and temperature condition resulting in mass loss due to reduction in the MC.

**Figure 3.** The variation of MOE (**a**), MOR (**b**), and deflection to failure (**c**) in weathered wood samples.

Despite the reduction in the MOE and MOR, weathering led to an increase in the deflection to failure in all wood species. This increment was 13%, 23%, 21%, and 15% for fir, poplar, alder, and oak wood after 240 h of exposure. This observation may be linked to the impact of ultraviolet condition on enhancing the viscoelasticity of wood. Fathi et al. [60] showed that the loss modulus and loss factor of the wood samples tested in this study increased under ultraviolet condition, proving that wood exhibits more viscoelasticity when exposed to UV degradation. This may result in an increase in the deflection to failure in the degraded samples. Feist [64] reported the leaching and plasticizing effects of water during the weathering process. This plasticizing effect can also facilitate the enlargement of micro-checks. Figure 4 shows the load-deflection diagram of the wood samples after different weathering time periods. It shows that the degradation resulted in having a larger deflection at certain load levels. It was also indicated that after 150 h or 240 h of exposure, the fir and poplar wood samples reached a deflection of 5 mm at a load level of around 1000 N. However, this deflection was achieved at a higher load level (~1500 N–1600 N) for hardwoods such as alder and oak. Apart from its impact on the MOE, MOR, and deflection to failure, the degradation affected the failure modes. Figure 5 shows the failure modes after the static bending tests for the oak samples under weathering at three exposure

time periods. It was observed that the failure mode was mainly governed by the tensile failure perpendicular to the grain in the radial-longitudinal plane (fiber compression was also observed in parallel). Consequently, increasing the exposure time resulted in having a deeper brittle fracture and splinter in tension. This can be due to the destruction of the middle lamella [65], which contains significant lignin content. Severe checking may develop with longer exposure times in the cell wall components resulting in the loosening and detaching of the fibrils and tracheids from the surface [65]. Feist [64] discussed that the weathering condition can result in the destruction of the middle lamella and cell wall layers. This can impact the cohesive strength of wood tissue [65] that may result in the mechanical degradation of the wood as well.

**Figure 4.** The load–deflection diagram for (**a**) fir, (**b**) alder, (**c**) oak, and (**d**) poplar wood samples exposed to different times of weathering.

#### *3.2. Color Change*

Tolvaj and Faix [82] stated that the light reflection decreased after UV radiation, resulting in the wood seeming darker. The reduction in the lightness of the samples with the exposure time can be linked to the negative ∆*L*, as shown in Figure 6. A similar trend of darkening of the wood appearance after UV radiation has been reported in the literature [35,82]. It can be seen in Figure 6 that after 24 h of exposure, there was a trivial change in the lightness with a minor difference between different wood species. In this time, poplar and oak showed more darkening (∆*L* = −2.5). By increasing the exposure time, fir underwent a significant change in ∆*L* while alder showed a minimal change in ∆*L*. After 240 h, alder accounted for the minimum change in the lightness (∆*L* = −4) followed

*Materials* **2021**, *14*, x FOR PEER REVIEW 9 of 19

by oak (∆*L* = −11.5) and poplar (∆L = −13.7), and the maximum change in the lightness occurred in fir wood (∆*L* = −25).

**Figure 5.** The fracture plane of the weathered oak wood at three exposure times: 24 h (**a**), 150 h (**b**), and 240 h (**c**) under the static bending test.

> (**b**), and 240 h (**c**) under the static bending test. *3.2. Color Change*  Tolvaj and Faix [82] stated that the light reflection decreased after UV radiation, resulting in the wood seeming darker. The reduction in the lightness of the samples with the exposure time can be linked to the negative *∆L*, as shown in Figure 6. A similar trend of darkening of the wood appearance after UV radiation has been reported in the literature [35,82]. It can be seen in Figure 6 that after 24 h of exposure, there was a trivial change in the lightness with a minor difference between different wood species. In this time, poplar and oak showed more darkening (*∆L* = -2.5). By increasing the exposure time, fir underwent a significant change in *∆L* while alder showed a minimal change in *∆L*. After 240 h, alder accounted for the minimum change in the lightness (*∆L* = -4) followed by oak (*∆L* = -11.5) and poplar (∆L = -13.7), and the maximum change in the lightness occurred in fir wood (*∆L* = -25). Variation in the yellowness (∆*b*) of the wood samples exposed to weathering is shown in Figure 7. It can be seen that the yellowness increased with exposure time and reached its maximum value after 150 h. After this time, fir poplar, and oak experienced a slight reduction in the yellowness, while it stayed almost unchanged for alder. The increase in the yellowness of the samples was discussed to be mainly due to the degradation of lignin [35,36,46]. The continuous increase of the yellowness followed by a slight reduction under artificial weathering was reported by Timar et al. [36] and Pandey [83]. Figure 7 indicates that lignin degradation occurred even after 24 h of exposure. The fir wood accounted for the highest rate of increment in the yellowness following the exposure, with the highest ∆*b* occurring after 150 h of exposure (∆*b* = 23.8). On the other hand, the smallest change in the yellowness was observed in alder, suggesting a lower lignin degradation of alder wood under ultraviolet conditions compared to other species. The variation in the yellowness was almost similar in the first 100 h of the exposure for poplar and oak wood. For longer exposure times (i.e., 150 h and 240 h), the yellowness of poplar was relatively higher than that for oak.

**Figure 5.** The fracture plane of the weathered oak wood at three exposure times: 24 h (**a**), 150 h

**Figure 6.** Variation in the lightness (∆*L*) of different wood species under artificial weathering.

**Figure 7.** Variation in the yellowness (∆*b*) of different wood species exposed to artificial weathering.

The variation in the redness (∆*a*) of the degraded wood samples is shown in Figure 8. It can be seen that there was a reduction in the redness of all wood species during the first 100 h of exposure. However, there was an increasing trend in the redness of the samples after 100 h. While Tolvaj and Faix [82] discussed that it is challenging to explain the wood color change in a definite way, a correlation between the ∆*a* and the extractives content in wood has been reported in the literature [41,84]. For example, Persze and Tolvaj [58] showed that the extractives content has an important role in thermal decomposition during photodegradation. They also reported that the ∆*a* is higher at elevated temperatures, whereas the thermal effect does not alter yellowing. It is also indicated in Figure 8 that after 240 h, fir and oak accounted for the highest increase in the redness (∆*a* = 1.9), while alder experienced a reduction in it. The color change parameters and the total color change for all wood species (∆*E*) are listed in Table 1. It was observed that the total color change increased with the exposure time. A general trend for ∆*E* of fir > poplar > oak > alder was observed at all exposure times. Interestingly, fir that experienced the maximum color change showed the highest reduction in the mechanical properties, and the alder with the smallest total color change showed the minimum mechanical degradation. This indicates

that the color change may be correlated with the mechanical degradation of wood and be used to predict MOE and MOR.

**Figure 8.** Variation in the redness (∆*a*) of different wood species exposed to artificial weathering.

\* Different letters (a–d) within a column for each wood species show the significant difference by Tukey's comparison test (*p* < 0.05).

#### *3.3. MOE and MOR Prediction*

The wood color parameters and type of the wood species were used as the input of the decision tree model to predict MOE and MOR. The results of machine learning modeling are shown in Table 2. It was observed that when the wood species type was combined with *a*, *b*, and *L*, the developed decision tree model predicted the mechanical properties of wood with an R2 of 0.84 and 0.77, respectively (test data). Figure 9 illustrates the relative importance of the selected input parameters used in the decision tree model and indicates that all of them were significant in developing the predictive model. It was shown that following the type of wood species, the level of redness and yellowness had the highest relative importance.

**Table 2.** The coefficient of determination obtained for the prediction of the wood mechanical properties using different input features.

**Figure 9.** The relative importance of the wood species, *a, b*, and *L* for predicting the (**a**) MOE and (**b**) MOR.

Table 2 shows that adding the total color change ∆*(E)* parameter as an input to the model improved the prediction accuracy. In this case, the MOE and MOR were predicted with an R2 of 0.87 and 0.88, respectively (test data). Figure 10 shows the relative importance of the parameters used in this model. It was indicated that all of the input parameters were important in the performance of the model, while the total color change had relatively a higher importance for the prediction of the MOR than MOE. It was also observed in Figures 9 and 10 that the lightness parameter had higher relative importance when predicting the MOR than MOE.

**Figure 10.** The relative importance of wood species, *a*, *b*, *L*, and ∆*E* for predicting the (**a**) MOE and (**b**) MOR.

The last model, which used the wood species, color parameters (*a*, *b*, *L*), total color change (∆*E*), and variation in the color parameters ∆*(a,* ∆*b*, and ∆*L*), did not make a noticeable improvement in the prediction accuracy of MOE (R2 = 0.88 for the test data) and

MOR (R2 = 0.90 for the test data). Overall, adding the three parameters ∆*a,* ∆*b*, and ∆*L* had a minor positive impact in the performance of the model. Figures 10 and 11 indicate that the wood species type and redness were still the most important parameters for the prediction of the MOE and MOR. However, in this model, ∆*b* had higher importance than ∆a. Markedly, while the level of redness (a), described in the literature to be linked to the extractives content, had relatively a high importance in the performance of the model, its variation (∆*a*) exhibited less importance in the performance of the predictive model. Overall, these findings indicate that all of the selected input parameters contributed positively to the predictive accuracy of the ML model, albeit with different levels of relative importance. These findings may suggest that there could be a dependency between the mechanical properties of weathered wood and its color parameters. However, further studies with a larger sample size could better clarify the details of these dependencies, especially when combined with the chemical composition analysis.

**Figure 11.** The relative importance of wood species, *a*, *b*, *L*, ∆*E*, ∆*a*, ∆*b*, and ∆*L* for predicting the (**a**) MOE and (**b**) MOR.

The results of variable clustering analysis were in accordance with those of the decision tree modeling. Figure 12 shows that the MOE and MOR had the highest similarity level with the redness, and form a cluster together. This cluster has the highest similarity with a cluster encompassing the total color change (∆*E*), yellowness (*b*), and variation in the redness (∆*a*) and yellowness (∆*b*). The MOE and MOR had the lowest similarity with the lightness (*L*) and variation in it (∆*L*), with a 28.44% similarity level. The general observations of the variable clustering analysis prove that the selected input parameters were correlated with the mechanical properties of degraded wood, which can be used for MOE and MOR prediction. Further study can be performed to outline these dependencies and similarities between the parameters when weathering conditions are changed.

#### **4. Discussion and Remarks**

The general trend of the mechanical degradation of the wood samples indicated the better resistance of alder and the higher vulnerability of fir to weathering condition. While it is not within the scope of a monitoring task, further research may be conducted to include the change in the chemical composition of wood to explain the role of extractives and degradation of lignin and how they can be linked to the mechanical properties of wood. The machine learning analysis showed the high importance of change in redness that is linked to the change in the extractives content in the literature. Future research considering the chemical composition of wood could better explain these observed trends. More research could also be performed to assess the relationship between the wood viscoelasticity behavior under artificial weathering and its failure mode and fracture behavior when subjected to loading. This study did not focus on evaluating the UV penetration depth, and aimed only at developing a monitoring model that reflects the combined effects of different parameters accountable for the mechanical degradation of wood under weathering. Future research aiming to explain the mechanism of degradation, rather than in situ monitoring, could be performed to separate the role of ultraviolet radiation, temperature condition, and MC and mass loss on the mechanical behavior of weathered wood.

The performance was obtained on the basis of analyzing a small set of defect-free wood samples. The scope of the experiment could be further expanded while performing the proposed methodology on real-sized timber, including defects such as knots. One of the main challenges associated with color measurement for the characterization of wood properties is non-homogeneous surface color variation [33], especially in real-sized timber, which necessitates multiple color measurements from different locations of the timber. This is an important challenge associated with using color parameters for monitoring the mechanical properties of timber. However, due to the simplicity of the color measurement, the color features can be combined with the data acquisition from other NDE methods (such as Lamb wave propagation) to result in a robust monitoring model. Nasir et al. [59] obtained a similar range for R2 while using Lamb wave features for predicting the mechanical properties of weathered wood. However, their model did not need to have the type of wood species as an input parameter, as the model was able to understand the difference between the wood species through the extracted Lamb wave features. Thus, if the type of wood species to be monitored is not available, a machine learning model based on the wave propagation features may be chosen. Both the color measurement and wave propagation

method should be applied to real-sized timber to have a more reliable comparative study. It should be mentioned that in real applications, other factors related to the environment (rain, decay, etc.) or the timber (grain orientation, defects, annual ring width, etc.) may impact the mechanical properties of degraded wood, and having a single monitoring method may not address both the surface and internal damages to the timber. In this case, combining color parameters that reflect the surface degradation with those of wave propagation that are linked to the internal structure of the wood can better show the mechanical degradation of large-size timber. Additionally, timber monitoring requires dataset of a larger size and perhaps other types of data-driven modeling. The choice of data-driven method depends on both the size and complexity of the data [85,86]. Different machine learning or deep learning models could be studied to choose the one that better fits the dataset of a larger size [87].

#### **5. Conclusions**

In this research, a machine learning model was developed based on the color parameters for predicting the MOE and MOR values of weathered wood. Artificial weathering led to a reduction in the wood mechanical properties. Wood species that experienced greater color changes exhibited a more highlighted mechanical degradation. This indicates that the color parameters may be linked to the MOE and MOR of weathered wood. Fir was shown to be more susceptible to artificial weathering, while alder was more resilient and experienced less reduction in its mechanical properties. The mechanical properties of the samples could be predicted by the color parameters, although the developed machine learning model needed the type of wood species as an input parameter for accurate prediction. It was also observed that the deflection to failure of the wood samples increased with the weathering and the failure mode under bending loading also changed. This may be due to the increased viscoelasticity of weathered wood samples. Future studies should be aimed at investigating the relation between the mechanical properties and the color parameters on large-sized timber to evaluate the reliability of the developed model based on the color parameters for NDE of timber structures. Furthermore, the chemical changes caused by weathering and the correlation between the color, mechanical and chemical properties of wood should be studied in future research. This study was performed on small and defect-free wood samples. Additionally, the sample size was small, which is a limitation of the statistical analysis. However, the decision tree modeling was able to successfully handle the dataset. Focusing on large-sized timber containing defects may require expanding the size of dataset in future studies.

**Author Contributions:** Conceptualization, V.N. and S.K.; methodology, V.N., A.F. and S.K.; experiment and data acquisition, H.F., software, V.N. and H.F.; validation, V.N.; formal analysis, V.N., S.K.; investigation, A.F.; resources, V.N., A.F., S.K., and P.A.; writing—original draft preparation, V.N., and S.K.; writing—review and editing, S.K., F.S., and P.A.; visualization, V.N., S.K. and H.F.; supervision, S.K.; project administration, S.K., F.S., and P.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**3. Results and discussion**

### **3.1 General must and wine composition**

General composition of must obtained with the two skin-contact treatment and conventional way from cv. Malvasia *aromatica* are given in **Table 1**. In the skincontact treatment assays, the total acidity of the must decreases along with a slight increase in pH. This is due to the transfer of cations from the skin to the must during the previous maceration stage, and results in a decrease of acidity in the form of potassium bitartrate together with a salification of the acids [18]. The results show an increase in Yeast assimilable nitrogen (YAN) according to the time of contact with the skin, being more notable with M18; M6 did not cause variations in YAN content. These results are in agreement with the studies carried out by other authors, where a period of contact with the skin favors the enrichment of the musts in terms of amino acid content [19, 20]. In general, the effect of skin-contact in both assays is not very pronounced, which could be related to the low temperature

*b Yeast assimilable nitrogen (YAN).*

#### **Table 1.**

*General composition of must obtained with different treatment: Conventional (C) and skin-contact treatment: Assay 1 (A1), 18 h (M18) and assay 2 (A2), 6 h (M6).*

*Influence of Skin-Contact Treatment on Aroma Profile of Malvasia* Aromatica *Wines… DOI: http://dx.doi.org/10.5772/intechopen.99216*

(10°C) and the time of contact, compared to other studies on white varieties (15.5°C, 20°C and 24°C [21]).


*Article* **A Theory of Autofrettage for Open-Ended, Polar Orthotropic Cylinders**

### **Marina Rynkovskaya 1,\*, Sergei Alexandrov 2,3 and Lihui Lang 2**

Received: 1 January 2019; Accepted: 19 February 2019; Published: 22 February 2019

**Abstract:** Autofrettage is a widely used process to enhance the fatigue life of holes. In the theoretical investigation presented in this article, a semi-analytic solution is derived for a polar, orthotropic, open-ended cylinder subjected to internal pressure, followed by unloading. Numerical techniques are only necessary to solve a linear differential equation and evaluate ordinary integrals. The generalized Hooke's law connects the elastic portion of strain and stress. The flow theory of plasticity is employed. Plastic yielding is controlled by the Tsai–Hill yield criterion and its associated flow rule. It is shown that using the strain rate compatibility equation facilitates the solution. The general solution takes into account that elastic and plastic properties can be anisotropic. An illustrative example demonstrates the effect of plastic anisotropy on the distribution of stresses and strains, including residual stresses and strain, for elastically isotropic materials.

**Keywords:** residual stress; residual strain; open-ended cylinder; autofrettage


**3.5 Auxiliary benefits for workers**

Habitually, residential waste is composed with different kinds of recovery and recycling products such as plastic materials, bottles, and metallic materials. These kinds of products are being sorted by the employees of these private waste companies; normally laborers in all the areas of operation do sell them to either full term scavengers or scraps vendors [18]. And these are further resold to recycling companies for production of new items, both on-sorted and sorted materials from municipal solid waste are further separated to form a better recyclable that can be transported to bigger industries in Lagos and beyond by these stakeholders. However, the benefits from such sales of recyclables in the first instance above do not go to the private companies directly, but to their workers who greatly benefit from it. An in-depth investigation of one waste private contractor company reveals that, before disposal, its workers, who are also stakeholders, typically sort 35.1 kg of reuse, recovered, and recycled materials from the waste they collect in the operational area assigned to them each day on average. They make approximately =N = 49,440 per month and =N = 433,280 year from sales of these materials. The materials that are being sorted and their corresponding unit costs are displayed in **Table 7**, **Figure 2**.

#### **Table 7.**

*Daily average sorted reusable materials and their value.*

#### **Figure 2**

*Recovery and recycling of MSW in ancient Kano city Nigeria. Source: Field survey 2021.*


**1. Introduction**

Dilated cardiomyopathy (DCM) is a heterogeneous primary muscle disease predominantly affecting men, with a male to female ratio 3:1. The prognosis of DCM has dramatically improved over

the last decades [1–3] and the occurrence of left ventricular reverse remodelling (LVRR) under optimal medical treatment has been shown as one of the main prognostic drivers [1,4,5].

Female sex has recently emerged as an important outcome modifier in DCM patients, being independently associated with more favourable long-term outcomes and with a lower incidence of cardiovascular events in comparison to the male counterpart [6–8]. However, little is known regarding the mechanism underlying this important sex-specific effect. So far, none of the available reports have evaluated whether this difference could be partially explained by a different response to treatment and a more frequent occurrence of LVRR in women.

The aim of the present study was to investigate the rates of LVRR in males and females, and the prognostic impact of the relationship between LVRR and sex in a well-selected large cohort of real-world DCM patients with a long-term follow-up.


The Future of Internal Combustion Engines

*Edited by Antonio Paolo Carlucci*

Published in London, United Kingdom

### *Supporting open minds since 2005*

The Future of Internal Combustion Engines http://dx.doi.org/10.5772/intechopen.76548 Edited by Antonio Paolo Carlucci

#### Contributors

Mateos Kassa, Carrie Hall, Akii Ibhadode, Raphael Ebhojiaye, Selahaddin Akansu, Mehmet İlhan İlhak, Nafiz Kahraman, Selim Tangoz, Antonio Paolo Carlucci

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