**2. Experimental**

efficiency and service life [1–4]. This is because inorganic nanoparticles embedded in conducting polymers can improve the mechanical, electrical, and optical properties such as nonlinear optical behavior, photoluminescence, electroluminescence, and photoconductivity [5–7]. Nanostructured composites or nanohybrid layers containing numerous heterojunctions can be utilized for optoelectronics, organic light emitting diodes (OLEDs), organic solar flexible cells (OSC) [8, 9], etc. Among conducting polymers, polyethylenedioxythiophene:poly(4-styrenesulfonate) (abbreviated to PEDOT:PSS) as a p-type organic semiconductor is well used for the hole transport layer in OLED [10] and OSC [4] as well as for the matrix materials in various sensors [11]. Various nanocomposite films consisting of conducting polymers mixed with carbon nanotubes (CNT) as an active material have been prepared for application in gas thin-film sensors. Recently, Olenych et al. [12] used hybrid composites based on PEDOT:PSSporous silicon-CNT for preparation and characterization of humidity sensors. The value of the resistance of the hybrid films was as large as 10 MΩ that may have caused a reduced

It is known that graphene possesses many excellent electrical properties, since it is an allotrope

atoms. Graphene quantum dots (GQDs), as seen in [13, 14], are a kind of 0D material made from small pieces of graphene. GQDs exhibit new phenomena due to quantum confinement and edge effects, which are similar to semiconducting QDs [15]. Graphene and related materials like graphene oxide (GO) or reduced graphene oxide (rGO) as materials used for chemical sensing have significant application potential. This is due to the two-dimensional structure that results in a high sensing area per unit volume and a low noise compared to other solidstate sensors. There were many works reporting on the use of graphene or graphene-related materials for monitoring gases and vapors [16, 17]. Especially, some of the works attempted to connect the advantages of nanoscale metals with that of graphene for the improvement of gas sensor applications [18, 19]. GQDs were mainly used in a single-electron transistor (SET). Besides detecting charge in SETs, GQDs have also been recruited to build electronic sensors

hybridized carbon

; it contributes

, etc., have large sensitiv-

accuracy in monitoring the resistance change versus humidity.

82 Nanocomposites - Recent Evolutions

for the detection of humidity and pressure [20].

based on nanostructured inorganic structures like SnO2

of carbon with a structure of a single two-dimensional (2D) layer of sp2

Ammonia is a compound of nitrogen and hydrogen with the formula NH3

significantly to the nutritional needs of terrestrial organisms by serving as a precursor to food and fertilizer. With the development of the chemical industry, more and more generation of ammonia gas is brought into the environment. It is known that ammonia gas is a toxic compound; consequently, it is harmful to human health when a large enough concentration of this compound is attained [21]. Thus, production of devices (or sensors) to detect ammonia gas with a large sensitivity and selective property is very important. Many scientific groups have researched and developed gas ammonia sensors for applications. Sensors

ity and response time, but the technology for producing both the materials and devices for gas sensors usually requests vacuum and high temperature that results in considerably large expenses [22]. With the aim to reduce these costs, many scientific groups have developed gas sensors based on conducting polymers [23–25]. The advantage of the polymer-based sensors consists of easy fabrication, low power consumption, room temperature operation, etc. [26]. Among the conducting polymers, polyethylenedioxythiophene + poly(4-styrenesulfonate)

, WO3

, TiO2

#### **2.1. Samples preparation**

## *2.1.1. Preparation of PEDOT-PSS + GQD + CNT (GPC) and PEDOT-PSS + GQD + AgNW (GPA) humidity sensors*

Firstly, GQDs were prepared; for this, a solution of graphite flake (GF), KMnO4 , and HNO3 with a weight ratio of 0.2 g:0.2 g:0.4 ml was prepared and put in a Pt crucible. This solution was then put in a microwave heaven for heating in 1 min to separate GF into laminar form (EG). The second solution was made from 0.2 g NaNO3 + 9.6 ml H2 SO4 (98%) + 1.2 g KMnO4 (called as NKH). EG was mixed with NKH solution and carefully stirred by use of a magnetic device for 2 h to have a GO solution. Adding to the GO solution 30 ml distilled water, and then 10 ml H2 O2 allowed us to get a dark-yellow solution. By spinning with a rate of 7000 rpm for 5 min, a GO powder was obtained and it was diluted in deionized water. In the next step, NH3 was added in the solution and stirred at 100°C for 5 h until a solution with a uniform dispersion of GQDs was reached. Finally, the GQDs dispersed solution was filtrated by using the "Dialysis" funnel to collect GQD powder with a volume of 0.2 g. This powder then was dissolved in 20 ml of twicedistilled water to get GQD-dispersion solution of 10 wt.% GQDs (abbreviated to GQD10).

Next, GQD + PEDOT:PSS + CNT composite solutions were prepared. Firstly, a powder of multiple wall carbonate tubes (shortly abbreviated to CNT) with an average size of 30 nm in diameter and 2 μm in length was embedded in 10 ml of the GQD10 solution without CNT and with three contents of CNT, respectively, 0.5, 1.0, and 1.5 mg. All of the solutions obtained are called GQC solutions. These solutions were treated by plasma in a microwave oven. Then, 2 ml of polyethylene-dioxythiophene + PEDOT:PSS (1.25 wt.% in H2 O) was poured into each GQC solution. The solutions of GQDs-PEDOT:PSS without and with CNT of the three abovementioned volumes of CNT were stirred by ultrasonic wave for 1 h. From all the volumes of chemicals such as GQDs, PEDOT:PSS, and CNT used for the film preparation, the CNT weight contents (wt.%) in the GQDs-PEDOT:PSS matrix have been calculated. It is seen that the samples embedded with the CNT volume of 0.5, 1.0, and 1.5 mg consist of 0.4, 0.8, and 1.2 wt.%, respectively.

Synthesis of Ag nanowires (AgNW) was carried out as follows. Firstly, 20 ml of ethylene glycol was heated within stirring in a 250ml Corning-0215 glass at 70°C for 15 min; then, 17 mg of NaCl was added. Raising temperature up to 100°C, 20 mg of AgNO3 was filled into the glass. The reaction between NaCl and AgNO3 occurred, resulting in formation of opaque AgCl solution. Ethylene glycol was decomposed in aldehyde that played a role of a catalyst for creating Ag nuclei. The next step, 5 mg of KBr was added to the glass and heated up to 140°C for 10 min, following 300 mg of PVP was filled and raising temperature to 160°C. The solution temperature was maintained for 15 min. Finally, 250mg of AgNO3 was added into the solution. The last solution was kept at 160°C for 30 min for growing silver nanowires. In the duration of this time, one can observe the change of the solution color from opaque to bright-gray, proving the formation of AgNWs in the solution. After the solution was cooled automatically to room temperature (in ~90 min), the solution was diluted by 80 ml of ethanol and kept for 10 h to deposit an AgNWs paste. This paste was put into a glass with 350 ml of distilled water for spinning with 6000 rpm for 30 min to get silver nanowires adhering to the glass walls. This AgNWs paste was removed from the glass and put into other glass with 200 ml of ethanol. By ultrasonic stirring, the AgNWs paste was dispersed completely in 2 h. Finally, 100 ml of distilled water was added into the AgNWs + ethanol solution; totally, 300 ml of the AgNWs solution was prepared for further studies.

*2.1.2. Preparation of P3HT + rGO + CNT film sensors*

heating solution of graphite filled in KMnO4

− and MnO2

P3HT, rGO, and CNTs were abbreviated to P3GC.

into Mn+

the glass beaker top was taken and kept in another glass beaker for further use.

SO4

Dc-bias applied to the two electrodes.

and 9.6 ml H2

O2

to separate MnO4

10 ml H2

All chemicals like PEDOT:PSS, P3HT, and multiple wall CNTs, with purity of ≥ 99.9% were purchased from Sigma-Aldrich Corporation. To prepare reduced graphene oxide (rGO), graphite flakes (GF) were taken off from graphite pieces with fewer layers by microwave

**Figure 1.** Image of a humidity sensor made from a single layer of composite films (a) and the schematic drawing of the device with the two planar electrodes (b). Humidity change is detected by the change in the current with a constant

and 28 ml of distilled water were poured into the glass beaker to get a liquid solution. Next,

The obtained solution was unmoved for 24 h, and at the glass beaker bottom, a paste-like layer with dark-yellow color was deposited, constituting the rGO paste. By slowly sucking, the solution above the rGO paste was completely taken from the glass beaker. Finally, 0.2 g of rGO paste was diluted in 40 ml of N,N-dimethylformamide (DMF) solvent in 50 ml-volume glass beaker and ultrasonically stirred for 900 s to get completely dispersive rGO in DMF (rGO-DMF). After 24 h waiting for the solution stabilization, 30 ml of rGO-DMF solution from

P3HT powder with a volume of 6 mg was mixed in 0.6 ml of rGO-DMF solution. This solution was ultrasonically stirred for 2 h at room temperature. At the same time, 1 mg of multiwalled carbon nanotubes (shortly called CNTs) was embedded in 0.5 ml of DMF (CNT-DMF) and also stirred by ultrasonic machine for 1 h. Finally, mixtures of the rGO-DMF and CNT-DMF solutions were put in a small glass beaker and carefully stirred for 5 h at 80°C by using a magnetic stirrer. For all the volumes of chemicals of P3HT, rGO, and CNTs used for further solid film preparation, the weight ratio of P3HT:rGO:CNTs was 100:20:10 (namely, the volume content of rGO and CNTs embedded in P3HT matrix was chosen to be, respectively, 20 and 10 wt.%. For simplicity in further analysis, the composite samples with such contents of

and HNO3

was added to this solution and ultrasonically stirred at room temperature for 8 h

were put in a 200 mL volume Corning-247 glass beaker, then 1.2 g KMnO4

Conducting Polymers Incorporated with Related Graphene Compound Films for Use…

. Mixtures of 0.2 g GF, 0.2 g NaNO3

http://dx.doi.org/10.5772/intechopen.79060

ions, yielding a solution with a bright-yellow color.

,

85

Next, to prepare PEDOT-PSS + GQD + AgNW solutions, we used GQDs+PEDOT:PSS mixture with a volume ratio 2/1 of 10 wt.% GQDs solution/PEDOT:PSS, further this solution is called as GPA. Next step, to the GPA solution, a small amount of the AgNWs paste was added. The AgNWs pastes were dispersed in the GPA solutions by ultrasonic wave for at 65°C 1 h.

Using spin coating, GQC and GPA solutions were deposited onto glass substrates which were coated by two silver planar electrode arrays with a length (L) of 10 mm and separated each one from the other by a distance (l) of 5 mm, as shown in **Figure 1**. In the spin-coating technique used for preparing composite films, the following parameters were chosen: a delay time of 100 s, a rest time of 45 s, a spin speed of 1500–1800 rpm, an acceleration of 500 rpm, and finally, a drying time of 3 min. To dry the composite films, a flow of dried gaseous nitrogen was used for 10 h. For a solidification avoiding the use of solvents, the film samples were annealed at 120°C for 8 h in a "SPT-200" vacuum drier.

For simplicity in further analysis, the GPC sensor samples without and with CNT of 0.4, 0.8, and 1.2 wt.% were abbreviated to GPC-0, GPC-1, GPC-2, and GPC-3, respectively, whereas GPA sensors with AgNW of 0.2, 0.4, and 0.6 wt.% are called GPA1, GPA2, and GPA3, respectively.

Conducting Polymers Incorporated with Related Graphene Compound Films for Use… http://dx.doi.org/10.5772/intechopen.79060 85

**Figure 1.** Image of a humidity sensor made from a single layer of composite films (a) and the schematic drawing of the device with the two planar electrodes (b). Humidity change is detected by the change in the current with a constant Dc-bias applied to the two electrodes.

#### *2.1.2. Preparation of P3HT + rGO + CNT film sensors*

and 2 μm in length was embedded in 10 ml of the GQD10 solution without CNT and with three contents of CNT, respectively, 0.5, 1.0, and 1.5 mg. All of the solutions obtained are called GQC solutions. These solutions were treated by plasma in a microwave oven. Then, 2 ml of polyethyl-

solutions of GQDs-PEDOT:PSS without and with CNT of the three abovementioned volumes of CNT were stirred by ultrasonic wave for 1 h. From all the volumes of chemicals such as GQDs, PEDOT:PSS, and CNT used for the film preparation, the CNT weight contents (wt.%) in the GQDs-PEDOT:PSS matrix have been calculated. It is seen that the samples embedded with the

Synthesis of Ag nanowires (AgNW) was carried out as follows. Firstly, 20 ml of ethylene glycol was heated within stirring in a 250ml Corning-0215 glass at 70°C for 15 min; then,

AgCl solution. Ethylene glycol was decomposed in aldehyde that played a role of a catalyst for creating Ag nuclei. The next step, 5 mg of KBr was added to the glass and heated up to 140°C for 10 min, following 300 mg of PVP was filled and raising temperature to 160°C. The

the solution. The last solution was kept at 160°C for 30 min for growing silver nanowires. In the duration of this time, one can observe the change of the solution color from opaque to bright-gray, proving the formation of AgNWs in the solution. After the solution was cooled automatically to room temperature (in ~90 min), the solution was diluted by 80 ml of ethanol and kept for 10 h to deposit an AgNWs paste. This paste was put into a glass with 350 ml of distilled water for spinning with 6000 rpm for 30 min to get silver nanowires adhering to the glass walls. This AgNWs paste was removed from the glass and put into other glass with 200 ml of ethanol. By ultrasonic stirring, the AgNWs paste was dispersed completely in 2 h. Finally, 100 ml of distilled water was added into the AgNWs + ethanol solution; totally, 300 ml

Next, to prepare PEDOT-PSS + GQD + AgNW solutions, we used GQDs+PEDOT:PSS mixture with a volume ratio 2/1 of 10 wt.% GQDs solution/PEDOT:PSS, further this solution is called as GPA. Next step, to the GPA solution, a small amount of the AgNWs paste was added. The AgNWs pastes were dispersed in the GPA solutions by ultrasonic wave for at 65°C 1 h.

Using spin coating, GQC and GPA solutions were deposited onto glass substrates which were coated by two silver planar electrode arrays with a length (L) of 10 mm and separated each one from the other by a distance (l) of 5 mm, as shown in **Figure 1**. In the spin-coating technique used for preparing composite films, the following parameters were chosen: a delay time of 100 s, a rest time of 45 s, a spin speed of 1500–1800 rpm, an acceleration of 500 rpm, and finally, a drying time of 3 min. To dry the composite films, a flow of dried gaseous nitrogen was used for 10 h. For a solidification avoiding the use of solvents, the film samples were

For simplicity in further analysis, the GPC sensor samples without and with CNT of 0.4, 0.8, and 1.2 wt.% were abbreviated to GPC-0, GPC-1, GPC-2, and GPC-3, respectively, whereas GPA sensors with AgNW of 0.2, 0.4, and 0.6 wt.% are called GPA1, GPA2, and GPA3, respectively.

CNT volume of 0.5, 1.0, and 1.5 mg consist of 0.4, 0.8, and 1.2 wt.%, respectively.

17 mg of NaCl was added. Raising temperature up to 100°C, 20 mg of AgNO3

solution temperature was maintained for 15 min. Finally, 250mg of AgNO3

O) was poured into each GQC solution. The

occurred, resulting in formation of opaque

was filled into

was added into

ene-dioxythiophene + PEDOT:PSS (1.25 wt.% in H2

84 Nanocomposites - Recent Evolutions

the glass. The reaction between NaCl and AgNO3

of the AgNWs solution was prepared for further studies.

annealed at 120°C for 8 h in a "SPT-200" vacuum drier.

All chemicals like PEDOT:PSS, P3HT, and multiple wall CNTs, with purity of ≥ 99.9% were purchased from Sigma-Aldrich Corporation. To prepare reduced graphene oxide (rGO), graphite flakes (GF) were taken off from graphite pieces with fewer layers by microwave heating solution of graphite filled in KMnO4 and HNO3 . Mixtures of 0.2 g GF, 0.2 g NaNO3 , and 9.6 ml H2 SO4 were put in a 200 mL volume Corning-247 glass beaker, then 1.2 g KMnO4 and 28 ml of distilled water were poured into the glass beaker to get a liquid solution. Next, 10 ml H2 O2 was added to this solution and ultrasonically stirred at room temperature for 8 h to separate MnO4 − and MnO2 into Mn+ ions, yielding a solution with a bright-yellow color. The obtained solution was unmoved for 24 h, and at the glass beaker bottom, a paste-like layer with dark-yellow color was deposited, constituting the rGO paste. By slowly sucking, the solution above the rGO paste was completely taken from the glass beaker. Finally, 0.2 g of rGO paste was diluted in 40 ml of N,N-dimethylformamide (DMF) solvent in 50 ml-volume glass beaker and ultrasonically stirred for 900 s to get completely dispersive rGO in DMF (rGO-DMF). After 24 h waiting for the solution stabilization, 30 ml of rGO-DMF solution from the glass beaker top was taken and kept in another glass beaker for further use.

P3HT powder with a volume of 6 mg was mixed in 0.6 ml of rGO-DMF solution. This solution was ultrasonically stirred for 2 h at room temperature. At the same time, 1 mg of multiwalled carbon nanotubes (shortly called CNTs) was embedded in 0.5 ml of DMF (CNT-DMF) and also stirred by ultrasonic machine for 1 h. Finally, mixtures of the rGO-DMF and CNT-DMF solutions were put in a small glass beaker and carefully stirred for 5 h at 80°C by using a magnetic stirrer. For all the volumes of chemicals of P3HT, rGO, and CNTs used for further solid film preparation, the weight ratio of P3HT:rGO:CNTs was 100:20:10 (namely, the volume content of rGO and CNTs embedded in P3HT matrix was chosen to be, respectively, 20 and 10 wt.%. For simplicity in further analysis, the composite samples with such contents of P3HT, rGO, and CNTs were abbreviated to P3GC.

Using spin coating, P3GC solutions were deposited onto glass substrates which were coated by two silver planar electrode arrays with a length of 4 mm and separated from each other by a distance of 2 mm, which is similar to the image in **Figure 1**. The following parameters were used for spin coating: a delay time of 100 s, a rest time of 45 s, a spin speed of 1500–1800 rpm, an acceleration of 500 rpm, and finally a drying time of 300 s. To dry the composite films, a flow of dried gaseous nitrogen was used for 10 h. For a solidification avoiding the use of solvents, the film samples were annealed at 120°C for 2 h in a "SPT-200" vacuum drier. To compare the performance efficiency of P3HT + rGO + CNT with the one for PEDOT:PSS + rGO + CNTbased sensors, PEDOT:PSS + rGO + CNT composites (shortly called PEGC) were prepared by the abovementioned procedure with replacement of P3HT by PEDOT:PSS polymer.

The P3GC film sensors were exposed to NH3

extraction of NH3

**3. Results and discussion**

*PSS + GQD + AgNW composite films*

tions are rarely formed.

steps.

gas with concentration (*Cgas*) that was controlled

http://dx.doi.org/10.5772/intechopen.79060

gas detecting to

87

with step decreases from 50 to 40, 30, 20, and 10 ppm. The repeatability in the resistance change of P3GC sensor was studied by measuring the resistance of sensor as a function of both the insertion/extraction of ammonia gas in/out chamber and measurement time. Each

saturation state, 50 s for maintaining exposed ammonia gas in chamber with abovementioned concentrations (namely *Cgas* = 50, 40, 30, 20, and 10 ppm), 30 s for extraction of Ar and ammonia gas out from chamber; then, 20 s for heating samples at 70°C for completing

and finishing the prior cycle. The next cycle was repeated by the same

Conducting Polymers Incorporated with Related Graphene Compound Films for Use…

measurement cycle consists of the following time durations: 20 s for NH3

**3.1. PEDOT-PSS + GQD + CNT and PEDOT-PSS + GQD + AgNW**

*3.1.1. Electrical property and morphology of PEDOT-PSS + GQD + CNT and PEDOT-*

**Figure 2.** A TEM micrograph of GQDs sample (a) and FE-SEM of GPC-3 composite film (b) [29].

From a TEM micrograph of a GQDs sample (**Figure 2a**), it is seen that the size distribution of the dots is considerably homogenous, as evaluated in this micrograph, the dots size ranged from 10 to 15 nm. **Figure 2b** is an FE-SEM micrograph of the GPC-3 sample where the CNT and GQDs clearly appeared while the conjugate polymer PEDOT:PSS exhibited a transparent matrix. This SEM micrograph also shows that in the GPC composite film, there are mainly heterojunctions of the GQD/PEDOT-PSS and CNT/PEDOT:PSS, whereas CNT/GQD junc-

From the thickness measurements, it can be seen that embedding CNT made the GPC samples considerably thicker. However, for the CNT-embedded GPC films, the CNT concentration was not much affected by the film thickness, so that the change in the thickness versus CNT

## **2.2. Characterization techniques**

The thickness of the films was measured on a "Veeco Dektak 6M" stylus profilometer. The size of GQDs and the surface morphology of the films were characterized by using "Hitachi" Transmission Electron Microscopy (TEM), Emission Scanning Electron Microscopy (FE-SEM), and NT-MDT atomic force microscope operating in a tunnel current mode. Crystalline structures were investigated by X-ray diffraction (XRD) with a Philips D-5005 diffractometer using filtered Cu-Kα radiation (λ = 0.15406 nm). The ultraviolet–visible absorption spectra were carried out on a Jasco UV–VIS–NIR V570.

For humidity sensing measurements, the samples were put in a 10 dm3 -volume chamber; a humidity value could be fixed in a range from 20 to 80% by the use of an "EPA-2TH" moisture profilometer (USA). The adsorption process is controlled by insertion of water vapor, while desorption process was done by extraction of the vapor followed by insertion of dry gaseous Ar. The measurement system that was described in [30] consisting of an Ar gas tank, gas/ vapor hoses and solenoids system, two flow meters, a bubbler with vapor solution, and an airtight test chamber connected with collect-store data DAQ component. The Ar gas played a role as carrier gas, dilution gas, and purge gas.

For each sample, the number of measuring cycles was chosen to be at least 10 cycles. The humidity flow taken for measurements was of ~60 sccm ml/min. The sheet resistance of the samples were measured on a "KEITHLEY 2602" system source meter. To characterize humidity sensitivity of the composite samples, the devices were placed in a test chamber and device electrodes were connected to electrical feedthroughs.

For monitoring gases, the prepared sensing samples were put in a testing chamber of 10 dm3 in volume. The gases value can be fixed in a rage from 10 to 1000 ppm by use of an "EPA-2TH" profilometer (USA). To characterize the gas sensitivity of the samples, the devices were placed in a test chamber at the room temperature (namely 300 K) and the Ar gas pressure of 101.325 kPa (or 1 Atm); the device electrodes were connected to electrical feedthroughs. The measurements that were carried out included two processes: adsorption and desorption. In the adsorption process, the gas (or vapor) flow consisting of Ar carrier and measuring vapor from a bubbler was introduced into the test chamber for an interval of time, following which the change in resistance of the sensors was recorded. In the desorption process, a dried Ar gas flow was inserted in the chamber in order to recover the initial resistance of the sensors. Through the recovering time dependence of the resistance, one can obtain information on the desorption ability of the sensor in the desorption process.

The P3GC film sensors were exposed to NH3 gas with concentration (*Cgas*) that was controlled with step decreases from 50 to 40, 30, 20, and 10 ppm. The repeatability in the resistance change of P3GC sensor was studied by measuring the resistance of sensor as a function of both the insertion/extraction of ammonia gas in/out chamber and measurement time. Each measurement cycle consists of the following time durations: 20 s for NH3 gas detecting to saturation state, 50 s for maintaining exposed ammonia gas in chamber with abovementioned concentrations (namely *Cgas* = 50, 40, 30, 20, and 10 ppm), 30 s for extraction of Ar and ammonia gas out from chamber; then, 20 s for heating samples at 70°C for completing extraction of NH3 and finishing the prior cycle. The next cycle was repeated by the same steps.


**Author details**

Dedik Budianta1 \*, Adipati Napoleon2 and Nanthi Bolan3,4

1 Faculty of Agriculture, Soil Chemistry and Fertility, Soil Science Department, Sriwijaya University, Indralaya, Indonesia

2 Faculty of Agriculture, Soil Biologi, Soil Science Department, Sriwijaya University, Indralaya, Indonesia

3 UWA School of Agriculture and Environment, The University of Western Australia, Perth, Australia

4 The UWA Institute of Agriculture, The University of Western Australia, Perth, Australia

\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
© 2022 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.


*3.3. Predictive Accuracy*

At univariate analysis (Table 4), for scanner 1 (Discovery 690) all PET/CT semiquantitative parameters and RF obtained an AUC value between 0.6 and 0.8. Regarding scanner 2 (Discovery STE), again all of the semiquantitative PET/CT parameters and RF reached a value of AUC between 0.6 and 0.8; in general, these values were lower than the those reported for scanner 1. Furthermore, the evaluation of *p*-value allowed the selection of some parameters with the best performances for the prediction of the final diagnosis of Tis, for both scanner 1 and scanner 2.

Considering the combined analysis of both the scanners together (scanner 1 + 2), in general PET/CT semiquantitative parameters revealed a higher AUC compared to RF, with significant *p*-value. Interestingly, the combined evaluation of both scanners revealed acceptable values of UAC with significant *p*-value for some RF, even if the same RF did not reach these values at the analysis for single scanner.

**Table 4.** Univariate analysis for semiquantitative PET/CT parameters and for radiomics features for the single scanner and for both scanners considered together. Only values with AUC > 0.6 and *p*-value < 0.05 are reported.

AUC: area under the curve.

After performing a bivariate analysis, for both the single scanners and for both of the scanners considered together, the best combinations between PET/CT semiquantitative parameters and RF are summarized in Table 5. Similarly to univariate analysis, none of the combinations reached an optimal AUC of 0.8 and the couples of parameters generally obtained higher AUC values on scanner 1 than on scanner 2. Furthermore, for this analysis, the p-values were statistically more significant on scanner 1 than on scanner 2. In this setting, even if a comparison between the couples of variables obtained before is complex given the heterogeneity between the two scanners, in general GLCM-related parameters variously combined resulted the ones with best performances. This is true for both scanner 1 and scanner 2 and these findings are confirmed by the good results at univariate analysis previously described. The GLRLM-related and GLZLM-related RF also revealed good

performances in this setting. Interestingly, PET/CT semiquantitative parameters were confirmed as good predictors only for scanner 2 (Figure 3).

**Figure 3.** Visual representations of the three combinations ((**A**) GLCM Entropy\_log10+GLZLM\_SZHGE, (**B**) GLCM Entropy\_log2+GLZLM:SZHGE; (**C**) GLCM Entropy\_lo10+GLRLM\_HGRE) with best performances at bivariate analysis for both scanners considered together.

**Table 5.** Bivariate analysis for clinical, semiquantitative PET/CT parameters and radiomics features for the single scanner and for both scanners considered together. For each analysis, only the couples with best performances are reported.

#### **Table 5.** *Cont*.

**Table 5.** *Cont*.

AUC: area under the curve.

#### **4. Discussion**

The aim of this study was to verify the predictive abilities of semiquantitative PET/CT parameters and of RF to discriminate between benignant and malignant nature of TIs revealed at 18F-FDG imaging.

On the basis of the resulting evidence we identified some remarkable points concerning the effect of different PET scanners on RF extraction and the predictive features and associated models.

In our experimental setting, we had to deal with images coming from different PET/CT tomographs and this fact required a preliminary investigation of the effect of different technologies in producing images and subsequent image features. The results showed that the scanner technology concretely affects some RF, as previously underlined in literature, and in clinical day practice the use of different tomographs in the same department is frequent [20,28–34]. In particular, the acquisition of the same phantoms on different tomographs with different scintillators and algorithm used for the reconstruction (number of iterations, number of subsets or on the presence of partial volume correction) demonstrated this evidence.

These findings suggest two relevant points: the former indicates that different scanners can potentially have different preferred features in terms of correlations with a clinical outcome; the second point suggests that we must critically consider radiomics models coming from centers adopting different technologies. In other words, on one hand a unique radiomic best model trained on many scanners is probably suboptimal for each of them and on the other hand, any radiomic model coming from different centers should be internally validated before considering its use in the daily practice. In particular, in the literature only one study which evaluated the predictive role of RF in TIs [23] used different scanners for the extraction of RF: this means that the reproducibility of the results (which is one of the biggest challenges in radiomics) still remain uninvestigated in this field. Furthermore, in our evaluation, only a small amount of RF demonstrated to be significantly different between the two scanners, together with SUVmax, but nevertheless the cross-correlation maps resulted quite similar, adding value to our results. In this setting, of the parameters that after bivariate analysis demonstrated the best performances, GLZLM SZHGE was the only one significantly different between the two scanners.

Regarding the predictive role of RF for the correct evaluation of Tis, at univariate and bivariate analysis a good percentage of the aforementioned parameters revealed an acceptable AUC between 0.6 and 0.8. However, none of them demonstrated an AUC above 0.8. Similarly, these AUC were coupled with a significant *p*-value in a high percentage of the cases. It is worth underlining the fact that at bivariate analysis performed for both the scanner considered together, the AUC values and the *p-*values were the best in the whole study. This fact underlines a good predictive ability of some RF such as GLCMrelated (in particular GLCM entropy\_log2 e GLCM entropy\_log10), GLRLM-related and GLZLM-related.

Only a small amount of works that investigate the predictive role of radiomics in the evaluation of TIs at 18F-FDG PET/CT are available in literature [21–24].

Even if not clearly characterized by the presence of a proper texture analysis, the first study to evaluate the distributive heterogeneity of 18F-FDG in TIs was produced by Kim et al. [24]. In this work, the authors revealed that this heterogeneity was a promising parameter which was able to predict the final nature of these TIs.

Subsequently, Sollini et al. [23] were the first to evaluate the predictive abilities of texture analysis in this setting. Data of this study underlined the fact that SUVstd (the standard deviation of the distribution of SUV inside the considered VOI), SUVmax, MTV, TLG, Histo skewness, Histo kurtosis and GLCM correlation were the only parameters that were able to predict the final diagnosis of TIs, with a general positive predictive value of 54% and a general negative predictive value of 85%.

A similar analysis was also performed by Aksu et al. [22], who underlined how the semiquantitative PET/CT parameters and some shape-related, GLCM-related, GLRLMrelated and GLZLM-related RF obtained AUC values superior to 0.7. These findings were partially confirmed in our study, where the same parameters confirmed these good results, with the exception of semiquantitative parameters and shape-related RF. Furthermore, the authors of the study developed a machine-learning algorithm using GLRLM RLNU e SUVmax with a good general AUC value (0.731).

Lastly, Ceriani et al. [21] demonstrated the ability to predict the final nature of TIs of some PET/CT semiquantitative parameters (SUVmax, SUVmean, SUVpeak, MTV e TLG) and some RF. In this case, the authors performed texture analysis with a different software from LIFEx and so RF resulted partly different in comparison to the ones used in our study. In general, some shape-related and GLCM-related features demonstrated good performances and multivariate analysis confirmed TLG, SUVmax and Shape sphericity as able to predict the final nature of Tis.

It is interesting to underline that PET/CT semiquantitative parameters resulted good predictors in all of the studies, while in our work only SUVmean obtained a certain predictive role at bivariate analysis. In this setting, we reported that AUC of semiquantitative parameters were quite similar to AUC of RF only at monovariate analysis. Given the fact that the bivariate predictive model did not confirm this evidence, we can assume that these parameters do not perform well when trying to build models with multiple variables as in our case. Furthermore, as previously described, data in literature about the role of these parameters for the assessment of TIs are really heterogeneous and our findings confirm these insights. Moreover, RF describe quality and parameters of images that cannot be visually assessed and this is why we focused our attention on the evaluation of these features, allowing us to better understand the role of 18F-FDG PET/CT in the prediction of Tis nature.

Our study surely presents some limitations. First of all, this is a retrospective study with the use of tomography that are not the actual state. Furthermore, the relatively low sample of patients included in the work, even if higher than similar studies, appears sub-optimal to clearly evaluate the predictive abilities of texture analysis. Furthermore, RF extrapolation with a single software appears another limit of our analysis. Lastly, the aforementioned problem of the reproducibility of radiomics analysis in terms of multicentric evaluation is still an open issue and, in this setting, further research in this field are mandatory.


**3. Results and Discussion**

Polyurea and MWCNT nanocomposites designed for ballistic protection applications must meet certain performance criteria related to their thermal and mechanical strength. Materials exposed to the impact with a projectile or at the action of a shock wave can suffer both considerable deformation (or failure of the structure) and thermal degradation. Thus, polyurea coatings have the role of reducing these devastating effects.

The first step in our study consisted in the morphology characterization of the nanocomposites using SEM analysis. In Figure 2 SEM images of the PU-NC4 sample are presented. The nanocomposite consists of a continuous film; however, it can be noticed that, in some areas (highlighted with a white ring), there is a lack of homogeneity. The explanation could be due to the MWCNTs' aggregation in the polymer matrices and their escape at the surface of the polymer films, respectively.

To strengthen the hypothesis about the aggregation of the MWCNTs in sample PU-NC4, the second step of this study involved an investigation of the distribution of the MWCNTs inside the polyurea-polyurethane matrix through a micro-CT technique. CTVol® Micro-CT Surface Rendering Software allowed a realistic visualization of the samples (Figures 3a–d and 4) and displayed a homogenous spatial distribution of the nanofiller inside the polymeric matrix. Reconstruction of the collected images was performed by the NRecon reconstruction software (local version), and the full set of reconstruction results

were visualized via DataViewer® 2D/3D Micro-CT Slice Visualization(Micro Photonics Inc., Allentown, PA, USA). Representative 2D cross-section slices of each reconstructed sample were illustrated in Figure 3e–h. As can be observed from Figure 3, the MWCNTs are well dispersed inside the polyurea-polyurethane matrix. Moreover, the increase of MWCNTs concentration is well distinguishable in the four micro-tomographs.

**Figure 3.** Micro-CT images (70 μm voxel size) of polyurea-polyurethane-MWCNTs nanocomposites comprising: (**a**,**e**) 0.05 wt.% MWCNTs, (**b**,**f**) 0.1 wt.% MWCNTs, (**c**,**g**) 0.2 wt.% MWCNTs, (**d**,**h**) 0.3 wt.% MWCNTs.

**Figure 4.** Micro-CT image (10 μm voxel size) of PU-NC4 sample.

Even if the nanocomposite samples containing 0.3 wt.% MWCNTs (PU-NC4) exhibited an apparent macro-homogeneity, there are regions where these nanoparticles tend to agglomerate, as can be observed in Figure 4.

Thermogravimetric analysis showed that the synthesized polyurea-polyurethane-MWCNTs films displayed a high thermal resistance, as can be observed in Figure 5 and Tables 2 and 3.

According to the TGA analysis, it appears that the introduction of carbon nanotubes into polyurea films had a slightly positive effect on thermal resistance, as their degradation process began later compared to the control sample (PU). However, as the concentration of carbon nanotubes increased, this effect decreased. The temperature corresponding to the maximum decomposition rate was also higher in the case of nanocomposites. Starting at 354 ◦C (in the case of PU samples) and at 366 ◦C, respectively (in the case of the nanocomposite samples), a significant weight loss process could be observed due to the decomposition process of the polymeric matrix (Figure 5).

**Figure 5.** TGA and DTG analyses of the polyurea-polyurethane-MWCNTs nanocomposites.

**Table 2.** Thermal properties of polyurea-polyurethane-MWCNTs nanocomposites.

T10% = decomposition onset temperature (measured at 10% weight loss); Tmax = the maximum decomposition temperature, corresponding to the maximum of DTG peak (the first derivative of the thermogravimetric curve).

Another important aspect related to the performance of polyurea-based materials designed for ballistic protection is related to their glass transition temperature. At lower temperatures, but still above the value of the glass transition temperature, polyurea tends to pass into the glass phase due to the energy dissipation phenomenon generated during a deformation caused by the action of a shock wave or impact with a projectile [17,20,31]. As can be observed in Figure 6, the synthesized materials exhibited two glass transition temperatures, each of them corresponding to the flexible and rigid nanodomains of the

polyurea nanocomposite, respectively. Table 4 summarizes the two Tg values obtained for the synthesized materials. It can be noticed that the values obtained for the glass transition temperatures associated with the flexible nanodomains (Tg1) were quite similar to the blank sample (PU) because there are no significant differences between the aliphatic chains situated at a considerable distance from the rigid nanodomains, but there is a visible difference between the Tg2 values obtained for the reference sample (PU) and the nanocomposite samples (PU-NC) because the carbon nanofiller joins the rigid nanodomains, thus reducing the mobility of the polymeric chains situated in their proximity.

**Figure 6.** DSC curves obtained for polyurea-polyurethane-MWCNTs nanocomposites.

**Table 4.** Glass transition temperature obtained for polyurea-polyurethane-MWCNTs nanocomposites (Tg1–glass transition corresponding to the flexible nanodomains; Tg2–glass transition corresponding to the hard nanodomains).

To evaluate the behavior of polyurea-polyurethane films under the mechanical stress action, the samples were subjected to tensile tests. Table 5 and Figure 7 illustrate the obtained results. As can be noticed, the introduction of carbon nanotubes into the polyurea matrix led to an increase of Young's modulus, which means that they have become more rigid than the reference sample.

Although these materials were stiffer than the control sample, they showed higher deformation energy, which suggests that these materials can dissipate more energy. Since these materials were specially designed for ballistic protection, their high capacity to absorb and dissipate energy represents a remarkable advantage.

The pre-dispersed MWCNTs achieved a good dispersion in the polymer matrix until it reached a concentration of 0.2% MWCNTs but, at higher concentrations, the nanofiller tends to agglomerate. This situation leads to a decrease in the tensile strength of the samples containing 0.3% MWCNTs (PU-NC4). Since PU-NC3 displayed the highest value of tensile true strain (σT) and the highest deformation energy (Figure 7 and Table 5), we

can affirm that these maximum values indicate the concentration of 0.2% MWCNTs as representing the optimal composition (Figure 8) for this type of polyurea nanocomposite. The results are also sustained by SEM and Micro-CT analyses through the aggregation of the MWCNTs in the case of PU-NC4.

**Table 5.** Mechanical properties of polyurea-polyurethane- MWCNTs nanocomposites.

\* Young's Modulus was calculated according to the mathematical model described by Xue and Hutchinson [32].

**Figure 7.** True stress–true strain plots for the synthesized polyurea-polyurethane nanocomposite films.

**Figure 8.** Variation of the maximum of true stress, depending on the concentration of the nanofiller.

The impact strength of a material is an index of its toughness. When a load is applied to a polymer, a part of the energy is dissipated throughout the polymer mass, while another part is stored in the material and will be released after the load is removed. The DMA analysis aimed to follow the evolution of storage (E ) and loss (E") modulus of the synthesized nanocomposites to evaluate their potential to be used as coatings for the structures that need improved ballistic protection. Figure 9 illustrates a comparative plot of the E and E" values for the blank sample (PU), and for the nanocomposite which displayed the best tensile test results (PU-NC3).

**Figure 9.** Comparative E and E" plot between polyurea and polyurea-polyurethane nanocomposites.

Both E and E" were higher in the case of PU-NC3, showing that this nanocomposite possesses the ability to store a significant amount of energy, and it can also dissipate a higher quantity of energy in comparison with the neat polyurea (PU). The peaks corresponding to the loss modulus can allow the estimation of the glass transition temperature at around −50 ◦C, indicating that this value obtained through DMA technique is following DSC analysis.

The characterization of these materials specially designed for ballistic protection would not be complete without experimental measurements which aim to evaluate their behavior at the impact with a projectile or the action of a shock wave. Therefore, for evaluating the efficiency of the obtained materials, round-headed projectiles were employed for impacting aluminum plates coated with the synthesized polyurea-polyurethane nanocomposites on the backside.

For the experimental testing in the dynamic regime, we used a Hopkinson bar set-up, with every sample being fixed and aligned for axial symmetry. An air gun, which allows the variation of pressure, launches the striker (the spherical head projectile in our case) towards the direction of the center of the tested specimen. Thus, the sample is rapidly impacted by the compressive stress wave generated. At impact, the samples undergo rapid and permanent deformations or fractures. Tables 6 and 7 and Figure 10 display the results obtained with the Hopkinson bar tests. As can be noticed in Table 7, at an initial projectile acceleration pressure of 0.2 bars, all the samples underwent deformations, but none of them suffered fissures. Starting with 0.3 bars, small cracks appeared in the aluminum plate, but the samples covered with polyurea, even if they displayed higher deformations, did not suffer fractures, meaning that neat polyurea already offers significant improvement. At a higher initial projectile acceleration pressure of 0.4 bars, the aluminum plate underwent a higher fracture, but the polyurea-coated specimens did not exhibit fissures. Only at 0.5 bars of initial projectile acceleration pressure could a small fissure be observed on the aluminum plate coated with polyurea, but the polyurea film remained unbroken due to its higher elasticity, and the specimens coated with the nanocomposite suffered only low

deformations and no fractures. At a higher initial projectile acceleration pressure of 0.6 bars, polyurea films PU and the nanocomposite containing the lowest MWNCTs concentration PU-NC2 started to crack. At 0.6 bars, the aluminum plate coated with PU-NC3 suffered low fractures, but this polymeric film resisted up to 0.8 bars. Therefore, we can affirm that, only at values higher than 0.8 bars for the initial projectile acceleration pressure, did both layers (metallic layer and polymeric nanocomposite coating) undergo visible fractures; at lower values, the metallic layer was the only one that cracked. In the case of PU-NC4 specimens, the metallic layer exhibited low fissures at 0.7 bars, and both layers (metallic plate and polymeric nanocomposite coating) were fractured at values higher than 0.8 bars. The increase in the resistance of the aluminum plates covered with PU and PU-NC can also be highlighted by the recordings made with the help of the pressure transducer (Table 6). From the values displayed in Table 6, it can be noticed that, for the tests performed on the uncoated metallic plates, the maximum value of the force was 2.6 kN at the pressure of 0.3 bar. This characteristic displayed a decreasing trend for the tests performed at higher pressures, and could be associated with the loss of the integrity of the plate (fracturing phenomenon). On the other hand, for the specimens coated with polyurea, the values measured with the pressure transducer were almost double for the maximum force, more precisely 5 kN for PU-NC2 at 0.5 bars. For higher initial projectile acceleration pressure, all specimens, including the ones coated with the nanocomposite, followed the same trend: after reaching a maximum value of the force, there was a decrease, a phenomenon associated with the loss of the integrity of the metal plates.

**Table 6.** Maximum force values obtained during the impact tests with Hopkinson bar.

**Figure 10.** Maximum force values obtained during the impact tests with Hopkinson bar.

**Table 7.** Images of the tested specimens, captured after the impact tests with Hopkinson bar (pressure in bar and pattern of deformation).

#### **Table 7.** *Cont.*

#### **4. Conclusions**

Polyurea-polyurethane and MWCNTs nanocomposites were obtained to be used for ballistic protection applications through a facile synthesis approach involving MWCNTs pre-dispersed in a polyester polyol-based resin. To demonstrate that these materials are suitable for this type of application, they were subjected to thermal and mechanical characterization using different analysis techniques (SEM, μCT, TGA, DSC, DMA, tensile tests), and they were also subjected to ballistic tests using a Hopkinson bar system. SEM and micro-CT analyses confirmed the homogenous dispersion of the MWCNTs inside the polyurea-polyurethane matrix for the samples containing 0.05%, 0.1%, and 0.2% MWC-NTs. In the nanocomposites containing 0.3% MWCNTs, it can be noticed that, in some regions, there is a lack of homogeneity due to the tendency to form aggregates at higher concentrations of this nanofiller. TGA showed that the nanocomposite films have good thermal stability (up to about 300 ◦C). The presence of MWCNTs delayed the onset of the decomposition process of polyurea-polyurethane films, and the maximum degradation temperature was about 10 ◦C higher than that of the reference polyurea sample (PU). DSC curves displayed two glass transition temperatures due to the coexistence of the two segregated nanodomains from the componence of polyurea matrix (flexible and rigid nanodomains). According to tensile test results, it turned out that, from all the synthesized materials, the polyurea-polyurethane nanocomposite film containing 0.2% MWCNTs is the optimal option for ballistic protection applications, since it possesses the highest deformation energy. DMA analysis also demonstrated that PU-NC3 samples had a remarkable capacity for absorbing and dissipating energy. Experimental testing in a dynamic regime of the polyurea-coated aluminum plates showed that the polymeric layer allows the metal plate to maintain its integrity at an acceleration pressure value that is almost three times higher than the one for the uncoated metallic specimen. Synthesized nanocomposites possess unique properties that recommend them to be used in the modernization of ballistic protection equipment and devices. Thus, military vehicles or bulletproof vests could be safer, but also lighter and less expensive.

We can conclude that the synthesized polyurea-polyurethane-MWCNTs nanocomposites possessing this exceptional combination of properties and advantages are suitable for employment as complementary materials for ballistic protection.

**Author Contributions:** Conceptualization, G.T., A.D. and E.R.; methodology, E.R., M.T. and A.R.; software, R.G.; validation, G.T., A.D., E.R., F.R., M.T. and E.T.; formal analysis, G.T., A.D., E.R. and R.G.; investigation, G.T., A.D., F.R., M.T., P.O.S., C.D., A.R., E.T., F.B. and R.G.; resources, F.R. and E.T.; data curation, G.T., A.D., P.O.S., C.D., A.R., F.B. and R.G.; writing—original draft preparation, G.T., A.D. and E.R.; writing—review and editing, G.T., A.D. and E.R.; visualization, G.T., A.D., E.R., F.R., P.O.S., C.D., E.T. and F.B.; supervision, A.D., E.R., M.T., and A.R.; project administration, E.R. and A.R.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was partially supported by the National Authority for Scientific Research from the Ministry of Education, Research and Youth of Romania through the National Project PN-III-P2- 2.1-PTE-2019-0400 Ctr. No. 49PTE/2020. The authors would also like to thank for the financial support provided by the National Authority for Scientific Research from the Ministry of Education, Research and Youth of Romania through the National Project PN-II-PT-PCCA-2013 No. 278/2014.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

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


2.3.2. Implementation

Although, in theory, twelve or more different analysis methods could be used in the virtual data fusion implementation for analysing the ASR-sensitive aggregate dataset, in practice such a full implementation was impractical. We selected a partial implementation of the scheme including only three analysis approaches. The choice included the approaches that would produce cracking data with sufficient accuracy for this specific application while simultaneously testing the effectiveness of the virtual data fusion concept for obtaining accurate and consistent results. A successful demonstration of the virtual data fusion method with only three analysis components would then give justification for the further development of the algorithm to gradually include additional analysis modules, simultaneously growing in accuracy and resiliency. Moreover, the modular architecture of the strategy could more easily be adapted to different problems than a full but rigid analysis.

In fact, for the specific ASR-sensitive aggregate analysis described in this paper, it was not important to separate internal pores from internal cracks since the surface area of both pores and cracks was vulnerable to ASR degradation. Thus, modules related to this differentiation could be left out of the analysis. Furthermore, since scans were not available at varying levels of degradation, all methods relying on changes in sample state relative to time (such as DVC-based methods) were also left out of this analysis. All image analysis described in this paper was completed using custom algorithms developed and implemented in MATLAB [49].

#### Module 1: Identification of Objects with Low Density

When cracks or pores have widths exceeding two voxels, the voxels in their centres are completely filled with air. Thus, these central voxels are characterized by a particularly low greyscale value. In order to separate these voxels, a threshold has to be selected and subsequently used for image binarization. All voxels darker than the threshold would then be transformed to white and all voxels lighter than the threshold transformed to black. Given that this threshold must be consistently applied for a large range of datasets, an automated selection method was implemented.

Using the triangle selection algorithm [50,51], a virtual line is drawn from the origin of the image histogram to the top of the largest histogram peak (excluding the initial peak at zero, which represents voxels from the air around the specimen) (Figure 4). A calculation is then conducted to determine which point on the histogram is furthest from the virtual line along an intersecting, perpendicular line. The location of that point is identified as a potential threshold. It was found, however, that such a threshold is rather over-encompassing, leading to the introduction of considerable noise into the resulting binarized images. Thus, a slightly more conservative threshold value was also implemented for this module that is 25% lower than the original threshold. An example of the results from this analysis can be observed in Figure 5.

**Figure 4.** Automated threshold selection using the triangle approach (based on figure in [10]).

**Figure 5.** Binarization of greyscale image using the conservative and over-encompassing thresholds identified using the automated selection method (based on figure in [10]).

### Module 2: Identification of Objects with High Gradient

When an object in a CT-image has either a much higher or a much lower greyscale value compared to the surrounding material, its edges can be identified as regions of high gradient. For this purpose, a gradient-magnitude image is calculated from the original CT-image through the use of the Sobel operator (Figure 6) [52]. Since the resulting gradient image consists of a wide range of grey values, it must also be binarized. For this purpose, a histogram of the gradient image grey values is computed and another automated triangular threshold selection is completed, this time from the right edge of the histogram (Figure 7). The resulting binary image contains the edges of both bright and dark objects in the original CT image (Figure 8). In order to remove the edges associated with the bright (high-density) objects in the CT-image as well as some of the noise, the binarized gradient image can be multiplied by a binarized CT-image (this time using the "over-encompassing void-solid threshold" identified in Figure 4) (Figure 9).

**Figure 6.** Generation of the gradient-magnitude image (based on figure in [10]).

**Figure 7.** Automated gradient threshold selection using the triangle approach (based on figure in [10]).

*Materials* **2020**, *13*, 3921

**Figure 8.** Binarization of the gradient image (based on figure in [10]).

**Figure 9.** Generation of an image containing the outer edges of only low-density objects (based on figure in [10]).

Module 3: Identification of Objects above a Given Size

Prior to noise identification and removal, the results from Modules 1 and 2 were combined into a single image (Figure 10). This ensured that the centres and edges of the cracks/pores would both be present and accounted for during object size analysis. The object size analysis was completed using a connected components algorithm. During this analysis, individual voxels are assessed to determine whether they are part of a larger object in the binary image by analysing whether they adjoin other voxels of the same color. For this analysis, white voxels with touching faces, edges or corners (also known as "26-connected") were identified as connected. Objects with voxel volumes smaller than 125 voxels were subsequently identified and eliminated (Figure 11).

**Figure 10.** Fusion of images resulting from Modules 1 and 2 (based on figure in [10]).

**Figure 11.** Removal of noise from binary crack and void image (based on figure in [10]).

It was found that the use of smaller cubes than 125 voxels (with five voxel long sides) tended to leave considerable noise within the image while the use of larger cubes resulted in the loss of a considerable number of voxels along the path of the cracks. Given that the full CT images each contained over seven billion voxels, such an object with a volume of 125 voxels represented less than one ten-millionth of the total image volume. A flowchart of the entire crack-detection process is provided in Figure 12.

**Figure 12.** Flowchart of crack-detection process (based on figure in [10]).