Identification of Wear Condition in IC Engine by Wear Debris Monitoring

*Mohit Kumar, R.B. Sharma, Kuldeep Chaudhary and Ranjeet Singh*

### **Abstract**

 The objective of the present work is to develop a simple technique to predict and quantify the presence of wear condition in an IC engine based on wear debris monitoring. Wear debris are formed in the engine oil mostly due to friction between two parts of engine and/or extraction of material from the various parts of the engine. A test rig has been developed based on the Ferrography principle to perform the experimental investigation for engine oil analysis. A series of engine oil samples from an IC engine during different time intervals were considered and the wear debris analysis was performed based on total particle concentration of different material (ferrous material and non-ferrous material), size of wear debris and visual inspection of particles. The results demonstrate that the concentration, size and visual inspection of ferrous and non-ferrous debris, which was present in the oil correlated well with the severity of the condition of the IC engine. This is also useful to diagnosis the fault present in the engine.

**Keywords:** wear monitoring, IC engine, engine oil, ferrography, wear debris analysis

#### **1. Introduction**

 IC engines are mostly used in the automotive sector due to their better fuel economy and low maintenance. Condition monitoring of the IC engine or its various components is very important for its appropriate functioning. The various studies that are available in the literature for condition monitoring of the machine components use different techniques such as vibration analysis, acoustic emission, wear debris analysis, and thermography. Some studies are also available regarding the modeling of acoustic emission, which are significant to comprehend the concrete mechanism of the generation of acoustic emission during the operating condition of the gear pair as well as rolling element bearing [1–3].  These problems are generated due to improper combustion, oil burning and friction between two parts of engine. The lubrication system used in IC engines is splash lubrication system, which is used to lubricate all parts of the engine. In the lubrication system, the oil flows through the splash system in which the crankshaft rotates which in turn splashes the oil on the various parts of the engine. The oil first lubricates the crankshaft then goes into the main gallery and

proceeds by connecting rod to piston and then cylinder. The oil then lubricates the camshaft proceed by rocker arm and then the valves. After lubricating the parts of engine, the oil flows back to the sump through the oil gallery [5]. The oil lubricating the engine parts carries the wear debris back into the sump.

The wear debris analysis technique is extensively used in monitoring the condition of an IC engine, machine and gearbox. Many researchers use oil-monitoring techniques for condition monitoring such as the ferrography technique [6], grey system theory [7, 8], FTIR [9], rotary particle depositor (RPD), particle quantifier (PQ ) and spectrometric oil analysis. Ferrography is used to quantify the amount of wear particles within a given oil sample and to conduct microscopic analysis of that debris in order to identify its size and shape [10]. Spectrometric oil analysis can determine the residual life of IC engines and gearboxes by performing tests on the engine oil at regular intervals. The tests performed on the engine oil reveal the chemical composition of any metal particles suspended in the oil samples, any pre-time wear of engine parts can be identified and preventive maintenance can be done [11]. The relationship between oil monitoring information and the residual life is established, which states that the predicted residual life may be proportional to the wear increment measured by the oil analysis. In spectrometric analysis, the metal concentration found includes many variables such as Fe, Cu and Al etcetera [12]. Color plays an important role in wear debris analysis [13, 14]. Wear can also be identified using their color features.


*3.1. Water Evaporation*

The results from the water evaporation model simulation at Lake Mead show an evaporation rate estimate of 1957 mm in 2018. This result is in agreement with the results of the study conducted by Moreo and Swancar [55] on Lake Mead during the period of March 2010 through February 2012 using the eddy covariance evaporation method. The study estimated the lake evaporation from March 2010 to February 2011, and from March 2011 to February 2012. According to the two authors, the evaporation rate for the first study period had a minimum value of 1958 mm and a maximum value of 2190 mm; while the minimum value was 1787 mm and the maximum value was 1975 mm for the second study period. The result obtained in this present study is located within the result interval of Moreo and Swancar's study. Another early study by Westenburg et al. provided the evaporation data for Lake Mead from 1997 to 1999 [95]. The average evaporation rate for that period was 2281 mm.

Figure 4a shows the monthly results of the evaporation rates' simulation using 2018 data. The evaporation rate is low in the winter and increases in summer. The evaporation rate at the peak of the summer, in June, is approximately five times more important than the lowest evaporation rate of the winter, in December. Figure 4b shows the daily evaporation estimates throughout the year.

**Figure 4.** Water evaporation simulation results for Lake Mead: (**a**) simulated evaporation values (mm) for each month of the year 2018; (**b**) simulated evaporation values (mm) for each day of the year 2018.

#### *3.2. Energy Production*

#### 3.2.1. FPV Operating Temperature Model

The multilinear regression on the collected data yielded the coefficients α0, α1, α2, and α3, which describe the relationship between the FPV effective operating temperature (*Teo*) and the

independent variables: the water temperature (*Tw*), the air temperature (*Ta*), and the solar irradiance (*IS*). The regression coefficients have been obtained with an R-squared value of 0.8276. Figure 5 shows the statistical results of the regression. The R-squared value combined with the random distribution of the residuals' plot on Figure 5b show that there is a linear relationship between *Teo* and the independent variables.

**Figure 5.** Multilinear regression results of the FPV panels' effective operating temperature (*Teo*): (**a**) simulated FPV temperature plotted against the measured temperature for 15 June 2020; (**b**) residuals' distribution plotted against the simulated FPV temperature for 15 June 2020.

Equation (28) is proposed as a model that represents the effective operation temperature of FPV mounted on a foam-based support.

$$T\_{\varepsilon 0} = -13.2554 - 0.0875 \times T\_w + 1.2645 \times T\_d + 0.0128 \times I\_S \tag{28} \tag{28}$$

Figure 6 shows the simulated operating temperature using the proposed model, the operating temperature of a titled aluminum pontoon-based mount FPV model based on the original Kamuyu et al.'s model (for pontoon-based tilted FPV), and the measured operating temperature for 15 June 2020. The simulated temperature is at times higher or lower than the measured temperature, but the overall trend of the two temperature profiles matches. The model proposed in this study is compared to the unadapted tilted FPV model and the current model (which is an adaptation of Kamuyu et al.'s model for foam-backed flat-surface FPV) and provides a better description of a foam-based FPV panel's operating temperature. The proposed model in this study has a similar profile to Kamuyu's model, and the proposed model provides a better description of the foam-based solar module's behavior.

**Figure 6.** Measured FPV operating temperature compared to simulated FPV operating temperature for 15 June 2020.

The temperature profile of a foam-based FPV panel installed on Lake Mead has been simulated using the proposed FPV operating temperature model and compared to a pontoon-based FPV as described by Kamuyu's model in Figure 7.

**Figure 7.** Operation temperature of an FPV installed on the surface of Lake Mead. (+) Operating temperature using the proposed model in this study for foam-based FPV. (**o**) Operating temperature using a ponton-based tilted FPV described by Kamuyu's model.

The maximum temperature obtained with the model proposed in this study is 48.7 ◦C and the minimum temperature is −8.5 ◦C. On the other hand, the maximum temperature and the minimum temperature obtained if the FPV system was tilted are, respectively, 58.2 ◦C and −3.4 ◦C. Overall, the temperature model used here based on experimental data during the summer months predicts a lower temperature when the panels are in direct contact with the water surface.

#### 3.2.2. Energy Yield and Water Savings of an FPV System Installed on Lake Mead

The temperature profile is used to estimate the electrical efficiency of the solar panel, which is in turn used to simulate the energy yield of an FPV system installed on Lake Mead with historical weather data. The energy yield has been simulated by assuming a coverage of the lake surface between 10 and 50% in 10% increments. The results are shown in detail for the 10% coverage case and the total energy production is shown for the other cases.

Figure 8 shows the comparison between the monthly energy production obtained using the proposed model and the energy production of a tilted FPV for 10% coverage of the lake's surface. As can be seen in Figure 8 and expected from Figure 7, the proposed model predicts a slightly higher energy production, about 3.5%, which is correlated with the lower operating temperature of the modules. The maximum energy per month production is 3.2 TWh and occurs in the month of June, while the minimum energy per month production is 1.1 TWh and occurs in December.

Figure 9 shows the result for the daily energy simulation when 10% of Lake Mead's surface is covered with a foam-based solar FPV system. The maximum daily energy production is 570 MWh on 6 January while the minimum daily production is 21 MWh on 18 June.

**Figure 8.** Monthly energy yield of a simulated foam-based FPV system installed on 10% of Lake Mead's surface using historical data from 2018. Comparison between the proposed model (c-Si flexible foam-backed FPV) and a tilted FPV based on Kamuyu's model (c-Si aluminum mount FPV).

**Figure 9.** Daily energy production results using the temperature model proposed in this study for 10% coverage of Lake Mead's surface.

Figure 10 shows the simulated annual energy production, and the water saving capabilities of a foam-based solar FPV system installed on the surface of Lake Mead as a function of coverage area from 10–50%. For a coverage of 10%, the annual production using collected temperature data is 25.59 TWh, corresponding to a saved water volume of 126.64 million m3. When the percentage coverage is increased, the energy production is increased linearly. For a coverage of 50% of the lake's surface, it is possible to harvest 127.93 TWh of electrical energy and save 633.22 million of m3 of water using foam-based FPV panels.

**Figure 10.** Simulated annual energy production (TWh) and water saving capability (millions of m3) of a foam-based solar FPV system installed on Lake Mead's surface using historical temperature data and the proposed model depending on the percentage coverage of the lake's surface.

Table 2 shows the annual water and energy savings estimates related to the water savings and energy production from the FPV. With houses with the least water consumption, the cost of the water saved is estimated to be USD 44 million when 10% of the lake surface is covered, and USD 220 million when 50% of the lake surface is covered. On the other hand, when the consumers' water consumption is on the high side, these costs increase, amounting to USD 172 million when 10% of the lake is covered and USD 861 million when 50% is covered. Furthermore, the results for the energy production show that USD 0.5 billion of energy can be generated when 10% of the lake surface is covered. The value of the energy generated when 50% of the lake surface is covered is estimated as USD 2.6 billion.

**Table 2.** Estimation of the yearly cost of water saved and energy produced using water and energy cost range from Nevada for an FPV system covering 10–50% of Lake Mead's surface.

The relative values of the water and energy provided by the foam-based FPV indicate that the electricity production from the FPV could be used to subsidize water conservation in arid and semi-arid areas. Thus, FPV could be a self-funded water conservation approach.

#### **4. Discussion**

The water evaporation calculation performed in this study predicts a significant water saving potential for foam-based FPV systems on Lake Mead. The evaporation calculation using historical data has shown an annual evaporation estimate of 1957.6 mm for the lake. The result of the calculation performed in this study is in agreement with previous evaporation studies on Lake Mead [55,95]. The simulation results show annual water savings ranging from 126.64 to 633.22 million m3 depending on the percentage of the lake surface covered by the FPV system. According to the United States Environmental Protection Agency (US EPA), each American uses, on average, 88 gallons of water per day, resulting in an annual water consumption of 32,120 gallons or 121.59 m3 per capita [96]. The amount of water saved using foam-based FPV on Lake Mead will therefore be enough to supply water to more than five million Americans in the case that 50% of the lake surface is covered. This would make a significant impact on the cities near Lake Mead. The value is more than the four million population of the second largest city in the country, Los Angeles [97] or the entire population of Nevada of 3.1 million [98]. When 10% of the lake is covered by FPV panels, the amount of water saved is enough to supply water to the populations of both Henderson (320,189) and Las Vegas (651,319) or Las Vegas and Reno (255,601) in Nevada [99]. According to an analysis performed by Barsugli et al., Lake Mead has a 50% percent chance of going dry between 2035 and 2047 if nothing is done to stop the current draw down and evaporation rate of the lake [100]. Other studies on the management of the lake have resulted in the same conclusion [101,102]. These studies have shown the need for new ways to mitigate lake evaporation not only on Lake Mead, but on other lakes in the world, especially those located in arid environments. Floating solar photovoltaic technology provides a solution to limit evaporation of water surfaces and provide electrical energy for the surrounding populations.

The energy production analysis has yielded an annual energy production ranging from 25.9 TWh to 127.93 TWh for a coverage of the lake of 10%, and 50%, respectively. The energy production profile is in accordance with a previous FPV study conducted by Kamuyu et al. [45], showing an improvement of 10% from a ground-mount system. This is confirmed by the study of Pierce et al., who determined that the energy production improvement of an FPV systems is 5–10% compared to a ground-mount system for mono and polycrystalline silicon [54]. This is due to the cooling effect of the water on the FPV modules. According to the United States Energy Information Agency (US EIA), the average American household electricity consumption is 10,649 kWh per year. This means that the energy production of an FPV system covering 10% of Lake Mead has the capacity to power more than two million American homes [103]. This is more than the electricity needed to power the homes in Las Vegas, Henderson and Reno combined. On the other hand, the total electricity consumption in the U.S. according to 2018 statistics is 4178 TWh. This implies that the electricity production from a solar FPV system covering 50% of Lake Mead can supply 3% of the total electricity consumed in the U.S. and can replace more than 11% of the coal-fired power plants operating in the country; thus, contributing in a significant way to the reduction in the national carbon dioxide emissions [104] and the concomitant air pollution-related mortality [105–108]. This study is in agreement with past work showing enormous potential for FPV on water bodies in the U.S. [109].

The results of this study show that there are several benefits to implementing a foam-based FPV solar plant. Foam-based FPV avoids the issues related to land use in ground-mounted solar PV [110] and since the floating device is made of low-cost material, the racking cost is lower than other raft racking FPV technologies [54]. In addition, FPV systems in general have the potential to form agrivoltaic type systems [111] by merging with aquaculture to form aquavoltaics [112,113]. The flexible foam-backed FPV approach used here even makes mobile FPV possible. The FPV approach demonstrated here is less expensive than conventional pontoon-based FPV and has a slightly higher energy output per W because of the modules' close proximity to the water. FPV racking in general is less costly than conventional ground mounted PV. Thus, as PV is already often the least costly method for new electricity production, it provides a potentially profitable means of reducing water evaporation in the world's dwindling bodies of fresh water. Overall, the results of this study appear extremely promising. Solar FPV is a fairly new technology that is growing at a tremendous rate, but for it to reach its full potential, future work is needed to explore policies that sustain the development of this technology while also minimizing negative externalities. To accomplish this, a full life cycle analysis (LCA) study is needed on this technology.

Future work is also needed to experimentally verify the results of this study in different locations throughout the world. In addition, future work is needed to investigate fouling (and means to prevent it) in different bodies of water. More data should also be collected to further refine the temperature model and improve the energy production accuracy of the results shown here. Foam-based technology used as a floating device needs to be investigated more in order to have a commercially viable mass-produced FPV foam racking. The work shown here and completed previously was accomplished using after-market alterations of flexible PV modules. It should be pointed out that economic calculations used here assumed a 25 year lifetime for the PV modules. Although they are rated for extreme environments, guaranteed to resist corrosion and waterproof, the flexible SunPower modules only carry a 5 year warranty rather than the industry standard 25–30 year warranty. Future work to test the long-term performance of such systems is needed to ensure the reliability and safety of a foam-backed FPV as described in this article. In addition, future technical work is needed to investigate the potential for making flexible modules rated for high voltages that would be more appropriate for utility scale systems such as described in this study. The cost of the FPV racking would be further reduced by integrating bulk purchased foam into the PV manufacturing process. In addition, closed loop, circular economy [114–116] and industrial symbiosis [117] could be applied to the FPV manufacturing process. This would be expected to further reduce the cost of the FPV as well but may also necessitate policy intervention to ensure end of life recycling [118]. The polyethylene foam used here could be fabricated from recycled plastic waste [119–121], thereby further improving the environmental balance sheet for foam-backed FPV. Future studies can potentially look into the long-term stability of foam in water by analyzing the effect of different qualities of water on this material. Another aspect of foam-based rack where future work is needed is the mooring technology used to secure the FPV. Finally, the environmental impacts of the floating solar systems on marine life have not been fully established [60] and will be an interesting subject for future studies.


*Article* **Antifungal and Anti-Biofilm Effects of Caffeic Acid Phenethyl Ester on Different** *Candida* **Species**

**Ibrahim Alfarrayeh 1,2,\* , Edit Pollák 3 , Árpád Czéh 4 , András Vida 4 , Sourav Das 5 and Gábor Papp 1**

**Abstract:** This study investigated the effect of CAPE on planktonic growth, biofilm-forming abilities, mature biofilms, and cell death of *C. albicans*, *C. tropicalis*, *C. glabrata*, and *C. parapsilosis* strains. Our results showed a strain- and dose-dependent effect of CAPE on *Candida*, and the MIC values were between 12.5 and 100 µg/mL. Similarly, the MBIC values of CAPE ranging between 50 and 100 µg/mL highlighted the inhibition of the biofilm-forming abilities in a dose-dependent manner, as well. However, CAPE showed a weak to moderate biofilm eradication ability (19-49%) on different *Candida* strains mature biofilms. Both caspase-dependent and caspase-independent apoptosis after CAPE treatment were observed in certain tested *Candida* strains. Our study has displayed typical apoptotic hallmarks of CAPE-induced chromatin margination, nuclear blebs, nuclear condensation, plasma membrane detachment, enlarged lysosomes, cytoplasm fragmentation, cell wall distortion, whole-cell shrinkage, and necrosis. In conclusion, CAPE has a concentration and strain-dependent inhibitory activity on viability, biofilm formation ability, and cell death response in the different *Candida* species.

**Keywords:** CAPE; *Candida*; antifungal; biofilm; apoptosis


*Article* **Enhanced Visible-Light Driven Photocatalytic Activity of Ag@TiO2 Photocatalyst Prepared in Chitosan Matrix**

**Ivana Grˇci´c 1,\* , Andreja Gajovi´c 2 , Milivoj Plodinec 3 , Kristina Šimunkovi´c 1 , Hrvoje Ivankovi´c 4 and Marc-Georg Willinger 5**

Received: 7 June 2020; Accepted: 4 July 2020; Published: 8 July 2020

**Abstract:** Ag doped TiO2 photocatalysts (Ag@TiO2) were prepared with an aim to extend the absorption range of TiO2 into the visible region, for tentative application under solar irradiation. Photocatalyst synthesized by the novel method using chitosan for reduction of Ag+ to Ag0 nanoparticles was compared to similar catalysts previously reported. The photocatalytic activity of Ag@TiO2 obtained by a simple novel method was evaluated based on degradation of salicylic acid as a model compound. The higher activity under visible irradiation can be attributed to the surface plasmon resonance and suppression of the electron-hole recombination when deposition of Ag nanoparticles on TiO2 was achieved using chitosan. The photocatalysts were characterized by X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), high resolution TEM (HRTEM), energy dispersive X-ray spectroscopy (EDXS), selected area diffraction (SAED), and diffuse reflectance spectroscopy (DRS). The photochromism of Ag was observed and explained.

**Keywords:** Ag-doped TiO2; chitosan; solar photocatalysis; photochromism

### **1. Introduction**

Advanced oxidation processes (AOPs) which involve the generation of highly reactive hydroxyl radicals (HO• ), have emerged as a promising water and wastewater treatment technology for the degradation of a wide range of organic contaminants [1–3]. High toxicity and resistivity to biodegradation of organic compounds like pharmaceuticals, pesticides, and hormones present in municipal and industrial wastewater pose a threat to the environment and human and animal health and may also affect aquatic organisms in an unpredictable way. In recent years, semiconductor photocatalysis has shown a great potential as a low-cost, environmental friendly and sustainable treatment technology to align with the "zero" waste scheme in the water/wastewater treatment. The ability of this advanced oxidation technology to achieve partial or complete mineralization of persistent organic compounds has been widely demonstrated [4–6]. Application of solar photocatalysis is particularly interesting [7].

The mechanism of a semiconductor photocatalysis has been thoroughly explained in the literature [8,9]. A large number of semiconductor oxides showed good photocatalytic activity. Among these, nanosized TiO2 is one of the most promising photocatalysts due to its many advantages, such as high photosensitivity, chemical stability, nontoxicity, easy availability, environmental acceptability, and low cost [10–12].

Still, there are also some shortcomings appearing during the practical application of TiO2. The band gap energy, e.g., is 3.2 eV for anatase and 3.0 eV for rutile and the light wavelength for such photon energy corresponds to λ < 400 nm. Therefore, TiO2 absorbs radiation of wavelength below the visible range of light spectrum. Thus, in wastewater treatment by TiO2 photocatalysis only UV light could be used. The overall efficiency is significantly reduced under sunlight, which consists of 43% visible and only a 5% UV fraction. Furthermore, the charge carrier recombination rate is high and photogenerated electron–hole pairs easily recombine in femtoseconds, which reduces photocatalytic efficiency [4,6,13].

To overcome these limitations of TiO2, many studies have been carried out to increase the lifetime of the charge carrier and to extend the absorption range of TiO2 into the visible region. These studies involved the addition of various dopants to TiO2 nanoparticles or TiO2 nanocomposites such as metals or metal ions (Fe, Mn, Cu, Zn, Zr, Sb, Sb, and Ce), non-metals (B, C, N, P, and S) [14–17], noble metals (Ag, Pt, and Pd), [11,17–19] and photosensitization of TiO2 with organic dyes, most often phtalocyanine dyes [20]. Among the noble metals used as electron traps, Ag is extremely suitable for industrial application due to its low cost and easy preparation. TiO2 doped with Ag has been a feasible approach to narrow the band gap of TiO2, thereby enhancing its visible light photocatalytic activity. The quantum-sized behavior of Ag nanoparticles has prompted considerable research interest in general. The Ag-doped TiO2 particles has therefore attracted much interest and the different methods of preparation are used, commonly the following methods: sol–gel, deposition–precipitation, photocatalytic deposition, and deposition–precipitation with photoreduction. In [12], the Ag-doped TiO2 photocatalyst was prepared by a sol–gel process. Degradation of organic dye Indigo carmine under visible irradiation indicated that prepared catalyst has the satisfactory photocatalytic efficiency. In [13], Ag/AgCl@TiO2 photocatalyst was prepared by the deposition–precipitation with photoreduction. The photocatalyst exhibited efficient photocatalytic activity for the degradation of 4-chlorophenol and photoreduction of Cr (VI) under visible light irradiation. In [21] Ag/AgBr@TiO2 photocatalyst was prepared by the deposition—precipitation method and photocatalytic deposition method. The catalyst showed high efficiency for the degradation of azo dyes and the destruction of bacteria under visible light irradiation.

This study demonstrates a new simple method for preparing Ag@TiO2 photocatalyst which is more efficient under solar irradiation than widely used commercial TiO2. The reported preparation methods [13,21] were repeated in the frame of this work with the aim to compare the respective efficiencies of above mentioned photocatalysts (hereby marked as S1a–c and S2) with the newly prepared Ag@TiO2 via method involving chitosan (marked as S3). The idea was to evaluate the possible application of different Ag doped catalysts in the photocatalytic wastewater treatment under visible and solar irradiation. A novel method for preparation of Ag@TiO2 from commercial titania was presented. The method was developed according to the preparation of silver composites with chitosan studied by Twu et al. [22]. The advantage of the presented method is the simplicity per se. Ag nanoparticles were prepared on TiO2 surface using basic chitosan suspension as both stabilizer and reductant without any additional chemicals (such as NaBH4) or irradiation. This kind of synthesis presents a facile green method of photocatalysts applicable for large scale production. The method does not require pre-synthesis of TiO2, since it uses commercially available TiO2 (P25) with confirmed photocatalytic activity. The use of toxic solvent is omitted thus presenting an environmentally acceptable solution. The synthesized photocatalysts were characterized and used for the degradation of salicylic acid under visible and simulated solar irradiation. The respective photocatalytic activities were compared with that of commercial non-dopedTiO2 (P25). Salicylic acid (SA) was chosen as a model pollutant since it has been identified as a water pollutant which arises from a number

of sources including paper milling, cosmetic industries, and landfill leachate. The destruction of SA is of considerable interest. The SA was listed as a pollutant in precipitation, surface waters (approx. 0.1 µg L−1 ) and as a constituent of humic material in drinking waters. It may be released to the aquatic environment in wastewater discharges from industry and even sewage treatment facilities [5,23]. In the light of presented results, newly prepared Ag@TiO2 (S3) deserves further research of the surface phenomena applicable in the field of solar photocatalysis and application on a larger scale. *Catalysts* **2020**, *10*, x FOR PEER REVIEW 3 of 17 of considerable interest. The SA was listed as a pollutant in precipitation, surface waters (approx. 0.1 μg L−1) and as a constituent of humic material in drinking waters. It may be released to the aquatic environment in wastewater discharges from industry and even sewage treatment facilities [5,23]. In the light of presented results, newly prepared Ag@TiO2 (S3) deserves further research of the surface phenomena applicable in the field of solar photocatalysis and application on a larger scale.

#### **2. Results and Discussion 2. Results and Discussion**

#### *2.1. X-ray Di*ff*raction (XRD) Analysis 2.1. X‐ray diffraction (XRD) Analysis*

Data of performed XRD analysis is given in Figure 1. As it can be observed, all samples contain almost the same proportion of anatase to rutile phase. Photocatalysts prepared by synthesis 1 (S1a, S1b, and S1c), i.e., by deposition–precipitation and photoreduction method using surfactant, contains a certain portion of AgCl on the surface (Figure 1b). Sample S2 contains a negligible portion of AgCl on the surface (Figure 1c). Having in mind unsuccessful reduction to Ag, samples S1a and b were completely excluded from detailed analysis, while photocatalytic activity was checked for S1c (for comparison), S2, and S3 only. Data of performed XRD analysis is given in Figure 1. As it can be observed, all samples contain almost the same proportion of anatase to rutile phase. Photocatalysts prepared by synthesis 1 (S1a, S1b, and S1c), i.e., by deposition–precipitation and photoreduction method using surfactant, contains a certain portion of AgCl on the surface (Figure 1b). Sample S2 contains a negligible portion of AgCl on the surface (Figure 1c). Having in mind unsuccessful reduction to Ag, samples S1a and b were completely excluded from detailed analysis, while photocatalytic activity was checked for S1c (for comparison), S2, and S3 only.

**Figure 1.** X‐ray diffraction (XRD) powder patterns of prepared Ag modified TiO2 photocatalysts compared to original TiO2 P25 (**a**): S1a–c from bottom to top (**b**), S2 (**c**), and S3 (**d**). **Figure 1.** X-ray diffraction (XRD) powder patterns of prepared Ag modified TiO2 photocatalysts compared to original TiO2 P25 (**a**): S1a–c from bottom to top (**b**), S2 (**c**), and S3 (**d**).

radians, θ denotes Bragg angle and λ is an X‐ray wavelength.

According to the Scherrer equation (Equation (1)), the average crystallite size (Dp) of the anatase and rutile in the powder was calculated from XRD data. Note that β stands for line broadening in

½ (1)

ൌ 0,94

According to the Scherrer equation (Equation (1)), the average crystallite size (Dp) of the anatase and rutile in the powder was calculated from XRD data. Note that β stands for line broadening in radians, θ denotes Bragg angle and λ is an X-ray wavelength.

$$D\_p = \frac{0,94\,\lambda}{\beta\_{1/2}\cos\theta} \tag{1}$$

In all XRD patterns, the peaks with highest intensities of anatase (101), (004), and (200) were used to evaluate the size of nanoparticles. For size of rutile nanoparticles we used intensities of (110), (101), and (111) peaks. Calculated Dp were 20.2(6), 22.8(7), and 22(1) nm for anatase and 30(1), 30(1), and 33.8(5) for rutile, in S1c, S2, and S3, respectively, which corresponds well with the average crystallite size calculated for pure TiO2 P25 (20.4(7) nm anatase and 31.6(6) nm rutile). Figure 1 indicate that amount of rutile in sample S3 (Figure 1d) is higher than in sample S2 or TiO2 P25. Since only the calcination above 915 ◦C would lead to definite transformation of anatase to rutile phase, and the highest calcination temperature in this work was 600 ◦C (sample S3), the peak differences were attributed to the uncertainty of quantitative determination of crystalline phases by XRD.

### *2.2. Raman Spectroscopy Measurements*

in the sample.

Raman spectroscopy results for samples S2 and S3 before photocatalytic reactions indicates that all the samples are dominantly in anatase form of TiO2, since all the bands characteristic to anatase (at around 145, 198, 395, 514, and 635 cm−1 ) were observed in the spectra (Figure 2a). Some small amount of rutile can exist in the sample S2 indicated by band at 440 cm−1 having low intensity (denoted by the arrow). Anatase dominated at least at the surface of the particles since Raman spectroscopy is more sensitive to the surface than to the bulk of the particles [24]. Raman scattering cross section of the anatase phase is larger than that of the rutile. Therefore, in the case of the small amount of rutile in comparison to the anatase in the TiO2 sample it is hard or impossible to observe bands characteristic for rutile. *Catalysts* **2020**, *10*, x FOR PEER REVIEW 5 of 17

**Figure 2.** (**a**) Raman spectra of the starting samples before photocatalytic reactions: sample S2, and sample S3 compared to TiO2 P25. (**b**) Comparison of Raman spectra of the sample S2 before photo‐ catalytic reaction (denoted by S2), after the reactions using simulated sunlight (i) and after reaction using only visible part of the spectrum (ii). (**c**) Comparison of Raman spectra of the sample S3 before photo‐catalytic reaction (denoted by S3), after the reactions using simulated sunlight (i) and after reaction using only visible part of the spectrum (ii). *2.3. Study by TEM Techniques* **Figure 2.** (**a**) Raman spectra of the starting samples before photocatalytic reactions: sample S2, and sample S3 compared to TiO2 P25. (**b**) Comparison of Raman spectra of the sample S2 before photo-catalytic reaction (denoted by S2), after the reactions using simulated sunlight (i) and after reaction using only visible part of the spectrum (ii). (**c**) Comparison of Raman spectra of the sample S3 before photo-catalytic reaction (denoted by S3), after the reactions using simulated sunlight (i) and after reaction using only visible part of the spectrum (ii).

The structural and morphological characterizations of samples S2 and S3 by TEM methods are shown in Figures 3 and 4. TEM images show that the nanocrystals of both S2 and S3 samples are spherical, cubic‐like or truncated tetragonal, and the particle size varies from less than 20 nm to about

are in good agreement with crystallite sizes of calculated from XRD patterns (Figure 1) for both samples. SAED patterns (Figures 3h and 4h) also consist of anatase and rutile diffraction points connected in the rings characteristic for monocrystalline materials. The number of the reflexes characteristic for the rutile phase is considerably smaller indicated smaller amount of the rutile phase

Sample S2 is recorded by Raman spectroscopy after the photocatalytic reactions that have been performed: (i) using simulated sunlight and (ii) using only visible part of the spectrum (Figure 2b). Raman spectroscopy results indicates that before and after reaction anatase form of TiO2 dominates, since all the bands characteristic to anatase (at around 144, 198, 395, 514, and 635 cm−1 ) were observed in the spectra while the band of rutile (at 438 cm−1 ) is hardly visible. The small shift of the anatase band at the 144 cm−1 to the higher wavenumbers in the samples recorded after reactions: to 145 cm−1 for the usage of sunlight (Figure 2b(i)), or to the 147 cm−1 for visible light (Figure 2b(ii)) indicated the decrease of the order in the crystal lattice of the anatase (nano)particles, or the possible formation of the defects.

Starting sample S3 show only anatase Raman bands, while after photocatalytic reaction using sunlight of the spectrum, rutile band of very low intensity at 438 cm−1 was observed (Figure 2c(i)). In the case of using visible part, anatase structure with only a small amount of rutile was observed as well (Figure 2c(ii)).

These results were in contradiction with the XRD results that suggested the highest ratio of rutile in S3 samples. Moreover, the Raman spectra of S3 showed certain baseline fluctuations that could cover the already weak rutile signals. Our findings showed the importance of combination of various methods for evaluation of material properties of a catalysts. In this case, one can conclude that the crystalline composition of TiO2 have not been changed during sample preparation or application.

### *2.3. Study by TEM Techniques*

The structural and morphological characterizations of samples S2 and S3 by TEM methods are shown in Figures 3 and 4. TEM images show that the nanocrystals of both S2 and S3 samples are spherical, cubic-like or truncated tetragonal, and the particle size varies from less than 20 nm to about 50 nm in S2, while in S3 somewhat larger particles are also visible. The faces of the crystallites were not well defined, but the lattice fringes observed by HRTEM clearly indicated both anatase and rutile phase of the crystallites. The observed size of the rutile and anatase nanoparticles (Figures 3 and 4) are in good agreement with crystallite sizes of calculated from XRD patterns (Figure 1) for both samples. SAED patterns (Figures 3h and 4h) also consist of anatase and rutile diffraction points connected in the rings characteristic for monocrystalline materials. The number of the reflexes characteristic for the rutile phase is considerably smaller indicated smaller amount of the rutile phase in the sample.

The presence of Ag in small amounts is observed in all analyzed samples before and after photocatalytic reaction by EDXS measurements (Figures 3g and 4g). The average atomic percentage of Ag indicated by multiple EDXS measurements in S2 was (0.5 ± 0.1) at %, while S3 contained somewhat larger amount of silver, (0.7 ± 0.2) at %. However, the nanoparticles of pure Ag were not observed, thus indicating that Ag atoms were inbuilt in crystal lattice of TiO2.

There are considerable differences in TEM images after photocatalytic reaction under sunlight and visible irradiation. After photocatalytic reaction, in the case of S2 samples, an amorphous layer of about one nanometer was observed on the surface of the catalyst after photocatalytic reaction using only visible irradiation (Figure 3d). The formation of this amorphous layer indicates that the products of catalytic reaction were accumulated on the surface of the catalysts. The reason for that accumulation could be incomplete degradation of the model pollutant (SA) under the visible light, so the formation of this amorphous layer could induce the decrease of the catalytic activity in time under only visible part of the irradiation spectrum. Moreover, the crystal structure of the TiO2 particles could be also affected at the surface, which could be inferred from comparison with the Raman spectroscopy measurements. In the Raman spectrum 144 cm−1 band shifting to higher wavenumbers was observed indicating disorder of TiO2 anatase crystal lattice, as was mentioned before.

*Catalysts* **2020**, *10*, x FOR PEER REVIEW 6 of 17

**Figure 3.** Sample S2: TEM (**a**) and high resolution transmission electron microscopy (HRTEM) (**b**) images before photo‐catalytic reaction, TEM (**c**) and HRTEM (**d**) images after photo‐catalytic reaction using only visible part of the spectra, TEM (**e**) and HRTEM (**f**) images after photo‐catalytic reaction using sun light, energy dispersive X‐ray spectroscopy (EDXS) of the sample after photo‐catalytic reaction using both visible irradiation and sun light (**g**) SAED of the sample after photo‐catalytic reaction using sun light (**h**). **Figure 3.** Sample S2: TEM (**a**) and high resolution transmission electron microscopy (HRTEM) (**b**) images before photo-catalytic reaction, TEM (**c**) and HRTEM (**d**) images after photo-catalytic reaction using only visible part of the spectra, TEM (**e**) and HRTEM (**f**) images after photo-catalytic reaction using sun light, energy dispersive X-ray spectroscopy (EDXS) of the sample after photo-catalytic reaction using both visible irradiation and sun light (**g**) SAED of the sample after photo-catalytic reaction using sun light (**h**).

*Catalysts* **2020**, *10*, x FOR PEER REVIEW 7 of 17

**Figure 4.** Sample S3: TEM (**a**) and HRTEM (**b**) images before photo‐catalytic reaction, TEM (**c**) and HRTEM (**d**) images after photo‐catalytic reaction using only visible part of the spectra, TEM (**e**) and HRTEM (**f**) images after photo‐catalytic reaction using sun light, EDXS of the sample after photo‐ catalytic reaction using sun light (**g**), SAED of the sample before photo‐catalytic reaction (**h**). The presence of Ag in small amounts is observed in all analyzed samples before and after **Figure 4.** Sample S3: TEM (**a**) and HRTEM (**b**) images before photo-catalytic reaction, TEM (**c**) and HRTEM (**d**) images after photo-catalytic reaction using only visible part of the spectra, TEM (**e**) and HRTEM (**f**) images after photo-catalytic reaction using sun light, EDXS of the sample after photo-catalytic reaction using sun light (**g**), SAED of the sample before photo-catalytic reaction (**h**).

photocatalytic reaction by EDXS measurements (Figures 3g and 4g). The average atomic percentage of Ag indicated by multiple EDXS measurements in S2 was (0.5 ± 0.1) at %, while S3 contained In the case of catalytic reaction using complete sunlight, the amorphous layer was not formed (or is considerably thinner) at the surface of the S2 catalyst (Figure 3e,f) thus indicating that the reaction was completed when both visible and UV part of the simulate sunlight spectrum were used for photocatalysis.

The formation of the thick amorphous layer is also observed in the case of photocatalysis with only visible irradiation in sample S3 (Figure 4c,d) which points to the accumulation of organics on the surface due to slower degradation as well. However, in the case of S3 sample when simulated sunlight was used for photocatalysis, there were not observed any amorphous layer on the sample surface (Figure 4e,f), thus the best catalytic performance was expected from this sample.

EDXS measurements of both samples after the reactions (Figures 3g and 4g) indicate that the Ag is still present in the samples after catalytic reaction no matter which irradiation was used. Moreover, the crystal structure of TiO2 nanoparticles is also preserved, so still the mixture of anatase and rutile crystallites was observed by SAED after the reactions, which is in accordance with the Raman spectroscopy measurements of the samples after catalytic reactions.

### *2.4. UV-Vis Di*ff*use-Reflectance Spectral Analysis*

*2.5. Detection of Ag Nanoparticles*

Figure 5 shows the UV–Vis diffuse-reflectance spectra of the as-prepared catalysts. These materials show a strong absorption in the UV region, a characteristic typical of semiconductors. Therefore, strong absorption below 350 nm corresponds with the band gap for TiO2 which comes from difference in position of valence and conductive band in prepared samples (Eg1 in Table 1). The series of Ag@TiO2 photocatalysts also exhibited broad absorption in the region of visible light. The absorption in the visible part should be assigned to surface plasmon resonance of silver nanoparticles, centered at 490–530 nm [12,22]. However, in case of S2 and S3, no nanoparticles were observed by HRTEM. Therefore, the band gap lowering might be due to Ag inbuilt in the crystal structure of TiO2 nanoparticles acting as a dopant in TiO2 semiconductor crystal that form additional energy levels in band gap region of TiO2. The samples S1c, S2, and S3 exhibit a visible light absorption, so corresponding band gaps (Eg2) can be calculated, as denoted in Table 1. *Catalysts* **2020**, *10*, x FOR PEER REVIEW 9 of 17 All three samples (S1c, S2, and S3) show similar values for band gap transitions in TiO2: 2.86, 2.86, and 2.98 eV, respectively (Table 1). These are the band gaps of TiO2 that were shifted to visible region due to the presence of Ag. The red‐shift of the band‐gap absorption was expected since it is noticed with metal and non‐metal loading of TiO2 [14]. Another transition which was occurred around 1.2 eV could be attributed to the additional energy levels in band gap region of TiO2 due to the Ag that acts as dopant.

**Figure 5.** UV–Vis diffuse‐reflectance spectra of prepared Ag@TiO2 photocatalysts S1c, S2, and S3. **Table 1.** Band gaps of investigated samples calculated from UV‐Vis measurements using formula **Figure 5.** UV–Vis diffuse-reflectance spectra of prepared Ag@TiO2 photocatalysts S1c, S2, and S3.

[F(R) × hυ]1/2 [26]. **Sample Eg1 (eV) Eg2 (eV)** S1c 2.86 1.19 The band gaps are calculated by known procedure [25,26]. According to the Kubelka–Monk theory of diffuse reflectance, the data were transformed using the function Equation (2) versus energy where R∞ is diffuse reflectance.

$$\mathbf{F}(\mathbf{R}\infty) = (1 - \mathbf{R}\infty)2\mathbf{\hat{z}}(2\mathbf{R}\infty) \tag{2}$$

In TEM images of S3, Ag is not identified as individual crystals, which indicates that Ag might be substituted in TiO2 crystal network. However, cell parameters (calculation not presented) did not **Table 1.** Band gaps of investigated samples calculated from UV-Vis measurements using formula [F(R) × hυ] 1/2 [26].

was kept indoor under ambient conditions (room temperature, ambient light, no UV) changed color

to greyish‐purple after few weeks. However, it was impossible to keep the observed color for possible analysis since it immediately turned back to white after the sample was shaken (Figure 6). The explanation for the observed phenomena, which resembles to photochromism [28] and clearly indicates the presence of Ag in S3 sample, was found in similarity with the two‐photon luminescence experiments [29]. Under ambient conditions, only low‐intensity visible‐NIR irradiation can reach the surface of the sample which after a longer period of exposure corresponds to visible‐NIR excitations regularly used in two‐photon luminescence experiments. Ag nanoparticle is then excited with "two‐ The data were plotted in the form of Tauc plots as [F(R) × hυ] n versus hυ, where n = 2 for direct transitions and n = 1 2 for indirect transitions. The value of n = 1/2 was used since both anatase and rutile phase of TiO2 have indirect band gap. The optical band gap sizes were estimated from the onset of absorption, as extrapolated from the line arising section of the curve that intersects with the baseline [25,26] and is listed in Table 1.

All three samples (S1c, S2, and S3) show similar values for band gap transitions in TiO2: 2.86, 2.86, and 2.98 eV, respectively (Table 1). These are the band gaps of TiO2 that were shifted to visible region due to the presence of Ag. The red-shift of the band-gap absorption was expected since it is noticed with metal and non-metal loading of TiO2 [14]. Another transition which was occurred around 1.2 eV could be attributed to the additional energy levels in band gap region of TiO2 due to the Ag that acts as dopant.

### *2.5. Detection of Ag Nanoparticles*

In TEM images of S3, Ag is not identified as individual crystals, which indicates that Ag might be substituted in TiO2 crystal network. However, cell parameters (calculation not presented) did not show much difference from an undoped TiO2. In literature [27] was reported that only a small number of Ag nanoparticles were found by TEM, and the majority of them were recessed in the TiO2 nanopores. Applied preparation method must have resulted in Ag nanoparticles finely dispersed on TiO2 surface. Growth of Ag nanoparticles in chitosan matrix ensures development of small nanoparticles [22] which can easily enter the TiO2 pores. The latter was expected since only S3 sample retain white color and other Ag doped catalysts were colored from pinkish to grey. It was observed that the color of S3 sample that was kept refrigerated in dark did not change. In contrary, sample that was kept indoor under ambient conditions (room temperature, ambient light, no UV) changed color to greyish-purple after few weeks. However, it was impossible to keep the observed color for possible analysis since it immediately turned back to white after the sample was shaken (Figure 6). The explanation for the observed phenomena, which resembles to photochromism [28] and clearly indicates the presence of Ag in S3 sample, was found in similarity with the two-photon luminescence experiments [29]. Under ambient conditions, only low-intensity visible-NIR irradiation can reach the surface of the sample which after a longer period of exposure corresponds to visible-NIR excitations regularly used in two-photon luminescence experiments. Ag nanoparticle is then excited with "two-photons" and fluorescence emission is observed when return to ground state. On the other hand, TiO2 plays important role in rapid color retrieval. If we assume that excited Ag loses electron (Equation (3)), and that electron freely circle in the TiO2 conduction band, when air rapidly flows through the sample ("shaking" of the sample), oxygen adsorbs on the TiO2 surface and captures the excess electron (Equation (4)). Eventually, superoxide anions recombine rapidly with Ag+ and the white color is observed again. *Catalysts* **2020**, *10*, x FOR PEER REVIEW 10 of 17 photons" and fluorescence emission is observed when return to ground state. On the other hand, TiO2 plays important role in rapid color retrieval. If we assume that excited Ag loses electron (Equation (3)), and that electron freely circle in the TiO2 conduction band, when air rapidly flows through the sample ("shaking" of the sample), oxygen adsorbs on the TiO2 surface and captures the excess electron (Equation (4)). Eventually, superoxide anions recombine rapidly with Ag+ and the white color is observed again. ଶ → ା ଶ ሺିሻ (3) ଶ ି → ଶ ି• (4) Justification for a tentative mechanism explaining the observed spontaneous photochromism of S3 sample is found in related researches. In [28], it was reported that interconversion between

$$\rm{Ag^{0}} + \rm{TiO\_{2}} \rightarrow \rm{Ag^{+}} + \rm{TiO\_{2}^{(e-)}} \tag{3}$$

$$O\_2 + e\_{cb}^- \to O\_2^{-\bullet} \tag{4}$$

plasmonic Ag nanoparticles and Ag+ is responsible for reversible color changes in case of multicolor

magenta shades with approximated corresponding wavelengths (λapprox) from 455 to 469 nm. Obtained λapprox correspond with the reported values absorption of Ag deposited on TiO2 [27,30,31].

To estimate the emission wavelength from excited particles, observed color was associated with

**Figure 6.** S3 sample after few weeks under ambient conditions (**1**), initial white color of S3 sample return after shaking (**2**). *2.6. Photocatalytic Activity* **Figure 6.** S3 sample after few weeks under ambient conditions (**1**), initial white color of S3 sample return after shaking (**2**).

The visible and full solar light driven photocatalytic activity of prepared Ag@TiO2 samples were investigated for the degradation of SA. Figure 7a,b show the kinetics and efficiency of photocatalytic degradation of SA. Commercial TiO2 P25 was tested as a reference. It can be seen that TiO2 did not show photocatalytic activity under visible irradiation and also photocatalyst S1c appeared as inactive, possibly due to residual AgCl on TiO2 surface that absorbed the useful irradiation and block active

Justification for a tentative mechanism explaining the observed spontaneous photochromism of S3 sample is found in related researches. In [28], it was reported that interconversion between plasmonic Ag nanoparticles and Ag+ is responsible for reversible color changes in case of multicolor photochromism, and electrons from small Ag nanoparticles are easily injected into TiO2, while O2 acts as electron acceptor for excited nanoparticles [27]. TiO2. These electrons and holes can recombine and result in the reduction in the photocatalytic efficiency, if TiO2 is used alone without any modification of it. Since TiO2 is modified, it is expected that presence of Ag could suppress the electron**–**hole recombination and increase efficiency [32] apart from surface plasmon resonance. Confirmation for the given explanation on enhanced activity is found in [33,34]. The loading of noble metal nanoparticles on TiO2 surface is one of possible approaches to reduce the high recombination rate process. After the light absorption, the promoted electrons from the valence band

of Ag throughout the sample. During photocatalytic processes, electrons and holes are excited in

*Catalysts* **2020**, *10*, x FOR PEER REVIEW 11 of 17 compared to TiO2 P25. Therefore, the best photocatalytic performance of this sample was shown, although the S3 sample has the largest band gap Eg1. The main reason for better performance could

To estimate the emission wavelength from excited particles, observed color was associated with colors from Pantone® color chart and British Standard BS4800 colors. Observed greyish-purple color (Figure 7) matches Pantone PMS 5275 and PMS 5285 and BS4800 22-C-37, all described as blue-magenta shades with approximated corresponding wavelengths (λapprox) from 455 to 469 nm. Obtained λapprox correspond with the reported values absorption of Ag deposited on TiO2 [27,30,31]. into conduction band of semiconductor is subsequently captured by noble metal which have a lower Fermi energy level than the semiconductor, leading to the separation of electron-hole pairs and an increase of the photocatalytic efficiency, particularly under UV irradiation. Instead, under visible light irradiation, the charge carriers in noble metal are injected from the excited plasmonic metal nanostructure into the semiconductor thus reducing the recombination rate process and simultaneously increasing the photocatalytic activity.

**Figure 7.** Kinetics and efficiency of photocatalytic degradation of salicylic acid (SA) under fullspectrum solar irradiation (**a**) and visible irradiation (**b**) using TiO2 P25 and Ag@TiO2 photocatalysts under given conditions. The grey areas represent the dark period prior reaction, i.e., time necessary to achieve sorption equilibrium. **Figure 7.** Kinetics and efficiency of photocatalytic degradation of salicylic acid (SA) under full-spectrum solar irradiation (**a**) and visible irradiation (**b**) using TiO2 P25 and Ag@TiO2 photocatalysts under given conditions. The grey areas represent the dark period prior reaction, i.e., time necessary to achieve sorption equilibrium.

The photodegradability of SA was also investigated in the absence of photocatalyst. The SA did

#### not undergo any degradation under visible light (Figure 7b) while under solar light was degraded *2.6. Photocatalytic Activity*

even around 50 % after 2 h as shown in Figure 7a. The photocatalytic degradation of SA follows the Langmuir**–**Hinshelwood kinetics model that is typically used for describing degradation kinetics over irradiated photocatalytic films [35]. As the initial concentration of SA is a millimolar solution (0.2 mmol L−1) and its concentration in comparison with the suspended catalysts loading (1 g L−1) is small, the model can be simplified to give a firstorder equation (Equation (5)). = −, (5) In the case of full-spectrum solar irradiation with an enhanced UV-B irradiation, the equation accounted rate of photolysis as shown in (Equation (6)). = −(ℎ + ,) (6) The visible and full solar light driven photocatalytic activity of prepared Ag@TiO2 samples were investigated for the degradation of SA. Figure 7a,b show the kinetics and efficiency of photocatalytic degradation of SA. Commercial TiO2 P25 was tested as a reference. It can be seen that TiO2 did not show photocatalytic activity under visible irradiation and also photocatalyst S1c appeared as inactive, possibly due to residual AgCl on TiO2 surface that absorbed the useful irradiation and block active sites necessary for generation of HO• radicals. The photocatalytic activities were observed as follows: S3 ≈ S2 under visible irradiation and S3 > TiO2 P25 ≈ S2 ≈ S1c under solar irradiation. As it can be seen, S3 seemed to be an efficient photocatalyst under both solar and visible irradiation. The complete degradation of SA was achieved for 60 min of irradiation while its activity is increased by about 20% compared to TiO2 P25. Therefore, the best photocatalytic performance of this sample was shown, although the S3 sample has the largest band gap Eg1. The main reason for better performance could be explained by modification made using chitosan. The method is not only facile and environmentally friendly, but the use of the viscous gel-like medium provided an even distribution of Ag throughout the sample. During photocatalytic processes, electrons and holes are excited in TiO2. These electrons and holes can recombine and result in the reduction in the photocatalytic efficiency, if TiO2 is used alone without any modification of it. Since TiO2 is modified, it is expected that presence of Ag could suppress the electron–hole recombination and increase efficiency [32] apart from surface plasmon resonance.

Confirmation for the given explanation on enhanced activity is found in [33,34]. The loading of noble metal nanoparticles on TiO2 surface is one of possible approaches to reduce the high recombination rate process. After the light absorption, the promoted electrons from the valence band into conduction band of semiconductor is subsequently captured by noble metal which have a lower Fermi energy level than the semiconductor, leading to the separation of electron-hole pairs and an increase of the photocatalytic efficiency, particularly under UV irradiation. Instead, under visible light irradiation, the charge carriers in noble metal are injected from the excited plasmonic metal nanostructure into the semiconductor thus reducing the recombination rate process and simultaneously increasing the photocatalytic activity.

The photodegradability of SA was also investigated in the absence of photocatalyst. The SA did not undergo any degradation under visible light (Figure 7b) while under solar light was degraded even around 50% after 2 h as shown in Figure 7a.

The photocatalytic degradation of SA follows the Langmuir–Hinshelwood kinetics model that is typically used for describing degradation kinetics over irradiated photocatalytic films [35]. As the initial concentration of SA is a millimolar solution (0.2 mmol L−1 ) and its concentration in comparison with the suspended catalysts loading (1 g L−1 ) is small, the model can be simplified to give a first-order equation (Equation (5)).

$$\frac{dc\_{SA}}{dt} = -k\_{SA,obs}c\_{SA} \tag{5}$$

In the case of full-spectrum solar irradiation with an enhanced UV-B irradiation, the equation accounted rate of photolysis as shown in (Equation (6)).

$$\frac{dc\_{SA}}{dt} = -(k\_{photolysis} + k\_{SA, obs})c\_{SA} \tag{6}$$

The *k*photolysis and *k*SA, obs are pseudo-first order rate constants for photolysis and photocatalysis of SA in investigated system, respectively, both given in min−1 . Under given conditions, the observed rate constant for photolysis, *k*photolysis was 6 × 10−3 min−1 .

Rate constants were evaluated from Equations (7) and (8) through the linear regression of ln(cSA/cSA,0) vs. Time where cSA is the SA concentration (mgL−1 ) at a time t (min) and cSA,0 is the initial SA concentration after adsorption (mgL−1 ).

$$
\ln \frac{\mathcal{C}\_{\rm SA}}{\mathcal{C}\_{\rm SA,0}} = -k\_{\rm SA,obs} t \tag{7}
$$

$$\ln \frac{\mathcal{C}\_{\rm SA}}{\mathcal{C}\_{\rm SA,0}} = -\left(k\_{\rm photolysis} + k\_{\rm SA,\ obs}\right)t\tag{8}$$

The corresponding values of the pseudo-first order rate constant *k*SA, obs as well as the degradation half-time, t1/2 and the determination coefficient R2 are given in Table 2. The comparison of photocatalytic activity using the simple kinetic model disregarding the catalysts surface area was justified since all catalysts have very similar specific surface areas. Namely, compared to commercial TiO2 P25 whose *S*BET was 56 m2 g −1 , the surface areas of *S*BET of S1c, S2, and S3 were 45, 47, and 52 m2 g −1 , respectively.

**Table 2.** The pseudo-first order rate constant (kSA,obs) for the degradation of SA in the flow cell irradiated with simulated solar and visible irradiation.

The rate of photocatalytic degradation of SA with all used photocatalysts is quite high when artificial solar light was applied and less than half an hour is needed for its half-degradation.

The photocatalytic activity of S2 and S3 was almost the same under visible irradiation. This can be explained by the insignificant difference in net contribution of Ag in the TiO2 lattice. Moreover, both S2 and S3 have an absorption band at 400–700 nm (Figure 5.) that could be assigned to surface plasmon absorption of silver, typically centered at 490–530 nm [21]. The broader band of S2 suggested

the larger Ag particles than those in S3. The small particle size and electron sharing phenomena are well described and incorporated in the explanation of the observed spontaneous photochromism (Section 2.5).

### **3. Materials and Methods**

### *3.1. Preparation of Ag*/*TiO2 Photocatalysts*

The photocatalysts were prepared by the known methods (S1a–c and S2) similar to those described in the literature [13,21], and by a novel method (S3) developed using literature on the preparation of Ag/chitosan composites [22]. The Ag@TiO2 photocatalysts marked as S1a and S1b were prepared by deposition precipitation with photoreduction, S1c was prepared by deposition precipitation and S2 by photocatalytic deposition. Photocatalyst marked as S3 was prepared by a novel method including deposition precipitation and reduction by chitosan. Detailed preparation routes are described in following subsections. In all procedures, TiO2P25 (Evonik, AEROXIDE ®) was used as starting titania material.

### 3.1.1. Preparation of S1a

TiO2 (0.20 g) and cationic surfactant N, N-Dodecyl-N, and N-dimethylammoniumchloride (hereafter: BARDAC) manufactured by Lonza, Switzerland (0.34 mL) were suspended in Milli-Q water (100 mL) and stirred for 60 min, followed by the addition of AgNO3 (0.1 M, 2.00 mL). The resulting suspension was stirred at room temperature for 60 min and then placed in UV chamber for 20 min. UV chamber consist of four 8 W lamps with emission output at λ = 300 nm. The suspension was filtered and washed with Milli-Q water. The obtained grey powder was dried at 80 ◦C for 12 h and then calcined at 300 ◦C for 3 h.

### 3.1.2. Preparation of S1b

TiO2 (0.20 g) and BARDAC (0.75 mL) were suspended in Milli-Q water (100 mL) and stirred for 60 min, followed by the addition of AgNO3 (0.1 M, 2.00 mL). The resulting suspension was stirred at room temperature for 60 min and then irradiated with ultraviolet lamp (λ = 254 nm, 8 W) for 20 min. The suspension was filtered and washed with Milli-Q water. The obtained greyish-pink powder was dried at 80 ◦C for 12 h and then calcined at 300 ◦C for 3 h.

### 3.1.3. Preparation of S1c

TiO2 (1.00 g) was suspended in Milli-Q water (100 mL) and sonicated for 30 min. After sonication, BARDAC (1.20 mL) was added and suspension was stirred for 30 min, followed by the addition of AgNO3 (0.21 g) and NH4OH (2.30 mL, 25 wt.% NH3). The resulting suspension was stirred at room temperature for 12 h. The product was filtered, washed with distilled water, and dried at 70 ◦C for 12 h. Finally, the grey powder was calcined under air at 500 ◦C for 3 h.

### 3.1.4. Preparation of S2

TiO2 (2.00 g) was suspended in aqueous solution of methanol (4 mL), AgNO3 (0.1 M, 3.80 mL) and NaCl (0.0018 M, 200 mL) in the round-bottom flask of 400 mL. The suspension was purged with a stream of nitrogen and irradiated with UV lamp (λ = 365 nm, 4 W) for 3 h. The product was filtered, washed with distilled water, and dried at 100 ◦C for 12 h.

### 3.1.5. Preparation of S3

Chitosan (Aldrich) (0.055 g) was dissolved in acetic acid (0.15 M, 25 mL) and the resulting biogel was added to NaOH aqueous solution (0.13 M, 100 mL). After a short mixing, TiO2 (6.00 g) and AgNO3 (0.13 M, 2.50 mL) were added simultaneously. Stirring was continued for 24 h in the dark. Finally, the suspension was filtered and then calcined at 600 ◦C for 3 h to eliminate chitosan.

### *3.2. Catalysts Characterization*

The samples were studied by X-ray diffraction (XRD) analysis using a Shimadzu XRD 6000 diffractometer with CuKα radiation. Data were collected over a 2Θ range between 10 and 70◦ 2Θ in step scan mode with steps of 0.02◦ and counting time 4 s per step. The identification of crystal phases was done using Joint Committee on Powder Diffraction Standards (JCPDS) card catalogue.

Raman spectroscopy measurements were performed using a Horiba Jobin-Yvon T64000 Raman system (Villeneuve d'Ascq, France) working in micro-Raman configuration. Coherent, Innova 400 (Santa Clara, CA, USA) argon ion laser operating at 514.5 nm was used for excitation. The Raman data were collected in a true backscattering geometry with the aid of a liquid nitrogen-cooled multi-channel charge-coupled device (CCD) detector. Nominal laser power of 20 or 7 mW (depending of the sample) was focused on the samples using a long working distance 50× objective lens with numerical aperture NA = 0.5 (Olympus, Tokyo, Japan).

High resolution transmission electron microscopy (HRTEM) was performed using Philips CM200-FEG transmission electron microscope operated at 200 kV, equipped with energy dispersive X-ray spectroscopy (EDXS) detector Genesis 4000. For the TEM measurements, the samples were first suspended in chloroform by sonication; then, a small drop of the suspension was transferred onto the carbon-coated copper grid. Finally, the grid was dried in air.

The UV–Vis spectra of the prepared samples were obtained using DRS (Shimadzu UV–3101PC) equipped with an integrating sphere. The spectra were recorded at room temperature in the wavelength range of 200–800 nm. BaSO4 was used as a reference. The diffuse reflectance spectra were presented with the reflectance of the "infinitely thick" layer of the solid, R (%).

The BET surface areas were estimated from nitrogen adsorption and desorption isotherm data using an ASAP 2000 apparatus (Micromeritics Corporation). Prior to analysis, samples were degassed (6.6 Pa) at 400 ◦C to remove any physically adsorbed gases.

### *3.3. Photocatalytic Experiments*

The photocatalytic activities of the prepared Ag-doped TiO2 photocatalysts were evaluated in terms of degradation of salicylic acid (SA) under solar and visible light. Salicylic acid (p.a. grade) was obtained as a free-of-charge sample from pharmaceutical industry Pliva in Croatia and used without further purification. The aqueous solution of 0.2 mmol L−1 of SA was prepared with distilled water and pH 4 of the solution was measured. Photocatalytic experiments were performed in a flow cell (Figure 8) using a peristaltic pump set at a constant flow rate of 67 mL min−1 . Prior to irradiation, photocatalysts (1 g L−1 ) were suspended in 40 mL of SA solution and homogenized in ultrasonic bath for 5 min and then recirculated in flow cell in the dark for 25 min to get an adsorption/desorption equilibrium. Total sorption time in dark was presented with grey area in Figure 7a,b. During photocatalytic experiments the flow cell was directly irradiated. The sources of irradiation were: (i) full-spectrum compact fluorescent bulb simulating solar spectra with enhanced UV-B irradiation (Exo Terra, 26 W) and (ii) incandescent bulb emitting only visible light (Osram Daylight, 100 W, *I*UV-B = *I*UV-A = 0.0 mW cm−2 ). All experiment were done under ambient conditions: T = 25 ± 2 ◦C.

triplicated and average results were reported.

The UV–Vis spectra of the prepared samples were obtained using DRS (Shimadzu UV–3101PC) equipped with an integrating sphere. The spectra were recorded at room temperature in the wavelength range of 200–800 nm. BaSO4 was used as a reference. The diffuse reflectance spectra were

The BET surface areas were estimated from nitrogen adsorption and desorption isotherm data using an ASAP 2000 apparatus (Micromeritics Corporation). Prior to analysis, samples were

The photocatalytic activities of the prepared Ag‐doped TiO2 photocatalysts were evaluated in terms of degradation of salicylic acid (SA) under solar and visible light. Salicylic acid (p.a. grade) was obtained as a free‐of‐charge sample from pharmaceutical industry Pliva in Croatia and used without further purification. The aqueous solution of 0.2 mmol L−1 of SA was prepared with distilled water and pH 4 of the solution was measured. Photocatalytic experiments were performed in a flow cell (Figure 8) using a peristaltic pump set at a constant flow rate of 67 mL min−1. Prior to irradiation, photocatalysts (1 g L−1) were suspended in 40 mL of SA solution and homogenized in ultrasonic bath for 5 min and then recirculated in flow cell in the dark for 25 min to get an adsorption/desorption equilibrium. Total sorption time in dark was presented with grey area in Figure 7a,b. During photocatalytic experiments the flow cell was directly irradiated. The sources of irradiation were: (i) full‐spectrum compact fluorescent bulb simulating solar spectra with enhanced UV‐B irradiation (Exo Terra, 26 W) and (ii) incandescent bulb emitting only visible light (Osram Daylight, 100 W, *I*UV‐B

= *I*UV‐A = 0.0 mW cm−2). All experiment were done under ambient conditions: T = 25 ± 2 °C.

The full‐spectrum solar bulbs were used to simulate the solar irradiation. Relative intensities over a range of wavelengths from 340 to 640 nm corresponded to the intensities of natural sunlight averaged around in year on northern hemisphere (~45° N). The UV‐B intensity of a lamp was slightly higher, mimicking the dessert sunlight. In the case of full‐spectrum solar bulb, the overall intensities in UV‐B and UV‐A region were measured by UVP UVX radiometer, fitted with the corresponding sensors. Given intensities on the cell top (suspension level) were: *I*UV‐B = 2.9 mW cm−2 and *I*UV‐A= 3.4 mW cm−2. At given time intervals the SA concentration was analyzed using HPLC, Shimadzu, with SUPELCO C18 column, length 250 mm, internal diameter 4.6 mm and UV (diode array) detection (DAD) at 303 nm. The analyses were carried out by binary flow elution with mobile phase solvents A and B at a flow rate of 0.6 and 0.4 mL min−1, respectively. Solvent A consisted of 0.5:99.5 (*v*/*v*)

presented with the reflectance of the "infinitely thick" layer of the solid, R (%).

degassed (6.6 Pa) at 400 °C to remove any physically adsorbed gases.

*3.3. Photocatalytic Experiments*

**Figure 8.** Scheme of the experimental set-up used for the photocatalytic degradation experiments; 1—irradiation source with reflective surface, 2—photocatalytic cell (dcell = 45 mm, reaction mixture level = 24 mm), 3—magnetic stirrer, and 4—peristaltic pump.

The full-spectrum solar bulbs were used to simulate the solar irradiation. Relative intensities over a range of wavelengths from 340 to 640 nm corresponded to the intensities of natural sunlight averaged around in year on northern hemisphere (~45◦ N). The UV-B intensity of a lamp was slightly higher, mimicking the dessert sunlight. In the case of full-spectrum solar bulb, the overall intensities in UV-B and UV-A region were measured by UVP UVX radiometer, fitted with the corresponding sensors. Given intensities on the cell top (suspension level) were: *I*UV-B = 2.9 mW cm−2 and *I*UV-A= 3.4 mW cm−2 . At given time intervals the SA concentration was analyzed using HPLC, Shimadzu, with SUPELCO C18 column, length 250 mm, internal diameter 4.6 mm and UV (diode array) detection (DAD) at 303 nm. The analyses were carried out by binary flow elution with mobile phase solvents A and B at a flow rate of 0.6 and 0.4 mL min−1 , respectively. Solvent A consisted of 0.5:99.5 (*v*/*v*) methanol: 0.1 M phosphoric acid and solvent B was 100% methanol. Experiments were done in triplicated and average results were reported.

### **4. Conclusions**

In this work, a series of Ag@TiO2 photocatalysts were prepared by five different synthetic pathways based on deposition–precipitation, photoreduction, photochemical deposition, and reduction in chitosan biogel. The synthesized photocatalysts were characterized by the means of XRD, Raman spectroscopy, TEM, HRTEM, EDXS, SAED, and UV-vis DRS and their photocatalytic activity was determined using salicylic acid as a model pollutant, chosen due to its persistent presence in the environment.

It was observed that the solar and visible photocatalytic activity of obtained photocatalysts strongly depends on the preparation methods. The rate of photocatalytic degradation of salicylic acid with all used photocatalyst is quite high when solar light was applied and less than half an hour is needed for its half-degradation. Ag@TiO2 prepared by a simple novel method based on reduction of Ag on TiO2 surface in chitosan biogel (sample S3) seems to be the most promising photocatalyst under solar irradiation (about 20% more efficient than TiO2) and deserves further attention. Although the activity under visible irradiation was the same for samples S2 and S3, the synthesis of S3 via chitosan is simple one-pot green synthesis readily available for large-scale production of photocatalysts.

Observed photocatalytic behavior of S3 could be explained by the fact that TiO2 lattice of this photocatalyst contains somewhat higher atomic percentage of silver that form additional energy level in band gap region of TiO2. On the other hand, the presence of Ag nanoparticle on TiO2 surface and inside the pores enhances the absorption in visible part of spectra and suppresses the electron–hole recombination and increase photoactivity. The contribution of Ag in the surface redox reaction was confirmed by spontaneous photochromism of a sample. The study confirmed that only visible part of solar spectra could alone induce the photocatalytic reaction, which is promising from the practical point of view: such catalysts could be used for outdoor applications in areas with lower daily solar irradiance.

**Author Contributions:** Conceptualization, I.G. and A.G.; validation, A.G. and H.I.; investigation, K.Š., I.G., M.P., and A.G.; resources, I.G., H.I., and A.G.; data curation, I.G., K.Š., and M.-G.W.; writing—original draft preparation, I.G. and A.G.; writing—review and editing, I.G. and A.G.; visualization, I.G.; supervision, A.G. and M.-G.W.; funding acquisition, I.G., A.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by European Regional Development Fund (ERDF) under the project "Waste & Sun for photocatalytic degradation of micropollutants in water" (OS-Mi), KK.<IP_ADDRESS>.0006., and by the Ministry of science and education of Croatia under the project HrZZ-IP-2018-01-5246. Authors acknowledge the Centre of Excellence for Advanced Materials and Sensing Devices, Ruder Boškovi´c Institute, Zagreb, Croatia, ¯ KK.<IP_ADDRESS>.0001 and partial support by European Regional Development Fund (ERDF) under the (IRI) project "Improvement of solar cells and modules through research and development" (KK.<IP_ADDRESS>.0115).

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

### **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).


**9. Conclusions and future perspectives**

Premium quality rice grain is the demand of a growing population with better living standards. Presently, the CRISPR/Cas9 system has all genome editing capabilities, e.g., knock-in, knockout, knockdown, and expression activation. This system has tremendous untapped potential, has formed an ever-expanding genetic toolbox for plant biologists to investigate functional genomics, and is a helping hand for breeders to integrate important genes into the genomes of important crops. The successful application of CRISPR/Cas9 for tissue engineering and human stem cell modification has led to further developments in the field of precise genome editing. The ability to target multiple genes via multiplexed genome editing strategies can facilitate pathway-level research to engineer complex multigenic rice grain quality attributes. Previously, few studies have been conducted that are related to targeted mutagenesis for rice grain quality improvement. The pathways of rice grain quality are not well understood, and they can be investigated for the genetic mechanisms controlling quality attributes. The development of novel regulatory components from naturally existing peripherals (genes, promoters, cis-regulatory elements, small RNAs, and epigenetic modifications) can facilitate the engineering of regulatory pathways for different elements of rice grain quality. The rapid shift of research toward the utilization of CRISPR/Cas9 systems for targeted mutagenesis could be a promising approach for overcome barriers to breeding improved quality rice.

*Plant Breeding - Current and Future Views*


**3. Autoimmunity in Severe Combined Immunodeficiency and Related Disorders**

The genetic and clinical variability among severe combined immunodeficiency (SCID) is remarkable and in some patients, the phenotype of early-onset severe infections can be associated with autoimmunity [61]. The case of Omenn syndrome (OS) is of particular interest, since in this condition the immune impairment coexists with a marked tendency towards lymphoproliferation and autoimmunity [62]. Children with OS develop severe invasive infections in the first months of life, and commonly show hepatosplenomegaly, diffuse lymphadenopathy, severe eczema, and alopecia [63]. Laboratory testing evidence peripheral eosinophilia, lymphopenia, and reduced serum immunoglobulin levels, associated with the peripheral expansion of self-reactive T cells, which represents the most peculiar aspect of OS [64].

The molecular basis underlying this clinical phenotype relies in most of the patients on a mutation of the recombinase activating genes (RAG) 1 and 2, which are central in the V(D)J recombination during T and B cell development. However, defects in other proteins (such as IL-7Ra, ZAP70, ARTEMIS, AK2, JAK3, and others) can be responsible for OS [61]. The pathogenesis of the autoimmune phenotype is not completely elucidated, but defects in central negative selection secondary to reduced AIRE expression and altered peripheral tolerance are implicated [65]. The prognosis of children with OS is severe [64], with death occurring in the first years of life unless they receive definitive treatment with HSCT [66]. The clinical expression of RAG mutations is not limited to OS. Mutations causing a partial loss of function of RAG cause an extremely variable clinical phenotype, with a wide spectrum of severity and clinical features of combined immunodeficiency, immune dysregulation with autoimmunity (mainly autoimmune cytopenia), and lymphoproliferation [62,67,68].

Among the non-OS phenotypic variants of SCID, patients with ARTEMIS deficiency show an ineffective DNA repair with genomic instability, with a consequent clinical picture of SCID associated with radiosensitivity and immune dysregulation with autoimmunity. Autoimmunity has also been described in a few cases of SCID carrying other molecular defects (i.e., IL-7Ra, ZAP70, ADA, and PNP deficiency) as the result of an altered central negative selection of T-cells [61]. Finally, mutations in the ORAI1 and STIM1 genes, encoding for calcium channels implicated in multiple cell functions (including B-cell receptor (BCR) and T-cell receptor (TCR) signaling), are responsible for a clinical phenotype featured by SCID-like manifestations, autoimmunity, hypotonia, and ectodermal dysplasia [65]. In all these conditions, HSCT represents the only curative therapeutic strategy.


**3. Hybrid quantum/classical vortex-in-cell method**

The vortex-in-cell method is a well-studied hybrid particle-mesh method for incompressible flows and is particularly well suited for flows in regular domains such that efficient Poisson solvers can be used. In the present work, the Fourier

**35**

**Figure 2.**

*simulation using full QFT.*

*Quantum Algorithms for Fluid Simulations DOI: http://dx.doi.org/10.5772/intechopen.86685*

∂ω

the QFT for the required discrete Fourier transforms.

→ \_\_\_ ∂*t* + *u* → ∂ω → \_\_\_ ∂*x* → = (ω →.∇)*u*

tions of three Poisson problems for stream function *A*

∆*A*

analysis approach to solving the problem in a fully periodic domain is used, using

The vortex-in-cell (VIC) method solves the incompressible-flow Navier-Stokes equations, transformed into the Helmholtz equations for vorticity evolution [5]:

> → + νΔω →;ω

→ = ∇*x A*

In simulations using the VIC, the flow evolves through a (large) number of time steps. During each of these time steps, the velocity field is recomputed using solu-

This part of the VIC is of particular interest here, as it represents the part that would be performed by the quantum processor in the quantum coprocessor model.

→ = −ω →;*u*

*Vortex-in-cell simulation of leapfrogging vortex rings. Effect of mesh refinement is shown. "Noiseless"*

→ = ∇*x u*

→ : → (1)

→ (2)

*Quantum Algorithms for Fluid Simulations DOI: http://dx.doi.org/10.5772/intechopen.86685*

*Advances in Quantum Communication and Information*

in the quantum circuit model.

*Quantum circuit for QFT on six-qubit register.*

transform in bit-reversed ordering.

rotation gate is not included in the quantum circuit.

**3. Hybrid quantum/classical vortex-in-cell method**

"k," i.e., 2*π*/2*k*

**Figure 1.**

storage of discrete data can be extended to multidimensional problems as well. For

mesh. In application in which multiple variables are to be stored in each mesh point, as in the discrete-velocity method discussed in the second part of this chapter, we add further qubits to the quantum register. Specifically, for each qubit added in the coherent register, the number of degrees of freedom is doubled. Once a mapping of the considered computational problem onto the quantum state vector has been designed, calculations are then performed through application of quantum gates as

The ability to implement the quantum Fourier transform (QFT) efficiently on a quantum computer is of paramount importance for many quantum algorithms. **Figure 1** shows the "standard" quantum circuit implementation of the QFT for an example register with 6 qubits. In **Figure 1**, "H" represents the one-qubit Hadamard gate, and the "Rk" gates are controlled rotation gates over an angle defined by index

chapter, the qubit register is represented vertically, with the leftmost qubit in the register at the top. The horizontal direction determines the sequence of gates that are applied to the quantum state. In the QFT example shown, it can be seen that the qubit indices have been reversed in the output state (right-hand side) relative to the input, to represent that this standard QFT circuit returns the discrete Fourier

A particular challenge is presented by the controlled rotation gates particularly

The vortex-in-cell method is a well-studied hybrid particle-mesh method for incompressible flows and is particularly well suited for flows in regular domains such that efficient Poisson solvers can be used. In the present work, the Fourier

those involving small angles. The QFT can be implemented approximately by removing all rotation gates with angles smaller than a certain threshold value, resulting in the approximate QFT (AQFT). In particular for fault-tolerant implementations, this is desirable as it greatly reduces the gate count. In the following, we define the approximation or "band-limiting" in the AQFT as follows. The rotation gates are eliminated above a limit value "k," i.e., for an angle smaller than 2*π*/2*k*

. In this circuit and all subsequent quantum circuits shown in this

regular

, the

example, 24 qubits suffice to store a single discretized function on a 2563

**2.2 Approximate quantum Fourier transform (AQFT)**

**34**

analysis approach to solving the problem in a fully periodic domain is used, using the QFT for the required discrete Fourier transforms.

The vortex-in-cell (VIC) method solves the incompressible-flow Navier-Stokes equations, transformed into the Helmholtz equations for vorticity evolution [5]:

$$\frac{\partial \vec{u}}{\partial t} + \vec{u}\frac{\partial \vec{u}}{\partial \vec{x}} = \{\vec{\alpha}\Delta\nabla\}\vec{u} + \nu\Delta\vec{\alpha}\vec{\alpha}; \vec{\alpha} = \nabla \propto \vec{u} \tag{1}$$

In simulations using the VIC, the flow evolves through a (large) number of time steps. During each of these time steps, the velocity field is recomputed using solutions of three Poisson problems for stream function *A* → :

$$
\Delta \vec{A} = -\vec{\alpha} \mathbf{j}; \vec{\mu} = \nabla \mathbf{x} \cdot \vec{A} \tag{2}
$$

This part of the VIC is of particular interest here, as it represents the part that would be performed by the quantum processor in the quantum coprocessor model.

**Figure 2.**

*Vortex-in-cell simulation of leapfrogging vortex rings. Effect of mesh refinement is shown. "Noiseless" simulation using full QFT.*

**Figure 3.**

*Vortex-in-cell simulation of leapfrogging vortex rings. For two different mesh resolutions, the effect of applying AQFT is shown ("k" limit is 5).*

**Figure 2** shows an example of a VIC simulation of two leapfrogging vortex rings, i.e., flow structures of fundamental importance in fluid mechanics. The lower vortex ring is stronger than the ring above it, and it will therefore convect upward faster, leading to the interaction of the vortex rings as shown. The iso-surface represents vorticity strength, i.e., a direct indicator of the "strength" of the considered vortex. Results are compared for two different meshes, 1283 and 2563 , to highlight the dependency of the solution on the chosen mesh size. Also, in the shown simulation, no quantum errors were simulated, and the full QFT was used. If we now replace the QFT with the AQFT, the results shown in **Figures 3** and **4** are obtained. In **Figure 3**, the "k" limit in the QFT is set to five for both meshes, showing that for the finer mesh this leads to unacceptable errors, while the coarser-mesh simulation still produces worthwhile results. If the "k" limit for the finer mesh is increased from 5 to 6, i.e., more controlled rotation gates are included in the AQFT circuit, the simulation on the finer mesh can also be made to produce similarly useful results. These example results show what level of approximation in the QFT is tolerable for application of the VIC method. For other QFT-based CFD solvers, a similar sensitivity study would need to be conducted.

**37**

**Figure 4.**

*mesh, "k" limit in AQFT is 6.*

the considered domain [10].

coordinates in (physical) space and *c*

*Quantum Algorithms for Fluid Simulations DOI: http://dx.doi.org/10.5772/intechopen.86685*

**4. Quantum algorithm for discrete-velocity method**

For a monatomic gas, the distribution function *f*(*x,*

space") is governed by the Boltzmann equation:

In computational fluid dynamics, the most widely used methods involve solving the Navier-Stokes equations for a continuum fluid, i.e., where fluid density, velocity components, and energy in each location in the computational domain are to be found from conservation equations. The vortex-in-cell method used in the previous section employs the Navier-Stokes equations in a transformed form involving vorticity rather than velocity, but importantly it still uses the continuum flow assumption. An alternative approach to the Navier-Stokes-based modeling is a description of the flow at a more detailed level, i.e., at the kinetic level [11]. Instead of governing equations for mass, momentum, and energy conservation, the flow is now described by the Boltzmann equation governing a particle distribution function in state space (or 3D velocity space for a 3D monatomic gas flow) for each location in

*Vortex-in-cell simulation of leapfrogging vortex rings. For 1283 mesh, AQFT ("k" limit 5) is used. For 2563*

→ *c*

→;*t*) with *x*

→ the molecular velocity (defined in "velocity

→ defining the

*Quantum Algorithms for Fluid Simulations DOI: http://dx.doi.org/10.5772/intechopen.86685*

*Advances in Quantum Communication and Information*

**Figure 2** shows an example of a VIC simulation of two leapfrogging vortex rings,

*Vortex-in-cell simulation of leapfrogging vortex rings. For two different mesh resolutions, the effect of applying*

and 2563

, to highlight

i.e., flow structures of fundamental importance in fluid mechanics. The lower vortex ring is stronger than the ring above it, and it will therefore convect upward faster, leading to the interaction of the vortex rings as shown. The iso-surface represents vorticity strength, i.e., a direct indicator of the "strength" of the considered

the dependency of the solution on the chosen mesh size. Also, in the shown simulation, no quantum errors were simulated, and the full QFT was used. If we now replace the QFT with the AQFT, the results shown in **Figures 3** and **4** are obtained. In **Figure 3**, the "k" limit in the QFT is set to five for both meshes, showing that for the finer mesh this leads to unacceptable errors, while the coarser-mesh simulation still produces worthwhile results. If the "k" limit for the finer mesh is increased from 5 to 6, i.e., more controlled rotation gates are included in the AQFT circuit, the simulation on the finer mesh can also be made to produce similarly useful results. These example results show what level of approximation in the QFT is tolerable for application of the VIC method. For other QFT-based CFD solvers, a similar sensi-

vortex. Results are compared for two different meshes, 1283

tivity study would need to be conducted.

**36**

**Figure 3.**

*AQFT is shown ("k" limit is 5).*

**Figure 4.**

*Vortex-in-cell simulation of leapfrogging vortex rings. For 1283 mesh, AQFT ("k" limit 5) is used. For 2563 mesh, "k" limit in AQFT is 6.*


**4.4.3 Fuzzy system rules**

156 Serial and Parallel Robot Manipulators – Kinematics, Dynamics, Control and Optimization

We present in figure 8 the fuzzy logic controller for the linear velocity. This controller determines whether the robot's speed should be increased or decreased. It is used to

The D, ϑO, and the ϑR represent respectively the distance between the robot and the nearest obstacle in the front area, the velocity of the nearest obstacle in the front area and the current velocity of the robot. The input and output membership functions of the fuzzy controller for

determine the speed change Δϑ (the linear velocity).

linear velocity are shown respectively in figure 9 and figure 10.

Fig. 9. Input membership functions for the linear velocity

Fig. 10. Output membership functions. Velocity up/down control.

the obstacle, it accelerates.

So, the speed of the robot can be influenced by the velocity of the obstacle in the front area. We present in algorithm 3 the reasoning process of the robot used to predict the velocity of the nearest obstacle in this area in order to determine its velocity. For example, if the velocity of the obstacle is smaller than the velocity of the robot, then the robot decreases its speed in order to avoid collision in the near future. If the velocity of the obstacle is larger than the velocity of the robot, the robot keeps the same speed if its velocity and the velocity of the obstacle are close. However, if there is a difference between the speed of the robot and

Fig. 8. Fuzzy controller of speed change

Robot navigation is actually governed by rules in the fuzzy controller which represent qualitatively the human driving heuristics. The fuzzy logic controllers are applied to coordinate and to combine the various behaviours of the robot in order to choose a suitable decision. The fuzzification converts the input variables into input grades by means of the membership functions shown in figure 5 and figure 9. The inference and aggregation generate a resultant output membership function with respect to fuzzy rules. The defuzzification gives the output membership function shown in figure 6 and in figure 10. Therefore, the total number of rules to determine the angular velocity is N= 24. We can reduce this number into 9 if-else rules by eliminating redundancies (rules that have different values of input variables and the same value of the output variable can be replaced by one rule). We present in table 2 the fuzzy rules for the angular velocity. For example, if DTCF=M (the obstacle in the front area is moving) and TO=F (the target is in front area) then Δθ=NT (the robot do not change its trajectory) whatever the value of DTCL and DTCR. Likewise, for the linear velocity there are N=18 rules, but we can reduce them into 9 if-else rules.

The use of a small number of rules is required in dynamic environments, this is especially important when a fast response to the changes is required (short execution and high frequency of sensor readings). We use the method of Center of Gravity Defuzzification to

Table 2. The fuzzy rules for the angular velocity.

A Reactive Anticipation for Autonomous Robot Navigation 159

nearest exit will be the opening at the right hand side where the wall ends (see figure 11.b). This method takes a large time to find the free end of the wall. However, the problem with trial-return motion is high power consumption and the time spent from start point to target is long when the obstacle is composed of complex traps like U-shape and T-shape. In (Zhu & Yang, 2004), the dead cycle problem is resolved by using a state memory strategy (see figure 11.c). Therefore, there is a situation where the robot cannot go straight toward the target. It has to keep turning around the obstacle (point "a") if the distance between the robot and the

**Scenario 2** We test our method in an environment composed of corridors (wide and narrow). As shown in figure 11.d the robot moves in a corridor in order to reach its target. The problem is how to ensure the motion's stability without oscillation. In our architecture, there is a compromise between "reach target" and "avoid obstacles". In region 1, the robot crosses a large corridor; it changes its direction when it detects a static obstacle. In region 2, the robot crosses a narrow corridor; in this situation the DTCR<∆T and the DTCL<∆T. Thus, the robot cannot change its trajectory, but it should only adjust its velocity with the velocity of the nearest obstacle in the front area. This reasoning allows the robot to reach its target

**Scenario 1** Generally it is difficult to deal with narrow passage cases when there are moving actors because there may be oscillation in the trajectory between multiple obstacles. However, we can show, in figure 12, the effectiveness of our method when dealing with this case. The robot navigates in an environment composed of corridors with a moving obstacle. At the first time, the corridor is narrow. In this setting the robot adjusts its velocity to avoid collision with the obstacle without changing the direction because the goal is in the front

Fig. 11. Experimental results in static environments

easily without oscillation.

obstacle is shorter than the distance between the robot and the target.

**5.3 Experimentation results in dynamic environments**

calculate the output for the two controllers. The constants of output membership function for the first controller (angular velocity) are shown in figure 6 and the values of the constants of output membership function for the second controller (linear velocity) are shown in figure 10. During simulation work, the robot's normal velocity VN is set to 0.5m/s.