3.2.4. Diagnosis

A new species of *Noblella* characterized by (1) skin on dorsum smooth to finely shagreen, skin on belly smooth, discoidal fold absent, dorsolateral folds absent; (2) tympanic annulus visible below skin, with the upper portion (1/4) covered by a supratympanic fold; tympanic membrane evident; columella present (Figure 6); (3) snout short, rounded in dorsal view and bluntly rounded to subtruncate in profile; (4) upper eyelid with minute tubercles, cranial crests absent; (5) dentigerous process of vomers absent; (6) vocal slits present; nuptial pads absent; (7) finger I shorter than finger II; tips of digits rounded, distally ending in papillae; Finger IV having two phalanges (Figures 2 and 7); (8) fingers with narrow lateral fringes; (9) ulnar tubercles absent; (10) short, oblique fold-like tubercle on the ventral part of tarsal region (Figure 2E); (11) no other tubercles on heel and tarsus; (12) inner metatarsal tubercle oval, of higher relief and about one and a half times the size of conical, rounded outer metatarsal tubercle; supernumerary plantar tubercles absent; (13) toes bearing narrow lateral fringes; webbing absent; toe V shorter than toe III; tips of digits weakly acuminate distally and expanded slightly in the digits II, III and IV; tips of the digits II, III, IV and V with discs slightly expanded, elongately acuminate, grooves present distally with papillae; (14) facial mask and lateral band dark brown with cream spots interrupted, extending from tip of snout along the flanks, almost reaching the point of insertion of thighs; (15) dorsum ocher gold to copper brown with or without irregularly-shaped middorsal dark brown marks; some specimens present a clear and slightly evident middorsal line that extends from middle of body to cloaca; dark brown suprainguinal stripes; interorbital bar present or absent; black or gray clear venter, always with irregular white markings; irregular white markings also present on the neck, thighs, and toes; (16) mean SVL 12.18 mm in females (range 9.74–13.60, *n* = 9), 10.08 mm in males (range 9.16–11.40, *n* = 12). Mean values and ranges of other morphological characters are provided in Table 1.

**Table 1.** Measurements (inmm) ofmales and females of the type series of*Noblella losamigos* sp. n. See section on morphological characters for definition of each character. Abbreviations: SD = standard deviation, SVL = snout-vent length, TL = tibia length, FL = foot length, HL = head length, HW = head width, ED = eye diameter, TY = tympanum diameter, IOD = interorbital distance, EW = upper eyelid width, IND = internarial distance, E–N = and eye–nostril distance, E-TY = eye to tympanum distance, ForL = forearm length, HaL = hand length, FIL = finger I length, FIIL = finger II length, TIL = toe I length, TIIL = toe II length. Ranges are included in parentheses.

**Figure 7.** Three-dimensional reconstructions based on μCT data, from (**A**) adult right hand paratype UMMZ 246570 of *Noblella losamigos* sp. n. and (**B**) adult right hand *Noblella myrmecoides* from Loreto UMMZ 246571. Finger phalanges (ph) and metacarpalia (mtc) are noted for fingers III and IV.

## 3.2.5. Comparisons with Described Species

*Noblella losamigos* sp. n. has two phalanges in Finger IV like *N. carrascoicola*, *N. lochites*, *N. myrmecoides*, and *N. ritarasquinae*. In contrast, *Noblella losamigos* differs from the following species, which have three phalanges in Finger IV: *N. coloma*, *N. duellmani*, *N. heyeri*, *N. lynchi*, *N. madreselva*, *N. pearsonina*, *N. peruviana, N. pygmaea* and *N. thiuni*. Externally, *N. losamigos* has an evident tympanum (absent in *N. carrascoicola*, *N. duellmani* and *N. ritarasquinae*; barely visible below skin in *N. madreselva, N. thiuni, P. bagrecito, P. chirihampatu, P. glauca and P. usurpator*). *N. losamigos* sp. n. is similar to *N. myrmecoides* and *N. ritarasquinae* by presence of papillae on the tips of the toes [6,12]. *N. losamigos* sp. n. has a short, oblique fold-like tubercle on the ventral part of tarsal region, similar to *N. ritarasquinae and P. glauca* (*N. peruviana and N. heyeri* have a prominent tubercle; *N. lochites and N. myrmecoides* have a tubercle transversely oriented; *N. coloma, N. lynchi, N. personina, N. thiuni, P. chirihampatu, P. usurpator* have a elongate tarsal tubercle; *N. carrascoicola* with a poorly marked tubercle; *P. bagrecito* have a smaller and sickle-shaped tubercle; *N. duellmani, N. madreselva* and *N. pygmaea* do not have a tubercle or tarsal fold. *N. losamigos* sp. n. is similar to *N. madreselva, N. myrmecoides, N. thiuni, P. chirihampatu, P. glauca* in that it has a heel lacking tubercles (*N. pygmaea* present a heel with one minute, round tubercle). The skin on the dorsum of *N. losamigos* is smooth to finely shagreen, similar to *N. carrascoicola*, *N. coloma, N. heyeri, N. lochites, N. myrmecoides, N. thiuni, P. bagrecito, P. chirihampatu, P. glauca and P. usurpator* (skin on the dorsum with small tubercles or pustules in *N. duellmani*, *N. madreselva*, *N. lynchi*, *N. personina* and *N. pygmaea*). *N. losamigos* presents suprainguinal spots similar to *N. carrascoicola, N. coloma, N. heyeri,* *N. lynchi*, *N. myrmecoides, N. ritarasquinae*, *N. thiuni* (diffuse suprainguinal stripes in *N. madreselva* or poorly defined in *N. personina*, longitudinal stripes in *P. chirihampatu and P. glauca*, absent in *N. duellmani*, *N. pygmaea*, and *P. usurpator*). *N. losamigos* has facial mask and lateral band of dark brown with creams spots interrupted and extending from the tip of the snout along the flanks, almost reaching the point of insertion of thighs (*N. duellmani* has a narrow dark brown post orbital stripe; *N. pygmaea* has a broad gray dorsolateral stripe that extends from upper eyelid to insertion of thigh; *N. personina* has a facial mask but lack a lateral dark band extending to the inguinal region [4,9,11]. The new species is larger in SVL (largest known female 13.60 mm, largest known male 11.40 mm) than *N. pygmaea* (largest known female 12.4 mm); it is similar in size to *N. myrmecoides* (largest known female 13.6 mm); and it is smaller than *N. coloma* (largest known female 16.03 mm, largest known male 14.55 mm)*, N. duellmani* (largest known female 20.00 mm), *N. heyeri* (largest known female 15.90 mm, largest known male 14.10 mm), *N. lynchi* (largest known female 20.20 mm), *N. lochites* (largest known female 19.4 mm), *N. madreselva* (largest known female 17.6 mm, largest known male 15.6 mm), *N. personina* (largest known female 17.90 mm, largest known male 16.30 mm), *P. bagrecito* (largest known female 18.60 mm, largest known male 16.30 mm), *P. glauca* (largest known female 19.80 mm), *P. chirihampatu* (largest known female 25.80 mm, largest known male 21.70 mm), *P. usurpator* (largest known female 24.1 mm, largest known male 20.3 mm).

## 3.2.6.  Choanae round, very small, positioned far anteriorly and laterally, widely separated from each other, slightly concealed by palatal shelf of maxilla; dentigerous process of vomer and vomerine teeth absent; tongue long and narrow; skin on dorsum finely shagreen; discoidal fold absent, dorsolateral folds absent; skin on flanks smooth; skin on ventral surfaces and gular regions smooth to finely areolate; pectoral fold poorly visible, discoidal fold not evident; cloaca protuberant; cloacal region bearing several small tubercles. Outer surface of forearm without tubercles; palmar tubercle flat and oval, approximately twice the size of elongate, thenar tubercle; tarsal tubercle small; supernumerary palmar tubercles present; subarticular tubercles like calluses, flat in ventral and lateral view, largest at the base of fingers; fingers without narrow lateral fringes; Finger IV with two phalanges; when adpressed, Finger 3 > 2 > 4 > 1 (Figure 2); tips of digits rounded, with distal grooves and papillae (Figure 2); forearm lacking tubercles. Hindlimb length moderate, tibia length 51% of SVL; foot length 43% of SVL; upper and posterior surfaces of hindlimbs without tubercles; heel without tubercles; outer surface of tarsus without tubercles; inner metatarsal tubercle, oval, of higher relief and about one time the size of conical, rounded outer metatarsal tubercle; low plantar supernumerary tubercles present; subarticular tubercles not evident in dorsal view; toes bearing narrow lateral fringes, basal webbing absent; tips of digits II, III, IV and V with discs slightly expanded, elongately acuminate, grooves present distally with papillae; digital tip of Toe V smaller than tips of Toes III—IV; when adpressed, relative lengths of toes: 4 > 3 > 5 > 2 > 1 (Figure 2).

Measurements of the holotype (in mm): SVL = 10.72, tibia length (TL) = 5.45, foot length (FL) = 4.60, head length (HL) = 3.12, head width (HW) = 3.55, interorbital distance (IOD) = 1.29, upper eyelid width (EW) = 1.00, internarial distance (IND) = 1.09, eye to nostril distance (E-N) = 0.78, snout to nostril distance (SND) = 0.50, eye diameter (ED) = 1.43, tympanum diameter (TD) = 0.45, eye to tympanum distance (ETD) = 0.19, forearm length (ForL) = 2.75, hand length (HaL) = 1.93, finger I length (FIL) = 0.91, finger II length (FIIL) = 1.30, toe I length (TIL) = 1.15, toe II length (TIIL) = 2.20.

## 3.2.7. Coloration of Holotype

In alcohol, the dorsal surface of the head (from the interorbital region) and body (to the cloaca) have dark brown marks of irregular shape; the dorsal surface of forelimbs is cream with a dark brown transverse bar in the shape of a wristband. The cloacal and suprainguinal regions present dark brown marks in dorsal view; those of the suprainguinal region are circular. The dorsal surfaces of the hind limbs do not have transverse dark bars, although small irregular dark bars are present in some specimens. The facial mask and lateral band are dark brown with creams spots. The lower part of the flank is dark brown. The iris is dark gray. All ventral surfaces are dark gray with minute cream spots (Figure 2). The coloration of the holotype in life is unknown.


**2. Methodology**

In this study, three major steps were employed, consisting of abrasive waterjet contour cutting experiments, regression modelling and optimisation. The experiment, modelling and optimisation procedures are presented in **Figure 1**. The experiment was conducted using the Taguchi L9 orthogonal array to analyse the impacts of input parameters, i.e., traverse speed, abrasive mass flow rate and waterjet pressure. Desirability analysis using response surface methodology is employed for the experimental results of material AISI 304L. In this desirability analysis, multi-responses are considered. It establishes the optimum set of the selected process parameters on the performance characteristics.

A regression model was developed using the machining process parameters from the experimental execution to extract mathematical models. A linear stepwise regression analysis was performed to predict the surface roughness, material removal rate

**Figure 1.**

*Multi-objective optimisation process flow chart.*

and kerf taper angle value. The reliability of the models generated was assessed based on coefficient of determination (R2 , R2 adj & R2 pred). However, supposing that regression models are not within the acceptable range or do not provide preferable values of coefficients of determination set by the decision-maker, it is anticipated that these models will not provide precise prediction. Therefore, the selected parameter setting conflicts with the response variables, denoting the necessity for modification of independent variables or experimental design [16].

*Multi-objective Optimisation in Abrasive Waterjet Contour Cutting of AISI 304L DOI: http://dx.doi.org/10.5772/intechopen.106817*

Referring to **Figure 1**, after achieving the fittest models, a multi-objective optimisation was performed by using response surface methodology with the objectives of maximising material removal, whilst minimising surface roughness and kerf taper angle. The number of solutions and iterations (i = 1 to n) may vary, depending on the machining process requirements to establish the best alternative or solution. Hence, if the composite desirability is not within the tolerable array, several iterations repeating the response surface optimisation were executed. Subsequently, if these repetitions reached the maximum number of iterations and the composite desirability is not attaining adequate values, modifying the design of experiments and the corresponding independent variables or its values is necessary [16]. Moreover, in some cases, other soft computing techniques should be considered [17].

### **2.1 Material and experimental design**

In this work, the material machined in the experiments was AISI 304L with varied thicknesses of 4, 8 and 12 mm. The assigned material thicknesses with differing uniform gaps were used to gain a better yield of variations in AWJM cutting behaviour. Stainless steel, such as AISI 304L, is widely used in fabrication industries, where it is recognised for its high strength and corrosion and heat resistance. This results from its high alloying content of Cr and Ni [18]. The chemical and mechanical composition of this material is detailed in **Table 1**.

The setup consisted of an OMAX MAXIEM 1515 abrasive waterjet machine, possessing a direct drive pump and dynamic cutting head with maximum pressure of 413.7 MPa and cutting area of 2235 mm length and 1727 mm width. The cutting head is comprised of a mixing chamber for abrasive and waterjet, along with a nozzle diameter of 0.56 mm and a jet impact angle of 90°. An abrasive garnet with a mesh size of #80 was utilised for abrasive waterjet cutting experiments. The unit is inclusive of IntelliMax software, where the experiment setup conditions were uploaded and entered. The cutting head can move in the Z-axis over a distance of 305 mm, with a maximum traverse speed of 12,700 mm/min. Standoff distance was designated to 1.5 mm in agreement with recommended range for abrasive waterjet machining in previous works [20, 21]. The AWJM setup and process parameters are demonstrated in **Figure 2**.

### **Table 1.** *Chemical and mechanical composition of AISI 304L [19].*

Upon completion of the experiments, the roughness of the machined surfaces was quantified by a surface roughness tester (TR200 model). **Figure 2** presents the cut surface captured by LEICA M80, which indicates the measurement area for the roughness. The kerf top and bottom width were measured using a LEICA M80 optical microscope model. Moreover, rate of material removal and kerf taper angle were calculated using Eqs. (1) and (2), respectively [11]. The roughness of the cut surface determined according to the ISO/TC 44 N 1770 standard, (μm); *Wt* is width of the cut surface at the jet inlet, (mm]; *Wb* is the width of the cut surface at the jet outlet, (mm); u is the angularity or perpendicular deviation, (mm); α°- inclination angle of the cut surface, (°); MRR is the Material Removal Rate, (mm3 /min); *t* is the thickness of the material (mm) [22].

$$MRR = ht\left(\frac{W\_t + W\_b}{2}\right) V\_f \tag{1}$$

$$KTA = Area \left(\frac{W\_t + W\_b}{2ht}\right) \tag{2}$$

The input parameters considered in abrasive waterjet contour cutting in this experiment included traverse rate (*Vf* ), abrasive flow rate (ma) and water pressure (*P*), as these parameters have been demonstrated in previous studies as having significant impacts in AWJM applications [10, 12, 23, 24]. Surface integrity, kerf geometries

**Figure 2.** *AWJM setup and process parameters.*

### *Multi-objective Optimisation in Abrasive Waterjet Contour Cutting of AISI 304L DOI: http://dx.doi.org/10.5772/intechopen.106817*

and low material removal rate evidence has been reported in machining of AISI 304L, requiring further improvement [4, 25]. Furthermore, taper angles formed in AWJM demonstrate different inclinations as contour curvature radius differs [26]. Hence, quality and productivity are an intensified demand in various manufacturing fields and are significant performance indicators for machining processes. Therefore, in this study, material removal rates (MRR), surface roughness (Ra) and kerf taper angle (KTA) have been chosen as process parameter characteristics for abrasive waterjet contour cutting investigations, due to their influence against the selected input parameters. The levels of the considered independent variables, responses and coding assignment have been detailed in **Tables 2** and **3**.

Abrasive waterjet cutting was executed for three different profiles, representing straight-line, inner arcs and outer arcs, as part of the completed twelve profiles, as demonstrated in **Figure 2**. The abovementioned profiles were selected to confirm a broad array of complicated machining profiling applications. The levels of profiles employed showed occurrences of surface roughness, low machining rate and inaccuracies of cut geometries in regard to previous works [27, 28], recommending further studies, predominantly for difficult-to-cut materials, such as AISI 304L (**Figure 3**).

The design of experimentation (DOE) was carried out using the Taguchi approach in MINITAB 19 software. The Taguchi method is useful in determining the best combination of factors under desired experimental conditions, reducing the large number of experiments which would be required in traditional experiments as the number of process parameter increases [29, 30].

In Taguchi's approach, selection of the appropriate orthogonal array depends on aspects such as: the number of input and response factors along with the interactions that are of key significance; number of levels of data for input factors; and required resolution of experiment and limitations cited on cost and performance [29, 31]. With this specific advantage, this method is suitable in conducting experiments with an appropriate number of tests to determine the optimal combination and significance of the selected factors [32]. The relevant variation in thicknesses dictates different material responses. Therefore, Taguchi L9 orthogonal array was executed for three

### **Table 2.**

*Levels of input process parameters.*

**Table 3.** *Output parameters for varied profiles.*

### **Figure 3.**

*Abrasive waterjet contour cutting profiles.*

### **Table 4.**

*Taguchi* L9 *orthogonal array.*

levels of material thicknesses (t), i.e., 4, 8 and 12 mm, as presented in **Table 4**. The AWJM performances were analysed accordingly by the applied material thickness.

### **2.2 Modelling and multi-objective optimisation**

A mathematical model was developed to associate the input process parameters to the response's characteristics. To achieve this, a linear regression was employed to develop models for the prediction of responses. The empirical model for the prediction of the responses in regard to controlling parameters was established by linear regression analysis. Regression analysis was then applied to obtain the interactions between independent and dependent variables [33]. Multi-linear regression involves regression analysis of dependent and independent variables exhibiting a linear relationship [34]. It stipulates the relationship between two or more variables and a response variable by fitting a linear equation to examine data. The value of the independent variable x or process parameter is correlated with a value of the dependent variable, y, which is the output parameter. In general, this analysis is applied to investigate the degree of relationship between multiple variables fitted by a straight line [33].

In general, regression model is expressed by Eq. (3) [33].

$$
\lambda y = \infty + \beta\_1 \mathbb{1}\_1 + e \tag{3}
$$

$$\text{Where}\\
\text{in}: \mathcal{e} = \mathcal{y}\_1 - \hat{\mathcal{y}}\_1 \tag{4}$$

where, *y* = dependent variable, α = constant, *x*1 = Independent variable, *β*1= coefficient of independent variable*x*1, e = error, *y*1= regression line values and *y*b1 = actual observation.

If this involves more than one variables, then it is categorised as multi-regression as shown in Eq. (5) [33].

$$\mathbf{y} = \mathbf{x} + \beta\_1 \mathbf{x}\_1 + \beta\_2 \mathbf{x}\_2 + \beta\_3 \mathbf{x}\_3 + \dots \dots \dots \beta\_n \mathbf{x}\_n + \mathbf{e}\_n \tag{5}$$

A multi-linear regression analysis can be employed *to fit a predictive model to an observed data set of values of output and input variables*. The obtained results of surface roughness, material removal rate and kerf taper angle were expressed in terms of the input parameters such as traverse speed (*X*1) abrasive mass flow rate (*X*2) and waterjet pressure (*X*3).

*The predicted values are functional for optimising the parameters by providing an adequate comprehension of the significant parameters.* The percentage of error between the experimental data and acquired predicted values has been calculated based on Eq. (6) [33]. The relative percentage of error was acceptable at <20% [35].

$$Error = \frac{1}{n} \sum\_{n}^{1} \left[ \frac{Response\_{(experiment)} - Response\_{(predicted)}}{Response\_{(experiment)}} \right] \text{(\text{\textquotedblleft}degree\text{\textquotedblright})} \tag{6}$$

The performance of the established regression model was assessed by statistical approaches to confirm the goodness-of-fit of the model and the impact of the predicted variables. Following this, the significance and effectiveness of the developed models were validated by *analysis of variance*. *Analysis of variance (ANOVA) is a statistical method that facilitates the evaluation of comparative influences for each control parameter* [36, 37]. The significance of input parameters including traverse speed, abrasive mass flow rate and waterjet pressure were investigated using *p-* values and determination of coefficient (R2 ). In this work, a confidence interval of 95% (*p* < 0.05) has been applied that is in alignment with previous works [29, 38, 39]. A 95% confidence interval means that there is only a 5% chance of being the wrong estimation; therefore, the influence of each process parameter or other interactions on the responses is considered insignificant if their *p*-values were estimated at more than 0.05 [37].

The determination of coefficient (R2 , R2 adj and R2 pred) refers to the percentage variation of responses ranging from 0–100%. These indicators determine the adequacy of the model against obtained experimental data and predicted observation. This R2 , R2 adj and R2 pred value of ≥80%, proved a better model fits of the obtained data [35].

Response surface methodology (RSM) can be utilised for multi-objective optimisation. This multi-desirability is based on multi-response optimisation using an objective function D(X), denoted as desirability function [40]. This method translates each response (*yi* ) into a desirability function (di), differing in the array of 0 ≤ di ≤ 1,

where desirability function =0 indicates an undesirable response and desirability function =1 represents a fully desired response [41]. The objective function D is specified by Eq. (7) [40].

$$D = (d\_1 X d\_2 X \dots \dots d\_n)^{\mathbb{V}\_n} = \left(\prod\_{i=1}^n d\_i\right)^{\mathbb{V}\_n} \tag{7}$$

The effectiveness of multi-objective optimisation is anticipated based on the method used for establishing priority weights for each response characteristics [42]. Generally, equal importance is set for selected responses; hence, weights may differ depending on the machining process requirements in order to establish the most suitable solution [43].

A simultaneous optimisation process was employed to determine the levels of resulting to the maximum overall desirability. The responses namely Ra, MRR and KTA were optimised concurrently to assess the set of input process parameters with the objectives of maximising MRR and minimising Ra and KTA.


**Author details**

Felipe de Paula Nogueira Cruz1,2, Andréa Cristina Bogas1,2 and Cristina Paiva de Sousa1,2\*

1 Laboratory of Microbiology and Biomolecules – LaMiB, Department of Morphology and Pathology, Federal University of São Carlos, Brazil

2 Biotechnology Graduate Program, Federal University of São Carlos, Brazil

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Light and Phages on Tackle of Infectious Diseases DOI: http://dx.doi.org/10.5772/intechopen.96425*


The reflective multi-pass photoreactor design introduced herein is one option to master the photoreaction engineering part of the overall challenge. The design can be made from cheap materials with established mass production techniques, and, if designed properly, exhibits a high spectral radiation transport efficiency. The derived design guideline, thereby, supports the selection of suitable materials and the determination of suitable characteristic dimensions resulting in a photoreactor suitable for a specific photocatalytic system. The design guideline, therewith, provides the basis for further development and adaptation of the proposed design to other photocatalytic systems. Even though demonstrated with a standard liquid phase photochemical reaction, the design approach is easily adaptable to photocatalytic reactions of all kinds, including gas, liquid and heterogeneous multi-phase catalysis in the fields of solar fuels, (waste) water treatment, or fine chemical synthesis.

# **9 Summary and conclusions**

With the work presented herein the multi-facetted materials and photochemical engineering challenge introduced in chapter 1 aiming for low-cost, high efficiency photocatalytic processes is discussed and analysed in detail.

Starting from an analysis of the status quo in chapter 2, the prerequisites and core challenges are worked out. Summarizing, the design of a high efficiency photocatalytic process is governed by the reaction engineering and optical properties of the light source, the photocatalyst, and the photoreactor. If the sun represents the light source, the design challenge naturally focuses on the photocatalyst and photoreactor. A suitable photocatalyst exhibits a high quantum yield throughout the relevant optical band that equals in case of a sunlight driven process the UV and Vis band. A suitable photoreactor ensures the operating conditions under which the quantum yield peaks and guides incident light into the reaction volume with as low as possible photon losses. Achieving low costs in both, fabrication and operation requires a reactor design that is manufacturable in cheap materials with standard, low-cost techniques. Additionally, the design must possess robust operating characteristics, especially with regard to the tolerance towards a varying direction of incident light resulting from a moving sun in the sky.

Further, the analysis in chapter 2 reveals that two core skills are essential in a photoreactor design process. Firstly, for the sake of the outstanding importance of the quantum yield of the employed photocatalyst, methods for the precise determination of the latter are indispensable. Of especial importance is, thereby, the ability to map the dependencies of the quantum yield on the operating conditions, namely temperature, reactant concentrations, wavelength, and local volumetric rate of photon absorption. These dependencies set the requirements on the photoreactor engineering side of the overall challenge. The second core skill is the ability to map radiation transport in complex multi-component systems such as photoreactors. The latter indirectly demands for the ability to determine optical transport properties, such as refractive indices, absorption and scattering coefficients, reflectivities, etc., of the employed photoreactor components and photocatalysts.

Within chapter 4 of the present work a precise and especially flexible radiation transport model is introduced. The model is implemented in MATLAB® and represents an implementation of a Monte Carlo ray tracing method. The implementation is in many aspects similar to methods in the family of Monte Carlo ray tracing methods known from rendering problems in computer graphics applications. The developed toolbox, thereby, allows to map radiation transport in three dimensions in multi-component systems built from transparent, translucent, and opaque objects. The simulation domain can comprise free-formed shapes with a high design freedom. However, the complexity of the geometry is constraint to extrudable geometries that can be projected into two dimensions. This seeming limitation is ideal for channellike geometries and accelerates significantly the numerical solution process. Together with the three additionally implemented acceleration techniques, parallelization, bounding volumes, and retracing, a computationally costeffective solution of different scientific and engineering issues is guaranteed. Validated via a set of literature-reported radiation transport problems with explicit solutions, the toolbox developed in the framework of the present work provides a robust basis for the various optical challenges encountered in the field of optical property determination, quantum yield measurements, and high efficiency photoreactor design.

To complete the introduced Monte Carlo Ray tracing toolbox and provide the basis for reasonable optical simulations using the environment, in chapter 6, a set of methods for the determination of optical transport properties is introduced and applied to both, a set of reactor component materials, and photocatalysts employed in the present work. Beyond the description of methods for the determination of optical transport properties of reactor component materials and photocatalysts, in chapter 6, additionally, methods for the fabrication of reactor components via additive manufacturing and the synthesis of both active materials and translucent active material supports are introduced. The fabrication of reactor components via powder bed fusion with laser beams of 316L stainless steel, thereby, turns out to show a limited suitability for the fabrication of optical components. The rather high mean surface roughness of the printed raw parts requires a time consuming post-treatment of printed optical surfaces via micro-milling and suspension-based polishing to achieve both high reflectivity and specular reflection characteristics as needed for high efficiency photoreactor design. In contrast, the described active material and photocatalyst support development resulted in a simple-to-synthesise active material for methanol photo-reforming with an evident photocatalytic contribution in the reaction mechanism and a photocatalyst support strategy

that is straight forward and based on well-known and easy to synthesize silica aerogels. Interestingly, the determined optical transport properties of prepared aerogel-supported photocatalysts imply that due to intense absorption and scattering, even in very dilute photocatalysts, the illumination of larger bulk volumes is impossible. Absorption and scattering coefficients are so high that typical penetration depths of photons into a photocatalyst bed lie more in the sub-millimeter to millimeter range than in the centimeter to meter range. The latter unambiguously underlines the need for micro process engineering in the field of photoreaction engineering.

In chapter 7 a hybrid approach for the precise determination of quantum yields in gas, liquid, and heterogeneously catalysed multi-phase reactions is introduced. 'Hybrid', thereby, refers to the combination of detailed experimentation delivering the number of production events and sophisticated optical simulations delivering the number of absorbed photons. The approach allows the variation of temperature, reactant concentrations, wavelength, and volumetric rate of photon absorption. The approach, therewith, allows a variation of all important operating conditions potentially influencing the quantum yield in a photoreaction. A special focus is laid on the design of the employed photoreactor that provides an isophotonic reaction volume, meaning a reaction volume with as low as possible gradients in the local volumetric rate of photon absorption. This unique system property is achieved by a numerical design optimization of the employed LED light source modules and the control of the optical density of the reaction volume via an optical dilution of the characterized active material. The approach is both validated via the determination of the quantum yield of the literature-known potassium ferrioxalate photoreduction and applied to the methanol photo-reforming photocatalyst described in chapter 6. The validation via the determination of the quantum yield of the potassium ferrioxalate photoreduction underlines the validity of the overall approach. The presented quantum yield data for the methanol reforming system further reveals interesting dependencies of the quantum yield on the operating conditions. These dependencies indicate a complex system behaviour being influenced by temperature, reactant concentration, wavelength, and local volumetric rate of photon absorption. The 'optical' operating conditions, wavelength and local volumetric rate of photon absorption, thereby, have the most significant impact on the quantum yield. Of utmost importance for photoreactor design is the observed dependency of the quantum yield on the local volumetric rate of photon absorption: The evident systematic decrease of the quantum yield with increasing local volumetric rate of photon absorption, or increased process

intensity, respectively, entails a trade-off situation demanding for a careful optimization in a photoreactor design process. A decreasing quantum yield with increasing local volumetric rate of photon absorption indicates that higher overall photocatalytic efficiencies can be achieved if incident photons are distributed to a larger amount of active material. For the sake of the high complexity of visible light active high quantum yield active materials it is to be expected that those materials are expensive and a spare but especially efficient use is necessary to ensure an overall low cost. The resulting trade off between high photocatalytic efficiency and low system cost will need a careful consideration on a case to case basis. A decreasing quantum yield with increasing local volumetric rate of photon absorption further demands for a sophisticated optical design ensuring a homogeneous distribution of incident photons into the reaction volume. Absorption hot spots would entail a local decrease of the quantum yield and a therefrom resulting decrease of the overall photocatalytic efficiency.

Lastly, in chapter 8, a low-cost and high efficiency photoreactor design is introduced, numerically optimized, and validated with a model photoreaction, the potassium ferrioxalate photoreduction. The design process relies on the methods and information introduced and described in chapters 4 and 6: (1) the validated radiation transport model, (2) the introduced manufacturing routes for photoreactor prototypes, (3) the synthesis routes for active materials and active material supports, (4) the set of methods for the determination of optical transport properties, and (5) the approach for the precise determination of quantum yields in photoreactions. Noteworthy, the introduced photoreactor design is not limited to an application in the field of homogeneous liquid phase photocatalysis, like for instance the potassium ferrioxalate photoreduction, but can easily be adopted to any gas, liquid, or heterogeneously catalysed multi-phase photocatalytic reaction. The proposed optical design is especially suited for photocatalysts that exhibit strong scattering. The photoreactor design, thereby, combines the beneficial radiation transport characteristics of literature-known concentrator cavity photoreactor designs and the low-cost and modular nature and broad acceptance angle characteristics of literature-reported polymer-based baggie and panel-like photoreactors. Despite the complex optical demands for a high efficiency photoreactor, the proposed design's basic geometry is still manufacturable via low-cost and established mass manufacturing techniques, polymer extrusion and polymer injection moulding. This paves the way for a potential commercialization of a photocatalytic process based on the proposed photoreactor design.

On an engineering point of view, the considerations regarding the achievable

limits for the photocatalytic efficiency in a photocatalytic process presented in chapter 8 not only support the interpretation of the performance of the optimized photoreactor but especially highlight once more the double challenge faced in high efficiency photoreactor design. It becomes clear that on the one hand, the quantum yield and its dependency on the wavelength set an ultimate limit for the achievable photocatalytic efficiency in any photocatalytic process. On the other hand, a theoretical limit for the efficiency of radiation transport from the reactor aperture into the reaction volume sets a second limit for any optimization. The achievable radiation transport efficiency limit, thereby, strongly depends on the optical transport properties of the employed materials and chosen characteristic dimensions of the photoreactor.

The conducted loss mechanism analysis that precisely delineates prevalent loss mechanisms in the proposed photoreactor design supports the interim conclusion of an outstanding importance of optical transport properties and characteristic dimensions for a successful high efficiency photoreactor. Only, if the latter are chosen properly, a photoreactor can guarantee a significant total absorption share which is the prerequisite for any photocatalytic reaction to take place.

Taken together, the considerations regarding the achievable limits and the loss mechanism analysis cumulate in a design guideline that supports the appropriate choice of materials and the determination of suitable characteristic dimensions. A detailed discussion further underlines that for most photocatalytic systems, polychromatic considerations are indispensable and trade-offs in between the performance in different optical bands are needed. The introduced geometry optimization tool delivering free-form optimized photoreactors, thereby, is one option to address these challenging trade-off situations.

Summarizing, the work presented herein first of all delivers a complete set of methods for the design of a high efficiency photocatalytic process. This includes (1) precise but computationally manageable methods for the simulation of radiation transport in multi component systems, (2) manufacturing techniques for photoreactor prototypes, (3) synthesis routes for model photocatalysts, including active materials and active material supports, (4) methods for the determination of optical transport properties, (5) methods for the reaction engineering characterization of photocatalysts, and (6) numerical free-form optimization tools for the optimization of photoreactors under the consideration of polychromatic sunlight. Beyond this method toolbox, the work presented herein outlines the complexity of high efficiency photoreactor design and sheds light on the pivotal roles of light source, photocatalyst, and

photoreactor. The presented quantum yield data of a model photoreaction highlights the need for diligent analysis of quantum yields in photoreactions and the careful definition of operating conditions in photocatalytic processes. Lastly, the proposed photoreactor design opens one way towards a low-cost photocatalytic process. Its potential is underlined by a patent application filed in summer 2022 [143]. The detailed analysis of the proposed design gives insights into the complex prevalent loss mechanisms and cumulates in a design guideline paving the way for adaption, further development, and potential commercial implementation of photocatalytic processes relying on the proposed reflective multi-pass photoreactor design.

# **10 Outlook**

Undoubtedly, materials are one corner stone in high efficiency photocatalytic process design. This includes reactor component materials but especially addresses the photocatalyst. In the present work, model photocatalysts are employed to demonstrate the validity of proposed methods and high efficiency photoreactor design. The challenge of the synthesis of an active material driving a relevant chemical reaction, for instance, the water splitting reaction, is not addressed. The need for a high quantum yield in the UV and Vis band, thereby, represents the core challenge that is not fully mastered until today. Indeed, there are many promising active materials reported. This includes, for instance, active materials for the water splitting reaction from the group around Kazunari Domen at the University of Tokyo. Those materials exhibit quantum yields close to unity but unfortunately are only active in the close UV and not in the Vis band. The challenge of an extension of the photocatalytic activity from the UV into the Vis band, thereby, is a well-known hurdle in the field of solar fuels. Further development, for instance, via the employment of new materials, like perovskite-based materials or cubic silicon carbide-based materials with suitable band gap, is undoubtedly needed. An encouraging fact is that many mechanisms and strategies for the governance of charge carrier dynamics and partial reactions, for instance, through a well-defined deposition of co-catalysts, are well understood and likely can be transferred from the experiences with UV active materials to new materials showing an activity in the Vis band as well.

As outlined in the present work, it is recommendable to impregnate active materials on translucent active material supports to ensure a good optical accessibility of the whole employed active material and control the operating conditions throughout the whole reaction volume. The proposed methodology via mixing of active material powders with silica aerogel powders is simple, functional, and easy to adopt to other material systems. However, the resulting photocatalysts exhibit rather high scattering coefficients which makes the illumination of larger reaction volumes impossible. Even though this is not necessarily a tremendous drawback, it makes the design of a high

efficiency photocatalytic process more difficult since the optical design of the photoreactor becomes more challenging in case of a strongly scattering photocatalyst. An improvement could be achieved by varying the aerogel impregnation strategy. Imaginable are, for instance, altered synthesis routes in which the active material is impregnated onto a silica alcogel in a wet impregnation process prior to the supercritical alcogel drying. Another approach could represent synthesis routes in which an active material slurry is added to the reacting sol in the alcogel synthesis. Preliminary studies in the framework of the present work imply, that these strategies may result in a more homogeneous dispersion of the active material in the aerogel support and a drastically reduced scattering coefficient of the resulting photocatalyst. As discussed in detail, both the active material development and the design of high efficiency photoreactors relies on methods for the precise determination of quantum yields. The approach for the precise determination of quantum yields introduced in the present work focuses on the provision of an isophotonic reaction zone, which, as shown in detail, indeed is highly relevant. The employed LEDs, however, are narrow banded but do not emit monochromatic light, which results in the challenge to differentiate the contributions of different wavelengths in a post experiment analysis. More elegant and more straightforward would be the employment of even narrower banded light sources or the employment of narrow band pass filters in the setup. Likely, this will also slightly alter the optical design of the employed photoreactor. The basic design idea of a diffusely reflecting cavity and a geometry optimized light source module, however, might still be a promising approach. The proposed light source optimization strategies could, thereby, be applied as demonstrated in the present work.

The reflective multi-pass photoreactor design concept developed in the present work is demonstrated in an additively manufactured lab demonstrator. As evident from the discussions regarding suitability of the employed manufacturing routes, additive manufacturing in the employed variant is not able to deliver the needed surface qualities. Additionally, from its nature, additive manufacturing is not the manufacturing route that would be chosen for mass production of photoreactors. Further, the materials employed in the lab demonstrator, 316L stainless steel, quartz glass, and silver coatings, are expensive and likely not the materials of choice in a photoreactor design intended for mass-production. The proposed employment of polymers, for instance polymethylmetharylate, and the employment of mass manufacturing routes, polymer extrusion and polymer injection moulding, remain to be demonstrated. The challenges that might be encountered in this research and

development task are (a) the surface quality of the optics module, which must be smooth enough to show specular reflection characteristics, (b) the application of a reflective layer, likely an aluminum layer, on the bottom side of the optics module, (c) the integration of the photocatalyst in the photoreactor channels, and (d) multiple additional material related issues that address the long-term stability of reflective multi-pass reactors. Those are, for instance, the resistance of the employed polymer towards the reactants, the resistance of the employed polymers towards UV irradiation, or the long term stability of the employed photocatalyst under realistic operating characteristics including diurnal and annual variations of the sunlight intensity. Moreover, the photocatalyst and photoreactor together do not result in a full process. Especially for an application in residential environments, fully integrated systems are needed. Such systems include the reactant conditioning upstream of the photoreactor and the product purification downstream of the photoreactor. The integration of a working photocatalyst-photoreactor combination with the necessary reaction engineering devices remains another open challenge. Summarizing, the work presented herein represents a significant contribution to the field of photochemical engineering. Nevertheless, it is only a small step in the overall way leading towards a low cost, high efficiency solar fuel process representing a corner stone of a future energy economy.

A long way towards a reasonable, sustainably living world society remains to be mastered.

# **Nomenclature**

#### Abbreviations

#### Molecular formulae

Z

#### Symbols

Z

# **Bibliography**

# **List of Figures**

# **List of Tables**

# **List of publications**

# **Appendix A: Test environment components**

The test environment described in detail in chapter 5 is built from a large set of commercial parts. The core components are listed in tables A.1, A.2, and A.3. The table explicitly does not include the hundreds of tubing pieces, valves and tube fittings and adaptors that together shape the gas phase and liquid phase tubing system of the test environment.

**Table A.1:** Component list of the employed test environment comprising the main components.

**Table A.2:** Component list of the employed test environment comprising the main components. Continuation of table A.1.

**Table A.3:** Component list of the employed test environment comprising the main components. Continuation of table A.2.

# **Appendix B: Experiment instructions**

The experiment environment described in detail in chapter 5 allows automated long-term experiments. This feature entails some challenges in the experiment preparation and evaluation. First of all, the user absence during the experiment demands for a rigorous planning that ensures that during the experiment time no errors occur that either result in a malfunction or a safety shut down of the test environment. Secondly, the large data sets generated by different test rig components – the measurement and control system log, the gas chromatograph's reports, the UV Vis spectra, camera takes, etc. – must be saved in a structured and especially lucid way. In best case, the data structure can be machine-read and pre-evaluated before a manual evaluation of larger data sets is conducted.

The automation checklists in figures B.1 and B.2 have been developed in the present work and were employed in the reaction technological experiments with the developed titania-based photo catalyst system described in chapters 6 and 7. Following the check lists ensures, that the experiment is conducted without interruptions and all experiment data is saved in a structured way accessible by the data scraper briefly described in section 5. The check lists are no scientific or engineering result but are a strong support for future work with the test environment by providing a detailed idea of the sum of steps and prerequisites that are necessary to ensure a safe and successful conduction of automated long term experiments.

**Figure B.1:** Automation checklist for experiments in the test environment using the solar simulator. The checklist was developed for the reaction technological experiments in the framework of the titania-based active material development described in chapter 6.

**Figure B.2:** Automation checklist for experiments in the test environment using reactor internal light sources. The checklist was developed for the reaction technological experiments in the framework of the quantum yield determination described in chapter 7.

Technologies for solar-driven synthesis of energy carriers and chemical feedstocks would be a substantial support for the development of a sustainable way of living. A high achieved photocatalytic efficiency and low cost of the technology, thereby, are mandatory. However, the state of the art in solar-driven synthesis lacks in technology readiness, shows low efficiencies, and relies on specialized and expensive components. This work provides an extensive toolbox and introduces strategies for the design process of a high efficiency and low-cost photocatalytic process for solar-driven synthesis. It comprises descriptions of numerical methods for the optimization, assessment, and analysis of photoreactors. Further, it introduces a low-cost and high efficiency photoreactor design concept that is manufacturable in polymers with standard mass-production techniques. Additionally, the work describes methods for the synthesis and optimization of photocatalysts. Thereby, it focuses on methods for the tuning of radiation transport properties of photocatalyst beds and the reliable determination of the quantum yield in photoreactions. With the introduced methods and strategies, the work supports the further development in the fields of photocatalyst engineering, photoreactor engineering, and photocatalytic process design. Further, the introduced low-cost and highly efficient photoreactor design concept, if combined with a suitable photocatalyst, could become a key component in a first the real-world implementation of a process for the competitive production of solar energy carriers and/or chemical feedstocks.

Optimizing photocatalysts and photoreactors for solar fuel synthesis

Paul Philipp Kant

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