**5. Conclusions**

IB-RSA is a safe and effective procedure without specific implant-associated complications at mid-term follow-up. Overall, using a 44-mm-diameter glenosphere compared to 40-mm-lead to an improved range of motion. Inverting biomaterials lead to a distinct kind of notching, which mainly showed mechanical features and no observed cases of grade 3 or 4. Scapular notching is associated with a high baseplate position and has a negative influence on range of motion and clinical outcome.

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

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

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of IRCCS Humanitas Research Hospital (protocol code 41/22 and 19 July 2022).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** Alessandro Castagna declares conflict of interest: Lima consultant and royalties. The other authors declare no conflict of interest.


Preface

Chapter 6 **SERS Research Applied to Polymer Based Nanocomposites 91** Sara Fateixa, Helena I.S. Nogueira and Tito Trindade

Chapter 7 **SERS-Based Sensitive Detection of Organophosphorus**

Qian Zhao, Guangqiang Liu and Weiping Cai

Chapter 8 **Raman Spectroscopy of Graphitic Nanomaterials 155**

Chapter 10 **Surface-Enhanced Raman Spectroscopy Characterization of Pristine and Functionalized Carbon Nanotubes and**

**Section 4 Raman Spectroscopy of Ferrite Nanomaterials 221**

Chapter 11 **Raman Spectroscopy in Zinc Ferrites Nanoparticles 223**

Chapter 12 **Structural Transformations in Ferroelectrics Discovered by**

**Section 5 Raman Spectroscopy Applied to Biomedical Sciences 273**

Chapter 13 **Raman Spectroscopy Applied to Health Sciences 275** Alexandra Nunes and Sandra Magalhães

Chapter 14 **Raman Spectroscopy for In Vivo Medical Diagnosis 293**

Miguel Ghebré Ramírez-Elías and Francisco Javier González

**Raman Spectroscopy 253**

Chapter 9 **Graphene Nanocomposites Studied by Raman**

Elena Iuliana Bîru and Horia Iovu

**Section 3 SERS and Raman Spectroscopy: Carbon Nanomaterials 153**

Daniel Casimir, Iman Ahmed, Raul Garcia-Sanchez, Prabhakar Misra

Sabina Botti, Alessandro Rufoloni, Tomas Rindzevicius and Michael

Pietro Galinetto, Benedetta Albini, Marcella Bini and Maria Cristina

Kai Jiang, Liping Xu, Jinzhong Zhang, Zhigao Hu and Junhao Chu

**Nerve Agents 127**

**VI** Contents

and Fabiola Diaz

**Spectroscopy 179**

**Graphene 203**

Stenbæk Schmidt

Mozzati

This book brings some examples of the state-of-the-art applications of Raman spectroscopy in characterization of materials and biomaterials, mainly through intensification processes, such as resonance Raman (RR) and Surface-enhanced Raman spectroscopy (SERS). The main goal of this book is to open up to an extended audience the wide possibilities of appli‐ cations of Raman spectroscopy for academic, industrial, biomedical, and environmental pur‐ poses. All authors and editors try to use fluent language in order to make the reading possible for a non-specialized public. In fact, this collective work will be beneficial to stu‐ dents, teachers, and researchers of many areas who are interested to expand their knowl‐ edge about Raman spectroscopy applied to nanotechnology, biotechnology, environmental science, inorganic chemistry, in situ and in vivo detection, and health sciences.

This book is organized starting from an introductory chapter that discusses basic aspects of conventional Raman Spectroscopy and also the special cases where the Raman scattering signal can be strongly amplified. Many examples are exploited. The book is organized in five main sections: (i) Introduction, (ii) Surface-Enhanced Raman Spectroscopy: Nanosub‐ strates and Applications, (iii) SERS and Raman Spectroscopy: Carbon Nanomaterials, (iv) Raman Spectroscopy of Ferrite Nanomaterials, and (v) Raman Spectroscopy Applied to Bio‐ medical Sciences.

The second section (ii) has three chapters that focus on preparation of highly sensible nano‐ materials to be used as efficient and reliable platforms for SERS measurements: (a) In the first chapter, the authors (M. Chirumamilla et al.) studied the synthesis and characterization of three-dimensional (3D) nanostructures with multiple branches (MB) as SERS substrates with breakthrough performances in hotspot-mediated ultra-sensitive detection; (b) in the follow‐ ing chapter (Marcin Pisarek et al.), the results of recent investigations into TiO2 nanotubular oxide layers on Ti metal loaded with Ag nanoparticles are investigated. The efficiency of these materials is discussed as surface plasmon resonators for precise surface analytical investiga‐ tions of numerous types of organic molecules at concentrations as low as, e.g., 10-9 M, and (c) finally, the synthesis of precisely controllable anisotropic noble metal nanoparticles (NPs) is reviewed (M. Xu and J. Zhang). This review has demonstrated the correlation of the key mor‐ phological parameters to achieve the strong E-field and ultra-sensitive SERS detection.

In fact, the SERS effect and also surface chemistry in general can be studied at molecular level when the conventional Raman system is joined to a scanning tunneling microscope (STM); in this case, the technique is appropriately named Tip-enhanced Raman spectroscopy (TERS-STM). The origin of the chemical enhancement has been the subject of much debate over the years. In this chapter (I. Rzeznicka and H. Horino), the effects of adsorption state of a molecule and its orientation over Raman signal are studied from the standpoint of surface

chemistry at the nanoscale. In addition, two chapters deal with special applications of SERS technique: (a) the recent research on the development of SERS substrates based on polymer nanocomposites and their applications in different fields is reviewed (S. Fateixa et al.). In addition, the joint use of Raman imaging and SERS in nanocomposite development is dis‐ cussed, and (b) the detection of organophosphorus molecules (W. Cai et al.) is the main fo‐ cus of the last chapter; the authors showed a route that could be suitable for detection of some organophosphorus nerve agents and other molecules weakly interacted with the coin metal substrates by choosing the appropriate modifiers.

Nogueira, Horia Iovu, Iman Ahmed, Izabela Rzeznicka, Jan Krajczewski, Jiatao Zhang, Jinz‐ hong Zhang, Junhao Chu, Kai Jiang, Kjeld Pedersen, Liping Xu, Manohar Chirumamilla, Marcella Bini, Marcin Holdyński, Marcin Pisarek, Maria Cristina Mozzati, Maria Janik-Cza‐ chor, Meng Xu, Michael Stenbæk Schmidt, Miguel Ghebré Ramírez-Elías, Mirosław Krawc‐ zyk, Peter Kjær Kristensen, Pietro Galinetto, Prabhakar Misra, Qian Zhao, Raul Garcia-Sanchez, Remo Proietti Zaccaria, Roman Krahne, Sabina Botti, Sandra Magalhães, Sergey I. Bozhevolnyi, S. Fateixa, Tomas Rindzevicius, Tomasz Płociński, T. Trindade, Weiping Cai, and Zhigao Hu. Special thanks go to Ms. Marina Dusevic for supporting and assisting with

> **Gustavo Morari do Nascimento** Federal University of ABC Santo André, Brazil

Preface IX

the book edition.

The third section (iii) brings chapters that used conventional Raman spectroscopy and SERS in the study of different carbon allotropes. The versatility of Raman spectroscopy is illustrat‐ ed in the characterization of single (SWNT) and multi-walled (MWNT) carbon nanotubes, few layers of graphene, and its functionalized forms, with an emphasis on gas-sensing appli‐ cations (P. Misra et al.). The characteristic features in Raman spectra of carbon allotropes are exposed, and the D and G band intensities are deeply investigated. In particular, one chapter is devoted to graphene and different types of graphene oxide and its nanocomposites (E.I. Bîru and H. Iovu). The last chapter (S. Botti and A. Rufoloni) in this section brings new results in the SERS studies of carbon nanotubes and graphene at pristine and also modified condi‐ tions. These materials can amplify the SERS signal mainly by chemical mechanism.

The fourth section (iv) brings two chapters that use Raman spectroscopy as a powerful tool for characterization of some important materials, such as ferrite nanoparticles and ferroelec‐ tric oxides. The first chapter gives a broad overview (P. Galinetto et al.) of the Raman spec‐ troscopy results in the characterization of ZnFe2O4 nanoparticles. The sensitivity of the Raman signal to probe cation disorder favored the appearance of several detailed works on a rich variety of nanosized zinc ferrites. An overview on the experimental results is reported and discussed at variance with synthesis methods, grain dimensions, and dopants. The sec‐ ond chapter systematically studies the structure of ferroelectric oxides and rare-earth ele‐ ment-doped ferroelectric materials (Junhao Chu et al.). Structural transformations that alter the crystal symmetry often have a significant effect on the Raman signal. The Curie tempera‐ ture (TC), distortion degree, and phase structure of the ferroelectric materials have been monitored by temperature-dependent Raman spectroscopy.

The last section (v) is composed of two chapters dedicated to special applications of Raman spectroscopy in biomedical area. The use of Raman spectroscopy as a tool for biochemical investigation is the main focus of the first chapter in this section (A. Nunes and S. Magal‐ hães). Raman spectroscopy, mainly SERS, was already applied to successfully diagnose sev‐ eral types of cancer and infections, and preliminary results are also promising in the context of Alzheimer's disease. The success of Raman spectroscopy in biomedical applications is based on the fact that the molecular composition of healthy tissue is different from diseased tissue; also several disease biomarkers can be identified in Raman spectra, which can be used to diagnose or monitor the progress of certain medical conditions (M. G. R-Elías and F. J. González). This chapter outlines an overview of the use of Raman spectroscopy for in vivo medical diagnostics and demonstrates the potential of this technique to address biomedical issues related to human health.Finally, I would like to give special thanks to all authors that contributed for this book (in alphabetical order): Alessandro Rufoloni, Alexander S. Roberts, Alexandra Nunes, Andrea Cerea, Andrea Toma, Andrzej Kudelski, Anisha Chirumamilla, Benedetta Albini, Daniel Casimir, Duncan S. Sutherland, Elena Iuliana Bîru, Esben Skovsen, Francesco De Angelis, Francisco Javier González, Guangqiang Liu, Hideyuki Horino, H.I.S.

Nogueira, Horia Iovu, Iman Ahmed, Izabela Rzeznicka, Jan Krajczewski, Jiatao Zhang, Jinz‐ hong Zhang, Junhao Chu, Kai Jiang, Kjeld Pedersen, Liping Xu, Manohar Chirumamilla, Marcella Bini, Marcin Holdyński, Marcin Pisarek, Maria Cristina Mozzati, Maria Janik-Cza‐ chor, Meng Xu, Michael Stenbæk Schmidt, Miguel Ghebré Ramírez-Elías, Mirosław Krawc‐ zyk, Peter Kjær Kristensen, Pietro Galinetto, Prabhakar Misra, Qian Zhao, Raul Garcia-Sanchez, Remo Proietti Zaccaria, Roman Krahne, Sabina Botti, Sandra Magalhães, Sergey I. Bozhevolnyi, S. Fateixa, Tomas Rindzevicius, Tomasz Płociński, T. Trindade, Weiping Cai, and Zhigao Hu. Special thanks go to Ms. Marina Dusevic for supporting and assisting with the book edition.

chemistry at the nanoscale. In addition, two chapters deal with special applications of SERS technique: (a) the recent research on the development of SERS substrates based on polymer nanocomposites and their applications in different fields is reviewed (S. Fateixa et al.). In addition, the joint use of Raman imaging and SERS in nanocomposite development is dis‐ cussed, and (b) the detection of organophosphorus molecules (W. Cai et al.) is the main fo‐ cus of the last chapter; the authors showed a route that could be suitable for detection of some organophosphorus nerve agents and other molecules weakly interacted with the coin

The third section (iii) brings chapters that used conventional Raman spectroscopy and SERS in the study of different carbon allotropes. The versatility of Raman spectroscopy is illustrat‐ ed in the characterization of single (SWNT) and multi-walled (MWNT) carbon nanotubes, few layers of graphene, and its functionalized forms, with an emphasis on gas-sensing appli‐ cations (P. Misra et al.). The characteristic features in Raman spectra of carbon allotropes are exposed, and the D and G band intensities are deeply investigated. In particular, one chapter is devoted to graphene and different types of graphene oxide and its nanocomposites (E.I. Bîru and H. Iovu). The last chapter (S. Botti and A. Rufoloni) in this section brings new results in the SERS studies of carbon nanotubes and graphene at pristine and also modified condi‐

The fourth section (iv) brings two chapters that use Raman spectroscopy as a powerful tool for characterization of some important materials, such as ferrite nanoparticles and ferroelec‐ tric oxides. The first chapter gives a broad overview (P. Galinetto et al.) of the Raman spec‐ troscopy results in the characterization of ZnFe2O4 nanoparticles. The sensitivity of the Raman signal to probe cation disorder favored the appearance of several detailed works on a rich variety of nanosized zinc ferrites. An overview on the experimental results is reported and discussed at variance with synthesis methods, grain dimensions, and dopants. The sec‐ ond chapter systematically studies the structure of ferroelectric oxides and rare-earth ele‐ ment-doped ferroelectric materials (Junhao Chu et al.). Structural transformations that alter the crystal symmetry often have a significant effect on the Raman signal. The Curie tempera‐ ture (TC), distortion degree, and phase structure of the ferroelectric materials have been

The last section (v) is composed of two chapters dedicated to special applications of Raman spectroscopy in biomedical area. The use of Raman spectroscopy as a tool for biochemical investigation is the main focus of the first chapter in this section (A. Nunes and S. Magal‐ hães). Raman spectroscopy, mainly SERS, was already applied to successfully diagnose sev‐ eral types of cancer and infections, and preliminary results are also promising in the context of Alzheimer's disease. The success of Raman spectroscopy in biomedical applications is based on the fact that the molecular composition of healthy tissue is different from diseased tissue; also several disease biomarkers can be identified in Raman spectra, which can be used to diagnose or monitor the progress of certain medical conditions (M. G. R-Elías and F. J. González). This chapter outlines an overview of the use of Raman spectroscopy for in vivo medical diagnostics and demonstrates the potential of this technique to address biomedical issues related to human health.Finally, I would like to give special thanks to all authors that contributed for this book (in alphabetical order): Alessandro Rufoloni, Alexander S. Roberts, Alexandra Nunes, Andrea Cerea, Andrea Toma, Andrzej Kudelski, Anisha Chirumamilla, Benedetta Albini, Daniel Casimir, Duncan S. Sutherland, Elena Iuliana Bîru, Esben Skovsen, Francesco De Angelis, Francisco Javier González, Guangqiang Liu, Hideyuki Horino, H.I.S.

tions. These materials can amplify the SERS signal mainly by chemical mechanism.

metal substrates by choosing the appropriate modifiers.

VIII Preface

monitored by temperature-dependent Raman spectroscopy.

#### **Gustavo Morari do Nascimento**

Federal University of ABC Santo André, Brazil

**Section 1**

**Introduction**

**Section 1**


**A Preventive Prebiotic Supplementation Improves the Sweet Taste Perception in Diet-Induced Obese Mice**

#### **Arnaud Bernard 1, Déborah Ancel 1, Audrey M. Neyrinck 2, Aurélie Dastugue 1, Laure B. Bindels 2, Nathalie M. Delzenne 2 and Philippe Besnard 1,\***

Received: 4 February 2019; Accepted: 27 February 2019; Published: 5 March 2019

**Abstract:** Orosensory perception of sweet stimulus is blunted in diet-induced obese (DIO) rodents. Although this alteration might contribute to unhealthy food choices, its origin remains to be understood. Cumulative evidence indicates that prebiotic manipulations of the gut microbiota are associated with changes in food intake by modulating hedonic and motivational drive for food reward. In the present study, we explore whether a prebiotic supplementation can also restore the taste sensation in DIO mice. The preference and licking behavior in response to various sucrose concentrations were determined using respectively two-bottle choice tests and gustometer analysis in lean and obese mice supplemented or not with 10% inulin-type fructans prebiotic (P) in a preventive manner. In DIO mice, P addition reduced the fat mass gain and energy intake, limited the gut dysbiosis and partially improved the sweet taste perception (rise both of sucrose preference and number of licks/10 s vs. non-supplemented DIO mice). No clear effect on orosensory perception of sucrose was found in the supplemented control mice. Therefore, a preventive P supplementation can partially correct the loss of sweet taste sensitivity found in DIO mice, with the efficiency of treatment being dependent from the nutritional status of mice (high fat diet vs. regular chow).

**Keywords:** Obesity; taste; eating behavior; prebiotics; microbiota

#### **1. Introduction**

Development of a nutritional obesity is a complex phenomenon depending on multiple causes among which food choice plays a significant role. By determining nutrient quality and acceptability, gustation is considered as an important sensory driver of the food selection and intake. However, the relationships between taste and obesity remains poorly understood. Sweet taste sensitivity is challenged in obese rodents. Rats and mice chronically subjected to an obesogenic high fat diet (HFD) become unable to detect properly low concentrations of sweet solutions during behavioral tests minimizing post-ingestive cues (e.g., neuro-endocrine regulations) [1]. A blunting of both peripheral detection and central perception to sweet stimuli might explain this relative loss of taste sensitivity. Indeed, sucrose-evoked calcium signaling is dramatically decreased in taste bud cells freshly isolated from diet-induced obese (DIO) mice [2]. Similarly, chronic HFD elicits a down-regulation of dopamine and opioid receptors [3,4] in the mesolimbic area leading to a progressive devaluation of the reward value of oral stimuli, as found with abuse drugs [5]. Such a diet-acquired sensory deficiency might explain the tendency of DIO rodents to overeat high rewarding foods [1], probably to gain the desired hedonic satisfaction [3].

This diet-induced gustatory disorder is widely corrected in rodents after bariatric surgery, leading to healthier food choices [6–8]. Understanding how this improvement of the sweet taste sensitivity takes place might open new insights in obesity treatment. In rats, Roux-en-Y gastric by-pass is associated with changes in gut microbiota similar to those found after a prebiotic (P) supplementation [9], known to affect the production of hormones controlling the eating behavior, such as glucagon-like peptide-1 (GLP-1) which has an anorexigenic effect. Interestingly, behavioral responses to sweet compounds (i.e., number of licks per 10 s) are reduced in GLP-1 receptor-null mice (GLP-1 R), as compared to wild-type animals [10]. Intestinal dysbiosis is also associated with a chronic low-grade metabolic inflammation by promoting intestinal permeation of lipopolysaccharides (LPS) derived from the outer membrane of Gram-negative bacteria [11,12]. These endotoxins promote the release of pro-inflammatory cytokines in various tissues by activating members of the Toll-like receptors (TRL). A set of observations suggests that this inflammatory environment might play a role in the change of taste sensitivity in DIO mice. Indeed, taste buds express the TLR4 signaling cascade and, thus, are LPS responsive [13]. Moreover, a chronic consumption of a HFD rich in saturated fatty acids elicits a pro-inflammatory gene profile in the gustatory papillae [14]. Finally, chronic endotoxemia reduces the number of taste buds in obese mice [15]. Collectively, these findings suggest an implication of the intestinal dysbiosis in the impairment of the sweet taste sensitivity observed in DIO mice that could be improved by a prebiotic supplementation.

To explore this hypothesis, the impact of a preventive prebiotic supplementation on the orosensory perception of a sweet stimulus was compared in lean and DIO mice. Specific gut bacteria, known to be involved in the regulation of the gut peptide production and/or the gut barrier function such as *Bifidobacterium* spp. and *Akkermansia muciniphila* [16,17], were analyzed in the caecal content of mice to highlight the prebiotic effect of inulin in our model.

#### **2. Materials & Methods**

#### *2.1. Animals*

This study was carried out in the strict accordance with European guidelines for the care and use of laboratory animals and protocol approved by the French National Animal Ethic Committee (CNEA n◦105). Six-weeks-old C57Bl/6 male mice were purchased from Charles River Laboratories (France). Animals were individually housed in a controlled environment (constant temperature and humidity, dark period from 7 p.m. to 7 a.m.) and had free access to tap water and chow. Experiments took place after a one-week acclimatization period. To study the impact of a preventive prebiotic treatment on the orosensory perception of sucrose during a diet-induced obesity, standard laboratory chow or custom high fat diet (Table 1) were supplemented with 10% prebiotic (P) and mice were split in four groups (*n* = 8–10): lean controls fed regular chow (C), lean controls fed supplemented regular chow (C+P), diet-induced obese mice (DIO) and supplemented diet-induced obese mice (DIO+P). Mice were fed *ad libitum* for 12 weeks. Inulin-type fructans (P—Fibruline®, Cosucra, Pecq, Belgium) was used as prebiotic. We have chosen a 10% prebiotic enrichment since this supplementation is known to promote metabolic [18] and cognitive benefits [19].

Evolution of the body composition (i.e., fat mass) was determined by nuclear magnetic resonance relaxometry (EchoMRI—Echo Medical Systems, Houston, TX, USA).

#### *2.2. Two-Bottle Choice Tests*

Tests were performed for 12 h at the beginning of the dark period in individually housed mice. Animals were food restricted during the duration of the experiment [20]. This protocol provides behavioral data combining orosensory sensations (i.e., oral detection and central perception) and post-ingestive cues. Mice were subjected to a choice between a control solution (0.3% xanthan gum in water to minimize textural influences) or a 1% sucrose in control solution. At the end of the test, fluid intake was measured for each bottle and the preference (i.e., ratio between experimental solution consumption and total intake) was calculated.

#### *2.3. Gustometer*

Licking behavior was studied using an original octagonal shaped gustometer of which each side has a computer-controlled shutter giving random access during a short time (10 s in the present study) to a bottle filled with a specific solution. All the bottles (five in the present study) are equipped with a lickometer. This original design, which forces the animal to move to access to the drinking source, allows a simultaneous analysis of the licking behavior, which mirrors the immediate pleasure gained from the consumption of a rewarding stimulus (i.e., "liking") and the motivation to drink (i.e., "wanting"). Concept and procedures are detailed in [21].

In brief, 20 h water-deprived mice were subjected to two training sessions before the taste-testing sessions (30 min, each). During the first training, all the doors were opened so that the mouse had free access to all the bottles filled with water. It was a time of habituation to a new environment. During training two, the mouse learned to drink according to the protocol used during the brief-access taste testing (i.e., random opening of shutters), all the bottles being filled with the control solution (i.e., water). Each mouse had access to a first bottle for 10 s after the first lick. After this trial, all doors remained closed for 10 s before another one was opened among the 4 remaining shutters, in a randomized manner. The program continued until the animal had licked all five bottles. This event constituted a block. At the end of one block, another block started, so that the number of blocks mirrored the motivation for the stimulus. A taste-testing session was performed in water and food deprived mice to explore their licking responsiveness to a set of sweet stimuli (0.01, 0.2, 0.6, 1.0 M sucrose).

#### *2.4. Blood Analysis*

Freshly drawn blood samples from fasted animals were centrifuged at 6000 g for 15 min (4 ◦C). Plasma was collected and kept at −80 ◦C. Glucose, total cholesterol and triglycerides were assayed in

plasma samples using commercial kits certified for in vitro diagnosis (colorimetric assays, ref#981780, 981786 and 981813) on Indiko device from Thermo (Waltham, MA, USA).

#### *2.5. Gut Microbiota Analysis*

Genomic DNA was extracted from the caecal content using a silica membrane-based purification technique with QIAamp DNA Stool Mini Kits (Qiagen, Hilden, Germany) according to the manufacturer's instructions, including a bead-beating step. Total bacteria, *Bifidobacterium* spp. and *Akkermansia* spp. were analyzed by quantitative PCR, as previously described [22].

#### *2.6. Statistics*

Results are expressed as Means ± SEM. The significance of differences between groups was evaluated with R software (v3.4.4; The R Foundation, Vienna, Austria). We first checked that the data for each group were normally distributed and that variances were equal. We then carried out either a Student's *t*-test or a Two-way ANOVA with the Tukey HSD post-hoc test. A principal component analysis (PCA), normalized and centered, was done with R software and the R-commander package (v2.4.4) on the different parameters studied.

#### **3. Results**

#### *3.1. Prebiotic Supplementation Attenuates the Negative Effects Elicited by a Diet-Induced Obesity*

Four groups of mice, subjected for 12 weeks to distinct diets (regulatory chow or obesogenic diet alone or supplemented with 10% P), were used to explore putative changes in orosensory perception of sweet stimuli (Figure 1A).

**Figure 1.** Comparison of body and biochemical parameters in mice subjected for 12 weeks to a regulatory chow or an obesogenic diet alone (C and DIO) or supplemented with 10% Prebiotic (C+P and DIO+P). (**A**) Time course of the experiment; (**B**,**C**) Evolution of the body and fat; (**D**) Daily energy intake; (**E**) Blood glucose; (**F**,**G**) Plasma triglyceride and cholesterol levels. Mean ± SEM, different letters indicate a statistical difference between groups. Significance was achieved at *p* < 0.05. C, C+P, DIO, *n* = 10. DIO+P, *n* = 8.

To verify the efficiency of this experimental design, body mass and composition, energy intake, various blood parameters, cecal tissue mass, mass of cecal content and cecal bacteria were analyzed. As expected, mice fed with the HFD displayed a greater gain in body weight and fat mass than animals fed the regular chow (Figure 1B). Prebiotic addition to the HFD led to a lower body mass gain as compared to DIO mice (Figure 1B) mainly attributable to a diminution in the relative fat mass (Figure 1C). Such a phenomenon was not observed in lean mice (Figure 1B,C). Prebiotic supplementation elicited a slight decrease in the energy intake whatever the diet, but this effect was insignificant (Figure 1D). According to previous data [23], blood glucose, plasma triglycerides and cholesterol levels were increased in DIO mice (Figure 1E–G). Surprisingly in our hands, these systemic changes were not improved in prebiotic-treated mice.

In agreement with the literature [23], chronic prebiotic consumption increased the caecal tissue mass and, in a lower extent, the fecal mass in caecum (Figure 2A,B). Prebiotic supplementation is known to modify gut microbiota composition [24]. To explore whether the bacterial signature was modified by our treatment, cecal bacterial content was studied by qPCR. As shown in Figure 2C, prebiotic supplementation tended to increase the cecal content of total bacteria whatever the diet. When abundance of selected bacterial displaying beneficial health effect was measured, a rise of *Bifidobacteria* and *Akkermansia* abundance was found in the DIO+P group (Figure 2D,E). We have previously described that ITF feeding promotes endogenous GLP-1 production through higher expression of proglucagon in the colon [25,26]. In the present study, we showed that the higher level of its expression was found in the cecal tissue from DIO+P mice (Figure 2F). Collectively, these data demonstrate the efficiency of our prebiotic protocol on DIO mice.

**Figure 2.** Comparison of bacterial parameters in mice subjected for 12 weeks to a regulatory chow or an obesogenic diet alone (C and DIO) or supplemented with 10% Prebiotic (C+P and DIO+P). (**A**) Cecal tissue mass; (**B**) Fecal mass in caecum; (**C**) total cecal bacteria; (**D**) *Bifidobacterium*; (**E**) *Akkermansia muciniphilla*; (**F**) relative proglucagon mRNA levels in caecum in reference to a housekeeper gene (RPL19). Mean ± SEM, different letters indicate a statistical difference between groups. Significance was achieved at *p* < 0.05. C, C+P, DIO, *n* = 10. DIO+P, *n* = 8.

#### *3.2. The Lower Sucrose Preference Found in DIO Mice Was Improved in Presence of Prebiotic*

The preference for a sweet stimulus was explored using a two-bottle choice test. Consistent with previous published data [27], DIO mice subjected to a long-term (12 h) two-bottle preference test showed a significant lower intake (Figure 3A) and preference for 1% sucrose solution than C or C+P groups (Figure 3B), suggesting a diet-induced modification of the orosensory perception of the sweet sensation. The prebiotic attenuated the effect of the DIO. Indeed, sucrose intake was higher in DIO-P group than DIO mice, this change improving their preference score to a level similar to C and C+P mice (Figure 3B).

**Figure 3.** Two-bottle choice test analysis of orosensory perception of a sweet stimulus in mice subjected to a regulatory chow or an obesogenic diet alone (C and DIO) or supplemented with 10% Prebiotic (C+P and DIO+P). Animals were simultaneously subjected for 12 h to a control solution (0.3% xanthan gum in water, *w*/*v*) and a test solution containing 1% sucrose (*w*/*v*) in the control solution. (**A**) Final consumption of control and experimental solution; (**B**) Preference i.e., ratio of the final consumption of control or experimental solution upon the final total liquid intake. Mean ± SEM. (**A**) Student *t* test: \*\* *P* < 0.01, \*\*\* *P* < 0.001; (**B**) 2-way ANOVA + Tukey HSD: different letters indicate a statistical difference between groups. Significance was achieved at *p* < 0.05. C, C+P, DIO, *n* = 10. DIO+P, *n* = 8. The dotted line represents the 50% preference.

#### *3.3. Prebiotic Supplementation Improves the Licking Behavior in Response to Sucrose Stimulus in DIO Mice*

Licking behavior was studied by using the gustometer FRM8 [21]. Training sessions failed to reveal any behavioral differences between mice suggesting that DIO and/or prebiotic supplementation did not affect animal adaptability to a new environment (training 1—Figure 4A) neither their ability to learn how the device works (training 2—Figure 4B). The fact that the number of total licks was similar between groups attests that mice did not present any oromotor or mobility defect (Figure 4C).

**Figure 4.** Gustometer analysis: training sessions in mice subjected to a regulatory chow or an obesogenic diet alone (C and DIO) or supplemented with 10% Prebiotic (C+P and DIO+P). 20 h water-deprived mice were subjected to 2 training sessions before the taste-testing sessions (30 min, each). (**A**) Training 1: Each mouse had a free access to the 5 bottles filled with water in order to determine the licking rate/bottle/30 min. It is time of adaptation to a new environment; (**B**) Training 2: Each learned to drink water according to the protocol used during the brief-access taste testing, i.e., a random and intermittent opening of shutters. The licking rate/bottle/30 min was determined; (**C**) Total licks for 30 min during the training 1 and 2. Mean ± SEM. 2-way ANOVA + Tukey HSD. ns non-significant. C, C+P, DIO, *n* = 10. DIO+P, *n* = 8.

In control mice, licking activity (licks/10 s) showed a typical dose-response curve in response to growing concentrations of sucrose, with a maximal frequency around 60 licks/10 s (Figure 5A). According to previous data [1], the number of licks/10 s elicited by sweet solutions was dramatically reduced in DIO mice as compared to controls (Figure 5A). Consistent with this observation, total licks (i.e., immediate pleasure gained from the consumption of a rewarding stimulus or "*liking*") and number of blocks (i.e., motivation to drink, "*wanting*") during the taste testing session (30 min) were significantly lower in DIO mice as compared to controls (Figure 5B,C). Altogether, these data confirm the existence of substantial differences in the licking responses to a sweet stimulus between C and DIO mice. A licking rate improvement was found in the prebiotic supplemented groups (Figure 5A). In response to the higher sucrose concentration, DIO+P mice displayed a similar licking response than C group, (Figure 5A), suggesting that this prebiotic treatment is able to counteract partially the orosensory deficit found in DIO mice. Despite of this significant improvement, only an upward trend in the total number of licks differentiated the DIO+P group from the DIO group (Figure 5B), the number of blocks remaining unchanged (Figure 5C).

**Figure 5.** Gustometer analysis of orosensory perception in response to various concentration of sucrose in mice subjected to a regulatory chow or an obesogenic diet alone (C and DIO) or supplemented with 10% Prebiotic (C+P and DIO+P). (**A**) Brief-access taste testing responses (licks/10 s) of naïve mice to control solution (0.3% xanthan gum in water) and ascending concentrations of sucrose (0.01, 0.3, 0.6, 1.0 M). Random access to bottles was computer controlled. Zero on the x-axis represents the licking rate obtained in response to the control solution; (**B**,**C**) Total number of licks (representative of the "Liking" component) and number of blocks (representative of the "Wanting" component) performed for 30 min. Mean ± SEM. 2-way ANOVA + Tukey HSD, significance was achieved at *p* < 0.05. Different letters indicate a statistical difference between groups, ns non-significant, C, C+P, DIO, *n* = 10. DIO+P, *n* = 8.

#### *3.4. Prebiotic Supplementation Brings the DIO Group Closer to Control Group*

In order to better delineate the global impact of a prebiotic supplementation in control and DIO mice, a multivariate analysis (principal component analysis—PCA) was performed from variable values at the time of behavioral phenotyping (Figure 1A). Values on the *x*-axis represent the component score for the dimension 1, and those on the *y*-axis for dimensions 2- and 3, accounting for 40.50, 16.63 and 11.32% of the inertia (total = 68.45%), respectively.

Confidence ellipse analysis highlighted that C and C+P groups were partly overlapping suggesting that prebiotic supplementation did not induce any discriminant change in lean mice according to the variables studied (Figure 6A,C). They were mostly defined on the dimensions 1 and 2 by variables related to efficient taste sensations (positive correlation with high number of licks, blocks and preference) and cecal bacteria content (Figure 6B,E).

As expected, they were clearly different from the DIO group, which was mainly characterized by variables linked to obesity (cholesterol, body mass, fat mass, energy intake, glycaemia and triglycerides—Figure 6E) on the dimension 1. The third group representing the DIO+P group was found in an intermediate position between lean and obese mice suggesting that the prebiotic supplementation improved the health status of DIO mice (Figure 6A,C). Interestingly, on the dimensions 2 and 3, the DIO+P group was positively correlated with variables linked to the high caecal bacteria content and related to taste sensations (Figure 6D,E). In brief: DIO+P mice exhibited a healthier pattern than DIO mice and were closer to C & C+P mice.

**Figure 6.** Principal component analysis performed from studied variables in mice subjected to a regulatory chow or an obesogenic diet alone (C and DIO) or supplemented with 10% Prebiotic (C+P and DIO+P). (**A**) Confidence ellipse analysis. Cluster distribution along the dimension 1 & 2. Each dot represents a mouse; (**B**) Arrows represent the direction of each variable in the 2-dimensional PCA space; (**C**) Cluster distribution along the dimension 1 & 3. Each dot represents a mouse; (**D**) Arrows represent the direction of each variable in the 2-dimensional PCA space; (**E**) Variables significantly representative of the 4 clusters (ranking in descending order of importance) and their respective abbreviations.

\$NN \$NNHUPDQVLD

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#### **4. Discussion**

Deciphering the functional links between the nutritional obesity, taste sensitivity and eating behavior is a challenge that might open new corrective nutritional and/or pharmacological interventions to fight obesity. Nevertheless, how diet-induced obesity affects the taste perception is not yet fully understood.

Chronic consumption of an obesogenic diet (e.g., *saturated high fat diet*) leads to a shift in the gut microbiota composition and a progressive accumulation of body fat. These changes might promote a chronic low-grade inflammation (e.g., endotoxin release and production of pro-inflammatory cytokines) and a new endocrine balance (e.g., drop of GLP-1 and rise of leptin) decreasing the orosensory acuity (at taste bud level) and related reward response (corticomesolimbic level). Taken together these alterations might promote an over-consumption of energy-dense foods to compensate the sensory deficit.

According to our present knowledge, the following scenario has been proposed [28]. Gut dysbiosis and increased fat mass elicited by the chronic consumption of an obesogenic diet, induce a low-grade inflammation (e.g., LPS release and proflammatory cytokines) associated with an endocrine unbalance (e.g., low GLP-1 and high leptin levels). Collectively, these systemic changes might blunt both the taste acuity (taste bud level) and the taste-driven reward behavior (corticomesolimbic level), modifying food choices to reach an expected hedonic satisfaction. This new gustatory phenotype might lead to a preferential consumption of highly palatable foods, rich in sugar and fat, and thus to a growing obesity (Figure 7). Especially, the putative role of the gut microbiota on the taste efficiency remains to be established.

**Figure 7.** Functional relationships between diet-induced obesity and taste sensitivity in the mouse: Working model.

In the present study, we explored the impact of a preventive prebiotic supplementation on the orosensory responsiveness to a prototypical tastant (i.e., sucrose) in mice fed a standard chow or an obesogenic diet. Multivariate analysis clearly shows that the prebiotic supplementation poorly affects studied variables in lean mice, C and C+P groups being partially overlapping in contrast to what was found in obese (DIO) mice. Indeed, DIO+P group appeared as an independent cluster found in an intermediate position between lean and obese mice, suggesting that addition of prebiotic partially counteracts the deleterious effects of HFD. Interestingly, DIO+P group was found to be mainly depicted by variables related to gut microbiota and taste sensations. Addition of 10% inulin, not only corrected the negative effects of HFD on gut microbiota in DIO mice, but also improved their orosensory response to sweet stimuli. This last prebiotic-mediated change was found using two complementary behavioral approaches (two-bottle choice test and gustometer exploration). Collectively, our behavioral data reported a partial covery of the sweet taste sensitivity in DIO+P mice, as compared to controls, in relation with a better hedonic response to sweet stimuli (i.e., rise of lick number = "liking") without motivational change (i.e., same block number = "wanting") resulting in a correction of the preference deficit observed in DIO mice. A distinct impact of P supplementation on "liking" and "wanting" component of the food reward was also reported using an operant-responding performance test in DIO mice [29]. Altogether, our data raise the question of the implied mechanisms.

GLP-1 is produce in the gut by the enteroendocrine L Cells via a differential post-transcriptional processing of the proglucagon gene. This peptide hormone regulates the digestive tract (ileal brake), glucose homeostasis (incretin action) and appetite/satiation (anorexigen effect) [30]. It is known that P increase the number of endocrine L cells in the jejunum and in the colon of rodents and promote the production and release of the active forms of GLP-1, thereby decreasing glycaemia [31]. Several lines of evidence support an implication of GLP-1 in the improvement of sweet taste sensitivity after

microbiota manipulation. Firstly, GLP-1 R is found both in afferent fibers innerving the gustatory papillae [32] and mesolimbic areas involved in the reward pathway (i.e., ventral tegmental area—VTA and nucleus accumbens—NAc) [33]. Secondly, GLP-1 R-null mice display a reduced response to sweet tastants during behavioral tests, suggesting that the GLP-1 signaling enhances the sweet taste sensitivity in this species [32]. Thirdly, GLP-1 reduces hedonic value of food by suppressing VTA and NAc dopamine signaling [34]. Finally, a rise of the proglucagon mRNA levels was specifically found in the caecum from DIO+P mice. Therefore, an involvement of prebiotic-induced GLP-1 release in regulation of sweet taste sensitivity is likely. Further studies combining GLP-1 R-null mice and prebiotic manipulations are required to fully establish the causal role of GLP-1 in the prebiotic impact on the gustation.

Obesity-induced endotoxemia is an alternative, but not exclusive, possible cause of implication of gut microbiota in the taste dysfunction since gustatory papillae are LPS-sensitive and display a pro-inflammatory gene profile in DIO mice [14]. However, a chronic infusion of LPS at a level similar to that observed in DIO mice was not sufficient to alter the spontaneous preference for oily solution in lean mice subjected to two-bottle choice tests. Although, the response to a sweet tastant was not analyzed in this study, it is likely that a LPS-induced low-grade endotoxemia alone does not explain the change in the orosensory perception observed in DIO mice [14].

In conclusion, this study brings the first demonstration that a gut microbiota manipulation can affect the orosensory perception of sweet compounds. These results are consistent with a recent study revealing that human volunteers shifting their habits towards a diet based on the daily consumption of P-rich vegetables showed a reduced desire to eat sweet, fatty, and salty foods, together with an increased hedonic attitude for some inulin-rich vegetables [30]. The present study also helps to reveal the complexity of the homeostatic system responsible for the proper functioning of the taste system whose disruption, undoubtedly, contributes to the establishment of nutritional obesity.

**Author Contributions:** Conceptualization, A.M.N., N.M.D. and P.B.; Data curation, A.B.; Formal analysis, A.M.N., N.M.D. and P.B.; Funding acquisition, N.M.D. and P.B.; Investigation, A.B., D.A., A.M.N., A.D. and L.B.B.; Methodology, A.B., D.A., A.D. and L.B.B.; Project administration, A.B. and P.B.; Supervision, P.B.; Writing—original draft, P.B.; Writing—review & editing, A.B., A.M.N., N.M.D. and P.B.

**Funding:** This study was supported by grants from the French National Research Agency: sensoFAT-2 project ANR-12-BSV1-0027-01 and ANR-11-LABX-0021-LipSTIC. NMD is a recipient of FRS-FNRS grants and of grant from Wallonia (FOOD4GUT project, convention 1318148).

**Acknowledgments:** We thank Jean-Christophe Blanchard, Guillaume Maquart and Jean-François Merlin for animal management and technical assistance.

**Conflicts of Interest:** None to disclose.

#### **Abbreviations**

#### **References**

© 2019 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/).


**4. Recent research advances**

The approach to progress further is to increase the efficiency as well as decrease the cost of the solar cells. Therefore new concepts and new cell structures should be brought in the development of the film solar cells. One of the plausible solutions is to implant nanostructures in the conventional thin film photovoltaic devices. Zinc oxide (ZnO) nanorod arrays are one of the nanostructures that can be implanted in the solar cells. ZnO nanostructures can be grown on top of the CIGS solar cells' window layers as an antireflective coating layer or implanted into the solar cells. On the one hand, the implanted nanostructures will decrease the reflection and increase the light path due to light coupling effects. On the other hand, the ZnO nanostructures will put the electrode close to the photoinduced carrier generation area with larger carrier collection function. It will assist in boosting the solar cells' efficiency by carrier collection enhancement.

#### **4.1. Photon management**

Thin film photovoltaic device technology relies on light management to enhance light absorption in thin absorber layers. One of the plausible solutions is to implant nanostructures in the conventional thin film photovoltaic devices. For example, the zinc oxide (ZnO) nanorod arrays can be implanted in the CIGS solar cells. The use of the ZnO nanorods in the thin film solar cells is an effective way to decrease the reflection. The variation of the geometrical parameters of the ZnO nanorods, such as the diameter, the height and the density can lead to an optimum which results in the maximal absorption in the absorber. An approach of a rigorous threedimensional (3D) modeling based on the finite element method (FEM) can be used to simulate and optimize the light absorption in the Cu(In,Ga)Se2 absorbers with nanostructures.

Modeling the optical properties of the Cu(In,Ga)Se2 absorbers with nanostructures starts by defining the characteristics of the incident light. In the stationary case, the electric and magnetic field can be expressed as follows.

$$E\left(\mathbf{x}, \mathbf{y}, z, t\right) = \bar{E}\left(\mathbf{x}, \mathbf{y}, z\right) e^{-i\alpha t} \tag{1}$$

$$H\left(\mathbf{x}, \mathbf{y}, z, t\right) = \tilde{H}\left(\mathbf{x}, \mathbf{y}, z\right) e^{-i\alpha t} \tag{2}$$

where *E* is the electric field, *H* is the magnetic field and *ω* is the angular frequency related by *ω* = 2π*f* to the frequency *f* of light. The mathematical model of the light propagation is Maxwell's equation:

$$\mathbf{E}\left(\mathbf{x},\mathbf{y},z,t\right) = \tilde{\mathbf{E}}\left(\mathbf{x},\mathbf{y},z\right)e^{-i\alpha t} \tag{3}$$

Copper Indium Gallium Selenide Thin Film Solar Cells http://dx.doi.org/10.5772/65291 191

$$
\nabla \cdot \boldsymbol{H} = 0 \tag{4}
$$

$$
\nabla \times \mathbf{E} = -\frac{\partial \mu \mathbf{H}}{\partial t} \tag{5}
$$

$$
\nabla \times \mathbf{H} = \mathbf{J} + \frac{\partial \epsilon \mathbf{E}}{\partial t} \tag{6}
$$

where *E* is the electric field, *H* is the magnetic field, *ρ* is the electric charge density, *ε* is the permittivity, *μ* is the magnetic permeability and *J* is the current density. In the time harmonic case the magnetic field can be determined by the electric field and vice versa. From Maxwell's equations we can find

**4. Recent research advances**

enhancement.

190 Nanostructured Solar Cells

**4.1. Photon management**

field can be expressed as follows.

equation:

The approach to progress further is to increase the efficiency as well as decrease the cost of the solar cells. Therefore new concepts and new cell structures should be brought in the development of the film solar cells. One of the plausible solutions is to implant nanostructures in the conventional thin film photovoltaic devices. Zinc oxide (ZnO) nanorod arrays are one of the nanostructures that can be implanted in the solar cells. ZnO nanostructures can be grown on top of the CIGS solar cells' window layers as an antireflective coating layer or implanted into the solar cells. On the one hand, the implanted nanostructures will decrease the reflection and increase the light path due to light coupling effects. On the other hand, the ZnO nanostructures will put the electrode close to the photoinduced carrier generation area with larger carrier collection function. It will assist in boosting the solar cells' efficiency by carrier collection

Thin film photovoltaic device technology relies on light management to enhance light absorption in thin absorber layers. One of the plausible solutions is to implant nanostructures in the conventional thin film photovoltaic devices. For example, the zinc oxide (ZnO) nanorod arrays can be implanted in the CIGS solar cells. The use of the ZnO nanorods in the thin film solar cells is an effective way to decrease the reflection. The variation of the geometrical parameters of the ZnO nanorods, such as the diameter, the height and the density can lead to an optimum which results in the maximal absorption in the absorber. An approach of a rigorous threedimensional (3D) modeling based on the finite element method (FEM) can be used to simulate

and optimize the light absorption in the Cu(In,Ga)Se2 absorbers with nanostructures.

( ,,, ,, ) ( ) *i t xyzt xyz e*-

( ,,, ,, ) ( ) *i t xyzt xyz e*-

( ,,, ,, ) ( ) *i t xyzt xyz e*-

where *E* is the electric field, *H* is the magnetic field and *ω* is the angular frequency related by *ω* = 2π*f* to the frequency *f* of light. The mathematical model of the light propagation is Maxwell's

Modeling the optical properties of the Cu(In,Ga)Se2 absorbers with nanostructures starts by defining the characteristics of the incident light. In the stationary case, the electric and magnetic

w

w

w

*E E* = % (1)

*H H*= % (2)

*E E* = % (3)

$$H = -\frac{1}{i\rho\sigma} \nabla \times \mathbf{E} \tag{7}$$

$$\mathbf{E} = \frac{\mathbf{l}}{\dot{\iota}\dot{\alpha}} \nabla \times \mathbf{H} \tag{8}$$

Thus the electric field in the electromagnetic will be taken into account in the simulation. The electric field distribution of the incident light is described by unpolarized stationary plane waves.

$$
\tilde{E} = A e^{i(\mathbf{k} \cdot \mathbf{r} + \boldsymbol{\varphi})} \tag{9}
$$

where *A* is the constant, *k* is the wave vector, *r* is the position vector and *φ* is the phase angle. For simplifying the simulation, the wave vector of the incident light is perpendicular to the surface of the Cu(In,Ga)Se2 absorbers and *ϕ* is taken as 0.

The nanorod arrays as a light coupling component can be incorporated into two types of solar cells, i.e., the bifacial solar cells and the superstrate solar cells. As shown in **Figure 7(a)**, both sides of the bifacial solar cells are illuminated and the ZnO nanorods work as a light-coupling component and a nanocontact electrode. The intrinsic ZnO nanorods in the superstrate solar cells play a buffer role. Optionally a buffer layer can be inserted between the ZnO nanorods and the absorber in the superstrate solar cells. Since both the bifacial and superstrate solar cells possess the same components which are glass/transparent conductive oxides/ZnO nanorods/ Cu(In,Ga)Se2 absorber, the same components are regarded as the simulated structure. As illustrated in **Figure 7(c)**, the simulated structure can be divided into two parts: the glass and the transparent conductive oxides/ZnO nanorods/Cu(In,Ga)Se2 absorber (TNA) structure.

**Figure 7.** (a) The schematic structure of the bifacial Cu(In,Ga)Se2 solar cell. (b) The schematic structure of the superstrate Cu(In,Ga)Se2 solar cell. (c) The schematic drawing of the simulated structure. The TNA is the abbreviation for the transparent conductive oxides/nanorods/absorber.

As shown in **Figure 8**, the total reflection consists of the specular reflectance and the scattered reflectance:

**Figure 8.** The schematic structure of the simulated structure. The structure is illuminated through the glass side. The figure defines the incident light intensity *I*0, the specular intensity *ITNA* illuminating the glass/TNA interface, the specular reflectance *R*s the scattered reflectance , *R*sc the specular reflectance of the *R*s TNA structure and the scattered reflectance of the *R*sc TNA structure.

where *RS* and *RSC* are the specular reflectance and the scattered reflectance, respectively. The specular reflectance is given as:

$$R^S = R\_{\text{glass}} + \left(1 - R\_{\text{glass}}\right)^2 \cdot \mathbf{r}\_{\text{glass}}^2 \cdot R\_{\text{TM}}^S \cdot \sum\_{\nu=0}^{\kappa} \left[R\_{\text{glass}} \cdot \mathbf{r}\_{\text{glass}}^2 \cdot R\_{\text{TM}}^S\right]^\nu \tag{11}$$

where *Rglass* is the reflectance at the air/glass interface with the incident of light illuminating the interface, is theabsorptance of the thick glassand is the reflectance of the TNA structure illuminated from the glass. The *Rglass* is calculated from the Fresnel's equation:

$$R\_{g\_{\rm glass}} = \left| \frac{\mathbf{n}\_{g\_{\rm glass}} - 1}{\mathbf{n}\_{g\_{\rm glass}} + 1} \right|^2 \tag{12}$$

$$
\mathfrak{m}\_{g\_{\text{class}}} = n + i \cdot k \tag{13}
$$

where *nglass* is the complex refractive index of the glass, *n* is the refractive index of the glass and *k* is extinction coefficient of the glass. The *τglass* is calculated as:

$$
\sigma\_{\text{glass}} = \exp\left(-\alpha\_{\text{glass}} \cdot d\_{\text{glass}}\right) \tag{14}
$$

$$a\_{\text{glass}} = \frac{4\pi k}{\lambda 0} \tag{15}$$

where *αglass* is the optical absorption coefficient of the glass and *λ*0 is the vacuum wavelength of light.

The reflection, transmission and absorption in TNA structure can be calculated using the finite element method (FEM) method. The FEM is a numerical technique used in finding solutions of Maxwell equations. The volume of the simulated structure is meshed and electromagnetic field components are computed. Once the simulation of the design (structure, boundary conditions, light sources and frequency range) is set up, FEM process operates through three steps: meshing, solving and postprocessing. In the first step, for a given wavelength, the volume of the designed structure is discretized. During the second step, the resulting system of the equations is solved. In the third step, the reflection, transmission and absorption of the simulated structure is computed. The reflectance *RTNA* is given by:

$$R\_{\text{TNA}} = R\_{\text{TNA}}^S + R\_{\text{TNA}}^{\text{SC}} \tag{16}$$

where is the scattered reflectance of the TNA structure. Since tracing the scattered reflected light of the TNA structure in the thick glass and air/glass interface dramatically boosts the computation amount, it is not possible to get the results using FEM. The total reflectance is approximately calculated as:

$$R^{Total} = R\_{g\_{\text{class}}} + R\_{\text{TNA}} \tag{17}$$

#### **4.2. Nanostructures**

**Figure 7.** (a) The schematic structure of the bifacial Cu(In,Ga)Se2 solar cell. (b) The schematic structure of the superstrate Cu(In,Ga)Se2 solar cell. (c) The schematic drawing of the simulated structure. The TNA is the abbreviation for

As shown in **Figure 8**, the total reflection consists of the specular reflectance and the scattered

**Figure 8.** The schematic structure of the simulated structure. The structure is illuminated through the glass side. The figure defines the incident light intensity *I*0, the specular intensity *ITNA* illuminating the glass/TNA interface, the specu-

where *RS* and *RSC* are the specular reflectance and the scattered reflectance, respectively. The

2 2 2 0

where *Rglass* is the reflectance at the air/glass interface with the incident of light illuminating

µ =

 t

= + é ù ×- ë × å û (11)

1 ··· *u S S S glass glass glass TNA glass glass TNA u*

structure illuminated from the glass. The *Rglass* is calculated from the Fresnel's equation:

*RR R R R R* t

the scattered reflectance , *R*sc the specular reflectance of the *R*s

( )

the interface, is theabsorptance of the thick glassand

*Total S SC R RR* = + (10)

TNA structure and the scattered reflec-

is the reflectance of the TNA

the transparent conductive oxides/nanorods/absorber.

reflectance:

192 Nanostructured Solar Cells

lar reflectance *R*s

TNA structure.

specular reflectance is given as:

tance of the *R*sc

An antireflective or antireflection coating (ARC) is a type of optical coating applied to the surface of lenses and other optical devices to reduce reflection. This improves the efficiency of the system since less light is lost. The ARC is generally made of a dielectric layer, e.g., MgF2, SiN, TiO2 or ZnS, with a thickness of a quarter-wavelength [22–25]. Another approach of

boosting light coupling is to structure the surface of the solar cells by means of the moth-eye effect. Moths' eyes have an unusual property: their surfaces are covered with a natural nanostructured film which eliminates reflections. The ZnO nanorods have an appropriate refractive index of ~2. Coating an absorber surface leads to continuously varying refractive index profiles in the tapered ZnO nanorods. Consequently they suppress the surface reflection via a subwavelength structure. Therefore the tapered ZnO nanorods are a promising light coupling layer for ARC of solar cells as well as solar thermal selective surfaces.

**Figure 9.** The ZnO nanowire arrays prepared by the hydrothermal method.

**Figure 2** has shown the cross section scanning electron microscopy (SEM) images of ZnO nanorods on CIGS. The ZnO nanorod arrays serving as an ARC were electrodeposited on thin film Cu(In,Ga)Se2 solar cells. According to the research, the weighted reflectance was reduced from 8.6 to 3.5%. Highest increases in both the saturation-current of 5.7% and the solar cells efficiency of 7.2% were achieved [26]. In addition, the ZnO nanorod arrays have been incorporated into a superstrate or a bifacial cell structure of the other thin film photovoltaic devices such as DSSCs [27], QDSCs [28] and organic solar cells [29].

ZnO nanostructures have been prepared by various methods [30–46]. The solution-based fabrication routes including hydrothermal method and electrochemical deposition (ECD) method are the only ways to grow ZnO nanostructures at a low temperature down to the range between 60°C and 90°C [35–46]. Meanwhile the growth can be achieved over large areas up to 10 cm × 10 cm [44–46]. **Figure 9** shows the SEM image of the ZnO nanowire arrays prepared by the hydrothermal method.

The ECD technique consists of an electrochemical cell and accessories for providing a galvanic current which flows through the electrochemical cell. The cell usually contains electrolyte and electrodes. The first of these electrodes has been named the anode. At an anode, electrons go away from the electrolyte to the anode. Hence, an anodic reaction must generate electrons. The second has been named the cathode. The cathode supplies electrons to the positively charged cations which flow to it from the electrolyte. Within the electrolyte, the current flow is always from the anode to the cathode, which means the electron transports from the cathode to the anode. In the external parts of the closed circuit ("external" relative to the electrolyte), the current flow is from cathode to anode, which equates the electron transport from the anode to the cathode [47].

**Figure 10.** (a) Two-electrode system and (b) three-electrode system.

boosting light coupling is to structure the surface of the solar cells by means of the moth-eye effect. Moths' eyes have an unusual property: their surfaces are covered with a natural nanostructured film which eliminates reflections. The ZnO nanorods have an appropriate refractive index of ~2. Coating an absorber surface leads to continuously varying refractive index profiles in the tapered ZnO nanorods. Consequently they suppress the surface reflection via a subwavelength structure. Therefore the tapered ZnO nanorods are a promising light

**Figure 2** has shown the cross section scanning electron microscopy (SEM) images of ZnO nanorods on CIGS. The ZnO nanorod arrays serving as an ARC were electrodeposited on thin film Cu(In,Ga)Se2 solar cells. According to the research, the weighted reflectance was reduced from 8.6 to 3.5%. Highest increases in both the saturation-current of 5.7% and the solar cells efficiency of 7.2% were achieved [26]. In addition, the ZnO nanorod arrays have been incorporated into a superstrate or a bifacial cell structure of the other thin film photovoltaic devices

ZnO nanostructures have been prepared by various methods [30–46]. The solution-based fabrication routes including hydrothermal method and electrochemical deposition (ECD) method are the only ways to grow ZnO nanostructures at a low temperature down to the range between 60°C and 90°C [35–46]. Meanwhile the growth can be achieved over large areas up to 10 cm × 10 cm [44–46]. **Figure 9** shows the SEM image of the ZnO nanowire arrays prepared

The ECD technique consists of an electrochemical cell and accessories for providing a galvanic current which flows through the electrochemical cell. The cell usually contains electrolyte and electrodes. The first of these electrodes has been named the anode. At an anode, electrons go away from the electrolyte to the anode. Hence, an anodic reaction must generate electrons. The second has been named the cathode. The cathode supplies electrons to the positively charged cations which flow to it from the electrolyte. Within the electrolyte, the current flow is always from the anode to the cathode, which means the electron transports from the cathode to the

coupling layer for ARC of solar cells as well as solar thermal selective surfaces.

**Figure 9.** The ZnO nanowire arrays prepared by the hydrothermal method.

such as DSSCs [27], QDSCs [28] and organic solar cells [29].

by the hydrothermal method.

194 Nanostructured Solar Cells

During the ECD process, the electrodeposited products are deposited on one of the electrodes. The electrode is the working electrode (WE). However, the WE is not enough for the ECD process. At least another electrode is necessary for allowing current to flow. In the simplest case a two-electrode cell is used for ECD (**Figure 10(a)**). The second electrode is used both as the reference electrode (RE) to measure the WE potential and as a counter electrode (CE) to allow current to flow. However, the potential of the second electrode changes accordingly when there is a current flow, which has been termed electrode polarization. In order to measure the accurate WE potential, a three-electrode cell containing a WE, a CE and a RE is more common (**Figure 10(b)**). A current flows between the WE and CE, while the potential of the WE is measured against the RE. No current flows in the circuit of the RE/WE, which therefore is not polarized [48]. The glass substrates coated by FTO and AZO transparent conductive oxides are used as the WE. The electrochemical cell was placed in a thermoregulated bath. The liquid electrolyte contains zinc salts and additives. During the experiments the liquid electrolyte in which there is a magnetic stirring bar is agitated. A schematic illustration of the setup for the ECD process is shown in **Figure 11**. The electrochemical process is controlled and recorded by a potentiostat/galvanostat.

**Figure 11.** Schematic description of the electrochemical deposition setup.


*2.2. Synthesis and Characterisation*

Synthesis of **H2L1**: A suspension of l-leucine (150 mg, 1.15 mmol) and 9,10-dimethyl-9,10-dihydro-2,3,6,7-tetracarboxyl-9,10-ethanoanthracene (190 mg, 0.463 mmol) was added to a round-bottomed flask with acetic acid (20 mL) and heated at 120 ◦C for four nights, during which time all solids were taken into solution. After this time the light-yellow solution was poured over ice, forming a light-yellow precipitate, which was recovered by filtration, washed with water and dried in vacuo. Yield: 172.5 mg, 62%. m.p. 195–198 ◦C. Found: C, 63.56; H, 5.96; N, 3.99%; C34H36N2O8. Requires: C, 63.32; H, 6.15; N, 3.89%. δH (400 MHz, *d*6–DMSO): 0.81 (m, 12H, CH2CH*Me*2); 1.36 (m, 2H, CH2C*H*Me2); 1.67 (s, 4H, C*H*2C*H*2); 1.8 (m, 2H, C*H*2CHMe2); 2.21 (s, 6H, Mecore); 2.16 (m, 2H, C*H*2CHMe2); 4.74 (dd, 3*J* = 12, 3*J* = 4.2 Hz, 2H, NC*H*); 7.78 (s, 4H, ArH). δC (100 MHz, *d*6–DMSO): 21.1; 23.5; 26.1; 37.1; 43.4; 44.3; 50.6; 116.6; 129.7; 152.8; 168.0; 171.3. υmax/cm−1: 2958w; 2871w; 1841w; 17,700m; 1702s; 1617m; 1534w; 1465w; 1442w; 1377s; 1321w; 1250m; 1205m; 1155m; 1088w; 991w; 911w; 869w; 752m. *m*/*z* (ES−): 504.1 ([M-Leu + OH]−, calculated for C26H26NO8−, 504.2), 100%; 599.2 ([M − H]−, calculated for C34H35N2O8−, 599.2) 93%.

Synthesis of **H2L2**: The synthesis was based on a literature report with minor modifications [52]. Pyromellitic dianhydride (0.707 g, 3.24 mmol) and l-alanine (0.752 g, 8.44 mmol) were added to a round-bottomed flask with acetic acid (40 mL) and heated under reflux overnight, giving a colourless solution. Concentrated hydrochloric acid (20 mL) was added to the solution, which was allowed to concentrate by evaporation overnight to give a white powder. The product was collected by vacuum filtration, washed with cold water, added dropwise, and dried in air. Yield: 0.703 g, 60%. δH (400 MHz, *d*6–DMSO): 1.59 (d, 6H, Me); 4.96 (q, 2H, NC*H*); 8.30 (s, 2H, ArH); 13.32 (s, 2H, CO2H). δC (100 MHz, *d*6–DMSO): 15.14; 48.02; 118.45; 137.26; 165.92; 171.14. υmax/cm−1: 3570w; 3146w; 3007w; 2953w; 1758w; 1702s; 1618w; 1561w; 1459m; 1383m; 1364m; 1304m; 1252w; 1208w; 1171w; 1154w; 1132w; 1104w; 1067m; 1018wm; 934m; 854m; 822w; 802w; 761w; 726m; 677w. *m*/*z* (ES−): 315.1 ([M-COO-H]−, calculated for C15H11N2O6−, 315.1) 100%; 359.1 ([M − H]−, calculated for C16H11N2O8−, 359.1) 20%.

Synthesis of [Cu4(**L1**)4(DMF)(OH2)3]·3.5DMF·9.5H2O (compound **1**): **H2L1** (10 mg, 16.7 μmol) and Cu2(OAc)4 (6.0 mg, 33.3 μmol) were added to DMF (1 mL) and sonicated to dissolve. The solution was heated at 85 ◦C for two nights during which time light blue/green block-shaped crystals formed, which were recovered by vacuum filtration. Yield: 2.2 mg, 61%. Found: C, 56.04; H, 5.58, N, 5.59%; C139H149N9O36Cu4·3.5DMF·9.5H2O ([Cu4(**L1**)4 (DMF) (OH2)3]·3.5DMF·9.5H2O). Requires: C, 56.14; H, 6.02; N, 5.31%. υmax/cm−1: 2955w; 2869w; 1771w; 1709 s; 1637m; 1465w; 1411m; 1377s; 1351m; 1259w; 1204w; 1156w; 1101w; 1062w; 992w; 913w; 870w; 805w; 771w; 755m; 729w; 660m. *m*/*z* (ES+): 2649.7 (cage + H)+, calculated for C136H136Cu4N8O32+, 2647.645; 2708.7 (cage + H2O + MeCN + H)+, calculated for C138H141Cu4N9O33+, 2706.683. TGA: On-set, 70 ◦C, mass loss = 10% (calculated 11.8% for loss of two non-coordinated H2O and 4.5 DMF molecules and all coordinated solvent); decomp, 350 ◦C.

Synthesis of [Co8(**L2**)8(DABCO)3(OH2)8]·solvent (compound **2**): **H2L2** (10 mg, 28 μmol), DABCO (1.6 mg, 14 μmol), and Co(NO3)2·6H2O (8.1 mg, 28 μmol) were added to DMF (2 mL) and sonicated to dissolve. The solution was heated at 100 ◦C for three nights during which time crystalline royal blue plates formed. Only one or two small single crystals formed, preventing bulk analysis. *m*/*z* (ES−): 955.9 ([Co4(**L2**)4(DABCO)(H2O)3(OH)]NO3)2−, calculated for C70H59Co4N11O392−, 956.0; 1841.9 ([Co4(**L2**)4(DABCO)]NO3)-, calculated for C70H52Co4N11O35−, 1842.0.