**Preface to "Mineralogy and Geochemistry of Ruby"**

The studies in this volume specifically focus on ruby in its diverse connotations, which inevitably incorporate studies of allied minerals that accompany the presence of ruby in nature. This applies to the enclosing host rocks, mineral intergrowths with ruby, and the internal mineral inclusions within the ruby crystal structures. Some of these allied minerals provide radiometric elements for geochronological dating that helps constrain the ages of ruby growth, while others provide important clues to the geological and tectonic environments that formed ruby and affected its growth.

This Special Issue investigates one of the most sought after and valuable gem minerals within the annals of natural gemstone studies, namely ruby, the red-colored gem variety of corundum. The color results from a sufficiency of the trace element chromophore cation Cr3+, with modifications in color induced by other trace elements. Most of the contributions in this volume include invaluable data on the trace element variations that can be considered against color images of the studied rubies.

It also follows up on the publication of a preceding Minerals Special Issue on research advances that covered a wider spectrum of gemstones, "Mineralogy and Geochemistry of Gems" [1]. That volume included several studies on ruby. These are referenced here as useful supplementary studies for readers to consider [2–4], along with the studies presented in this Special Issue on ruby. During production of this Special Issue, other publications related to research topics on ruby continued to appear in the literature. One of special interest showed the extent that Cr can substitute into the ruby structure [5]. Ruby from secondary deposits from volcanic exposures at Mount Carmel, Israel, recorded contents up 32 wt % Cr2O3. These high levels were attributed to exceptional reducing conditions of formation, although the precise source of the ruby remains for further study.

### **Frederick Lin Sutherland, Khin Zaw**

*Editors*


2.1.2. Life Cycle Inventory (LCI)

This phase aims to model the system via data collection. Data collection must follow the system boundaries and FU definition. Both scenarios are based on real case studies data from the operational power units. Data was collected within the time frame of one year. For the CCT units, data was collected from the technical project reports [12,13].

#### 2.1.3. Life Cycle Impact Assessment (LCIA)

LCIA comprises classification, characterization, normalization, and weighting process, where the energy and mass flows of the previous step are transformed into environmental impacts. These environmental impacts are calculated according to the selected impact method. Each substance of the assessed systems is multiplied by the characterization factors that determine the potential contribution to the specific environmental impact. An optional step for LCIA is normalization. Normalization enables the comparison between different impact categories. Normalization uses the dataset of the reference indicators of environmental impacts for the European region or worldwide. Thus, the results are values that show the contribution to the sum of European (or world) impacts in the specific environmental category [14].

In this study, the chosen LCIA method is the ReCiPe v.1.08 method of GaBi software at the midpoint level. Based on Carvalho et al. [2], the ReCiPe method is the LCIA, which is intended and tailored for the comprehensive environmental process impact assessment. Also, the ReCiPe method is highly recommended by the EU commission [15]. The characterized midpoint environmental indicators are ozone depletion (OD), human toxicity (HT), ionizing radiation (IR), photochemical oxidant formation (POF), particulate matter formation (PMF), terrestrial acidification (TA), climate change (CC), terrestrial ecotoxicity (TET), agricultural land occupation (ALO), urban land occupation (ULO), natural land transformation (NLT), marine ecotoxicity (MET), marine eutrophication (ME), fresh water eutrophication (FE), fresh water ecotoxicity (FET), fossil depletion (FD), metal depletion (MD), and water depletion (WD) [16].

#### 2.1.4. Interpretation

In this phase, the results are further processed and discussed. The interpretation identifies the significant environmental problems and suggests the optimization of the process toward lowering the impacts. Moreover, it describes the hotspots from assessed processes and indicates significant impact categories. The identification of the significant environmental problems can be done through a Pareto analysis, using the statistical Pareto rule (80/20 rule) [17]. It determines that 20% of all impact categories contributes to 80% of the total environmental impact [2]. Data for Pareto analysis is normalized.

To summarize, the LCA method used in this study was performed by the ReCiPe method. The characterization and normalization were done according to the midpoint level of ReCiPe v 1.08. Moreover, additional Pareto analysis were done to specify key environmental impacts, and further analysis of the concrete processes was made to identify the influence on the environmental impacts.


**1. Introduction**

198 Electropolymerization

Microarrays are becoming a common tool in biology for screening large numbers of samples. However, the relevance of such an approach depends on the reproducibility of measurements that is directly linked to the stability of the probes grafted on the chip especially when many cycles of regeneration are performed. Indeed, regeneration of microarray chips is of great interest in improving the throughput and reducing the costs. The impact of treatments performed to remove bound ligands in order to reuse the chip depends partially on the grafted probe and on the characteristics of the probe-ligand interaction. Thus DNA microarrays are considered to be stable as oligonucleotides are highly stable molecules and hybridization reaction depends very little on the conformation of the partners; so, multiple regeneration/rehybridization procedures can be carried out without major loss of signal intensity (Benters et al., 2002, Donhauser et al., 2009). Stability of the probes is much more difficult to achieve when proteins are used. Indeed, such molecules are very complex and heterogeneous, thus there are no general rules to account for their behaviour upon different regeneration steps. Moreover, protein-protein interactions are highly susceptible to partner conformation. This is particularly true in the case of antigen (Ag) – antibody (Ab) binding, and several papers mention a loss of signal after the second or third regeneration step (Barton et al., 2008, Yakovleva et al., 2003).  Thus they are often used for antibody profiling (Cherif et al., 2006, Halperin et al., 2011, Neuman de Vegvar et al., 2003). However, there is no study dealing with the stability of such microarrays during a large samples screening (>20). Furthermore, a conformational change is not the only parameter which can impact on ligand binding. In this chapter, our aim was to analyze the evolution of the signals during samples screening and to determine which parameters are involved in the decay of the chip efficiency: grafting method, saturation step, probe itself or probe-ligand interactions, presence of protease activity in the sample. We use a microarray system based on pyrrole electropolymerization

Stability of Peptide in Microarrays: A Challenge for High-Throughput Screening 201

France) and Dr P. Morand (Centre Hospitalo-Universitaire, Grenoble, France). Serums were

Glass prisms coated with a 50 nm gold layer were obtained from Genoptics-HORIBA Scientific (Chilly-Mazarin, France). Electrodeposition was performed using an Omnigrid Micro robotic arrayer (Genoptics-HORIBA Scientific). Surface Plasmon Resonance (SPR) signals were monitored using a surface plasmon resonance imager (SPRi-Plex from Genoptics-HORIBA Scientific). Measurements were performed using SPRi dedicated software (Genoptics-HORIBA Scientific). Sample injections were ensured by a 231XL

Peptides (100µM) were grafted at least in triplicat on the gold surface of the biochip by electrochemical copolymerization of pyrrole-peptide conjugates using a solution containing 20 mmol/L pyrrole, 100 µmol/L of pyrrolated peptides and 10% glycerol in phosphate buffer (50 mmol/L). The polymerisation step was performed by a short 100 ms electrical pulse (2 V) between the counter electrode located in the needle of the microarrayer and the prism gold layer (Cherif et al., 2006, Villiers et al., 2009). Another grafting method based on electro-deposition of diazonium-peptide adducts (Corgier et al., 2009) was also used, as indicated in the text. The prism was rinsed with distilled water and saturated at room temperature for 2h using various mediums as indicated in the text. After washing with

All reactions were carried out at room temperature, in phosphate-buffered saline (PBS)/0.01%Tween 20. The flow rate in the chamber was 37 µL/min. Reflectivity was measured at 810 nm, at a fixed incidence angle (55°<<56°). After injection of serum (500 µL, 1/50 or 1/200 as indicated in the text), the biochip surface was rinsed with running buffer (10 min) to remove unbound ligands and specific binding was quantified by measuring the change in reflectivity (R) obtained after 10 min washing. The chip was regenerated using 0.1 M HCl-Glycine (pH 2.3) solution for 10 min and stabilized in the running buffer (10 min). Every twelve injections, a cleaning step was performed by injection of 1% SDS (sodium dodecyl sulphate) in water for 10 min followed by running buffer for 20 min. Saturation with NIS (1/25 in PBS), PLL-PEG or PVP as indicated in the text was realized after each

**3.1 Is a grafted peptide sufficiently stable to allow a multiple re-use of the chip?**  To assess the stability of the peptide chip during samples screening, we immobilized C131 peptide using pyrrole electropolymerization and performed multiple injection/regeneration cycles using non immune serum (NIS) with periodical injections of anti-C131 serum to monitor the reactivity with the grafted probe. SDS cleaning was realized every twelve injections, as described in §2.4. The SPR (Surface Plasmon Resonance) signal obtained for each injection was monitored. Results from eight independent experiments are presented in

sampling injector coupled to a 832 temperature regulator (Gilson, Roissy, France).

distilled water, the prism was positioned in the SPRi-Plex and used immediately.

stored at -20°C.

**2.2 Materials**

**2.3 Peptide immobilization on gold**

**2.4 SPRi interaction monitoring**

**3. Results and discussion**

cleaning step.

Fig. 1.

for probe immobilization and surface plasmon resonance imaging (SPRi) for ligand detection. The biological model consists in antigen-antibody interactions, where probes are peptides used as antigens and ligands are antibodies (Ab) contained in serum samples. Our data suggest that modification of the peptide conformation is the main parameter involved in the decrease of the signal observed upon successive uses of the chip. This conformational change leads to both a progressive reduction of the signal due to a decrease of the peptide reactivity with Ab, and the selection of Ab with the highest affinity. This phenomenon can be evaluated and must be taken into account in the analysis of the data resulting from samples screening.


**2.2 Biological mechanisms**

448 Solar Radiation

hydroxyl radicals. Among various AOPs, photocatalytic processes are very attractive for the mineralization (conversion to carbon dioxide, water, and other mineral species) of aqueous pollutants and inactivation of pathogenic microorganisms (Gelover et al., 1999; Bandala and Estrada, 2007; Bandala et al., 2007 & 2008). The use of AOPs for water disinfection, using solar radiation as the energy source, usually referred to as *enhanced photocatalytic solar disinfection* (ENPHOSODIS), has allowed the efficient deactivation of highly resistant microorganisms. Specifically, heterogeneous and homogeneous photocatalysis are the AOPs with the most technological applications because of their ability to remove organic pollutants and their capability to inactivate nuisance microorganisms. Regarding heterogeneous photocatalysis, the use of titanium dioxide (TiO2) as a catalyst has been widely tested and proven effective for deactivating several microorganisms as well as carcinogen cells (Dunlop et al., 2008; Rincon and Pulgarin, 2003; Reginfo et al., 2008; Castillo-Ledezma et al., 2011). In comparison with the heterogeneous arrangement, homogeneous processes also rely on the generation of hydroxyl radicals. Nevertheless, it has been proposed that other highly oxidant species could be involved in pollutant degradation and microorganisms deactivation. Fenton and Fenton-like processes are among the most widely studied methodologies (Bandala et al., 2009; Guisar et al., 2007; Bandala et al., 2011). From the economic point of view, the possibility of using solar energy to promote both homogeneous and heterogeneous photocatalytic processes is an interesting alternative to the use of these technologies in developing countries (Bandala et al., 2011; Blanco et al., 2007). The aim of this chapter is to review the state-of-the-art in the use of solar driven Fenton-like processes for the deactivating waterborne pathogens. It also the goal of this work to discuss the advantages and potential limitations of these treatment processes while analyzing the challenges and opportunities for the application of such technologies at real scale in poor,

isolated regions in developing countries with no access to safe drinking water.

**2. Chemical and biological mechanisms involved in homogeneous**

Fe2+ +H2O2 → Fe3+ +HO• + OH−

[FeOH]2+ +H2O2↔ [Fe(OH)(HO2)]+ +H+

• + HO−

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Fe2+ +HO• → Fe3+ +OH− Fe3+ +H2O ↔ [FeOH]2+ +H+ Fe3+ + H2O2↔ [FeHO2]2+ +H+

[FeOH]2+→ Fe 2++ HO• [FeHO2]2+ → Fe 2+ + HO2• [Fe(OH)(HO2)]+ → Fe 2++ HO2

RH + HO• → R• + H2O

Table 1. Chemical reactions involved in the Fenton reaction (Fe (II) and H2O2).

al., 2008; Gallard and De Laat, 2000; Gallard et al., 1999).

The chemical mechanisms involved in the Fenton reaction are well known since early of the past century. The reactions of iron (II) salts with hydrogen peroxide have been widely studied for decades and the main reactions involved are summarized in Table 1 (Orozco et

**photocatalysis 2.1 Chemistry**

In general, photocatalytic processes, both homogeneous and heterogeneous, in the presence of iron and hydrogen peroxide have been demonstrated effective against a wide variety of resistant microorganism such as viruses (Kim et al., 2010), helminth eggs (Bandala et al., 2011a,b; Guisar et al., 2007), bacteria and spores (Dunlop *et al*., 2008; Bandala *et al*., 2009; Bandala et al., 2011c; Sichel *et al*., 2009; Castillo-Ledezma et al., 2011).

The main mechanism involved in deactivating pathogenic microorganisms is suggested to be related with the cellular damage produced by so-called reactive oxygen species (ROS), mainly hydroxyl (HO•) and superoxide radical (O2 •) as shown in Tables 1 and 2. According to different studies, these ROS are able to modify and eventually destroy the structure of the cell membrane (Alrousan *et al*., 2009, Malato *et al*., 2009), mainly as the result of lipid peroxidation (Dunlop *et al*., 2008; Alrousan *et al*., 2009). The initial damage is produced in the outer lipopolisaccarid and peptidoglycan walls, followed by lipid peroxidation and protein and polysaccarides oxidation (Malato *et al*., 2009; Dalrymple *et al*., 2010) affecting the regulatory function of the cell membrane for the internal and external interchange. The damage produced will further produce failure in the cell's respiratory activity and decrease its permeability, allowing the attack of inner cell components leading to its death (Alrousan *et al*., 2009).

Some studies have also demonstrated that microorganism's deactivation is also improved by the presence of iron derivatives, which have been suggested to show an important inhibitory activity in important microbiological processes such as biofilm generation (Dunlop *et al*., 2008). Cells are used to regulate iron adsorption as a defense mechanism against hydroxyl radical; however once hydroxyl radicals are generated in the intracellular media, as a result of the Fenton-like process by direct attack of the ROS, they are free for reacting with biomolecules (Darlymple *et al*., 2009).

During cellular metabolism some ROS are produced, such as superoxide (O2 •), hydroxyl radical and hydrogen peroxide (H2O2), as a result of cell respiration. However, these oxidizing species are in equilibrium with the immune system defense mechanism through anti-oxidizing enzyme production related to superoxide dismutase (SODs), catalase (CAT) and glutathione peroxidase (GPX) families (Castillo-Ledezma et al., 2011). When microorganisms are exposed to a major oxidative stress, for example ROS produced during a photocatalytic process, enzyme production is no longer capable of eliminating excess radical allowing deep cell damage. In the same way, ROS may produce additional oxidative stress in the cells through Fenton and Heber-Weiss reaction (Dunlop *et al*., 2008) generating damage in all the cell components including proteins, lipids and DNA. In the case of DNA damage, produced by pyrimidine dimmers formation by the generation of covalent bonds among the bases in the same DNA chain (Sichel *et al*., 2009), it generates mutations that may lead to loss of functional capability and death of cell (Malato *et al*., 2009). At the same time, when microorganisms are exposed to ultraviolet radiation (UV, λ ≤ 400 nm) during the photocatalytic reaction, DNA damage occurred directly through the radiation absorption by cell chromophores, which absorb radiation and produce heat. This interaction leads to an increase in ATP and RNA synthesis, jointly with the increase of ROS production. Microorganisms receiving a sub-lethal dose of UV radiation may become resistant to induced oxidative stress, partially recover their defense mechanisms and adapt to oxidative stress generated by exposure to UV radiation alone (tanning effect) (Bandala et al., 2011b).