• CTLA-4:

CTLA-4 is a leukocyte differentiation antigen and a transmembrane receptor on T cells, which shares the B7 ligand with its co-stimulator molecule receptor (CD28). When CTLA-4 binds to B7 it stimulates T-cell anergy, i.e., it participates in the negative regulation of the immune response by inducing a lack of T-cell response and preventing T-cell activation. The antibody to CTLA-4 has the following anti-tumour mechanisms: (1) modulation of tumour-specific immune effector cells, such as CD8+ T cells, to promote their clonal proliferation, (2) removal of Tregs to reduce inhibition of tumour-associated immune response [46].


4. Conclusion

Figure 8.

p.p)

p.p)

a Present work. b Ref. [17]. c Ref. [27].

34

Table 5.

Centre of lens (equator and

Centre of lens (equator and

centerd on equator temporal and nasal.

Ratio of the doses along the plaque's central axis for 20 mm COMS plaque (gold backing + Silastic insert) fully

Theory, Application, and Implementation of Monte Carlo Method in Science and Technology

Silastic insert only

IR06-103Pda

16.21 18.98 17.23 19.00

Gold backing only

b. 16 mm COMS Water medium Gold backing + Silastic IR06-103Pda Theragenics 200b IR06-103Pda Theragenics 200b 6711-125I

15.63 16.34 11.83 12.5 17.58

Gold backing + Silastic

c

loaded with IR06-103Pd, model 200 and model 2335 seeds [17] to the doses in water medium.

Centre of eye 29.08 34.31 30.82 34.37 Macula (equator and p.p) 15.57 17.22 16.55 18.29 Optic disk (temporal) 7.52 8.81 7.98 8.85 Optic disk (nasal) 18.29 21.47 19.42 21.44

Sclera 351.81 367.38 374.68 383.73 Apex 85.00 85.00 85.00 85.00 Lacrimal gland (nasal) 4.82 5.59 5.12 5.62 Opposite side 3.39 3.94 3.59 3.95

Centre of eye 22.52 22.75 17.8 18.3 23.79 Macula (equator and p.p) 10.61 11.43 9.8 8.089 12.82 Optic disk (temporal) 6.32 7.193 5.12 5.35 8.98 Optic disk (nasal) 19.63 21.65 13.67 14.13 21.02

Sclera 297.41 287.92 261.4 211 222.9 Apex 85.00 85.00 68.23 68.7 74.43 Lacrimal gland (nasal) 3.72 — 2.75 3.03 — Opposite side 3.63 3.77 2.22 2.94 5.55

Doses in grey at points of interest for (a) 20 mm (b) 16 mm COMS eye plaque loaded with IR06-103Pd seeds compared with the doses at the same points for model 200 (103Pd) and model 6711 (125I), in 16 mm COMS eye plaque. Eye plaque centerd in the midway of equator and posterior pole (equator and p.p) and

Points of interest a. 20 mm COMS

Water medium

In this study, the dosimetry of a new design brachytherapy seed IR06-103Pd was determined by Monte Carlo using MCNP (version 5). Simulations were considered in three seed orientations with the result that there are no significant statistical differences among the orientations (i.e., ideal, vertical and diagonal). The dosimetric parameters of the new seed are presented in TG-43U1 format. These parameters are in acceptable agreement with those of other commercially available seed models. Thus, the IR06-103Pd seed is dosimetrically suitable for use in routine brachytherapy where the other similar seeds are employed. Also in this study, the dosimetry of IR06-103Pd seed was evaluated in COMS eye plaques and compared to results for commercially available 103-Pd and 125I seeds in the same plaque geometries.

The COMS Silastic insert has a significant effect in reducing dose along the plaque central axis. The presence of gold backing enhances the dose near the plaque gold surfaces. This effect is due to secondary fluorescence photons arising from the backing material. Due to the energy of these emissions, the effect is significantly attenuated at a distance >2 mm. The dose decrease depends on the composition of the plaque backing material and therefore on the emitted photon spectrum of the seeds and the fluorescence of the backing material.

The combination of gold backing with the Silastic insert decreases the dose relative to water by 19% at 1 cm along the plaque central axis.

This study shows that this 19% effect is lower than for either model 200 or 2335 103-Pd seeds. Doses to interest points including the macula, optic disk, lens, sclera and lacrimal gland have been determined; and also the effects of plaque backing material and Silastic insert have also been studied at these points. The study affirms that dose and dose rate at these points of interest in COMS plaques are routinely lower when using 103-Pd rather than 125-I seeds. The dose to the proximal sclera, underlying the plaque, is greater using 103-Pd seeds due to lessened penetration than 125-I seeds.


**5. Conclusion**

The high entropy alloys in the CrFeMoNbTaTiZr, CrFeMoNbTaTi, CrFeMoNbTaZr, CrFeMoTaTiZr, CrFeTaNbTiZr, CrTaNbTiZrMo, and FeTaNbTiZrMo alloying systems can be produced in the RAV furnace provided that the batch volume is large enough to allow the dissolution of hard-to-melt elements. Raw materials need to be introduced in the form of smaller grain particles (below 1 mm in diameter) in order to favor the dissolution phenomena by diffusion in the common molten metal bath. Moreover, the liquid state holding time must be increased so as to achieve the complete dissolution of particles from hard-to-melt elements. Heat treatments produce superficial oxidation effects, forming oxide layers of approx. 42 microns thick, partly fractured or peeled. The hardness value in case of the CrFeMoTaTiZr alloy increased from 800 HV0.2 to approx. 1290 HV0.2 after applying the heat treatment. The epifluorescence microscopy technique showed that mesenchymal stem cells adhered to the surface of the alloy.

## **Acknowledgements**

The researches were financed by the Executive Agency for Higher Education, Research, Development and Innovation (CNCS CCDI—UEFISCDI), under the grant project no. PN-III-P1-1.2-PCCDI-2017-239/60PCCDI 2018—Obtaining and expertise of new biocompatible materials for medical applications—MedicalMetMat within PNCDI III.

**Author details**

Victor Geanta1

**195**

, Ionelia Voiculescu1

*High Entropy Alloys for Medical Applications DOI: http://dx.doi.org/10.5772/intechopen.89318*

1 University Politehnica of Bucharest, Bucharest, Romania

2 Gheorghe Asachi Technical University of Iasi, Romania

© 2019 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,

\*Address all correspondence to<EMAIL_ADDRESS>
provided the original work is properly cited.

\*, Petrica Vizureanu2 and Andrei Victor Sandu2

#### **Thanks**

The authors would like to thank their colleagues and collaborators Mirela Codescu, Alina Vlădescu, Hajnal Kelemen, Sanda Manu, and Dumitru Mitrică for their contribution to the testing and characterization of experimental materials.

*High Entropy Alloys for Medical Applications DOI: http://dx.doi.org/10.5772/intechopen.89318*

**4. Cell viability preliminary tests**

*Engineering Steels and High Entropy-Alloys*

AM and Propidium Iodide.

**Acknowledgements**

within PNCDI III.

**Thanks**

**194**

**5. Conclusion**

In order to evaluate the cell viability of the new alloys with high entropy in simulated biological environments, a series of experiments on bone fragments taken from bone extractions from human patients were performed. Bone fragments were transferred to minimally modified Dulbecco essential medium (DMEM) (Sigma cat. No. D6046) supplemented to the final concentration of 1% with penicillin/streptomycin (Sigma cat.no.P4333) and 10% fetal bovine serum (FBS) (Sigma F7524) and centrifuged at 4000 rpm for 10 min to dislodge stem cells. Bone fragments were cultured as explants in a vial with a surface of 75 cm2 (Eppendorf No.<PHONE_NUMBER>) at 37°C, in a humid atmosphere, with 5% CO2. After 7–10 days, the first cells that migrated from the explants were observed under the microscope. Cells directly in contact with the alloy samples were analyzed in the Leica DMi8 inverted microscope, using FITC and Rhodamine fluorescence cubes, after incubation with Calcein

The high entropy alloys in the CrFeMoNbTaTiZr, CrFeMoNbTaTi, CrFeMoNbTaZr, CrFeMoTaTiZr, CrFeTaNbTiZr, CrTaNbTiZrMo, and

FeTaNbTiZrMo alloying systems can be produced in the RAV furnace provided that the batch volume is large enough to allow the dissolution of hard-to-melt elements. Raw materials need to be introduced in the form of smaller grain particles (below 1 mm in diameter) in order to favor the dissolution phenomena by diffusion in the common molten metal bath. Moreover, the liquid state holding time must be increased so as to achieve the complete dissolution of particles from hard-to-melt elements. Heat treatments produce superficial oxidation effects, forming oxide layers of approx. 42 microns thick, partly fractured or peeled. The hardness value in case of the CrFeMoTaTiZr alloy increased from 800 HV0.2 to approx. 1290 HV0.2 after applying the heat treatment. The epifluorescence microscopy technique showed that mesenchymal stem cells adhered to the surface of the alloy.

The researches were financed by the Executive Agency for Higher Education, Research, Development and Innovation (CNCS CCDI—UEFISCDI), under the grant project no. PN-III-P1-1.2-PCCDI-2017-239/60PCCDI 2018—Obtaining and expertise of new biocompatible materials for medical applications—MedicalMetMat

The authors would like to thank their colleagues and collaborators Mirela Codescu, Alina Vlădescu, Hajnal Kelemen, Sanda Manu, and Dumitru Mitrică for their contribution to the testing and characterization of experimental materials.


*Engineering Challenges Associated with Welding Field Repairs DOI: http://dx.doi.org/10.5772/intechopen.104263*

triangle with two equal leg sizes. However, it is difficult to make a fillet weld that formulates a perfect right triangle. There is a reasonably generous tolerance for the shape of a fillet weld. The weld throat is assumed to be the length of the hypotenuse, across the weld shape. Fillet welds must maintain a certain size relative to the thickness of the base metal. In other words, the throat of the weld cannot be too convex or too concave. A fillet weld with a throat that is too convex means there is not enough penetration into the joining material. A fillet weld with a throat that is too concave results from not enough weld metal applied. Either condition creates a weak weldment, and both should be flagged for repair, at present.


*Supporting open minds since 2005*

Human Blood Group Systems and Haemoglobinopathies http://dx.doi.org/10.5772/intechopen.82992 Edited by Osaro Erhabor and Anjana Munshi

#### Contributors

Bruna Policiquio, Vivian Gonzaga, Cristiane Wenceslau, Irina Kerkis, Fatima A. Aldarweesh, Jolanta Korsak, Anna Piotrowska, Osaro Erhabor, Teddy Charles Adias, Tosan Erhabor, Iwueke Ikechukwu, Maria Rios, Emilia Sippert, Evgeniya Volkova, Rocio Aguilar-Quesada, Veronica Valdivieso-Gomez, Javier Garrancho-Pérez, Inés Aroca-Siendones, Hassan Njifon Nsangou, Régine Scelles, Vincent Kabwe Mulunda-A-Mulunda, Pierre Kouam, Taty Oke Ingwen, Parijat De, Jessica Siew Hua Lee, Kinda Al-Hourani, Edith Christiane Christianne Bougouma, Sodiomon Bienvenu Sirima

#### © The Editor(s) and the Author(s) 2021

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**1. Introduction**

Peptide-based synthetic vaccines have attracted a significant amount of attention as a new generation of vaccines, because of their safety benefits and ease of production when compared with that of conventional whole pathogen-based vaccines [1,2]. However, poor immune responses are induced when only minimal antigenic epitopes are used without combining suitable adjuvants (immune stimulants), which are sometimes toxic. Nanocarrier-based delivery systems are a promising approach to overcome those drawbacks of peptide vaccines. In designing the nanocarrier, it is important to consider the interaction between the nanocarrier surface and cells, such as antigen presenting cells (APCs).

Over the past few decades, various nanocarriers have been developed, including liposomes [3–5], polymeric nanoparticles [6–8], and polymeric micelles [9]. In many of these systems, building block molecules for the construction of nanocarriers are first synthesized and then combined with antigenic peptides via several procedures, including nanomaterial formation and loading of antigenic peptides (encapsulation, chemical immobilization or physical adsorption), to give a nano-formulation. Recently, the use of antigenic peptides that are pre-conjugated to self-assembly motifs has attracted attention as an easier and simpler procedure to produce nano-formulations [10,11]. This self-assembly approach ensures highly efficient drug loading without laborious procedures or the use of synthetic components, which sometimes exhibit toxicity. In addition, because the resulting nanostructures consist of a single component, the physicochemical and structural features of these nanostructures can be simply tuned by the design of the building block peptide, and variation in drug loading efficiency among different nanostructures can be eliminated.

Among the various molecular blocks (e.g., lipids [12–14] and hydrophobic polymers [15,16]) used to assemble antigenic peptides into nanostructures, β-sheet-forming-peptides are extremely attractive because: (i) They can assemble in aqueous solution to give nanofibers (NFs) with highly regulated structures, even when functional molecules with a relatively large molecular weight are conjugated; (ii) the resulting well-ordered β-sheet structures allow the integration of antigens at high density; and (iii) they are relatively easy to synthesize and have high biocompatibility. These advantages make NF-vaccines a good alternative to traditional vaccines. Immune induction by NFs formed from antigenic peptides conjugated to β-sheet-forming-peptides have been reported [17–21]. For example, Rudra et al. reported that NFs composed of an antigenic epitope peptide conjugated to self-assembling peptide Q11 were subcutaneously administered, and elicited a strong antibody response [17]. They have also demonstrated that the β-sheet peptide NF system can be applied to various types of antigens, including a malaria epitope [18], a *Staphylococcus aureus* epitope [19] and a tumor-associated antigen MUC1 glycopeptide [20]. However, fundamental studies on how the hydrophilic-hydrophobic balance of NF components affects their cellular interaction—including cellular uptake, cytotoxicity, and immune stimulation response—has not been reported. Recently, studies on other particulate systems reported that surface hydrophobicity is an important factor for determining cellular response [22–32]. In addition to cellular internalization and nontoxicity (i.e., safety), the ability of nanocarriers to stimulate an immune response is an essential property in nanocarrier-based vaccine applications, because uptake of nanocarriers containing an antigen by APCs that do not induce an immune response may lead to unwanted tolerance toward the antigen. Thus, to design nanomaterial-based vaccines that elicit strong immunity without toxicity using a β-sheet assembly system requires a clear understanding of how the hydrophilic-hydrophobic balance of NF components affects their cellular interactions and response.

In previous work, we reported the preparation of antigen-loaded NFs by exploiting the self-assembly of β-sheet peptides [33,34] conjugated to antigenic peptides and hydrophilic chains, such as oligo-ethylene glycol (EG) [35–37]. MHC class I restricted epitope (SIINFEKL) from ovalbumin was selected as a model antigenic peptide. In addition, the structure of the NFs was analyzed in detail by various techniques, including wide-angle X-ray diffraction (WAXD), small-angle X-ray scattering (SAXS), Fourier transform infrared spectroscopy (FT-IR), circular dichroism (CD), transmission electron microscopy (TEM), and atomic force microscopy (AFM). Interestingly, structural analysis revealed that the shape of the NFs is rectangular, rather than a cylinder-like structure observed for filament micelles, possibly because of the lamination structures of β-sheets. Based on this finding, the structural model was proposed as shown in Figure 1b, which shows that the surface of the NFs is not covered with EG chains homogeneously [35,36]. Thus, we hypothesized that the EG chain length is an important parameter for tuning the cellular interactions of NFs, including cellular uptake, cytotoxicity, and immune stimulation response.

In this study, the effect of the EG chain length in building block molecules, which form peptide NFs, on their cellular interaction was investigated. The self-assembling behavior of three kinds of building block peptides with different EG lengths was evaluated by determining their critical aggregation concentration (CAC) and the critical concentration for nanofiber formation (CFC). The structures of the resulting NFs were analyzed by TEM and CD, and their surface hydrophobicity was evaluated using a hydrophobic fluorescence probe. Cellular uptake, cytotoxicity, and immune stimulation ability of the three kinds of NFs were examined in vitro using DCs. In addition, interaction of cells with micelle-like aggregates that were composed of the same building blocks as the NFs were also investigated. Cellular

interaction of the NFs was found to be significantly dependent on EG length, whereas that of micelles was independent of EG length. Notably, uptake by DC of NFs composed of EG with a moderate length was effective, and the NFs activated DC without exhibiting significant cytotoxicity. The findings provide useful design guidelines for the development of effective nanofiber-based vaccines.

**Figure 1.** (**a**) Design of the building block peptides (EG*n*) that are composed of a β-sheet-forming sequence (FVIFLD), a flexible-linker block (GSG), a model antigen sequence (SIINFEKL from OVA), and oligo-ethylene glycol. (**b**) Schematic illustration of the self-assembly process for nanofiber formation and the proposed model of highly antigen-loaded nanofibers based on previous structural study [36]. The schematic illustration was created by modification of Figures 1 and 2 of reference [35].

### **2. Results**


**7. References**

Allen, G. (1879). *The Colour-sense: Its Origin and Development*. London: Trubner & Co.

The Contribution of Color to Object Recognition 87

Mangun, G. R., & Hillyard, S. A. (1991). Modulations of sensory-evoked brain potentials

*Experimental Psychology: Human Perception and Performance, 17*, 1057-1074. Marr, D., & Nishihara, H. (1978). Representation and recognition of the spatial organization

McKeefry, D., & Zeki, S. (1997). The position and topography of the human colour centre as revealed by functional magnetic resonance imaging. *Brain, 120*, 2229-2242. McPherson, W., & Holcomb, P. (1999). An electrophysiological investigation of semantic

Nagai, J., & Yokosawa, K. (2003). What regulates the surface color effect in object

Naor-Raz, G., & Tarr, M. J. (2003). Is color an intrinsic property of object representation?

Oliva, A., & Schyns, P. G. (2000). Diagnostic colors mediate scene recognition. *Cognitive*

Osorio, D., & Vorobyev, M. (1996). Colour vision as an adaptation to frugivory in primates.

Ostergaard, A., & Davidoff, J. (1985). Some effects of color on naming and recognition of

Pratarelli, M. (1994). Semantic processing of pictures and spoken words: Evidence from

Price, C., & Humphreys, G. W. (1989). The effects of surface detail on object categorization and naming. *The Quarterly Journal of Experimental Psychology, 41*, 797-827. Regan, B. C., Julliot, C., Simmen, B., Viénot, F., Charles-Dominique, P., & Mollon, J. D.

Riddoch, M. J., & Humphreys, G. W. (1987). Visual Object Processing: A Cognitive

Rossion, B., Gauthier, I., Tarr, M. J., Despland, P., Bruyer, R., Linotte, S., et al. (2000). The

Rossion, B., & Pourtois, G. (2004). Revisiting Snodgrass and Vanderwart's object pictorial

Schendan, H., & Kutas, M. (2002). Neurophysiological evidence for two processing times for

Schendan, H., & Kutas, M. (2007). Neurophysiological evidence for the time course of

visual object identification. *Neuropsychologia, 40*, 931–945.

objects. *Journal of Experimental Psychology: Learning, Memory and Cognition, 11*, 579-

(2001). Fruits, foliage and the evolution of primate colour vision. *Philosophical*

Neuropsychological Approach. In G. W. Humphreys & M. J. Riddoch (Eds.), *Picture*

N170 occipito-temporal component is delayed and enhanced to inverted faces but not to inverted objects: an electrophysiological account of face-specific processes in

set: The role of surface detail in basic-level object recognition. *Perception, 33*, 217-

activation of global shape, part, and local contour representations during visual object categorization and memory. *Journal of Cognitive Neuroscience, 19*, 734-749.

priming with pictures of real objects. *Psychophysiology, 36*, 53-65.

*Proceedings of the National Academy of Sciences, 263*, 593-599.

Event-Related brain potentials. *Brain and Cognition, 24*, 137-157.

*Transactions of the Royal Society, 356*, 229–283.

the human brain. *Neuroreport, 11*, 69-74.

*Naming*. London: Erlbaum UK.

269-294.

587.

236.

*Cognition, 28*, 1-4.

*Perception, 32*, 667-680.

*Psychology, 41*, 176-210.

indicate changes in perceptual processing during visual-spatial priming. *Journal of*

of three-dimensional shapes. *Proceedings of the Royal Society of London, Series B, 200*,

recognition: Color diagnosticity or category? *Technical Report on Attention and*

86 Advances in Object Recognition Systems

Bramão, I., Reis, A., Petersson, K. M., & Faísca, L. (2011). The role of color information on object recognition: A review and meta-analysis. *Acta Psychologica, 138*, 244–253.

Davidoff, J., & Ostergaard, A. (1988). The role of colour in categorial judgement. *Quarterly*

Davidoff, J., Walsh, V., & Wagemans, J. (1997). Higher-level cortical processing of color. *Acta*

Gegenfurtner, K. (2003). Cortical mechanisms of colour vision. *Nature Reviews Neuroscience,*

Gegenfurtner, K., & Rieger, J. (2000). Sensory and cognitive contributions of color to the

Goffaux, V., Jacques, C., Mouraux, A., Oliva, A., Schyns, P., & Rossion, B. (2005). Diagnostic

Hamm, J., Johnson, B., & Kirk, I. (2002). Comparison of the N300 and N400 ERPs to picture

Humphreys, G. W., Goodale, M. A., Jakobson, L. S., & Servos, P. (1994). The role of surface

Humphreys, G. W., Price, C., & Riddoch, M. J. (1999). From objects to names: A cognitive

Humphreys, G. W., & Riddoch, M. J. (2006). Features, objects, action: The cognitive

Humphreys, G. W., Riddoch, M. J., & Quinlan, P. (1988). Cascade processing in picture

Jacobs, G. H. (1993). The distribution and nature of color vision among mammals. *Biological*

Jacobs, G. H., & Rowe, P. W. (2004). Evolution of vertebrate color vision. *Clinical and*

Johnson, J., & Olshausen, B. (2003). Timecourse of neural signatures of object recognition.

Kiefer, M. (2001). Perceptual and semantic sources of category-specifc effects: Event-related

Kutas, M., & Hillyard, S. (1980a). Event-related potentials to semantically inappropriate and

Kutas, M., & Hillyard, S. (1980b).  Electrophysiological evidence

Lueck, C. J., Zeki, S., Friston, K., Deiber, M., Kennerd, C., & Frackowiak, R. (1989). The color

surprisingly large words. *Biological Psychology, 11*, 99-116.

centre in the cerebral cortex of man. *Nature, 340*, 386-389.

semantic incongruity. *Science, 207*, 203-205.

potentials during picture and word categorization. *Memory and Cognition, 29*, 100-

for effects of color knowledge in object recognition. *Neuroscience Letters, 469*, 405–

colours contribute to the early stages of scene categorization: Behavioural and

stimuli in congruent and incongruent contexts. *Clinical Neurophysiology, 113*, 1339-

information in object recognition: Studies of a visual form agnosic and normal

neuropsychology of visual object processing, 1984–2004. *Cognitive Neuropsychology,*

recognition of natural scenes. *Current Biology, 10*, 805–808.

neurophysiological evidence. *Visual Cognition, 12*, 878-892.

neuroscience approach. *Psychological Research, 62*, 118-130.

identification. *Cognitive Neuropsychology, 5*, 67-103.

Davidoff, J. (1991). *Cognition Through Color*. Cambridge: MIT Press.

*Journal of Experimental Psychology, 40*, 533-544.

*Psychologica, 97*, 1-6.

subjects. *Perception, 23*, 1457-1481.

*Experimental Optometry, 87*, 206-216.

*Journal of Vision, 3*, 499-512.

*4*, 563-572.

1350.

*23*, 156-183.

116.

410.

*Reviews, 68*, 413-471.

**Section 3**

**Optical Correlators,**

**and Artificial Neural Networks**


*Article* **Design of Amorphous Carbon Coatings Using Gaussian Processes and Advanced Data Visualization**

**Christopher Sauer \*, Benedict Rothammer, Nicolai Pottin, Marcel Bartz, Benjamin Schleich and Sandro Wartzack**

Engineering Design, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstraße 9, 91058 Erlangen, Germany<EMAIL_ADDRESS>(B.R<EMAIL_ADDRESS>(N.P<EMAIL_ADDRESS>(M.B<EMAIL_ADDRESS>(B.S<EMAIL_ADDRESS>(S.W.)

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** In recent years, an increasing number of machine learning applications in tribology and coating design have been reported. Motivated by this, this contribution highlights the use of Gaussian processes for the prediction of the resulting coating characteristics to enhance the design of amorphous carbon coatings. In this regard, by using Gaussian process regression (GPR) models, a visualization of the process map of available coating design is created. The training of the GPR models is based on the experimental results of a centrally composed full factorial 23 experimental design for the deposition of a-C:H coatings on medical UHMWPE. In addition, different supervised machine learning (ML) models, such as Polynomial Regression (PR), Support Vector Machines (SVM) and Neural Networks (NN) are trained. All models are then used to predict the resulting indentation hardness of a complete statistical experimental design using the Box–Behnken design. The results are finally compared, with the GPR being of superior performance. The performance of the overall approach, in terms of quality and quantity of predictions as well as in terms of usage in visualization, is demonstrated using an initial dataset of 10 characterized amorphous carbon coatings on UHMWPE.

**Keywords:** machine learning; amorphous carbon coatings; UHWMPE; total knee replacement; Gaussian processes


**3. Results**

16 Hydrodynamics

In order to understand the interactions in this kind of systems, we have to note that the standard theory of colloidal interactions, the DLVO theory, fails to explain several experimental observations. For example, an attractive interaction is observed between the particles when the electrostatic potential is obtained. This is a effect that has been previously observed in experiments on suspensions of confined equally-charged microspheres (Behrens & Grier (2001a;b); Grier & Han (2004); Han & Grier (2003); Larsen & Grier (1997)). Grier and colleagues listed several experimental observations using suspensions of charged polystyrene particles with diameters around 0.65 microns at low ionic strength and strong spatial confinement. They note that such effects appear when a wall of glass or quartz is near the particles. Studying the *g*(*r*) function and its relation to the interaction potential, given by expression 36, they showed the appearance of a minimum on the potential located at *z* = 2.5 microns of the wall and a distance between centres to be *r*min = 3.5 microns. This attraction cannot be a Van der Waals interaction, because for this type of particle and with separations greater than 0.1 micrometres, this force is less than 0.01 *kBT* (Pailthorpe & Russel (1982)), while

The same group (Behrens & Grier (2001b)) extended this study using silica particle suspensions (silicon dioxide, SiO2) of 1.58 microns in diameter, with a high density of 2.2 g/cm3, using a cell of thickness *h* = 200 *μ*m. In this situation, even though the particles are deposited at a distance from the bottom edge of the particle to the bottom wall equal to *s* = 0.11 *μ*m, no minimum in the interaction energy between pairs appears, being the interaction purely repulsive, in the classical form of DLVO given by Eq.34. In that work, a methodology is also provided to estimate the Debye length of the system and the equivalent load *Z*∗ through a study of the presence of negative charge quartz wall due to the dissociation of silanol groups in presence of water (Behrens & Grier (2001a)). However, Han & Grier (2003) observed the existence of a minimum in the potential when they use polystyrene particles of 0.65 micron and density close to water, 1.05 g/cm3, with a separation between the walls of *h* = 1.3 microns. What is more, using silica particles from previous works, they observe a

The physical explanation of this effect is not clear (Grier & Han (2004)), being the main question how to explain the influence on the separation of the two walls in the confinement cell. However, some criticism has appeared about this results. For example, about the employment of a theoretical potential with a DLVO shape. An alternative is using a Sogami-Ise (SI) potential (Tata & Ise (1998)). Moreover, Tata & Ise (2000) contend that both the DLVO theory and the SI theory are not designed for situations in confinement, so interpreting the experimental data using either of these two theories may be wrong. Controversy on the use of a DLVO-type or SI potentials appears to be resolved considering that the two configurations represent physical exclusive situations (Schmitz et al. (2003)). In fact, simulations have been performed to explore the possibility of a potential hydrodynamic coupling with the bottom wall generated by the attraction between two particles (Dufresne et al. (2000); Squires & Brenner (2000)). However, the calculated hydrodynamic effects do not seem to explain the experimental minimum on the potential (Grier & Han (2004); Han & Grier (2003)). Other authors argue that this kind of studies should be more rigorous in the analysis of errors when extracting data from the images (Savin & Doyle (2005; 2007); Savin et al. (2007)) and other authors claim that the effect on the electrostatic potential may be an artefact (Baumgart et al. (2006)) that occurs because of

a incorrect extraction of the position of the particles (Gyger et al. (2008)).

**2.4.3 Anomalous effects**

this attractive interaction is about 0.7 *kBT*.

minimum separation between walls of *h* = 9 *μ*m.

Our experimental system is formed by a MRF composed of colloidal dispersions of superparamagnetic micron-sized particles in water. These particles have a radius of 485 nm and a density of 1.85 g/cm3, so they sediment to an equilibrium layer on the containing cell. They are composed by a polymer (PS) with nano-grains of magnetite dispersed into it, which provide their magnetic properties. The particles are also functionalized with carboxylic groups, so they have an electrical component, therefore, they repel each other, avoiding aggregation. This effect is improved by adding sodium dodecyl sulfate (SDS) in a concentration of 1 gr/l.

The containing cell consists on two quartz windows, one of them with a cavity of 100 *μ*m. The cell with the suspension in it is located in an experimental setup that isolate thermically the suspension and allows to generate a uniform external magnetic field in the centre of the cell. The particles and aggregates are observed using video-microscopy (see details for this experimental setup on (Domínguez-García et al. (2007))). Images of the fluid are saved on the computer and then analysed for extracting the relevant data by using our own developed software (Domínguez-García & Rubio (2009)) based on *ImageJ* ( *U. S. National Institutes of Health, Bethesda, Maryland, USA, http://rsb.info.nih.gov/ij/* (n.d.)). In Fig.3, we show an example of these microparticles and aggregates observed in our system.

The zeta potential of these particles is about −110 to −60 mV for a pH about 6 - 7. Therefore, the electrical content of the particles is relatively high and it is only neglected in comparison with the energy provided by the external magnetic field. However, the colloidal stability of these suspensions is not being controlled and it may have an effect on the dynamics of the clusters, specially when no magnetic field is applied. In any case, as we will see, even when a magnetic field is applied, it is observed a disagreement between theoretical aggregation times and experimental ones.

Fig. 3. Superparamagnetic microparticles observed when no external magnetic field is

where the exponent *ξ* is an exponent that depends on the dimensionality of the system, so if *d* ≥ 2, *ξ* = 1/2. Using Monte Carlo simulations they obtain that *ξ* � 0.51, and therefore that *z*

337 Hydrodynamics on Charged Superparamagnetic Microparticles

in Water Suspension: Effects of Low-Confinement Conditions and Electrostatics Interactions

In the case of our experiments, we have experimentally obtained that the *z* exponent in aggregation is contained in the range of 0.43 − 0.67 (Domínguez-García et al. (2007)) with an average value of *z* ∼ 0.57 ± 0.03. These experimental values do not depend on the amplitude of the magnetic field nor on the concentration of particles, but they seem to depend on the ratio *R*1/*R*0, which is a sign of the more important regime of aggregation. The dependency on this ratio also appears when the morphology of the chains is studied (Domínguez-García, Melle & Rubio (2009); Domínguez-García & Rubio (2010)). Besides, the scaling behaviour given by Eq.7 is experimentally observed and checked. We have compared our experimental results with Brownian dynamics simulations based on a simple model which only included dipolar interaction between the particles, hard-sphere repulsion and Brownian diffusion, neglecting inertial terms and effects related with sedimentation or electrostatics. The results of these simulations agree with the theoretical prediction, whereas the experimental aggregation time, *t*ag, appears to be much longer than expected (Cernak et al. (2004); Domínguez-García et al. (2007)), about three orders of magnitude of difference. The formation of dimers (two-particles aggregates) in the experiments lapses *t* ∼ 102 seconds, but Brownian simulations show that this lapse of time is about *t* ∼ 0.1 s. This last value can be easily obtained by assuming that the equation for the movement between two particles with

*Mr*¨ + *γ*0*r*˙ + 3*μμ*0*m*2*r*−4*π*−1 = 0 where *M* is the mass of the particles. Because of Reynolds number (Eq.19) is very low, we neglect the inertial term on this equation. If the particles are separated a initial distance *d* = *R*0

If we express this equation in function of the *λ* parameter 43 and of the diffusion coefficient

1 *λD φ*−5/2

15*μsμ*0*m*2 *φ*−5/2

2*D*

2*D* (47)

*t*ag ∼= 32*πγ*0*a*5

*t*ag ∼= 2*a*2 15

applied (Left) and when it is applied (Right).

dipolar magnetic interaction is:

given by the Stokes-Einstein equation 13:

we can obtain that:

is *z* � 0.61.

#### **3.1 Control parameters**

We have already defined some important parameters as the Péclet number, Eq.29, and the Reynolds number Eq.19. However, in our system we need to define some external parameters related with the concentration of particles and the intensity of the magnetic field. The concentration of volume of particles in the suspension, *φ*, is defined as the fraction of volume occupied by the spheres relative to the total volume of the suspension. In a quasi-2D video-microscopy system is useful to take into account the surface concentration *φ*2*D*.

For measuring the influence of the magnetic interaction we used the *λ* parameter, defined as:

$$
\lambda \equiv \frac{\mathcal{W}\_m}{k\_B T} = \frac{\mu\_s \mu\_0 m^2}{16 \pi a^3 k\_B T} \tag{43}
$$

as the ratio of *Wm* = *Ud ij*(*r* = 2*a*, *α* = 0), i.e., the magnetic energy, and the thermal fluctuations *kBT*. Here, *μs* is the relative magnetic permeability of the solvent, *μ*0 the magnetic permeability of vacuum and *m* the magnetic moment. The parameters *λ* y *φ*2*D* allow to define a couple of characteristic lengths. First, we define a distance *R*1 for which the energy of dipolar interaction is equal to thermal fluctuations:

$$R\_1 \equiv 2a\lambda^{1/3} \tag{44}$$

Finally, we define a mean distance between particles:

$$R\_0 \equiv \sqrt{\pi} a \,\phi\_{2D}^{-1/2} \tag{45}$$

The comparative between these two quantities allows to distinguish between different aggregation regimes. When, *R*1 < *R*0, the thermal fluctuations prevail over the magnetic interactions so diffusion is the main aggregation process. If *R*1 > *R*0, the aggregation of the particles occurs mainly because of the applied magnetic field.

#### **3.2 Aggregation and disaggregation**

Studies about the dynamics of the irreversible aggregation of clusters under unidirectional constant magnetic fields have used a collection of experimental systems. For example, electro-rheological fluids (Fraden et al. (1989)), magnetic holes (non-magnetic particles in a ferrofluid) (Cernak et al. (2004); Helgesen et al. (1990; 1988); Skjeltorp (1983)), and magneto-rheological fluids and magnetic particles (Bacri et al. (1993); Bossis et al. (1990); Cernak (1994); Cernak et al. (1991); Fermigier & Gast (1992); Melle et al. (2001); Promislow et al. (1994)).

These studies focus their efforts in calculating the kinetic exponent *z* obtaining different values ranging *z* ∼ 0.4 − 0.7. The different methodologies employed can be the origin of these dispersed values. However, more recent studies (Domínguez-García et al. (2007); Martínez-Pedrero et al. (2007)) suggest that this value is approximately *z* ∼ 0.6 − 0.7 in accordance with experimental values reported for aggregation of dielectric colloids *z* ∼ 0.6 (Fraden et al. (1989)) and with recent simulations of aggregation of superparamagnetic particles (Andreu et al. (2011)). Regarding hydrodynamics interactions Miguel & Pastor-Satorras (1999) proposed and effective expression for explaining the dispersed value of the kinetic exponent based on logarithmic corrections in the diffusion coefficient (Eqs. 26 and 27):

$$\mathcal{S}(t) \sim (t \ln \left[ \mathcal{S}(t) \right])^{\tilde{\mathbb{S}}} , \tag{46}$$

18 Hydrodynamics

We have already defined some important parameters as the Péclet number, Eq.29, and the Reynolds number Eq.19. However, in our system we need to define some external parameters related with the concentration of particles and the intensity of the magnetic field. The concentration of volume of particles in the suspension, *φ*, is defined as the fraction of volume occupied by the spheres relative to the total volume of the suspension. In a quasi-2D

*kBT* = *μsμ*0*m*2

fluctuations *kBT*. Here, *μs* is the relative magnetic permeability of the solvent, *μ*0 the magnetic permeability of vacuum and *m* the magnetic moment. The parameters *λ* y *φ*2*D* allow to define a couple of characteristic lengths. First, we define a distance *R*1 for which the energy of dipolar

*R*0 ≡ √*πa φ*−1/2

The comparative between these two quantities allows to distinguish between different aggregation regimes. When, *R*1 < *R*0, the thermal fluctuations prevail over the magnetic interactions so diffusion is the main aggregation process. If *R*1 > *R*0, the aggregation of the

Studies about the dynamics of the irreversible aggregation of clusters under unidirectional constant magnetic fields have used a collection of experimental systems. For example, electro-rheological fluids (Fraden et al. (1989)), magnetic holes (non-magnetic particles in a ferrofluid) (Cernak et al. (2004); Helgesen et al. (1990; 1988); Skjeltorp (1983)), and magneto-rheological fluids and magnetic particles (Bacri et al. (1993); Bossis et al. (1990); Cernak (1994); Cernak et al. (1991); Fermigier & Gast (1992); Melle et al. (2001);

These studies focus their efforts in calculating the kinetic exponent *z* obtaining different values ranging *z* ∼ 0.4 − 0.7. The different methodologies employed can be the origin of these dispersed values. However, more recent studies (Domínguez-García et al. (2007); Martínez-Pedrero et al. (2007)) suggest that this value is approximately *z* ∼ 0.6 − 0.7 in accordance with experimental values reported for aggregation of dielectric colloids *z* ∼ 0.6 (Fraden et al. (1989)) and with recent simulations of aggregation of superparamagnetic particles (Andreu et al. (2011)). Regarding hydrodynamics interactions Miguel & Pastor-Satorras (1999) proposed and effective expression for explaining the dispersed value of the kinetic exponent based on logarithmic corrections in the diffusion

16*πa*3*kBT* (43)

*R*1 ≡ 2*aλ*1/3 (44)

*S*(*t*) ∼ (*t* ln [*S*(*t*)])*ξ* , (46)

2*D* (45)

*ij*(*r* = 2*a*, *α* = 0), i.e., the magnetic energy, and the thermal

video-microscopy system is useful to take into account the surface concentration *φ*2*D*. For measuring the influence of the magnetic interaction we used the *λ* parameter, defined as:

*λ* ≡ *Wm*

**3.1 Control parameters**

as the ratio of *Wm* = *Ud*

interaction is equal to thermal fluctuations:

**3.2 Aggregation and disaggregation**

Promislow et al. (1994)).

coefficient (Eqs. 26 and 27):

Finally, we define a mean distance between particles:

particles occurs mainly because of the applied magnetic field.

Fig. 3. Superparamagnetic microparticles observed when no external magnetic field is applied (Left) and when it is applied (Right).

where the exponent *ξ* is an exponent that depends on the dimensionality of the system, so if *d* ≥ 2, *ξ* = 1/2. Using Monte Carlo simulations they obtain that *ξ* � 0.51, and therefore that *z* is *z* � 0.61.

In the case of our experiments, we have experimentally obtained that the *z* exponent in aggregation is contained in the range of 0.43 − 0.67 (Domínguez-García et al. (2007)) with an average value of *z* ∼ 0.57 ± 0.03. These experimental values do not depend on the amplitude of the magnetic field nor on the concentration of particles, but they seem to depend on the ratio *R*1/*R*0, which is a sign of the more important regime of aggregation. The dependency on this ratio also appears when the morphology of the chains is studied (Domínguez-García, Melle & Rubio (2009); Domínguez-García & Rubio (2010)). Besides, the scaling behaviour given by Eq.7 is experimentally observed and checked. We have compared our experimental results with Brownian dynamics simulations based on a simple model which only included dipolar interaction between the particles, hard-sphere repulsion and Brownian diffusion, neglecting inertial terms and effects related with sedimentation or electrostatics. The results of these simulations agree with the theoretical prediction, whereas the experimental aggregation time, *t*ag, appears to be much longer than expected (Cernak et al. (2004); Domínguez-García et al. (2007)), about three orders of magnitude of difference. The formation of dimers (two-particles aggregates) in the experiments lapses *t* ∼ 102 seconds, but Brownian simulations show that this lapse of time is about *t* ∼ 0.1 s. This last value can be easily obtained by assuming that the equation for the movement between two particles with dipolar magnetic interaction is:

$$M\ddot{r} + \gamma\_0 \dot{r} + 3\mu\mu\_0 m^2 r^{-4} \pi^{-1} = 0$$

where *M* is the mass of the particles. Because of Reynolds number (Eq.19) is very low, we neglect the inertial term on this equation. If the particles are separated a initial distance *d* = *R*0 we can obtain that:

$$t\_{\rm ag} \cong \frac{32\pi\gamma\_0 a^5}{15\mu\_s\mu\_0 m^2} \,\phi\_{2D}^{-5/2}$$

If we express this equation in function of the *λ* parameter 43 and of the diffusion coefficient given by the Stokes-Einstein equation 13:

$$t\_{\rm ag} \cong \frac{2a^2}{15} \frac{1}{\lambda D} \phi\_{2D}^{-5/2} \tag{47}$$

For completing this study, we also have shown results of disaggregation, that is, the process that occurs when the external magnetic field is switched off and the clusters vanish. For this process we study the kinetics in the same way that in aggregation, by searching power laws behaviours and calculating the kinetic exponents *z* and *z*� (Domínguez-García et al. (2011)). We have also developed Brownian dynamics simulations to be compared with the experiments. The Fig.4 summarizes some of our results in aggregation and disaggregation. The experimental kinetic exponents during disaggregation range from *z* = 0.44 to 1.12 and *z*� = 0.27 to 0.67, while simulations give very regular values, with *z* and *z*� ∼ 1. Then, the kinetic exponents do not agree, being also the process of disaggregation much faster in simulations. From these results, we conclude that remarkable differences exist between a simple theoretical model and the interactions in our experimental setup, differences that are

339 Hydrodynamics on Charged Superparamagnetic Microparticles

in Water Suspension: Effects of Low-Confinement Conditions and Electrostatics Interactions

specially important when the influence of the applied magnetic field is removed.

suspension.

In all these experiments some data has been collected before any external field is applied. That allows us to study the microstructure of the suspensions by calculating the electrostatics potential using the methods previously explained. The inversion of the O-Z equation reveals an attractive well in the potential with a value in its minimum in the order of −0.2 *kBT*, similar to other observations of attractive interactions of sedimented particles in confinement situations. Moreover, these values of the minimum in the potential seems to depend of the concentration of particles (Domínguez-García, Pastor, Melle & Rubio (2009)), something which is expected, if it is related in some way with the electrical charge contained in the

Fig. 5. Left: *g*(*r*) function, Right: Electrostatic potential calculated by inverting the O-Z equation (all the approximations give the same result) Inset: a detail for *U*(*r*) in the region of

As a confirmation of these results, we show here a calculation of the electrostatic potential using a long set of images of charged superparamagnetic microparticles spreading in the experimental system described above. We have obtained images of the suspension during more than an hour, with a temporal lapse between images of 0.3 seconds. This data allow us to produce a very defined graph for the pair correlation function, showed in the Fig.5. In the right side of the Fig.5, we plot the electrostatic potential and in its inset we can see that the minimum has a value of about −0.1 *kBT*, confirming the previous results obtained in this

However, this result may be an effect of an imagining artefact. About that question, some of the studies which use particle tracking only apply some filters to the images for detecting brightness points and then extracting the position of the particles. Our image analysis

the minimum. Number density *n* = 0.0009

experimental setup.

For example, the aggregation processes for *S*(*t*) in the work of Promislow et al. (1994), show an aggregation time of 200 seconds. The paramagnetic particles used in that work have a diameter of 0.6 *μ*m and a 27% of magnetite content. Using the Stokes-Einstein expression, *D* = 0.86 *μ*m/s2 is obtained, supposing that these particles do not sediment. Using *φ* = 0.0012 and *λ* = 8.6, we can obtain that *t*ag ∼ 122 seconds, in the order of their experimental result. In the case of our experiments, we obtain the same values using Eq.47 that using Brownian simulations.

These discrepancies may be related with hydrodynamic interactions which should affect the diffusion of the particles. From Eq.47, we see that some variation on the diffusion coefficient of the particles can modify the expected aggregation time for two particles. For testing that, we made some microrheology measurements using different types of isolated particles according to the theory of sedimentation and with the corrections on the values of the diffusion coefficient. The experimental values agree very well with the theoretical ones calculated from the expression 2.3.4 (Domínguez-García, Pastor, Melle & Rubio (2009)) but they imply a reduction on the diffusion coefficient a factor of three as a maximum, no being sufficient for explaining the discrepancy in the aggregation times.

Fig. 4. Experiments of aggregation and disaggregation. The experimental process of aggregation (a) begins with *λ* = 1718, *φ*2*D* = 0.088 while disaggregation is shown in (b). Brownian dynamics simulations results with *λ* = 100, *φ*2*D* = 0.03 are shown for aggregation (c) and disaggregation (d). Data from Refs.(Domínguez-García et al. (2007; 2011))

20 Hydrodynamics

For example, the aggregation processes for *S*(*t*) in the work of Promislow et al. (1994), show an aggregation time of 200 seconds. The paramagnetic particles used in that work have a diameter of 0.6 *μ*m and a 27% of magnetite content. Using the Stokes-Einstein expression, *D* = 0.86 *μ*m/s2 is obtained, supposing that these particles do not sediment. Using *φ* = 0.0012 and *λ* = 8.6, we can obtain that *t*ag ∼ 122 seconds, in the order of their experimental result. In the case of our experiments, we obtain the same values using Eq.47 that using Brownian

These discrepancies may be related with hydrodynamic interactions which should affect the diffusion of the particles. From Eq.47, we see that some variation on the diffusion coefficient of the particles can modify the expected aggregation time for two particles. For testing that, we made some microrheology measurements using different types of isolated particles according to the theory of sedimentation and with the corrections on the values of the diffusion coefficient. The experimental values agree very well with the theoretical ones calculated from the expression 2.3.4 (Domínguez-García, Pastor, Melle & Rubio (2009)) but they imply a reduction on the diffusion coefficient a factor of three as a maximum, no

being sufficient for explaining the discrepancy in the aggregation times.

Fig. 4. Experiments of aggregation and disaggregation. The experimental process of aggregation (a) begins with *λ* = 1718, *φ*2*D* = 0.088 while disaggregation is shown in (b). Brownian dynamics simulations results with *λ* = 100, *φ*2*D* = 0.03 are shown for aggregation

(c) and disaggregation (d). Data from Refs.(Domínguez-García et al. (2007; 2011))

simulations.

For completing this study, we also have shown results of disaggregation, that is, the process that occurs when the external magnetic field is switched off and the clusters vanish. For this process we study the kinetics in the same way that in aggregation, by searching power laws behaviours and calculating the kinetic exponents *z* and *z*� (Domínguez-García et al. (2011)). We have also developed Brownian dynamics simulations to be compared with the experiments. The Fig.4 summarizes some of our results in aggregation and disaggregation. The experimental kinetic exponents during disaggregation range from *z* = 0.44 to 1.12 and *z*� = 0.27 to 0.67, while simulations give very regular values, with *z* and *z*� ∼ 1. Then, the kinetic exponents do not agree, being also the process of disaggregation much faster in simulations. From these results, we conclude that remarkable differences exist between a simple theoretical model and the interactions in our experimental setup, differences that are specially important when the influence of the applied magnetic field is removed.

In all these experiments some data has been collected before any external field is applied. That allows us to study the microstructure of the suspensions by calculating the electrostatics potential using the methods previously explained. The inversion of the O-Z equation reveals an attractive well in the potential with a value in its minimum in the order of −0.2 *kBT*, similar to other observations of attractive interactions of sedimented particles in confinement situations. Moreover, these values of the minimum in the potential seems to depend of the concentration of particles (Domínguez-García, Pastor, Melle & Rubio (2009)), something which is expected, if it is related in some way with the electrical charge contained in the suspension.

Fig. 5. Left: *g*(*r*) function, Right: Electrostatic potential calculated by inverting the O-Z equation (all the approximations give the same result) Inset: a detail for *U*(*r*) in the region of the minimum. Number density *n* = 0.0009

As a confirmation of these results, we show here a calculation of the electrostatic potential using a long set of images of charged superparamagnetic microparticles spreading in the experimental system described above. We have obtained images of the suspension during more than an hour, with a temporal lapse between images of 0.3 seconds. This data allow us to produce a very defined graph for the pair correlation function, showed in the Fig.5. In the right side of the Fig.5, we plot the electrostatic potential and in its inset we can see that the minimum has a value of about −0.1 *kBT*, confirming the previous results obtained in this experimental setup.

However, this result may be an effect of an imagining artefact. About that question, some of the studies which use particle tracking only apply some filters to the images for detecting brightness points and then extracting the position of the particles. Our image analysis

seems to be. Indeed, this "detaining" effect of the particles inside the clusters is not observed

341 Hydrodynamics on Charged Superparamagnetic Microparticles

in Water Suspension: Effects of Low-Confinement Conditions and Electrostatics Interactions

What is more, we have also observed that the kinetic exponents during aggregation are different and slower if we add salt to the suspension (Domínguez-García et al. (2011)). This last effect may be related with an unexpected interaction of the particles with the charged quartz bottom wall by means of a a spontaneous macroscopic electric field. When the particles and clusters have no electrical component, they should be highly sedimented at the bottom of the quartz cell and the resistance to their the movement should be increased (Kutthe (2003)),

In this chapter, we have reviewed the main interactions, with focus on hydrodynamics and from a experimental point of view, that can be important in a confined colloidal system at low concentration of microparticles. We have used charged superparamagnetic microparticles dispersed in water in low-confinement conditions by means of a glass cell for the study of irreversible field-induced aggregation and disaggregation, as well as the microstructure of the suspension. Regarding aggregation characteristic times and basic behaviour on the disaggregation of the particles, we have observed significant discrepancies between the experimental results and the theory. Morover, anomalous effects in the electrostatic behaviour have been observed, showing that, in this kind of systems, the electro-hydrodynamics interactions are not well understood at present and deserve more theoretical and experimental

We wish to acknowledge Sonia Melle and J.M. Pastor for all the work done, J.M M. González, J.M. Palomares and F. Pigazo (ICMM) for the VSM magnetometry measurements and J.C. Gómez-Sáez for her proofreading of the English texts. This research has been partially supported by M.E.C. under Project No. FIS2006-12281-C02-02, M.C.I under FIS2009-14008-C02-02, C.A.M under S/0505/MAT/0227 and UNED by 2010V/PUNED/0010.

Andreu, J. S., Camacho, J. & Faraudo, J. (2011). Aggregation of superparamagnetic colloids in

Bacri, J. C., Djerfi, K. & Neveu, S. (1993). Ferrofluid viscometer - transient magnetic

Baumgart, J., Arauz-Lara, J. L. & Bechinger, C. (2006). Like-charge attraction in confinement:

Behrens, S. H. & Grier, D. G. (2001a). The charge of glass and silica surfaces, *J. Chem. Phys.*

Behrens, S. H. & Grier, D. G. (2001b). Pair interaction of charged colloidal spheres near a

Bensech, T. & Yiacoumi, S. (2003). Brownian motion in confinement., *Phys. Rev. E* 68: 021401. Bossis, G., Mathis, C., Minouni, Z. & Paparoditis, C. (1990). Magnetoviscosity of micronic

Brenner, H. (1961). The slow motion of a sphere through a viscous fluid towards a plane

magnetic fields: the quest for the equilibrium state, 7: 2336.

birrefrigence in crossed fields, *J. Mag. Mat.* 123 (1-2): 67–73.

myth or truth?, *Soft Matter* 2: 631–635.

charged wall, *Phys. Rev. E* 64: 050401(R).

particles, *Europhys. Lett.* 11: 133.

surface., *Phys. Rev. E* 68: 021401.

115: 6716–6721.

in experiments with added salt.

**4. Conclusions**

investigations.

**6. References**

**5. Acknowledgements**

generating that the kinetic exponents reduce their value.

software (Domínguez-García & Rubio (2009)) employs open-sourced algorithms for detecting the centres of mass of the particles by detecting the borders of each object and then obtaining its geometrical properties. As an example, we have tried to evaluate how this border detection can have an influence on the result of the electrostatic potential. A measured apparent displacement Δ(*r*) = *r*� − *r* should affect to the radial distribution function in the following form: *g*(*r*) = *g*� (*r* + Δ(*r*))(1 + *d*Δ(*r*)/*dr*) (Polin et al. (2007)). From that expression, the variation in the electrostatic potential is:

$$
\beta l l'(r) - \beta l l(r) \cong -\beta \frac{d l l(r)}{dr} \Delta(r) + \frac{d \Delta(r)}{dr} \tag{48}
$$

For obtaining Δ(*r*) we have extracted a typical particle image and we have composed some set of images which consist on separating the two particles a known distance (*r*) in pixels. Next, we apply our methods of image analysis for obtaining the position of those particles and calculate the distances (*r*� ). Then, the apparent displacement, Δ(*r*) = *r*� − *r*, is observed to grow when the particles are very near. In Fig.6, we display the results of our calculations on the possible artefact in the analysis of the position of the particles by image binarization and binary watershed, a method for automatically separating particles that are in contact. The figure reveals that the correction on the electrostatic potential for this cause is basically negligible, because the correction in the potential is zero for distances *r* > 1.2 *μ*m. In the inset of the figure we can see some of the images we have employed for this calculation, showing the detected border of the particles among the images themselves.

Fig. 6. Estimation of a possible artefact in the analysis of the position of the particles. In the inset we have included some examples of the images used for this calculation.

In any case, the possibility of an artifact can be the cause of these observations in the electrostatic potential cannot be descarted. However, the direct or indirect presence and influence of these attractive wells has been detected in many other situations in these experiments. For example, the attractive interaction disappears when we added a salt, in our case KCl, to the suspensions, confirming the electrostatic nature of the phenomena (Domínguez-García, Pastor, Melle & Rubio (2009)). In disaggregation it is observed how the particles move inside the chains without leaving them (Domínguez-García et al. (2011)). The lapse of time that the particles are in this situation depends on the initial morphology of the aggregates, something which has been observed to depend on the ratio *R*1/*R*0 (Domínguez-García, Melle & Rubio (2009); Domínguez-García & Rubio (2010)). Then, this effective lapse of time depends of how many particles are located near the other in a short distance. In that situation, the attractive interaction should play a role in disaggregation, as it

seems to be. Indeed, this "detaining" effect of the particles inside the clusters is not observed in experiments with added salt.

What is more, we have also observed that the kinetic exponents during aggregation are different and slower if we add salt to the suspension (Domínguez-García et al. (2011)). This last effect may be related with an unexpected interaction of the particles with the charged quartz bottom wall by means of a a spontaneous macroscopic electric field. When the particles and clusters have no electrical component, they should be highly sedimented at the bottom of the quartz cell and the resistance to their the movement should be increased (Kutthe (2003)), generating that the kinetic exponents reduce their value.


**4. Generation of recombinant VZV using a highly efficient homologous recombinant system**

To test the novel VZVLuc system for studying VZV pathogenesis, five single ORF deletion mutants were first generated, starting from ORF0 to ORF5, via the homologous recombination system harbored in DY380 *E. coli*. Afterwards, the VZVLuc was used for genome-wide mutagenesis to systematically delete each individual VZV ORF for functional characterization of the VZV genome.


**3. Halal tourism and Islamic tourism terminology**

Malaysia, the UAE and Singapore lead the Halal Travel Indicator that focuses on the health of

There is now a growing interest in Halal tourism from the perspectives of both industry and academic research. Many stress the fact that any strategy to develop or market Halal tourism products and services must be guided by Islamic teachings and principles in all their aspects [3, 6, 7]. There are several terms that define the concept of Halal tourism and provide a suggested guideline to help clarify the matter. Building on this foundation, this chapter proposes that there exist differences in the meaning of the terms 'Halal', 'Islamic' and 'Muslim-friendly' in the context of tourism. It is hoped that a clearer understanding of the above terms will address the concern among many researchers and marketers regarding Muslim traveller behaviour in Halal tourism.

One challenge faced by academicians and practitioners is to identify the right terminologies as well as the proper clarifications of the concepts. At the moment, the most commonly used terms are 'Halal tourism' and 'Islamic tourism'. At the moment, due to the multidisciplinary scope of the subject matter, there exist confusions regarding the two terms. As a result, the terms are often used interchangeably by researchers in both conceptual and empirical papers as if the two concepts are similar [1, 3, 6, 8–12]. However, since the Quran does differentiate between the terms 'Mu'minoon' ('Islamic person') and Muslim (persons who profess to be Muslims), it is necessary to clarify the difference between the terms 'Islamic', 'Halal' and 'Islam related'. At the outset, it must be understood that 'Islam' is the name of the religion, whose first prophet was Adam and whose final prophet was Muhammad. One of the meanings of the term Islam is 'a state of peace achieved through surrender to God' [13]. Another meaning of the term Islam is 'submission'. Therefore, the term 'Muslim' means a person who submits to God and

the family-friendly/Halal travel ecosystem a country has relative to its size.

**Figure 1.** Global Muslim market. Source: State of the global Islamic economy 2014–2015 [5].

4 Mobilities, Tourism and Travel Behavior - Contexts and Boundaries

**2. Is it Halal or Islamic?**

is used for an adherent of the Islamic faith.

According to UNWTO definition, 'Tourism comprises the activities of persons travelling to and staying in places outside their usual environment for not more than one consecutive year for leisure, business and other purposes' [20]. Cook et al. ([21]; p. 3) defined Tourism as 'the temporary movement of people to destinations outside their normal places of work and residence, the activities undertaken during their stay in those destinations, and the facilities created to cater to their needs'. It is noted from the previous definition that the tourism includes the movement of people—either Muslim or non-Muslim tourists—which represents the demand side and activities/facilities to cater to tourists' needs (destination) which represent supply side. The point here is, to understand Muslim tourists' behaviour, marketers need to understand the demand side: the needs of Muslim travellers in the context of Halal tourism.

Some researchers have tried to define Islamic tourism and Halal tourism in tourism and destination marketing literatures [1, 3, 12, 22–24]. However, most have not taken into consideration the Islamic law (Sharia), the target customers (i.e. Muslims or non-Muslims), the location of activity (i.e. Muslim vs. non-Muslim country), the product and service offered (i.e. food, facilities) and the purpose of travel. Most of these definitions are loosely defined and used interchangeably. In other words, Halal tourism and Islamic tourism are treated as similar concepts. For example, Jafari and Scott [1] defined Islamic tourism as 'The encouragement of tourists likely to meet the requirements of Sharia law'. The definition focuses on Islamic law and its requirements to meet the tourist needs but ignored the religion of tourists (Muslims) and other dimensions. In our opinion, Jafari and Scott's definition is more appropriate for 'Halal tourism' rather than 'Islamic tourism'.

When Muslims travel to another destination for leisure less than one year, this travel is considered as tourism. The question that is often asked is whether the activity is to be referred to as Halal tourism or Islamic tourism. In our opinion, the activity can be referred to as Halal tourism if all the activities, facilities, actions and objectives are permissible according to Islamic teachings. The same thing applies in the case of non-Muslim tourists. So a non-Muslim tourist can claim that he/she consumes Halal food and attends Halal entertainment outlets as long as the activities related to his claim are genuinely permissible in Islam. However, to describe the traveling activities as being 'Islamic tourism' requires another very crucial element, that is, whether the activity is accompanied by *Niyyah* or intention on the part of the traveller.

If the intention of travelling is to seek the pleasure of God or in order to strengthen his faith, then it will be both Halal and Islamic. Therefore, the term 'Islamic tourism' is more appropriate. However, the place may not necessarily be located in a Muslim country or in religious locations. For example, if a man travels to London to visit the London museum in order to study the history of British colonization of the Muslim world for the sake of God, the travel can be classified as *Ibadah* (a religious act) deserving rewards from God and therefore can be appropriately referred to as 'Islamic tourism'.

However, if the intention is not in accordance with Islamic teaching, then the travelling is not Islamic. For example, even if a man travels to the holy city of Makkah or Madinah during Hajj seasons, but with the evil intention to steal things from *Hajj* or *Umrah* travellers, then the action cannot be classified as Islamic. To reinforce my point of the importance of the element of *Niyyah* or intention, I would like to highlight the fact that the term 'Islamic food' does not exist. The reason is food is not a living object and will never have any aspect of 'intention'. For food, therefore, the term to be used is *not* Islamic food but rather 'Halal or non-Halal food'.

The term 'Muslim-friendly' in tourism industry denotes an attempt to make the tourism experience enjoyable to observant Muslims. As such it is close to the concept of 'Halal tourism' but not the same. Using 'Halal' is a wider context than 'Muslim friendly'. So when the company provides Halal products and services, it should be fully Halal and not allowed to provide non-Halal products and services. However, Muslim-friendly company could provide both Halal and non-Halal products and services but not in same place or section. In other words, Muslim-friendly destinations offer plenty of 'Halal' services (such as Halal food and beverages, sex-segregated swimming pools, etc.) along with comfortable places for Muslims to perform their daily prayers. Finally, the case of non-Muslims travelling to destinations in Muslim countries to visit Islamic historical religious and cultural sites could be an argument. Is it Islamic tourism? It is suggested NOT be referred to as Islamic tourism but rather 'Islamrelated tourism'.