Eric Badel
email: badel@nancy-engref.inra.fr

P Perré
email: perre@engref.fr

M Zwieniecki

The shrinkage of oak predicted from its anatomical pattern: validation of a cognitive model

Keywords: Modelling, Morphology-based, Anatomy, Swelling, Shrinkage, Wood, Oak, Cognitive

This paper deals with the ability of a scientific strategy to compute the shrinkage behaviour of any oak sample, regardless of its origin, density, growth ring, etc. This approach uses the description of the actual oak structure at the annual ring level (i.e. the spatial organisation of the radial ray-cells, fibre, parenchyma areas and large vessels), to evaluate its shrinkage/swelling and elastic properties in the transverse directions. For the shrinkage properties, computed results were compared with experimental values measured on a set of samples depicting a very large diversity of anatomical patterns. The accuracy of our prediction is about 5% in the tangential direction and 20% in the radial direction, which is much better than statistical models over a wide range of variables. These results are discussed and a few microscopic observations with ESEM allow explanation of anomaly points of behaviour to be formulated. Such good results could allow this approach to be used to study the influence of growing conditions or of global changes upon physical wood properties.

Introduction

Building a model that is able to predict wood properties is a common goal of many scientists who try to address different demands. Some of these demands come from industry and are focused on the optimised use of the raw material they have to transform (Seco and Barra 1996). Others try to establish relationship between growth conditions, anatomy, stand characteristics and the physical properties of wood [START_REF] Berges | Long-term changes in wood density and radial growth of Quercus petraea Liebl. in northern France since the middle of the nineteenth century[END_REF][START_REF] Beismann | Mechanical properties of spruce and beech wood grown in elevated CO2[END_REF][START_REF] Woodcock | Wood specific gravity and its radial variations: the many ways to make a tree[END_REF]. Because of it technical importance in the use of wood products, shrinkage, especially in the transverse directions, has been paid close attention for decades. Shrinkage experiments are time consuming. Therefore, the main objectives of modelling works are focused on the possibility of predicting these properties. Literature shows several ways to investigate this problematic theme. On the one hand, huge experimental data describe a forestry resource (Eyono Owoundi 1992;[START_REF] Zhang | Modelling intratree wood shrinkage in european oak by measuring wood density[END_REF][START_REF] Moguedec | Modélisation de propriétés de base du bois et de leur variabilité chez le chêne sessile (Quercus petrea Liebl.)[END_REF]. Thanks to these data, macroscopic parameters (annual ring width, cambial age, position in the trunk, etc) can be used as inputs for statistical models that estimate the mean properties of the resource and its variability. These models can be useful at the stand of the forest level. But they are not relevant to estimate the properties of one individual wood sample [START_REF] Badel | Investigation of the relationship between anatomical pattern, density and local swelling of oak wood[END_REF]). On the other hand, cognitive studies try to formulate, and to understand and simulate the relationship between the structure of the material and its properties. For gymnosperms that have a simple anatomical structure, the variations inside the annual-ring width are almost only along the radius. This is why specific attention has been focused on the effect of the cell arrangement on the local properties. The microscopic structure of cells (organisation of the multilayers, microfibril angle of the cell wall), their shape (spatial organisation of the cells) or their chemical compositions, [START_REF] Salmen | Micromechanical understanding of the cell-wall structure[END_REF], are Springer commonly involved in the transverse shrinkage and/or mechanical properties. For 40 years, several theoretical models have been elaborated (Barber 1968;[START_REF] Koponen | Modelling longitudinal elastic and shrinkage properties of wood[END_REF]Farruggia 1998;[START_REF] Watanabe | Transverse shrinkage of coniferous wood cells examined using replica method and power spectrum analysis[END_REF][START_REF] Holmberg | Nonlinear mechanical behaviour and analysis of wood and fibre materials[END_REF][START_REF] Yamamoto | A model of the anisotropic swelling and shrinking process of wood. Part 1[END_REF][START_REF] Amri | Détermination du comportement élastique d'un ensemble de fibres de bois à partir de son organisation cellulaire et d'essais mécaniques sous microscope[END_REF][START_REF] Yamamoto | A model of anisotropic swelling and shrinking process of wood; Part 2. A simulation of shrinking wood[END_REF][START_REF] Pang | Predicting anisotropic shringkage of softwood. Part 1: theories[END_REF][START_REF] Perré | Wood as a multi-scale porous medium: observation, experiment, and modelling[END_REF].

For angiosperms, and especially for ring-porous species like oak, anatomical structure varies in both radial and tangential directions and the complex two-dimensional (2-D) interaction that exists at the ring-width level between the different tissues is of great importance. Except for numerous work dealing with the effect of ray cells on the transverse anisotropy of angiosperms [START_REF] Keller | Influence des gros rayons ligneux sur quelques proprietes du bois de hetre[END_REF][START_REF] Kawamura | Studies on the properties of rays I[END_REF][START_REF] Kawamura | Studies on the properties of rays II[END_REF][START_REF] Burgert | The influence of rays on the transverse elastic anisotropy in green wood of deciduous trees[END_REF]), few studies have been performed to investigate the properties of such materials. A mechanical model has been proposed by Guitard and El Amri (El Amri 1987) to understand the elastic properties of oak. In the formulation, the proportion and a schematic arrangement of the tissues (in parallel or in series) only have been taken into account. [START_REF] Clair | Relationship between growth stress, mechanical-physical properties and proportion of fibre with gelatinous layer in chestnut (castanea sativa mill.)[END_REF] studied the effect of tension wood upon shrinkage and mechanical behaviour for chestnut at the annual ring level.

Yet, in the case of oak wood, the heterogeneity at the annual-ring level called for an explanation at this tissue level. This paper is the culmination of a whole modelling work. For several years, we have been presenting the different elements of a complete modelling tool chain. In two previous papers, [START_REF] Badel | Predicting oak wood properties using X-ray inspection: representation, homogenisation and localisation. Part I: Digital X-ray imaging and representation by finite elements[END_REF][START_REF] Perré | Predicting oak wood properties using X-ray inspection: representation, homogenisation and localisation. Part II: Computation of macroscopic properties and microscopic stress fields[END_REF], we described a modelling tool that is able to predict the shrinkage/swelling and the elastic properties of oak wood in the transverse directions. This model is based on the description of the actual anatomical structure of the annual ring and takes the microscopic properties of each tissue into account [START_REF] Badel | Détermination des propriétés élastiques et du retrait d'un cerne annuel de chêne dans le plan transverse: description de la morphologie, mesures des propriétés microscopiques et calculs d'homogénéisation[END_REF]Perré 1999, 2001). The validity of this theoretical approach should be tested by comparison with experiments.

In order to validate this chain of predicting tools, a wide range of anatomical patterns was selected from samples coming from 288 oak trees. Swelling experiments have been performed on microsamples prepared from the selected anatomical patterns. Then, the modelling tool chain has been applied, starting from X-ray images of these samples. Based on these results, the objective of this paper is the experimental validation of the cognitive model: the evaluation of its ability to really predict the swelling properties of any oak sample is discussed.

Method

The modelling principles

The modelling tool chain has been detailed in previous papers [START_REF] Badel | Predicting oak wood properties using X-ray inspection: representation, homogenisation and localisation. Part I: Digital X-ray imaging and representation by finite elements[END_REF][START_REF] Perré | Predicting oak wood properties using X-ray inspection: representation, homogenisation and localisation. Part II: Computation of macroscopic properties and microscopic stress fields[END_REF]. The main principles are summarised as follows. The deterministic approach uses the techniques of composite material sciences. Assuming that the local properties of each component and their spatial distribution are known, homogenisation techniques allow the macroscopic properties of the heterogeneous material to be computed. The mathematical formulation leads to the following equation (Sanchez-Huber and Sanchez-Palencia 1992) that makes a distinction between two separate terms:

A ijkh Homogenised Values = a ijkh
Average of the microscopic values + a ijlm e ylm (X kh )

Corrective term

(1)

This equation tells us that the macroscopic property is the sum of the average of the microscopic properties plus a second term, called "the corrective term", which represents the effect of the microscopic morphology, i.e. the spatial organisation of the components withing the unit cell.

In practical terms, for oak wood, this approach involves the characterisation of two types of data (Fig. 1):

-The local (or "microscopic") properties of each tissue, -The description of the morphology of the anatomical structure of the annual ring.

The first sets of data have been experimentally determined with the help of specific devices:

-Tensile tests under microscope allowed the individual mechanical properties (elastic Young modulus and Poisson's ratio) of fibre zones, ray cells and parenchyma cells to be measured; both in radial and tangential directions [START_REF] Badel | Détermination des propriétés élastiques d'éléments individuels du plan ligneux du chêne par des essais de traction sur micro-éprouvettes[END_REF]). -The comparison of images collected by a digital X-ray imaging device at different relative humidity conditions, allowed the swelling coefficients of these same tissues to be evaluated [START_REF] Badel | Using a digital X-ray imaging device to measure the swelling coefficients of a group of wood cells[END_REF].

On the other hand, X-ray images of the transversal structure are used to describe the structure of the annual ring and to separate the diverse tissues. MeshPore, a software tool developed in LERMAB [START_REF] Perré | Meshpore: A software able to apply image-based meshing techniques to anisotropic and heterogeneous porous media[END_REF], allows the boundaries of the tissues area to be described using image segmentation and description of contours by segment chains. In this way, we define the four types of tissues: radial ray-cells, fibre, parenchyma areas and large vessels. This new vector-valued information becomes the input data of freeware mesh generator (Easymesh (c) ) that builds a Finite Element mesh. Triangular elements are chosen to follow the complex shapes of the tissues. The refinement of the description is totally controlled by MeshPore to ensure the most realistic description of the true anatomi- It describes the material as a composite made of big vessels, ray cells, parenchyma and fibre zones as close as possible to the actual structure cal oak structure. Figure 2 depicts the main steps of this procedure.

Finally, MorphoPore, a software tool also developed in LERMAB, reads the mesh, attributes the individual properties of the tissues (Table 1) and computes the macro-scopic properties of the heterogeneous structure, i.e. the swelling/shrinkage and the elastic mechanical properties in the radial and tangential directions. The experimental protocol Assuming that the anatomical morphology of wood is the result of the genetic origin and of the growth conditions of the tree, the ultimate ambition of our model is to predict the behaviour of any oak sample without the knowledge of the forest of origin, the age, the species (Quercus robur or petraea), the density, etc. The sampling has been chosen to be as representative as possible of the anatomical variability one could observe in a huge database of 37,000 annual rings coming from 288 oak trees from eight forests scattered across France [START_REF] Bakour | Influence de l'espèce et de la provenance des deux principaux chênes franc ¸ais (Quercus Robur L.; Quercus Petrea Liebl.) sur la structure anatomique et les propriétés du bois de merrain[END_REF][START_REF] Badel | Investigation of the relationship between anatomical pattern, density and local swelling of oak wood[END_REF]. The annual ring width, the density (measured on X-ray micrographs) and the species have been combined to select 12 samples, which include typical and untypical samples (i.e. large annual ring with low density or the contrary).

E R (MPa) E T (MPa) ν TR G TR α R (%/%) α T (%/%)
The predicting formulation computes the behaviour of a material made of a periodic repetition of the same annual ring pattern. This led us to select only steady growth zones made up of three similar annual rings in the radial direction and three or four zones between large rays in the tangential direction. Moreover, the model is a 2-sD approach. The cutting to shape of the sample (only 1 mm thick in the longitudinal direction, radial-tangential plane perpendicular to the spiral grain, etc) was carefully done to be in accordance with the model's hypothesis.

The 12 samples were all placed inside a digital X-ray imaging system at the same time. This device was designed to control the dry bulb temperature and the relative humidity of the air surrounding the samples. The accuracy and the stability of the device have been tested over several weeks. The stability of the dry temperature was characterised by a standard deviation equal to ± 0.038 • C, with a maximum value of 0.28 • C peak-to-peak. The stability of the relative humidity was ± 1%; the latter was measured using a capacity sensor calibrated with the aid of a dew point analyser. Five hygrometric steps were performed along a sorption cycle and a desorption cycle. Each step was long enough for the equilibrium state to be reached. At the end of each step, an X-ray image of each sample was recorded. Finally, an image correlation process based on the grey level distribution of the whole image compared the different records to determine the strain field due to shrinkage or swelling. The swelling coefficient is defined as the slope of the strain curve plotted against relative humidity. Because the algorithm is based on images, these swelling coefficients are extracted simultaneously in radial and tangential directions [START_REF] Badel | Investigation of the relationship between anatomical pattern, density and local swelling of oak wood[END_REF]. Let us note that the samples were initially already air dried before the experiments. So, the hysteresis of the first drying is removed. Moreover, in this range of relative humidity, we assume that the behaviour between shrinkage and swelling is more or less similar. To free the confusion between the two terms, the same calculation is performed for both the steps and the definition of the swelling coefficient is used for swelling and shrinkage stages.

Results and discussion

During the computation of the properties, the software (Mor-phoPore) imposes periodic displacements over the calculation domain in the radial and tangential directions (Fig. 3). In order to reduce the computational procedure (image segmentation, FE mesh generation, computational time), only the central annual ring of the sample was used to represent the periodic structure. Because we have chosen samples with regular anatomical patterns, it is important to notice that this procedure perfectly matches the above mentioned hypothesis of periodic conditions. The computed part of the image is called the "Representative Elementary Volume" (R.E.V.). This R.E.V. is physically linked to neighbouring parts of the sample. According to this configuration, the shrinkage (swelling) of the entire sample is macroscopically free while the shrinkage (swelling) of the R.E.V. is constrained by its similar neighbouring parts. Figures 4 and5 depict the R.E.V. and the boundary representation of each sample.

The calculations were performed for the 12 samples. The computed swelling coefficients are listed in Table 2 and are compared to the experimental values. The gap between com- In both directions, the evaluation is quite good. For the first time, a cognitive modelling chain is able to predict the swelling behaviour of oak in radial and tangential directions with an acceptable accuracy; without any information concerning the origin and/or the history of the sample (age, from the pith or from the bark, position in the trunk, etc.). These results exhibit four comments:

-The large natural variability of property that can be observed with the experimental data is clearly reproduced by the predictive model. -The computed values fit well with the experimental values. The average error is only 9.4% in the tangential direction. -Although the prediction is not as good in the radial direction, the average error remains acceptable (error: 21.9%), Springer Fig. 5 X-ray image of samples 7 to 12, Representative Elementary Volume (R.E.V.) and contour chains resulting from image segmentation. The computation of the macroscopic properties is performed using triangular Finite Element meshes generated from these contours -In both cases, the model overevaluates the shrinkage coefficients.

At this stage, it is necessary to stipulate that to obtain accurate results, the model must represent the anatomical structure of the sample as closely as possible. A previous study about the sensibility of the model showed the significant role of ray cells [START_REF] Badel | Détermination des propriétés élastiques et du retrait d'un cerne annuel de chêne dans le plan transverse: description de la morphologie, mesures des propriétés microscopiques et calculs d'homogénéisation[END_REF]. Keeping in mind that the REV used in this work contains only one large ray cell, it becomes obvious that a bad description of this element can Springer affect the computed result. In order to increase the quality of the prediction, we choose to recompute the properties of the samples taking into account the exact ray cell proportion in the annual ring of each actual sample. This proportion was measured by image analysis (Visilog software) on the initial x-ray images. Using this value, new finite element meshes were built, taking care to adjust the width of the single large ray cells so that the REV respects the average ray density.

The new results are listed in Table 3, and Fig. 6 depicts the confrontation of the predicted values with the measured data. Paying particular attention to the exact proportion of ray cells significantly improved the predictive capacity of the model:

-Almost all predicted values fit better than previously the experimental data in both directions.

Predicted values

Swelling coefficients (% / %) Fig. 6 Accuracy of the predicted values. Twelve samples have been chosen to exhibit a large range of anatomical patterns. They show a large range of swelling behaviour that is well predicted by the cognitive model. The relative average error of the prediction is around 20% in the radial direction and 5% in the tangential direction (the error is evaluated according to the Eq. ( 2)) -In the tangential direction, the average error has been reduced from 9.4 to 5.7%. -In the radial direction, the improvement is not significant and the error remains clearly worse than for the tangential direction (error: 20.4%). The prediction still overestimates the experimental data. Note that, even in this case, these relatively poor predictions remain much better than models based on macroscopic parameters [START_REF] Badel | Investigation of the relationship between anatomical pattern, density and local swelling of oak wood[END_REF]).

Indeed, Fig. 6 exhibits a few points for which the correspondence between the prediction and the experiment is less accurate. Samples 2 and 12, for example, present the worst predictions in the tangential direction. These two points are, in fact, of great interest to go further in the understanding of oak shrinkage.

Sample number 2 exhibits the strangest behaviour. The experimental swelling coefficients are very low in the two transverse directions (α R = 0.104%/% and α T = 0.320%/%). The model predicts this behaviour well. However, it clearly overestimates the value in the radial direction and underestimates in the tangential direction. Several views were performed using an Environmental Scanning Electronic Microscope (ESEM, Quanta 200). This equipment allows wood to be observed without any coating and without complete desiccation, which dramatically reduces any artefact. The solid blocks from which samples 2 and 12 have originally been sliced were used for the observation. The preparation consisted in a slight superficial planing, which was done using a sledge microtome, in order to produce a clean transverse surface. Figure 7 shows the local cellular structure. This picture depicts the typical conformation of cell walls that buckled (arrows). This state is characteristic of the collapse that can occur during the removal of liquid water. Indeed, when water is removed from wood, capillary forces generate Springer Fig. 7 Local buckling in sample 2. The collapse occurs along radial bands that buckle simultaneously and accentuates the tangential shrinkage. (ESEM, LERMAB by F. Huber) a depression inside the lumen, and the structure buckles in its weakest areas. This behaviour generates large displacements that are blocked in the radial direction by large ray cells and by the typical alignment of cell wall along the radial direction resulting from cambial activity. In the tangential direction however, the deformation is almost free. This is why collapse usually occurs along radial bands that buckle simultaneously and produces a very large tangential shrinkage. This explains the drastic anisotropic behaviour due to a relatively higher deformation in the tangential direction and the lower deformation in the radial direction found for sample 2 that cannot be predicted by the current model.

The experimental shrinkage of sample 12 is lower than what the model predicted. This is true for both directions (tangential and radial). A microscopic observation proves that a significant proportion of the fibres contains G layers. This anatomical feature is the characteristic of tension wood (Fig. 8). Very little literature exits on the shrinkage behaviour of tension wood in the cross section. Indeed, most works on tension wood are focused on the longitudinal shrinkage. It is usually admitted that the presence of such cells leads to a higher longitudinal deformation than for normal wood. This effect is considered as the consequence of the presence of the gelatinous layer inside the lumen [START_REF] Clair | Relationship between growth stress, mechanical-physical properties and proportion of fibre with gelatinous layer in chestnut (castanea sativa mill.)[END_REF]. For the transversal direction, only Washusen [START_REF] Washusen | Relationship between shrinkage and tension wood from three provenances of Eucalyptus globulus Labill[END_REF] observed a positive correlation between the proportion of tension wood and the tangential shrinkage. This hypothesis could explain the relatively high-swelling values (second-highest experimental result in the tangential direction) but does not explain the difference from the model prediction. So far, we have to state the main assumption of this work: the tissue morphology is able to explain a large part of oak variability in terms of shrinkage. For both these particular cases, the sample specificity lies in the variability of the tissue behaviour that is not taken into account in our predictive model. This would be possible, but requires information about the structure at the cell level. This is not integrated into the current computations just because cell features and their effects on the tissue properties would require observations and measurements at a much closer scale.

Coming back at the tissue level, it is interesting to know what is the benefit of accounting for the shape (morphology) of tissue in the prediction of macroscopic properties. For this purpose, the comprehensive model can be compared to a simple model that takes into account only the proportion of the tissues, regardless of the element shapes. This simplified model consists in accounting only for the first term of the right hand side of Eq. (1). Figure 9 depicts the results. At first glance, the results could be qualified as acceptable. However, the computation in the tangential direction is clearly affected by this simplified model. The range of the values and the tendencies seem to be satisfactory placed along the bisector line but the prediction potential decreases drastically and the average error increases from 5 to 18%. For the radial direction, the global error of prediction is not worse than with the complete model (the average error remains around 20%) but looking at in more detail, the comparison with Fig. 6 shows a tendency along the bisector line that is worse in Fig. 9. This leads to the conclusion that, consistently, this simplified model is able to predict the average property, which is weighted by the tissue proportion, rather than individual values. 1). This influence of the corrective term is on average around 18% in the tangential direction and only 12% in the radial direction. It proves that the spatial organisation of the components plays a more dominant role in the tangential direction for swelling properties than for the radial direction. This can be explained by the significant role of the large ray cells. Their long shape in the radial direction constrains the displacements along this direction while keeping a free displacement of the others tissues in the tangential direction.

Conclusions and perspectives

For the first time, a morphology-based tool chain has been used to predict the swelling properties of oak wood in the transverse plane. This cognitive model has been confronted with experimental data. The results confirm that the annual ring is the most relevant level of observation to propose a modelling approach for oak wood. The anatomical pattern and the distinction of the four main components of oak (large vessels, large ray cells, fibre zones and parenchyma zones) can explain a great part of the properties, provided their spatial organisation is carefully taken into account. The quality of the model prediction is impressive in the tangential direction for which the error is only about 5%. The results are not so satisfactory in the radial direction, but still are much better than the statistical approaches. No satisfactory explanation can be given to explain the general shortfall in behaviour of the prediction in this direction. The comparison with the simplified model shows that the tangential direction is more sensitive to the change of morphology. The limitation of the performance is linked to special microscopic features like the presence of tension wood or the presence of very dense fibre zones. So, adding the knowledge of the structure at a smaller level (tissue density, etc) to detect such cases would probably increase the quality of the prediction.

The anatomical structure of oak wood, which is the result of the genetic origin and of the growth conditions of the tree, provides enough information to predict the physical properties of the material. This way is, in its spirit, completely different from many other models, which estimate the properties from macroscopic values such as density using statistical correlations. Because all the required data can be obtained using Non-Destructive Techniques, this kind of approach is well adapted to study the behaviour of a single annual ring for which the measurements are not possible (standing trees, old wood, etc). For example, a simple core can be used to characterise the morphology and to predict the swelling behaviour with good accuracy. A first application is a Second computation: taking care to adjust the width of the single large ray cells in order to match the average ray density.

Springer in progress in collaboration with ecophysiologists to study the link between growth conditions (especially the climate variations) and wood properties. Note that the computation time is only a few seconds but the building of a precise finite element mesh is still time consuming.

As a prospect of development for the coming years, the mechanical properties could also be predicted using the same modelling tool chain. Presently, the computation of the mechanical elastic properties is already available but the validation step has to be done.

Fig. 1 Fig. 2

 12 Fig. 1 Principle of homogenisation: this paper is devoted to the experimental validation of the cognitive model

Fig. 3

 3 Fig. 3 Homogenisation concept applied to the anatomical structure of oak. The annual ring is the Representative Elementary Volume of a periodic material

Fig. 8

 8 Fig. 8 Microphotograph of sample number 12 (fibre zone in latewood): the shape of the cells and the thickness of the cell walls is typical of tension wood with G-layer.(ESEM, LERMAB by F. Huber)

Fig. 9

 9 Fig.9Values predicted by the simplified model. This model uses the proportion and the properties of each tissue but it does not account for the spatial organisation of these tissues

Table 1

 1 Individual properties of each component of the annual ring a

Table 2

 2 Swelling coefficients a

		Experimental data	Predicted values
	Sample	α R (%/%)	α T (%/%)	α R (%/%)	α T (%/%)
	1	0.198	0.357	0.216	0.376
	2	0.104	0.32	0.171	0.297
	3	0.258	0.44	0.243	0.420
	4	0.199	0.377	0.200	0.377
	5	0.253	0.396	0.221	0.402
	6	0.155	0.29	0.192	0.300
	7	0.147	0.323	0.205	0.400
	8	0.139	0.231	0.166	0.308
	9	0.183	0.366	0.228	0.394
	10	0.228	0.386	0.242	0.411
	11	0.243	0.372	0.201	0.377
	12	0.191	0.382	0.266	0.453
	a Confrontation of predicted values with experimental data. Raw data:
	first computation.

Table 3

 3 Confrontation of predicted values with experimental data a

		Experimental data	Predicted values
	Sample	α R (%/%)	α T (%/%)	α R (%/%)	α T (%/%)
	1	0.198	0.357	0.217	0.361
	2	0.104	0.32	0.174	0.277
	3	0.258	0.44	0.250	0.416
	4	0.199	0.377	0.202	0.352
	5	0.253	0.396	0.237	0.400
	6	0.155	0.29	0.194	0.282
	7	0.147	0.323	0.198	0.343
	8	0.139	0.231	0.158	0.261
	9	0.183	0.366	0.230	0.376
	10	0.228	0.386	0.240	0.381
	11	0.243	0.372	0.201	0.359
	12	0.191	0.382	0.258	0.423

a Second computation: taking care to adjust the width of the single large ray cells in order to match the average ray density.

Table 4

 4 depicts the respective influence of both terms of Eq. (

Table 4

 4 Confrontation of predicted values with experimental data a

		Average of the	Corrective term
		microscopic properties
	Sample	α R (%/%) α T (%/%) α R (%/%) Relative influence:	α T (%/%) Relative influence:
					Correc-		Correc-
					tive/macroscopic		tive/macroscopic
					( × 100) Radial		( × 100) Tangential
	1	0.212	0.326	0.005	2.4	0.034	9.5
	2	0.157	0.240	0.017	9.5	0.037	13.3
	3	0.213	0.330	0.037	14.7	0.086	20.7
	4	0.193	0.299	0.009	4.5	0.054	15.2
	5	0.210	0.324	0.027	11.5	0.076	19.1
	6	0.167	0.254	0.027	14.0	0.027	9.7
	7	0.179	0.274	0.019	9.6	0.069	20.1
	8	0.113	0.171	0.045	28.4	0.090	34.5
	9	0.215	0.332	0.015	6.6	0.045	11.9
	10	0.201	0.311	0.039	16.4	0.070	18.3
	11	0.173	0.265	0.028	14.1	0.094	26.3
	12	0.233	0.362	0.025	9.8	0.061	14.4


Ben Nasrallah
email: sassi.bennasrallah@crten.rnrt.tn

Shrinkage/swelling behaviour of knitted fabrics during relative humidity cycles determined by x-ray imaging

Keywords: deformation, x-rays, digital image, steam, shrinkage, swelling, NDT

A digital x-ray imaging system was equipped to measure the shrinkage/swelling behaviour of a knitting cotton Jersey ready-to-wear clothing under the influence of the variations of climatic conditions during an adsorption/desorption cycle. Images of the samples are recorded at different equilibrium states of the cycle. Then, the 2D strain field is computed using an image correlation process.

These original experimental results establish clear anisotropic shrinkage/swelling behaviour.

They are explained by the local structure due to the manufacturing process. In addition, the results show hysteresis between the adsorption and the desorption steps, which denotes the effect of the material's history. Finally, ESEM observations show the yarn's structure that could explain shear effect.

Introduction

The natural textile materials are highly hygroscopic. They change their moisture content and their dimensions (shrinkage/swelling) under the influence of climatic conditions (temperature and relative humidity) [START_REF] Benamar | Etude expérimentale et modélisation des transferts de masse et de chaleur à travers un tissu vestimentaire en régime dynamique: phénomène de sorption, de mouillage et de capillarité[END_REF]. Cotton is made of cellulose, i.e. each glucose has three hydroxyl groups. This generates a very hydrophilic behaviour that causes morphological changes of the material.

Many works studied the coupled heat and mass transfer phenomena in woven fabrics using laws of the water transport (Ogniewicz and Tein 1990;[START_REF] Motakef | Simultaneous heat and mass transfer with phase change in porous slabs: Analytical solutions and experimental results[END_REF]) and consisting to the steam's adsorption / desorption phenomena [START_REF] Li | A model of heat and moisture transfer in porous textiles with phase change materials[END_REF][START_REF] Li | Numerical simulation of heat transfer coupled with moisture sorption and liquid transport in porous textiles[END_REF]. Few of them were interested in the modelling of the sorption's kinetics to determine the steam diffusion in textile materials [START_REF] Li | A two stages sorption model of coupled diffusion of moisture and heat in wool fabrics[END_REF]Li andLuo 1999, 2000). Others were interested in the modelling of the adsorption and the desorption isotherms to study the evolution of the sorption hysteresis according to the temperature, the product structure or the material's history [START_REF] Nordon | Sorption kinetics of water vapor in wool fibers: Evaluation of diffusion coefficients and analysis of integral sorption[END_REF][START_REF] Jannot | Isothermes de sorption: Modèles et détermination, Communication of LEPT[END_REF][START_REF] Tvardovski | Sorptive deformation of organo-substituted laminar silicates and hysteresis phenomena[END_REF][START_REF] Wang | Dynamic water vapor and heat transport through layered fabrics Part I: Effect of surface modification[END_REF]Barnes and Holocombe 1996).

The mechanical behaviour of textile materials was approached by Magno [START_REF] Magno | A mesoscopic wave model for textile materials in large deformations[END_REF] who developed a mathematical model that describes the behaviour of textile materials under the influence of large stress. The modelling approach of Ben [START_REF] Boubaker | Modèles discrets de structures tissées: Analyse de stabilité et de drapé[END_REF][START_REF] Jannot | Isothermes de sorption: Modèles et détermination, Communication of LEPT[END_REF] focused on the estimation of the internal efforts between warp yarns and weft yarns of the same material. Others studies were interested in drapes such as Nadjombe [START_REF] Nadjombe | Modélisation mathématique et simulation numérique du drapé d'un textile[END_REF] who elaborated a specific mathematical model to describe the behaviour of a textile material around a table corner.

Nevertheless, literature remains poor concerning the textile behaviour according to hygrometric variations. Technological properties, such as shrinkage or swelling, are badly known. This field of interest is widely treated in case of others hygroscopic materials such as wood [START_REF] Badel | Détermination des propriétés élastiques et de retrait d'un cerne annuel de chêne dans le plan transverse: Description de la morphologie[END_REF][START_REF] Botosso | Une méthode de mesure du retrait microscopique du bois : Application à la prédiction du retrait tangentiel d'éprouvettes de bois massif de Spin Pectiné[END_REF][START_REF] Mariaux | Anisotropie de retrait et structure du bois: Recherche de l'influence des caractères morphologiques transverses des fibres[END_REF][START_REF] Koponen | Modelling elastic and shrinkage properties of wood based on cell structure[END_REF]Watanabee et al. 1998;[START_REF] Yamamoto | A model of the anisotropic swelling and shrinking process of wood[END_REF], concrete [START_REF] Dwiargo | Détermination expérimentale de l'influence de l'hystérésis sur les propriétés hydriques de matériaux poreux du génie civil[END_REF], or agro alimentary materials [START_REF] Baucour | Mesure des isothermes de sorption dans les hautes humidités: Modélisation du couplage transfert d'eau et croissance bactérienne en surface des viandes[END_REF][START_REF] Bizot | Using the "GAB" model to construct sorption isotherms[END_REF][START_REF] May | The importance of considering exchange surface area reduction to exhibit a constant drying flux period in foodstuffs[END_REF][START_REF] Bizot | Water vapor sorption hysteresis in potato starch[END_REF].

In this work, the shrinkage swelling behaviour of a knitting cotton Jersey ready-to-wear clothing was measured. For that reason, an x-ray imaging device was coupled with a climatic chamber to have regular cycles of adsorption / desorption. Electronic images of the structure were recorded in order to explain the samples behaviours.

Experimental set-up

Materials

The digital x-ray imaging system was initially designed to study wood shrinkage [START_REF] Badel | Using a digital X-ray imaging device to measure the swelling coefficients of a group of wood cells[END_REF]. This experimental device (Figure 1) includes a micro-focus x-rays source, a sample chamber and a 2D detector that are aligned on the same axis. The sample partly attenuates the incident polychromatic x-ray beam according to its thickness, density and chemical constitution.

The transmitted beam is converted into visible light by a scintillator situated in front of a cooled CCD detector, which is connected to a computer to record the images. Compared to visible light, the use of x-ray light led to several advantages:

-the x-ray path is always a straight line. It brings no reflection problem as visible light does,

-the grey levels on the image does not vary a lot between dry and wet states, -appropriate processes generate very homogeneous light on the sample, -x-rays don't cause shadow effects through materials. It removes the most usual difficulty when using visible light.

The complete details of this device are available in [START_REF] Badel | Using a digital X-ray imaging device to measure the swelling coefficients of a group of wood cells[END_REF]. Let us sum up the main characteristics of its components:

The micro focus x-ray source (Hamamatsu L6731) generates a polychromatic beam from a very small emission point (8 µm). It allows x-ray geometrical magnification to be performed. The voltage can be adjusted up to 80 kV and the current up to 100 µA.

The scintillator, which is placed after the sample, converts x-ray photons into visible light. It consists of a thin layer of CsI(Tl) coupled to a Fiber Optic Plate (FOP). The field of view is 20 x

30mm 2 .
The camera (RTEA/CCD-1317-K) is based on a 1317 x 1035 pixels detector. The CCD is cooled at -30°C to reduce the electronic noise. It allows acquire time up to 30 minutes.

The sample holder is made of a circular metallic plate with 14 holes, each of them is covered by a 100µm thick Mylar sheet, almost transparent to the x-ray beam. The samples are simply placed on the tight film to ensure their free deformation. Two of these holes are kept free for incident beam calibration. Finally, 12 samples can be placed simultaneously in the climatic chamber.

Preliminary experiments showed that the fabric rolled-up during desorption steps. Then the shrinkage/swelling measurement has been spoiled. A second concentric PVC plate was positioned one millimetre above the metallic plate (Figure 2). This configuration allows free displacement in the two plane directions of the fabrics but restrict them in the third direction (perpendicular to the plane).

An external air generator was plugged in the chamber that is isolated by glass wool. Then dry temperature and relative humidity of the air are controlled with reliable accuracy [START_REF] Badel | Investigation of the relationship between anatomical pattern, density and local swelling of oak wood[END_REF].

Raw images contain many defaults. Most of them can be removed with the help of appropriate treatments.

• Settings of the x-ray source

Assuming that signal/noise (S/N) increases with the photons number, intensity is set at the maximum level (100µA). For the same reason, the voltage is optimised. A previous work [START_REF] Badel | Using a digital X-ray imaging device to measure the swelling coefficients of a group of wood cells[END_REF] showed that very good results are obtained using 70 kV even with low-density materials. This range of energy matches with the spectral performances of the CsI(Tl) scintillator.

• Settings of the camera

The quality of an image is mainly characterised by the spatial resolution and the S/N ratio:

The spatial resolution depends on the x-ray spot size, the scintillator, the camera detection and the

x-ray magnification. According to the size of our samples, a compromise between these parameters is defined and the spatial resolution reaches about 30 µm.

The S/N ratio increases as the square root of the number of detected photon. Assuming that the xray source and the scintillator's efficiency determine the incoming flux, two ways are possible to improve the S/N ratio: to increase the detection time or to modify the image parameters. Indeed, the pixels of the CCD can be grouped in order to generate "super-pixels". Super-pixels made of 3x3 pixels reduce the spatial resolution, but allow the photons number to be multiplied by 9 and hence the S/N to be tripled.

The acquisition time is adjusted in order to fill the 12 bits potential of the CCD detector (90 seconds during these experiments). For each sample and each equilibrium state, 26 consecutive images are recorded and digitally accumulated in order to ensure a very good S/N ratio. Finally, the non-homogeneous x-ray light is corrected according to the well known "flatfield" process:

( ) ( ) ( ) ( ) ( ) y x Background y x Flatfield y x Background y x age y x age raw , , , , Im , Im - - = (1)
The background image is recorded without light and the flatfield image is recorded with light but without sample. This process ensures that the final grey level represents the physical attenuation property of the material for each pixel. Figure 4a depicts an example of the data collection. We observe the regular structure of the Jersey knitted fabric. A small aluminium marker (arrow) is placed in a corner. It allows the sample to be placed in the same position at the different humidity steps.

Method: Experimental protocol for shrinkage/swelling experiments

A constant temperature of 35°C, close to the temperature of human body, is used. Only the relative humidity of the air varies during the experiment. At first, the relative humidity increases through four steps from 20% to 87% (20% -41% -62% -87%). Then, a desorption phase brings back the relative humidity to the initial state (Figure 4) following the same four steps.

• Kinetics consideration

Preliminary sorption experiments were performed to evaluate the minimum waiting time to ensure stable moisture content of the samples for every climatic condition. These specific experiments were performed using a micro-balance (Sartorius MC 2 , sensitivity 0,1µg, accuracy 2µg). That was installed in a climatic chamber where temperature and relative humidity are carefully controlled [START_REF] May | The importance of considering exchange surface area reduction to exhibit a constant drying flux period in foodstuffs[END_REF]. The mass measurement was recorded during a complete sorption / desorption hygroscopic cycle. The relative humidity of the air was ranged from about 20% to 80%. The moisture content was defined by the following equation:

( ) ( )

100 0 0 % × - = M M t M t X
(2) -X%(t) is the moisture content of the sample, -M (t) is the mass of the sample, -M 0 is the dry mass of the sample (4 hours at 104°C).

Figure 6 depicts two parts of the evolution of the moisture content versus the time during adsorption (6a) and desorption (6b) cycle. It shows that the kinetic of knitted fabrics is very rapid and it follows the kinetic of the climatic chamber.

• Sample preparation and experimental protocol for x-ray imaging

The initial sample dimensions are 30mm x 25mm (Table 1). They are placed on the sample holder in the x-ray imaging device. The relative humidity of the air is fixed at 20% and the dry temperature is fixed at 35°C. After a stabilisation step of 6 hours, a first x-ray image is recorded for each sample. It is the "reference" image that refers to the initial state. Then, the relative humidity is modified according to the adsorption/desorption cycle (Figure 5). An image is recorded for each step after a waiting time of 6 hours to ensure a complete stabilisation of the conditions.

• Data analysis: computation of the deformation

The deformation is measured by image analysis using a correlation process. For that purpose, the grey level distribution is exploited. The strain field is supposed to remain constant over the entire image. Assuming that image 1 is the initial state and image 2 is the deformed one, an arbitrary 2-D strain field (often the vector zero) is applied to the initial image (image 1). The new position of each pixel is computed, which allows the grey levels of a new virtual image to be determined. This virtual image can be compared to the real deformed image (image 2) by the following objective function (Figure 7):

( )

2 2 N image computed i i i gl gl C N - = ∑ (3) gl i
image 2 is the grey level of the i th pixel on the image 2 gl i computed is the grey level of the i th pixel on the virtual computed image N: is the total pixel number of the images.

Function "C" is minimised using the simplex method [START_REF] Teukolsky | Numerical Recipes in Fortran[END_REF]) and finally the six parameters (solid displacement + strain) for each relative humidity are determined.

The analytical expression of the strain ε in the plan is composed of three coordinates: ε 11 is the elongation along the horizontal axis of the image. ε 22 is the elongation along the vertical axis of the image. ε 12 is the shear that depicts the deviation's angle between the horizontal and vertical directions. Because the main axes of the fabric are not directed exactly along the physical axes of the CCD detector, the deformation values are corrected by applying a solid rotation θ, which is measured by image analysis (Visilog 5©).

Finally, the new values of the deformation are calculated along the Course (C) and Wale (W) directions of the fabric according to the following equations.

( ) ( ) ( ) ( ) ( ) ( )        - + =       - - = + + = θ θ ε ε θ ε θ ε θ ε ε θ ε ε θ θ ε ε θ ε θ ε cos sin 2 cos

Results and discussions

Four identical pieces of fabric have been tested. For each one, the image, which was obtained at each relative humidity step, has been compared with initial step (relative humidity = 20%). The strain parameters (ε cc , ε ww , ε wc ) represent the overall deformation of the samples.

Figures 8,9 and 10 represent the evolution of the elongations (ε cc and ε ww ) and shear (ε wc ) as functions of the relative humidity of the air. All the samples show very similar behaviour. This proves the excellent reproducibility of the measurements.

Along the course direction, which represents the width of the knitted (Figure 8) and along the wale, which represents the length of the knitted (Figure 9), we observe a hysteresis of the cotton jersey fabrics during the entire cycle of climatic conditions. Indeed, during the adsorption step, the swelling increases very slowly when the sample is at low humidity conditions. Then, the slope of the curve increases quickly. We observe the same behaviour during the desorption steps: the shrinkage occurs more slowly at the beginning then the slope of the deformation increases.

Nevertheless, the complete adsorption / desorption cycle is a closed loop and the dimensions come back to their initial state. The slopes of this curve could be explained by geometrical considerations. The structure of fabric contains macroscopic pores between the yarns. During the adsorption step, the yarn fibres swell almost freely and mainly in their transversal direction. They fill the micro-pores without deformation of the overall structure. Then, when the contact is established between the yarns, their transversal swelling generates an overall and large deformation. During the desorption step, the yarns start to shrink individually with a low overall deformation of the material. The micro-porosity is restored during this stage. Finally, we observe that the fabric comes back to its initial state. This last step is resulting to memory effect of the initial yarns curvature.

The deformation magnitude is clearly lower in the direction of the wale, what reveals the anisotropic character of the cotton knitted fabric. Indeed, the manufacturing process generates a continuous and sinuous yarn along the width of the material (Figure 4b and Figure 4c) that build up a very flexible structure mainly in this direction. This anisotropic behaviour is well known for the mechanical properties of the Jersey that is more rigid in the course direction [START_REF] Rios | A study of damage development in a weft knitted fabric reinforced composite: Part I-Experiments using model sandwich laminates[END_REF][START_REF] Boisse | Analyses of fabric tensile behaviour: Determination of the biaxial tension -strain surfaces and their use in forming simulations[END_REF]. Figure 11 depicts the evolution of the strain along the course versus the strain along the wale direction. It shows the evolution of the anisotropic behaviour during the adsorption / desorption cycle. This graph exhibits new information: the hysteresis affects the anisotropy. During the first adsorption step (points 1 to 3), the slope of the curve is very high (the anisotropic ratio is around 2). Then the behaviour tends to become isotropic.

The shape and the manufacturing parameters of the stitches can explain the first part of the curve.

As said before, the yarns can swell in their transversal directions almost freely. They do it according to the geometrical shape of the stitches (ε cc >ε ww ) until the micro-pores are filled. Here, the ratio between the stitch number along the wale and along the course directions (Table 1) has a great importance on the anisotropic behaviour of the material. As the main part of the yarn is in the wale direction, the overall swelling occurs in the course direction. Finally, at the end of the desorption step, the loop is closed in accordance with the ε cc and ε ww curves.

Figure 10 reveals that a shear deformation (ε wc ) occurs during adsorption (Figure 4a). Then, this deformation decreases during desorption, and finally, the structure comes back to its initial state. This shear effect cannot be explained by the macroscopic structure of the stitches, which is regular and symmetrical. However, at the yarn level, the ESEM views show the highly twisted fibres (Figure 12). On the front face, the angle of the twist (straight lines) is different between the two wings of a stitch. Indeed, the samples are made by a circular knitting machine (Table 1). This machine produces continually deviated stitches with helical form. It results a non-symmetrical yarn and the twist between the wings shows different angles. At this level, the structure is not symmetrical anymore, it brings a macroscopic shear deformation that is observed on Figure 4a. Indeed, the twist factor between the two wings of the same stitch is different, therefore, when the fibres swell, the yarn untwists, causes non-homogeneous length in stitches that generate the shear strain of structure.

Conclusion

We coupled an x-ray imaging device and a climatic chamber to evaluate the deformation (swelling and shrinkage) of ready-to-wear knitted cotton Jersey during an adsorption / desorption cycle. Thanks to the x-ray device, the 2D strain was evaluated without contact by comparison of images. The first results reveal the excellent reproducibility of the experimental protocol and proved that the method is efficient to measure such deformations.

For the two material directions, we observe a hysteresis that reveals the relationship between the behaviour and the history of the sample. A great anisotropic behaviour is also observable. The deformation is always lower along the wale direction than along the course direction. This anisotropic behaviour, already well known for the mechanical properties, is explained by the macroscopic structure of the stitches. The parameters used in the manufacturing process have a major role in this behaviour. Surprisingly, a shear deformation is also measured. This is explained at the yarn level by the non-symmetrical structure of the wings. Again, the manufacturing process is involved in the property.

Complementary investigations are in progress to study the sorption properties and to understand the relationship between the fabric history, the structure and the behavior of the material when relative humidity varies.

Shrinkage/swelling behaviour of knitted fabrics during relative humidity cycles determined by x-ray imaging

Bhouri N. 1,2 *, Badel E. 1 , Ben Nasrallah S. 2 , Perré P. 1 1 LERMAB-UMR INRA 1093, ENGREF 14, rue Girardet, 54042 Nancy Cedex, France badel@nancy.inra.fr, perre@engref.fr 2 LESTE, ENIM Avenue IBN ELJAZZAR, 5019 Monastir, Tunisie bhourinaoufel@yahoo.fr*, Sassi.Bennasrallah@crten.rnrt.tn

List of Figures

1: Principle of the 2D X-ray imaging device and general view of the experimental device (the X-ray imaging system is coupled with the air conditioner).

Fig. 2 :

 2 Fig.2: The climatic chamber allows several samples to be placed simultaneously in the same conditions. The captors allow the record of the relative humidity and the dry temperature.

Fig. 3 :

 3 Fig. 3: Samples holder: the fabric is positioned on a thin Millar sheet. A PVC plate, 1 mm above, limits the vertical displacement of the boundaries. The holes in the 2 plates allow the X-ray to through the sample holder. The plate can rotate in order to place each sample in the path of the Xray beam.

Fig. 4 :

 4 Fig. 4: Structure of the Jersey knitting cotton ready-to-wear clothing: Image obtained by x-ray attenuation (a). Micrographs obtained by electronic microscopy (ESEM). Front face (b) and back face (c). (LERMAB by F. Huber)

Fig. 6 :

 6 Fig. 6: Kinetics measurements: Adsorption (a) and desorption (b) kinetics versus Relative Humidity condition: The complete stabilization is almost reached after 1 H.

Fig. 7 :

 7 Fig.7: The 2D strain field determination process. A virtual strain field is applied to the initial image until the resulting image fits the experimental deformed image. Six parameters are evaluated: solid translation (2), solid rotation (1) and strain (3).

Fig. 8 :

 8 Fig. 8: Evolution of the strain versus the relative humidity of the air during adsorption and desorption cycle (along course direction).

Fig. 9 :

 9 Fig. 9: Evolution of the strain versus the relative humidity of the air during adsorption and desorption cycle (along wale direction).

Fig. 10 :

 10 Fig. 10: Evolution of the shear versus the relative humidity of the air during adsorption and desorption cycle.

Fig. 11 :

 11 Fig. 11: Anisotropy. The strain along course direction is plotted versus strain along wale direction. Open marks represent the individual measurements. Line and plain marks represent the average values. The anisotropic behaviour is involved in the first adsorption step.

Fig. 12 :

 12 Fig. 12: Micrograph of the Jersey stitch obtained by electronic microscopy (ESEM). The angle of the twist is not the same on the left and right wings. This difference causes the shear deformation.(LERMAB by F. Huber)

Fig. 2 :Fig. 3 :Fig. 5 :Fig. 6 :

 2356 Fig.2: The climatic chamber allows several samples to be placed simultaneously in the same conditions. The captors allow the record of the relative humidity and the dry temperature.

Fig. 7 :Fig. 8 :Fig. 9 :

 789 Fig.7: The 2D strain field determination process. A virtual strain field is applied to the initial image until the resulting image fits the experimental deformed image. Six parameters are evaluated: solid translation (2), solid rotation (1) and strain (3).

Fig. 10 :Fig. 11 :

 1011 Fig. 10: Evolution of the shear versus the relative humidity of the air during adsorption and desorption cycle.

Fig. 12 :

 12 Fig. 12: Micrograph of the Jersey stitch obtained by electronic microscopy (ESEM). The angle of the twist is not the same on the left and right wings. This difference causes the shear deformation. (LERMAB by F. Huber)

Table 1 :

 1 Properties of the Knitting cotton ready-to-wear clothing

Shrinkage/swelling behaviour of knitted fabrics during relative humidity cycles determined by x-ray imaging

Bhouri N. 1,2 *, Badel E. 1 , Ben Nasrallah S. 2 , Perré P. 1

1 LERMAB-UMR INRA 1093, ENGREF 14, rue Girardet, 54042 Nancy Cedex, France badel@nancy.inra.fr, perre@engref.fr 2 LESTE, ENIM Avenue IBN ELJAZZAR, 5019 Monastir, Tunisie bhourinaoufel@yahoo.fr*, Sassi.Bennasrallah@crten.rnrt.tn Table 1: Properties of the Knitting cotton ready-to-wear clothing

List of Tables


Robert Edgington
email: r.edgington@ucl.ac.uk

Agnes Thalhammer

Joseph O Welch

Alexandre Bongrain

Philippe Bergonzo

Emmanuel Scorsone

Richard B Jackman

Ralf Schoepfer

Patterned neuronal

Introduction

Nanodiamond (ND) coatings are extremely biocompatible substrates for interfacing with living cells and are excellent platforms for supporting even the most environmentally demanding cell types [START_REF] Lechleitner | The surface properties of nanocrystalline diamond and nanoparticulate diamond powder and their suitability as cell growth support surfaces[END_REF][START_REF] Thalhammer | The use of nanodiamond monolayer coatings to promote the formation of functional neuronal networks[END_REF] using minimal culture conditions. In particular, detonation nanodiamond (DND) monolayers have been shown [START_REF] Thalhammer | The use of nanodiamond monolayer coatings to promote the formation of functional neuronal networks[END_REF] to support cultures of hippocampal neurons -arguably one of the most environmentally demanding cell types.

In this case nanodiamond monolayers promote the attachment and formation of functional neuronal networks even in the absence of the otherwise prerequisite procedure of laminating culture surfaces with adhesion promoting extracellular matrix (ECM) proteins (e.g. laminin)

prior to cell seeding. Moreover, DND monolayers show a remarkable ability to support neuronal networks in stark comparison to the similar material of nanocrystalline diamond (NCD) thin films [START_REF] Thalhammer | The use of nanodiamond monolayer coatings to promote the formation of functional neuronal networks[END_REF]. Considering the difference in ability of DNDs and NCD to support neuronal cultures, yet the seemingly similar surface properties of these materials, the mechanism of neuronal attachment onto DND monolayers remains to be addressed. The proteomic dimensions, chemical inertness and organic surface functionalisations of monodispersed nanodiamonds and their coatings suggest DNDs to have an influence on neuronal adhesion on a nanoscale subcellular level. Therefore in this study the size, surface chemistry, source and deposition method of nanodiamond coatings have been varied and parametrised in order to investigate their influence on neuronal adhesion and gain insight into the mechanism of neuronal adhesion observed on ND-coated substrates. Furthermore, in view of the promising nature of ND monolayers for in vitro and in vivo brain-machine interface application due to their ease of deposition in 3D on most materials [START_REF] Hees | Electrostatic self-assembly of diamond nanoparticles[END_REF], porous nature and electrode functionality [START_REF] Zang | Electrochemical properties of nanodiamond powder electrodes[END_REF], the usage of micropatterning for the directed growth of hippocampal neurons is demonstrated using patterned DND coatings of varying line widths from 5 to 70 µm.

Experimental Methods

Chemicals

All chemicals were from Sigma (St Louis, MO), Tocris (Tocris Cookson Ltd, Avonmouth, UK) or Invitrogen (Carlsbad, CA), unless stated otherwise.

For all experiments substrates of borosilicate cover slip glass were used Glass (13 Ø cover glass, VWR, UK). Samples were sterilized by autoclave following ND deposition, which did not affect ND surface functionalisation, as determined by Fourier Transform Infrared Spectroscopy (FTIR) (data not shown). Various ND types and deposition methods were used as follows. Deposition methods: Ultrasonication (US): Samples were submerged in 0.5 g/L ND hydrocolloid (mDND used) and subjected to 10 mins ultrasonication, then dried by N2 gun. Spin coating (SC): 0.5 g/L ND hydrocolloids (mDND and aDND used) were coated on whole substrates, then spun at 2000 rpm for 20 seconds and dried by N2 gun. PVA/PDDAC electrostatic coating: NDs (aDND used) were deposited using electrostatic attraction as described previously [START_REF] Girard | Electrostatic Grafting of Diamond Nanoparticles: A Versatile Route to Nanocrystalline Diamond Thin Films[END_REF], where negatively charged NDs electrostatically bind to positively charged polydiallyldimethylammonium chloride (PDDAC). Remaining solvent and polymers were removed by a short (20-40s) hydrogen microwave plasma using a Seki AsteX AX6550 reactor and the following parameters: MW power, 600 W; H2, 200 sccm; pressure, 20 mbar in order to keep the measured temperature to 500˚C (as probed using an optical pyrometer Ircon Modline 3 series 3L). Dip: Samples were coated in PDDAC and dipped in 3 g/L HPHT NDs. PDDAC was removed using the same hydrogen plasma as above.

Following coating, some samples were treated to a short microwave plasma enhanced chemical vapour deposition (MWPECVD) process that served to fixate the NDs on the surface, also resulting in a hydrogenated diamond surface. The parameters used were as above for the PVA/PDDAC removal, except with a 1% methane addition and a run time of 11 minutes.

Patterning

Patterning of NDs was achieved as described previously [START_REF] Bergonzo | 3D shaped mechanically flexible diamond microelectrode arrays for eye implant applications: The MEDINAS project[END_REF] using a photolithography mask comprised of a filled surface (control), a grid of constant pitch (200 µm) and gradated line widths of [START_REF] Girard | Electrostatic Grafting of Diamond Nanoparticles: A Versatile Route to Nanocrystalline Diamond Thin Films[END_REF][START_REF] Williams | Nanocrystalline diamond[END_REF][START_REF] Kotov | Inorganic Nanoparticles as Protein Mimics[END_REF][START_REF] Hynes | Integrins: Bidirectional, Allosteric Signaling Machines[END_REF][START_REF] Roach | Surface tailoring for controlled protein adsorption: Effect of topography at the nanometer scale and chemistry[END_REF]30,35,40,50, 60 and 70 µm (Figure 3a). In brief, following ND coating an Al hard mask was deposited and patterned using AZ NLOF 2020 photoresist and AZ351B developer, which simultaneously developed the resist and etched the Al hard mask. The exposed NDs were then etched away using an O2/Ar reactive ion etch and the remaining resist and Al removed, followed by multiple deionized water rinses. AFM AFM measurements were carried out using a NanoScope V (7.0) controlled Dimension V Scanning Probe Microscope equipped with an XYZ Hybrid Scanner (Veeco Instruments Inc., Mannheim, Germany), using aluminium-coated silicon AFM probes with a resonant frequency of 190 kHz (Nanoscience Instruments, Inc., Phoenix, USA). The AFM was operated in tapping mode with a VT-103-3K Acoustic/ Vibration Isolation System and the VT-102 Vibration Isolation Table at room temperature in air. Statistical analysis and image processing on AFM images was carried out using Gwyddion software. Firstly topographical data was tilt corrected using a first order plane fit subtraction followed by scar removal and median line correction where necessary. Average roughness, Ra, was calculated using the following formula Σ where N is the number of data points and Z is the topographical data (hereafter N refers to seeding density of NDs). Particle analysis was then performed on all images.

Firstly a low band pass 1D FFT filter was applied to all images to filter out high frequency noise (Fourier modulus density of > 0.2 k nm -1 removed). Particle marking was performed using a semiautomatic process of either a watershed or threshold detection method followed by manual editing of the particle masks. Falsely marked particles of insufficient size were removed using a threshold area filter. Particle statistics of seeding density (N) and equivalent disc radius (EDR), which is the radius of a disc of the same area as the marked grain, were then calculated. The standard error of the mean EDR was then calculated to represent the breadth of each EDR distribution for each layer. It should be noted EDR does not truly reflect particle size due to AFM tip convolution effects. Tip convolution resulted in overestimates of EDR by ca. 10nm, as estimated from the x-axis intercept of the z-axis height of grains vs. their EDR.

Raman

Raman spectroscopy was carried out with a Renishaw Invia Raman Spectrometer using a laser wavelength of 514.5 nm. Thicker ND coatings were used for characterisation, prepared by evaporating ND hydrocolloid drops on Si wafers in order to be visible under the Raman Spectrometer.

Primary neuronal cultures

Murine hippocampal neurons were obtained at embryonic day 18, essentially as described in [START_REF] Specht | Subcellular localisation of recombinant alpha-and gamma-synuclein[END_REF]. For cells cultured on ECM proteins, glass coverslips were incubated overnight in 100 µL of a 3 µg/mL poly-ornithine (p-ORN) plus 2 µg/mL laminin (LN) solution in PBS. Cells were seeded in attachment medium (AM; MEM with Earl's salts and glutamine, 10% fetal bovine serum (FBS), 33 mM glucose, 1 mM pyruvate) at 100,000 cells/cm 2 .

After 3-4 h the AM was replaced with maintenance medium (MM; Neurobasal medium, 2% B27 supplement, 2 mM GlutaMAX-I, 100 U/ml penicillin, 0.1 mg/ml streptomycin, 33 mM glucose). Cultures were kept for up to 7 days in vitro (DIV7). All animal work was undertaken under the auspices of the UK Home Office Project and Personal Licenses held by the authors in their designated laboratories.

Immunochemistry and imaging

Samples were fixed with 4% paraformaldehyde/4% sucrose in phosphate buffered saline (PBS; 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4) for 10 min, washed with PBS before permeabilisation of cells with 0.2% TritonX-100 in PBS for 10 min. After 1-2 h in blocking solution (BS; 5% horse serum in PBS) samples were incubated for 1-2 h with primary antibodies (mouse anti-MAP2, Chemicon, MAB364, 1:500 in BS; rabbit anti-GFAP, DakoCytomation, Z0334, 1:500 in BS). F-actin was stained for 10 min at RT with 2 U/ml rhodamine-phalloidin (Molecular Probes R415) and nuclei for 5 min at RT with Hoechst 33258 (Molecular Probes, 1 mg/ml in PBS). Samples were washed repeatedly and incubated with secondary antibody (Cy2-and Cy5-conjugated goat anti-mouse IgG, Jackson; 1:300 in BS, 115-095-003 and 115-175-003, respectively) for 1 h. Samples were washed with PBS, and stored at 4 ˚C in sterile-filtered PBS until imaging or mounted after wash in Gelmount (Sigma).

Confocal microscopy was performed on a Bio-Rad Radiance 2100MP

Confocal Microscope with a 20x (Nikon, Plan Flour, 0.75) water-immersion objective using Bio-Rad Lasersharp 2000 software. Alternatively a Leica TCS SPE confocal microscope equipped with a 40x oil-immersion objective (Zeiss, ACS APO, 1.15) was used in combination with LAS AF software, V1.7.0. Areas for imaging were randomly chosen. 2 independent sets of experiments were performed for unpatterned cultures, and 1 independent set for patterned cultures. Total neurite length (TNL) measurements were performed using a modified version of the NeuriteTracer ImageJ plugin [START_REF] Pool | NeuriteTracer: A novel ImageJ plugin for automated quantification of neurite outgrowth[END_REF] that does not require nuclei markers. TNL was calculated on one set of confocal images of unpatterned DIV2 neuronal cultures. Image processing was performed on the neuronal marker confocal image channel, which had been recorded using the same confocal microscope settings for each image.

Processing thresholds were chosen individually for each image to most faithfully trace the neurites. TNL was measured as an average of 4 quadrants of each confocal image in order to reflect the heterogeneity of neuronal cultures, and its associated error is presented as the standard error of the mean. Each quadrant has a surface area of 98,775 µm 2 .

Pearson product-moment correlation coefficients of TNL with respect to ND coating morphology parameters were calculated using OriginPro 8.6 software, where p-values were calculated using an unpaired Student's 2tailed t-test (significant for p<0.05).

Results

ND coating and characterisation

To investigate the dependence of ND coating type on neuronal adhesion, ten different types of coatings have been prepared on glass slides using different ND sources, deposition methods, and with or without the addition of a chemically modifying PECVD treatment. Firstly, monodispersed DNDs (mDNDs) were deposited on glass cover slips using ultrasonic seeding (US) as reported previously [START_REF] Thalhammer | The use of nanodiamond monolayer coatings to promote the formation of functional neuronal networks[END_REF] (Figure 1a 1 st column).

The same mDNDs were next deposited by spin coating (SC), resulting in more aggregated, sparse DND coatings (Figure 1a, 2 nd column, rows 1 & 2). In order to investigate the effect of ND particle size on neuronal adhesion, aggregated DND (aDND) hydrocolloids were then prepared, deposited via the methods of spin coating (Figure 1a, 3 rd column, rows 1 & 2) and electrostatic binding using ND/PVA hydrocolloids on PDDACcoated glass substrates (hereafter referred to as 'PVA'; see Experimental Methods) (Figure 1a, 4 th column, rows 1 & 2). US seeding was not used for aDNDs due to it possibly effecting aggregate size. Finally, NDs synthesised by the HPHT method were used in order to investigate ND coatings of different origin. HPHT NDs have a similar but differing surface chemistry, size (ca. 20 nm) and morphology to NDs of detonation origin.

For each coating type above additional duplicate samples were then prepared which, following deposition, were subjected to a short MWPECVD treatment in order to result in better-adhered ND coatings and also hydrogenated ND surfaces, as opposed to untreated, predominantly oxygenated ND surface functionalisation [START_REF] Girard | Surface properties of hydrogenated nanodiamonds: a chemical investigation[END_REF]. Following MWPECVD treatment, the increased adhesion of ND coatings was observed via the improved resistance of the coatings to mechanical abrasion. Furthermore the improved adhesion of ND layers was demonstrated by the elimination of unwanted AFM measurement phenomena associated with measuring loose nanopowders, such as inconsistent feature scanning from particles being moved around by the tip and tip artefacts (anomalous reoccurring shapes convoluted into the AFM image) from NDs becoming stuck onto the tip. MWPECVD processing is commonly used to grow nanocrystalline diamond (NCD) films [START_REF] Williams | Nanocrystalline diamond[END_REF] from ND seeded coatings, therefore, in order to not result in an accrued mass of diamond on the NDs nor significantly alter their morphology, the fixing step was kept short (11 mins) and at relatively low power in comparison to typical diamond growth parameters. AFM image pairs of most coatings (Figure 1a, 1b) revealed no discernable differences in the morphology of the coatings before and after fixation outside of the heterogeneity expected from the employed deposition methods, except for perhaps the sample 'fixated aDND PVA', which had a skewed average roughness (Ra) measurement that could be ascribed to an atypical coating heterogeneity for the fixated sample area measured. The average roughness (Ra) and ND particle statistics of equivalent disc radius (EDR) and seeding density (N) were measured from AFM images as described in Experimental Methods, and parametrised ND coating properties are displayed in Figure 1b.

In order to confirm the lack of increase in ND particle size after MWPECVD fixation treatment, Raman spectroscopy was performed on thicker (ca. 100 nm) mDND layers before and after treatment. All samples were not tested due to the smallest diamond seeds being the most sensitive to changes in Raman spectra with respect to increased particle size (see below). Raman spectra in Figure 1c show the crystalline carbonaceous structure of the fixated mDND to be very similar to that of untreated nanodiamond coatings and distinct from grown NCD films [START_REF] Williams | High Young's modulus in ultra thin nanocrystalline diamond[END_REF].

Pertinently, the redshift of the sp 3 diamond peak from 1333 cm -1 to 1327 cm -1 observed in both Raman spectra indicates the primary particle size of the mDNDs to remain unaltered by the MWPECVD fixation (the redshift arises from phonon confinement in nanodiamond and is related to particle size [START_REF] Sun | Raman spectroscopy of single nanodiamond: Phonon-confinement effects[END_REF]).

Neuronal cell attachment on ND coatings

Primary neuronal cultures were seeded onto the ten differently ND-coated samples and control sample of a glass cover slip coated with LN/p-ORN.

Confocal microscopy images of immunostained neuronal cultures after 2 days in vitro (DIV2) revealed attachment and outgrowth of neurons on all samples tested (Figure 2). In order to quantify neuronal outgrowth on the different ND coatings, the total neurite length (TNL) was measured as described in Experimental Methods. Average TNL per quadrant for all samples is shown in the bottom right hand panel of Figure 2. For NDcoated substrates neurite extension was similar or in excess to the control LN/p-ORN coated substrate. Notably, no discernable differences between untreated and hydrogenated fixated ND coatings were observed, except for samples untreated and fixated aDND SC that showed a reduced neurite extension post-fixation (Figure 2, 3 rd row).

Having parametrised the degree of neurite extension on the different NDcoated samples, the Pearson product-moment correlation coefficients, r, and their significance, s, were calculated in order to test for any significant linear correlation between TNL and the associated ND coating parameters of Ra, EDR and N. Scatter plots of Ra, EDR and N vs. TNL are shown in Figure 3, accompanied by their associated r and s values inset in each panel. The largest and only statistically significant (r=-0.77 p=0.01) correlation observed is EDR vs. TNL, which suggests that the size of particles could have a determining factor on neuronal adhesion and development in the nanoscale range tested. Ra also shows a negative but insignificant correlation coefficient over the Ra range tested. No significant correlation to TNL is observed for the N range tested, however the correlation coefficient for N vs. TNL is positive (r=0.4, p=0.25) as expected (as N tends to zero so should TNL). Cultures were maintained up to at least DIV7, forming apparent neuronal networks on all substrates tested (data not shown).

ND-directed neuronal patterning

Patterned ND coatings were deposited as described in Experimental Methods using the photolithography mask pictured in Figure 4a. The ND type and deposition method of untreated aDND SC was used due to its spin coating process being readily incorporated into photolithography processing and it also having a high seeding density for ease of feature identification using an AFM. ND patterns were etched with little residual ND particulates and Figure 4b shows an AFM image of a crossroad junction of 15 µm thick ND tracks. Neuronal cultures were performed as above on top of patterned substrates. Confocal microscopy images after DIV2 reveal NDs to be able to directly pattern neuronal adhesion down to line widths of at least 10 µm (Figure 4c). Upon inspection of Figure 4c.i one can see the majority of neuron nuclei sit over the junction of the ND tracks for 10 µm line widths, whereas for larger widths (Figure 4c.ii, iii) nuclei are dispersed seemingly irrespective of the patterned features. For the unpatterned area of Figure 4c.iv, less neurite branching is observed than on the patterned areas, with the neurons taking on a more circular morphology, as also observed in aDND SC in Figure 2. After DIV7 (Figure 4c.v) neurons can be seen to have formed physically connected, ordered networks on top of the ND tracks and, in comparison to the unpatterned mask area (Figure 3c.vi), the neurons are not fasciculated.

Discussion

Mechanism of neuronal adhesion with respect to ND surface properties

Whilst cellular adhesion is regularly achieved on artificial substrates via biomolecule and peptide lamination, some artificial substrates have been shown to be able to promote cellular adhesion. The most primitive mechanism known to promote cellular adhesion on artificial substrates is the electrostatic binding of negatively charged cell membranes to positively charged surfaces of materials such as polylysine [START_REF] Rainaldi | Positively charged polymer polylysine-induced cell adhesion molecule redistribution in K562 cells[END_REF]. NDs could also have a similar effect on neuronal adhesion especially considering their polar surfaces, however, a common fate for cells adhering in such manner is apoptosis that is caused by cells' subsequent inability to secrete ECM proteins [START_REF] Bacakova | Cell adhesion on artificial materials for tissue engineering[END_REF]. NDs could be providing an electrostatically attractive substrate for initial cellular adhesion and moreover, due to their nanoscale porosity, also be providing a platform for endogenous ECM formation and continued cell survival. However, this is unlikely because neuronal growth is supported on both hydrogenated and oxygenated coatings (Figure 2) which possess opposite charges.

Considering the remarkable ability of NDs to support and direct neuronal adhesion for extended periods of time, it is tempting to entertain the possibility that NDs are mimicking the functional properties of ECM proteins and are directly interfacing with cells and initiating cellular attachment and development. Afterall, the organic functionality, size, charge and shape of ND particles is similar to that of globular proteins, and other nanoparticles have already been shown to mimic other biological processes [START_REF] Kotov | Inorganic Nanoparticles as Protein Mimics[END_REF]. In the particular case of NDs being anchors for the development of focal adhesions with neurons, NDs would have to mimic the function of adhesion molecules (AMs) of the ECM by mimicking AM peptide motifs such as (for laminin) RGD, YIGSR, IKVAV, etc. [START_REF] Rao | Adhesion molecule-modified biomaterials for neural tissue engineering[END_REF], which in turn interface with cells via membrane proteins called integrins [START_REF] García | Get a grip: integrins in cell-biomaterial interactions[END_REF]. In this context, similarities can be drawn between ND surface functionalities and the functionalities of R-groups of amino acids of AM motifs, such as the amine groups on Arginine (R) and carboxylic groups on Aspartic Acid (D) of the common RGD motif. Whilst it is not inconceivable that functionalities on NDs could be serendipitously arranged as to occasionally mimic AMs' motifs (especially considering organic RGD mimetics exist [START_REF] Shimaoka | Therapeutic antagonists and conformational regulation of integrin function[END_REF]), direct ND-cell interaction is still highly unlikely.

Without integrin signalling cells die in a process called anoikis, and even slight changes in integrins or AM motif sequences cause anoikis or severe loss of function in cells [START_REF] Mager | Exploring and exploiting chemistry at the cell surface[END_REF][START_REF] Hynes | Integrins: Bidirectional, Allosteric Signaling Machines[END_REF]. Therefore, it would be surprising that NDs -crudely forged in detonation chambers -could mimic the highly specific, evolved interactions of AMs with integrins. In this context direct cell-ND interaction is refuted by the observation of neuronal adhesion on both untreated and hydrogenated NDs; the latter of which could not have such AM motif mimetic surface chemistry. Instead, it is much more likely that NDs provide a substrate for the optimal adsorption of AMs, such as vitronectin and fibronectin from serum proteins used in the attachment stage of cell culturing and the subsequent ECM proteins produced by cells themselves [START_REF] García | Get a grip: integrins in cell-biomaterial interactions[END_REF], much in the same way that tissue culture polystyrene promotes cellular adhesion [START_REF] Steele | Polystyrene Chemistry Affects Vitronectin Activity -an Explanation for Cell Attachment to Tissue-Culture Polystyrene but Not to Unmodified Polystyrene[END_REF][START_REF] Steele | Adsorption of Fibronectin and Vitronectin Onto Primaria(Tm) and Tissue-Culture Polystyrene and Relationship to the Mechanism of Initial Attachment of Human Vein Endothelial-Cells and Bhk-21 Fibroblasts[END_REF]. Indeed, in the absence of serum neurons do not attach to ND-coated substrates (n.b. serum also contains other growth factors essential to cell attachment that are not AMs) and the slight fasciculation of neurons observed in Figure 4c.v (after DIV7) can be ascribed to neurons attaching onto their own matrix of endogenous AMs, and not the underlying surface (n.b. fasciculation has also been observed previously [START_REF] Thalhammer | The use of nanodiamond monolayer coatings to promote the formation of functional neuronal networks[END_REF] on control ECM coated substrates and therefore can also be ascribed to varying culture conditions).

From previous studies [START_REF] Thalhammer | The use of nanodiamond monolayer coatings to promote the formation of functional neuronal networks[END_REF] it has been shown that nanocrystalline diamond (NCD) films, which are similar in surface functionalisation and bulk constitution to NDs but differ in surface morphological properties such as curvature, do not usually promote murine hippocampal neuronal growth.

The remarkable attachment of neurons observed in [START_REF] Thalhammer | The use of nanodiamond monolayer coatings to promote the formation of functional neuronal networks[END_REF] and herein on NDcoatings can most probably be explained by functional, well-orientated AM protein adsorption onto NDs as a result of their small radii of curvature; not simply an apt average nanoscale roughness, as well as their high affinity for protein adsorption [START_REF] Wasdo | Differential binding of serum proteins to nanoparticles[END_REF] (n.b. NCD used in [START_REF] Thalhammer | The use of nanodiamond monolayer coatings to promote the formation of functional neuronal networks[END_REF] was of similar roughness to ND coatings). The NDs used in this study all have radial curvatures in the order of that of proteins, resulting in point-contact adsorption that allows proteins to stay strongly electrostatically adhered to NDs but remain in their native structural and functional shape, as dictated by the hydrophobic force. For example, it has recently been shown that the bovine serum albumin (BSA) protein adsorbs with high affinity to NDs whilst retaining its structural features [START_REF] Wang | Study on protein conformation and adsorption behaviors in nanodiamond particleprotein complexes[END_REF]. In this context, the correlation coefficient analysis in Figure 3 between the neurite extension and parametrised morphological measureables of the ND coatings show a significant correlation between the radii of curvature (proportional to EDR) of the ND particles and the total neurite length measured (r=-0.77, p=0.01), whereas the more commonly studied biomaterial metric of average roughness is only weakly correlated to neurite extension (r=-0.48, p=0.16). The negative correlation measured agrees with the above hypothesis that as the ND particles tend towards the size of proteins, the surface becomes more biocompatible and more neurite extension is observed.

In consideration of which AMs could be responsible for initial attachment of neuronal cells to ND-coatings, vitronectin has been shown to absorb with high affinity to NDs from serum [START_REF] Wasdo | Differential binding of serum proteins to nanoparticles[END_REF] and is known to be an important AM for initial cell attachment [START_REF] García | Get a grip: integrins in cell-biomaterial interactions[END_REF][START_REF] Steele | Adsorption of Fibronectin and Vitronectin Onto Primaria(Tm) and Tissue-Culture Polystyrene and Relationship to the Mechanism of Initial Attachment of Human Vein Endothelial-Cells and Bhk-21 Fibroblasts[END_REF]. Furthermore, vitronectin is of similar size to BSA and is made up of globular domains that are easily denatured on low curvature surfaces during absorption [START_REF] Roach | Surface tailoring for controlled protein adsorption: Effect of topography at the nanometer scale and chemistry[END_REF][START_REF] Roach | Modern biomaterials: a review-bulk properties and implications of surface modifications[END_REF], which could explain the increased adhesion of neurons on ND-coatings and reduced adhesion on facetted low curvature NCD.

Conclusions

Various nanodiamond coatings of different origin, size, aggregation and deposition method have been investigated for their ability to support neuronal adhesion and all ND coatings are shown to universally promote murine hippocampal neuronal adhesion. In particular interest, predominantly oxygenated (untreated) and hydrogenated, fixated nanodiamond coatings show no apparent differences in their ability to promote neuronal adhesion. The possible mechanisms for neurons adhering to ND coatings are discussed, and via parametric correlation coefficient analysis of the morphological properties of ND coatings and their corresponding neuronal cultures, the small radii of curvature of NDs, and thereof their ability to adsorb ECM proteins in their functional state, is implicated as a determining factor in the ability of ND coatings to support neuronal adhesion and growth. Using photolithography and reactive ion etching, nanodiamond patterns are fabricated and the direct patterning of neuronal adhesion using nanodiamond is demonstrated, making ND patterning a promising technique for the in vitro study of ordered neuronal networks without using additional biomolecules to promote adhesion. Furthermore, patterned ND coatings show great potential for directing neurons towards electrodes of neuroprosthetics such as artificial retina microelectrode arrays or cochlear implants.

  ND types: Monodispersed DNDs (mDND, 6-10 nm) were purchased from New Metals & Chemicals Corporation, Japan, and larger, purposefully aggregated (aDND, ca. 6-20 nm) DND hydrocolloids from the same source were prepared by Dr Oliver A Williams (Fraunhofer Institute for Applied Solid State Physics, Freiburg, Germany). High Pressure High Temperature (HPHT) Syndia SYP 0-0.02 GAF aqueous solution nanodiamonds were purchased from Van Moppes (ca. 20 nm).

FiguresFigure 1 :

 1 Figures

  Figure 1: (a) 1 µm square AFM images of ND-coated substrates coated using a variety of ND types and deposition methods. Row 1 are untreated coatings and Row 2 are MWPECVD fixated resulting in a hydrogenated surface. No differences in coating are apparent before and after fixing. Scans are representative of the whole sample surface and were selected at random over the entire substrate surface. (b) Corresponding average roughness, Ra; equivalent disc radius, EDR; and seeding density, N of AFM images. EDR measurements are subject to AFM tip convolution contributions resulting in an overestimate of particle size by ca. 10 nm. (c) Raman spectra of untreated and fixated mDNDs show no significant differences in spectra. Pertinently the 1327 cm -1 peak has not shifted, indicating no change in core particulate size from the fixing treatment.

Figure 2 :

 2 Figure 2: Confocal microscopy images of immunostained neuronal cell cultures after DIV2 on ND-coated and laminin (LN)/poly-DL-ornithine (p-

Figure 3 :

 3 Figure 3: Scatter plots showing the relationships between the AFMdetermined morphological properties (Figure 1b) of the ND-coated samples against the total neurite extension measured from confocal microscopy images shown in Figure 2. Data point fill colour: ND coating type. Line

Figure 4 :

 4 Figure 4: (a) Diagram of photolithography mask used for ND patterning (9 mm square). Black areas are ND filled. Grid pitch is 200 µm and incremental gradated line widths of 5, 10, 20, 25, 30, 35, 40, 50, 60 and 70 µm increase from left to right every 4 th to 5 th grid unit. The central unpatterned region is used as an ND control surface for each sample. The indicated circular regions correspond to the confocal images presented in panel (c). (b) 70 µm square AFM image of patterned aDND SC tracks on glass substrate. Width: 15 µm. Height: ca. 35 nm. (c) Confocal microscopy images of immunostained neuronal cultures on untreated aDND SC patterned glass substrates. Cells have MAP2 and f-actin staining as in Figure 2 and an additional nuclei stain using Hoechst 33258. After DIV2neurons can be seen to be following ND-tracks down to at least 10 µm (c.i) and for larger line widths as well (c.ii, c.iii). After DIV7, neurons have

Acknowledgements

The work was financially supported by awards to RBJ and RS by the UK's Engineering and Physical Sciences Research Council (EPSRC, EP/F026110) and an EU Framework 6 STREP programme 'DREAMS' (FP6-NMP-033345). Dr Oliver A. Williams (IAF, Freiburg, Germany now at University of Cardiff, UK) is thanked for his skillful preparation of the dispersed ND solutions that were used throughout. Dr Madeline Pool (McGill University, Canada) is thanked for her kind provision of the modified NeuriteTracer software. At UCL Dr Steve Hudziak is acknowledged for his assistance with the AFM measurements.