Alexis Nebout
email: alexis.nebout@irisa.fr

Weijie Wei

Zhi Liu

Lijin Huang

Olivier Le Meur
email: olemeur@irisa.fr

PREDICTING SALIENCY MAPS FOR ASD PEOPLE

Keywords: saliency map, ASD, prediction, visual attention

This paper presents a novel saliency prediction model for children with autism spectrum disorder (ASD). We design a new convolution neural network and train it with a new ASD dataset. Among the contributions, we can cite the coarse-to-fine architecture as well as the loss function which embeds a regularization term. We also discuss about some data augmentation methods for ASD dataset. Experimental results show that the proposed model performs better than 6 models, one supervised model finetuned with the ASD dataset. Contrary to control people, our results hint that no center bias apply in visuall attention for autistic children.

INTRODUCTION

People with autism spectrum disorder (ASD) apprehend differently natural scenes in comparison to neurotypical people [START_REF] Klin | Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism[END_REF]. As the ASD diagnosis may require a long and difficult subjective procedure, relying on behavioural, historical and parent-report information [START_REF] Yaneva | Detecting autism based on eye-tracking data from web search-Fig. 6: Positional bias of predicted (left) and ground truth (right) saliency map for ASD people computed over the test dataset. ing tasks[END_REF], new bio-markers are required to identify potential ASD patients. In the recent years, and with the emergence of low-cost eye tracking devices, more and more studies investigate the peculiarities of ocular movement of control vs ASD people. Eye tracking techniques turn out to be fundamental to monitor the gaze deployment in a noninvasive fashion and to reveal deficits related to social cognition such as facial recognition, difficulty of eye contact, to name a few [START_REF] Mercadante | Saccadic movements using eyetracking technology in individuals with autism spectrum disorders: pilot study[END_REF][START_REF] Thorup | Altered gaze following during live interaction in infants at risk for autism: an eye tracking study[END_REF][START_REF] Dawson | Understanding the nature of face processing impairment in autism: insights from behavioral and electrophysiological studies[END_REF].

The recent advent of deep learning techniques show tremendous progress in the ability to predict where an observer stares within a scene has made huge progress. New deep-learning-based saliency models [START_REF] Cornia | Predicting human eye fixations via an lstm-based saliency attentive model[END_REF][START_REF] Pan | Salgan: Visual saliency prediction with generative adversarial networks[END_REF] perform much better than non-supervised models such as [START_REF] Riche | Rare2012: A multi-scale raritybased saliency detection with its comparative statistical analysis[END_REF][START_REF] Zhang | Sun: A bayesian framework for saliency using natural statistics[END_REF][START_REF] Garcia-Diaz | Saliency from hierarchical adaptation through decorrelation and variance normalization[END_REF]. However, all these methods have been designed for predicting the salience induced by natural scenes and for healthy people. There has been very few attempts to specialize saliency models for specific cases. We can however cite the saliency prediction for children [START_REF] Le Meur | Visual attention saccadic models learn to emulate gaze patterns from childhood to adulthood[END_REF][START_REF] Krishna | Gaze distribution analysis and saliency prediction across age groups[END_REF]. In the context of ASD, some recent studies proposed deep networks for saliency prediction [START_REF] Jiang | Learning visual attention to identify people with autism spectrum disorder[END_REF][START_REF] Duan | Learning to predict where the children with asd look[END_REF]. The proposed paper is in the continuity of these studies. We aim to leverage a new ASD datasets by training a new deep network.

The paper is organized as follows. Section 2 presents the architecture of the proposed saliency network. Section 3 elaborates on the training strategy and presents the performances for the proposed method. Section 4 concludes the paper.

PROPOSED DEEP NETWORK

Architecture

The proposed architecture, inspired from 3 previous saliency models, namely CASNet model [START_REF] Fan | Emotional attention: A study of image sentiment and visual attention[END_REF], deep gaze network [START_REF] Kümmerer | Deepgaze ii: Reading fixations from deep features trained on object recognition[END_REF] and the multi-level deep network of [START_REF] Cornia | A Deep Multi-Level Network for Saliency Prediction[END_REF], is based on a two-stream VGG-16 network architecture [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF]. Figure 1 presents the architecture of the proposed model. The model takes as input RGB images. The first stream extracts high-resolution deep features of images (400 × 300 pixels) which represent fine-scale information. The second stream is used for coarser-scale images 200 × 150 for extracting low-resolution deep features, which account for contextual information. For both streams, we extract 1280 feature maps from layers conv3 pool, conv4 pool, conv5 3. Feature maps of layers conv4 pool and conv5 3 are rescaled to get feature maps with a similar spatial resolution. For each stream, the 1280 feature maps go through a 2D convolutional layer (kernel=3 × 3, stride=1, out-put=128 maps), a pyramid of dilated convolution (kernel=3 × 3, stride=1, dilation factors={1, 3, 6, 12}, output=4×32) and a 2D convolutional layer (kernel=3 × 3, stride=1, output=32 maps). The fine and coarse-based maps (2 × 32) are then concatenated. Following the proposition of [START_REF] Fan | Emotional attention: A study of image sentiment and visual attention[END_REF], a 2 × 2 max pooling is applied on the 64 channels of concatenated feature maps to reduce their spatial variance. We then use a 2D locally convolutional layer to determine the relative importance of the different maps. These maps are then used to weight the 64 channels of concatenated feature maps by a pixelwise multiplication. A saliency map is finally determined thanks to a 1×1 convolutional layer. The different layers have a Relu activation.

Optimization and loss function

The network was trained using stochastic gradient descent. To prevent over-fitting, a dropout layer is added in each stream before the first 2D convolutional layer. The rate of dropout is set to 0.25. During training, the network was validated against the validation set after every iterations to monitor convergence and over-fitting. The learning rate is set to 0.001. To test the model, the predicted map is first resized to get the original image resolution and is then filtered by a Gaussian filter to smooth the generated saliency map.

The loss function L between the ground truth map S and the prediction Ŝ is similar to the loss used in [START_REF] Cornia | A Deep Multi-Level Network for Saliency Prediction[END_REF]:

L(S, Ŝ) = 1 N × M N ×M i=1 1 α -Si (Si -Ŝi) 2 + β × Ri (1)
with, alpha = 1.1 and Ri a regularization term as shown in the equation 2 below. We assume that S and Ŝ are in [0, 1]. N × M represents the image resolution. We empirically set the parameter β to 0.1, even though it could be optimized to improve final results.

Ri = ( Ŝi -Bi) 2 (2)
The map B, as illustrated in Figure 2 (right), represents the positional bias observed with ASD people. The map B is simply the average saliency maps computed over the training dataset. Figure 2 also illustrates on the left-hand side the positional bias for people without ASD. We observe a strong difference between both populations. The center bias is much less important for people with ASD than for the control population. The total trainable parameters of the proposed model is 65 357 441. Compared to existing models, this number is quite reasonable. For instance, the very recent CASNet model [START_REF] Fan | Emotional attention: A study of image sentiment and visual attention[END_REF] requires more than 142 million of trainable parameters whereas Sam-VGG and Sam-ResNet [START_REF] Cornia | Predicting human eye fixations via an lstm-based saliency attentive model[END_REF] require between 50 and 70 million of trainable parameters.

EXPERIMENTS

Training, validation and test datase

ASD dataset is presented in [START_REF] Duan | A dataset of eye movements for the children with autism spectrum disorder[END_REF]. It is composed of 300 images selected among the MIT1003 dataset. The experiment was carried on 14 autistic children and 14 typically developping (TD) children. ASD children were between 5 and 12 years old with a mean of 8 years old. Both TD and ASD population were 8 years old in average. We used the first 240 images of the ASD dataset for the training and fine-tuning the proposed method. The next 30 images are used for validation whereas the last 30 images are used for testing.

Data augmentation

In order to get more training samples, we augment the data in different ways as illustrated in Figure 3. We assume that used transformations do not significantly modify gaze behaviors:

• Horizontal flip: this transformation allows to flip an image right/left (e.g. mirror image).

• Blur: we add some blur to images by considering that it does not change the visual gaze deployment [START_REF] Judd | Fixations on lowresolution images[END_REF].

• Noise: we add some noise to images assuming that it does not change the visual gaze deployment. • Grayscale: we convert color images into grayscale images.

Based on the proposed architecture illustrated in Figure 1, we define and assess different variants of the proposed method. These variants are described below. They are composed of two main parts: the first one is related to a training process from a random initialization whereas the second concerns a fine-tuning process. The model is then fine-tuned with the ASD dataset. We use the loss L plus the regularization term (see equation 1 and 2). The first method M1 implements the horizontal flip as data augmentation, while method M2 adds blurred images. We also modify the loss L of M2 in order to leverage the complementary between MLNET loss, Kullback-Leibler loss and the regularization term.

Performances

To carry out the evaluation, we use quality metrics used in the MIT benchmark [START_REF] Bylinskii | Mit saliency benchmark[END_REF]: CC (correlation coefficient, CC ∈ [-1, 1]), SIM (similarity, intersection between histograms of saliency, SIM ∈ [0, 1]), AUC (Area Under Curve, AU C ∈ [0, 1]), NSS (Normalized Scanpath Saliency, N SS ∈ ]-∞, +∞[) and KL (Kullback Leibler divergence, KL ∈ [0, +∞[). Details of these metrics can be found in [START_REF] Bylinskii | Mit saliency benchmark[END_REF][START_REF] Meur | Methods for comparing scanpaths and saliency maps: strengths and weaknesses[END_REF]. Figure 4 illustrates an example of saliency maps predicted by the different models. Table 1 presents the results of the proposed methods as well as the performance of existing methods. Several observations can be made.

How do existing saliency models perform? Seven existing models are first evaluated. As expected, non-supervised models, such as Rare2012 [START_REF] Riche | Rare2012: A multi-scale raritybased saliency detection with its comparative statistical analysis[END_REF], Hou [START_REF] Hou | Image signature: Highlighting sparse salient regions[END_REF], AWS [START_REF] Garcia-Diaz | Saliency from hierarchical adaptation through decorrelation and variance normalization[END_REF], GBVS [START_REF] Harel | Graph-based visual saliency[END_REF], SUN [START_REF] Zhang | Sun: A bayesian framework for saliency using natural statistics[END_REF] do not perform well. The SAM-VGG [START_REF] Cornia | Predicting human eye fixations via an lstm-based saliency attentive model[END_REF] performs better than unsupervised model, but its performances are lower than the proposed methods, as we shall see. The SalGAN model [START_REF] Pan | Salgan: Visual saliency prediction with generative adversarial networks[END_REF], when fine-tunes with the training ASD dataset, presents the best performances among the existing models.

Performance of the proposed methods. The best results are obtained by the method FT-M1. The best 2 and worse 2 predicted saliency maps are illustrated on figure 5. Among the different setups, we observe that the blur augmentation method decreases SIM, CC, KL, NSS and AUC-J. It may be correlated with the fact that ASD people give more importance to details in natural scenes [START_REF] Alink | Individuals with clinically relevant autistic traits tend to have an eye for detail[END_REF].

The third augmentation supports this claim, especially with noise. Furthermore, our results on RI are worse than SalGAN, or at least similar. On the other hand, the increase in SIM, CC, AUC-J and AUC-B scores on the two last lines indicates that the fine-tuning of the proposed method improves the performances; in this context, the model outperforms the state-of-the-art models. The blur augmentation employed in RI-M2 may be the cause of the drop in performance, supporting the point stated above.

Comparing FT-M1 method with fine-tuned SalGAN. In Table 2, we further analyze the performance of the best proposed model per image of the test dataset. For each image, we compare the correlation coefficient of the best proposed method and the SalGan (finetuned on ASD dataset) model. Some annotations are also given indicating what are the most important elements in the scene.

Do predicted saliency maps present the same positional bias as ground truth ones? We also assess whether or not we retrieve a positional bias, as the one illustrated in Figure 2 (right). For that, we average the 30 predicted saliency maps. Figure 6 illustrates the Fig. 5: Predicted saliency maps computed by the best performing method FT-M1 method. From left to right: original image, ground truth saliency map, and predicted saliency map. From top to bottom: top 2 predictions with a CC score of 0.8992 and 0.8653, respectively. The last two rows present the worst prediction with a CC score of 0.4869 and 0.4295, respectively. bias of our predicted saliency maps. We observe that the positional bias of predicted saliency map does not compare well with the one of Figure 2 (right). To make clear this point, we compute the positional bias of the test dataset. It appears that the bias of the test dataset does not present the same bias as the bias of the training set.

CONCLUSION

In this paper, we have presented a novel saliency prediction model dedicated to children with ASD. The proposed coarse-to-fine architecture allows to combine fine details as well as global information. Thanks the training strategy, based on the proposed loss function, the fine-tuning and the data augmentation, the proposed model outperforms existing saliency models. Future works will concern mainly the dataset extension.

Fig. 1 :

 1 Fig. 1: Proposed deep architecture.

Fig. 2 :

 2 Fig. 2: Averaged colored saliency maps computed over the training dataset for people without ASD (left) and with ASD (right). Horizontal and vertical marginal distributions are also plotted.

Fig. 3 :

 3 Fig. 3: Different data augmentation methods. From left to right: original, blurred, flipped, noisy and grayscale image.

•

  Random init. (RI):We randomly initialize the weights of the network. Then, we augment the training dataset according to 3 methods. Method M1 implements the horizontal flip as data augmentation, while method M2 also adds blurred images. Method M3 combines flip, blur, noise and grayscale data augmentations.• Fine-tuning (FT): We pre-trained the network with Judd dataset.

Fig. 4 :

 4 Fig. 4: Predicted saliency maps for the different tested saliency models. From left to right, first row: original image and ground truth; second row presents the predictions from RARE2012, Hou, AWS, SUN, GBVS, SAM-VGG. From left to right, third row presents the predictions from SalGAN, RI-M1, RI-M2, RI-M3, FT-M1, FT-M2. The red frame indicates the prediction having the highest CC.

Table 1 :

 1 Performance of the proposed model and comparison with existing saliency models. SalGAN ( * ) =SalGAN model finetuned by MIT1003 and training dataset. RI= Random Initialization; FT=Fine-Tuning. The last line present the results of FT-M2 on test set of 200 natural images from [19]. Results on bold show best scores, while results on italic in the last line show the greater score on the other dataset.

	Existing models
	RARE2012	0.5317 0.4240 0.7754 0.8632	0.7224	0.7058
	Hou	0.5304 0.3934 0.7127 0.7452	0.7088	0.6940
	AWS	0.5178 0.3777 0.8024 0.7551	0.6973	0.6897
	SUN	0.4834 0.2442 0.8842 0.5144	0.6376	0.6301
	GBVS	0.5990 0.5541 0.5426 0.9919	0.7642	0.7555
	SAM-VGG	0.5453 0.5961 3.3719 1.3182	0.7758	0.6576
	SalGAN ( * )	0.6353 0.6866 1.5651 1.3074	0.7829	0.7551
	Proposed methods
	RI -M1	0.6237 0.6808 2.5995 1.2709	0.7833	0.7520
	RI -M2	0.6211 0.6587 2.0173 1.2140	0.7810	0.7540
	RI -M3	0.5833 0.5655 1.9500 0.9714	0.7573	0.7297
	FT -M1	0.6590 0.6983 0.9480 1.2637	0.7955	0.7739
	FT -M2	0.6099 0.5883 0.6368 1.0274	0.7712	0.7468
	Results on other dataset
	FT -M1	0.6308 0.6822 0.9023 1.4193	0.8106	0.7850

Model SIM ↑ CC ↑ KL↓ NSS ↑ AUC-J ↑ AUC-B ↑

Table 2 :

 2 CC score on test dataset, according to SalGAN and FT-M1 models. Images are sorted by descending CC on FT-M1.

	Image Image context CC FT-M1 CC SalGAN Image	Image context	CC FT-M1 CC SalGAN
	295	People	0.8992	0.6005	300	Nature	0.7025	0.7701
	292	People	0.8653	0.7963	274	Building	0.7018	0.3562
	275	Toilet	0.8235	0.8355	283	People	0.7011	0.8358
	291	People	0.8172	0.6836	288	Statue of a man	0.6609	0.6506
	296	People	0.7946	0.6650	293	Infrastructure	0.6574	0.6673
	281	Street	0.7887	0.7073	278	Bed	0.6506	0.5973
	289	People	0.7838	0.6562	277	Bed	0.6179	0.5471
	294	People	0.7759	0.7455	290	People	0.6148	0.7256
	279	People	0.7629	0.7519	271	Car	0.5981	0.5599
	287	People	0.7605	0.8517	299	People	0.5860	0.7881
	284	People	0.7603	0.6868	297	People	0.5769	0.5318
	280	Street	0.7545	0.5972	272	People	0.5114	0.6697
	276	Toilet	0.7529	0.7513	273	People	0.4958	0.5600
	298	People	0.7181	0.8246	282	People	0.4869	0.7227
	285	People	0.7089	0.8565	286	Ducks	0.4295	0.6067

This work was supported by the National Natural Science Foundation of China under Grant No. 61771301.


Agathe Hubau
email: a.hubau@brgm.fr

Alexandre Chagnes

Michel Minier

Solène Touzé

Simon Chapron

Anne-Gwenaelle Guezennec

Anne-Gwénaëlle Guezennec

Recycling-oriented methodology to sample and characterize the metal composition of waste Printed Circuit Boards

Keywords: sampling, characterization, printed circuit boards, e-waste

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Many countries including the European Union (EU) are highly dependent on imports of raw materials that are crucial for a strong industrial base. The increasing global demand for minerals and metals and the price volatility of certain raw materials, as well as the market distortions imposed by some countries on several raw materials, have highlighted the importance of raw materials for the global economy (UNEP, 2013).

Furthermore, the supply of raw materials is also threatened by the depletion of many primary deposits that are easily accessible and easy to process. Consequently, the recycling of secondary resources such as waste electronic and electrical equipment (WEEE) is a good opportunity to diversify raw material supply. In particular, metals contained in spent printed circuit boards (PCBs) are economically and strategically relevant and make them one of the most valuable components in WEEE [START_REF] Goosey | Recycling technologies for the treatment of end of life printed circuit boards (PCBs)[END_REF].

Presently, pyrometallurgy is the most common technology implemented to recover metals from e-waste. In pyrometallurgical processes, spent PCBs are treated with ores in smelters to recover mainly copper and precious metals. This technology presents several disadvantages, such as metals losses in the slags (rare earth elements, tantalum), high energy consumption, toxic emissions, etc. [START_REF] Cayumil | High temperature investigations on optimising the recovery of copper from waste printed circuit boards[END_REF][START_REF] Cui | Metallurgical recovery of metals from electronic waste: A review[END_REF].

Furthermore, only high-grade PCBs, containing high gold concentration (> 400 mg.kg -1 ; [START_REF] Hagelüken | Recycling of electronic scrap at Umicore's integrated metals smelter and refinery -World of Metallurgy Erzmetall[END_REF], are processed in the pyrometallurgical route, as the addition of PCBs in smelters represents at most 15% of the inlet flow of materials, the rest being ores [START_REF] Ghosh | Waste Printed Circuit Boards recycling: an extensive assessment of current status[END_REF][START_REF] Goosey | Recycling technologies for the treatment of end of life printed circuit boards (PCBs)[END_REF]. [START_REF] Goosey | Recycling technologies for the treatment of end of life printed circuit boards (PCBs)[END_REF] estimated that, in the United Kingdom in 2003, only 15% of PCB scraps were subjected to any forms of recycling, the rest being consigned to landfill.

Printed circuit boards composition is dependent on the location, year and the type of appliance in which they were used. The average composition of PCBs is 40% of metals, 30% of ceramics and glasses and 30% of plastics [START_REF] Kumar | Characterization of the non-metal fraction of the processed waste printed circuit boards[END_REF].

Many works are dedicated to the development of the different operations involved in WEEE recycling (Akcil et al., 2015;[START_REF] Ghosh | Waste Printed Circuit Boards recycling: an extensive assessment of current status[END_REF] such as dismantling, mechanical treatments (crushing, grinding), physical separation (magnetic separation, electrostatic separation, gravimetric separation), leaching and bioleaching, chemical separation and extraction (liquid-liquid and liquid-solid extraction). In addition to these conventional processes, original dedicated technologies are currently under development to address the specificities of e-wastes. As an example, non-classical reagents can be used such as supercritical CO2 for the extraction of polymers (Calgaro et al., 2015) and molten salts for the dissolution of glasses, oxides and the destruction of plastics without oxidizing the most valuable metals [START_REF] Flandinet | Metals recovering from waste printed circuit boards (WPCBs) using molten salts[END_REF]. The use of ionic liquids have also been investigated for the dissolution of epoxy resins [START_REF] Zhu | Treatment of waste printed circuit board by green solvent using ionic liquid[END_REF]. Usually these unit operations are combined in a common treatment flowsheet, which gives a wide diversity of alternative processes that might be used for WEEE recycling. For instance, Mäkinen developed recently a process relying on the combination of flotation and bioleaching [START_REF] Mäkinen | The effect of flotation and parameters for bioleaching of printed circuit boards[END_REF].

The development of the complex above-mentioned operations involves the establishment of material balances in order to assess and optimize the recovery of valuable elements in PCBs. Consequently, it is necessary to know accurately the raw material compositions and to be able to get reproducible sampling at the laboratory scale.

The Gy sampling theory was developed to ensure a good reproducibility and representativeness of samples from primary resources at various scales [START_REF] Gy | Sampling of heterogeneous and dynamic material systems, theories of heterogeneity,sampling and homogeneizing[END_REF]. This theory enables an estimate of the error generated by dividing samples into smaller samples. Some other studies dealt with sampling of municipal waste, plastics, metallurgical powders, or soils, with adjustments from Gy theory. Likewise, the These methods often also describe analytical methodologies to obtain reliable characterization. However, there is no similar theory applied to e-waste sampling and more especially PCBs sampling. Today, the implementation of reproducible and representative sampling of e-waste remains a great challenge because of their huge heterogeneity.

International

PCBs sampling relies also on the implementation of an efficient method to reduce the particle size of PCBs in order to release metals from PCB matrix. [START_REF] Yamane | Recycling of WEEE: Characterization of spent printed circuit boards from mobile phones and computers[END_REF] reported that above 1 mm, metals were not completely accessible from polymers and ceramics. In order to achieve an efficient metal release, Wang et al. recommended using an impact or knife milling [START_REF] Wang | Crushing performance and resource characteristic of printed circuit board scrap[END_REF]. However, several issues were reported with shredding due to the presence of ductile metals [START_REF] Ernst | Analysis of eco-relevant elements and noble metals in printed wiring boards using AAS, ICP-AES and EDXRF[END_REF][START_REF] Wienold | Elemental analysis of PCB considering the ROHS regulation[END_REF][START_REF] Yamane | Recycling of WEEE: Characterization of spent printed circuit boards from mobile phones and computers[END_REF]. Unfortunately, very few data are available from the recycling industry concerning the sampling procedure, including size reduction, material division and the methods implemented to evaluate PCBs composition of large samples.

Spent PCBs can be characterized by dry techniques and wet techniques. For the sake of illustration, some of the techniques used to prepare samples and analyze them are reported in Table 1. It could be of great interest to combine several methods to have a full characterization of the wastes (metal contents, elemental speciation, etc.) since these techniques are usually complementary. The combination can include various analytical methods such as elemental analyses after digestion with adequate reactive, X-Ray Fluorescence spectroscopy (XRF), Scanning Electron Microscopy with Energy-Dispersive X-ray spectroscopy (SEM-EDX) and Transmission Electron Microscopy (TEM). However, some of these techniques (particularly XRF analysis or microwaveassisted digestions) characterize only very small amounts of materials (below 1 g); this can lead to issues of representativeness and reproducibility, and therefore large errors of characterization. Due to high temperatures, techniques such as calcination and fusion are not compatible with the characterization of volatile elements. With an easy implementation and the possibility to treat large quantities of PCBs, aqua regia digestion is the most frequently used technique in the literature. Inductively coupled plasma-atomic emission spectroscopy (ICP-AES) or inductively coupled plasma-mass spectrometry (ICP-MS), as well as atomic absorption spectroscopy (AAS), are widely used to analyze leachates of spent PCBs digestion.

The aims of the present work are (i) to establish and evaluate a sampling procedure from a large sample of spent PCBs in order to obtain reproducible sub-samples and (ii) to develop a methodology to determine the metal composition of the PCBs. The experimental protocol proposed, tested and assessed in this study was designed specifically to be applied to large amounts of PCBs that are further used for the development of dedicated metallurgical treatments, from laboratory to pilot scale. The objective was to produce subsamples of the same composition from a few grams to several kilograms, which then enables the calculation of mass balances at every step of the process and the assessment and the comparison of the efficiency of the metal recovery. The protocol developed in this study includes several milling steps, the division of the materials in subsamples and the determination of metal contents, as well as their partitioning in the different size fractions. This protocol was applied to 526 kg of spent PCBs and the accuracy of the analytical methods and the reproducibility of the subsamples was assessed. The developed methodology was evaluated according to the following requirements:

-Concerning the reproducibility, less than 10% of variation of metal concentrations between two samples was expected.

-Concerning their characterization, to obtain the closest metal concentration to the true value, the most complete digestion possible was required. 98% (in mass) of metal digested was considered satisfactory.

Materials and methods

Electronic scrap sample

A sample of 526 kg of medium-grade spent PCBs from the small waste electrical and electronic equipment category (sWEEE) was collected and provided by the WEEE sorting center "Envie 2E Midi-Pyrénées" (France). The grade of the sample, which was medium (with 100 to 400 mg.kg -1 of gold; [START_REF] Hagelüken | Recycling of electronic scrap at Umicore's integrated metals smelter and refinery -World of Metallurgy Erzmetall[END_REF], was estimated by the sorting center from the origin of the PCBs (type of appliances), and not by elemental analyses. These PCBs were collected from small appliances such as information technology (IT) equipment, audio and video appliances, toys, personal care products, culinary equipment, etc.

Samples preparation

Figure 1a shows the successive steps of milling and sampling procedure implemented in the present study : (i) PCBs were shredded to reduce the particle size below 30 mm with a shear shredder from Bohmier Maschinen GmbH; (ii) the particles were afterward shredded to a size less than 10 mm with the same equipment; (iii) particle size reduction was then carried on using a laboratory knife mill (Retsch SM-2000) with bottom sieve mesh size of 750 µm. Intermediary bottom sieves with mesh sizes of 10 mm, 4 mm, 2 mm and 1 mm were used for this step. To reduce the particle size to smaller diameter, the following apparatuses were tested on two 500 g-samples: laboratory knife mill (Retsch SM-2000) with bottom sieve mesh size of 500 µm and disk mill (Bico Inc).

In each reduction steps, particles retained on the bottom sieve of the mill were added to the ground sample in order to ensure a good representativeness.

After size reduction steps at 30 mm, 10 mm and 750 µm, the milled samples were divided into smaller samples so that the final mass of the final samples was equal to approximately 4 kg (initial mass of the batch = 526 kg). The details of these sample divisions are given in Figure 1b. These operations were carried out with a rotary divider for large samples (fraction 30 mm) and a riffle-divider for smaller ones (fractions 10 mm and 750 µm) with openings of the riffle-divider larger than three times the particle size (see supporting information).

For the sake of comparison of sampling procedures, the Umicore procedure is also presented in Figure 1a. To our knowledge, this procedure is the only industrial methodology published in the literature until now [START_REF] Hagelüken | Recycling of electronic scrap at Umicore's integrated metals smelter and refinery -World of Metallurgy Erzmetall[END_REF]. At each step of the procedure, the size reduction and mass ratios obtained in this study were in the same order of magnitude than those reached in the Umicore's procedure.

Particle size distribution

Particle size distributions were determined for three samples: (i) a sample reduced with bottom sieve mesh size of 750 µm (step 3), (ii) a sample reduced with bottom sieve mesh size of 500 µm (step 4) and (iii) a sample ground with disk mill (alternative step 4). These distributions were obtained by wet sieving of 290 g samples at 1.25 mm, 1 mm, 800 µm, 400 µm, 200 µm, 100 µm and 63 µm. Each size fraction was dried at 40 °C before weighting and performing any analyses.

Analyses

Considering the particles' size, analysis should be performed on 40 g-samples (see Section 3.2). However, for safety reasons, aqua regia digestions were performed on 5 gsamples. Consequently, initial 40 g-samples were divided into 8 sub-samples by quartering method. 5 g-PCBs samples were leached by 56 mL of aqua regia (HNO3:HCl 1:3) prepared by mixing 42 mL of 32%w HCl with 14 mL of 67-69%w HNO3, or reverse aqua regia (HNO3:HCl 3:1) (14 mL of 32%w HCl+42 mL of 67-69%w HNO3). Nitric acid (67-69%w, trace metal grade) and hydrochloric acid (32%w, certified for analysis) were provided by Fisher Chemical. The leaching reactors were heated to 200 °C during 2 hours by means of a Kjeldatherm-Gerhardt digestion system. After cooling, solid/liquid filtration was performed with cellulose nitrate membrane filters (Sartorius Stedim Biotech, pore diameter=0.45 µm) in order to separate the leaching solutions and the residues. The residues were rinsed with water and dried at 40 °C. Metal concentrations in the filtrate were determined after dilution in 0.5 mol.L -1 HNO3 prepared by dilution of concentrated nitric acid in milliQ water (resistivity= 18.2 MΩ.cm). A Varian SpectrAA-300 flame atomic absorption spectrometer (FAAS) was used to determine Fe, Cu, Zn, Pb, Co and Ni concentrations. An ICP-AES Horiba Jobin Yvon Ultima 2 or an ICP-MS Thermo Scientific X Series were also used to determine other metals concentrations : Ag, Al, Au, Cr, Ga, Ge, In, Mg, Mn, Mo, Pd, Sb, Sn, Ta, V and W.

After characterizing these 8 subsamples by the methodology above-mentioned, metal concentrations in 40 g-samples were calculated by using the following equation:

𝑚 "#$ 𝑥 "#$ = ∑ 𝑚 ) *$ 𝑥 ) *$ + ),- [1]
Where 𝑚 "#$ , 𝑥 "#$ , 𝑚 )*$ and 𝑥 )*$ denote effective mass of the 40 g-sample, metal concentration in the 40 g-sample, effective mass of 5 g-subsample i and metal concentration in 5 g-sub-sample i, respectively.

The same methodology was used for reverse aqua regia digestion of one 40-g sample and for reverse aqua regia digestion of each size fraction.

Solid residues resulting from partial leaching by aqua regia were ground for 20 min at 710 rpm in a planetary mill containing ferro-chromium grinding bowls (Siebtechnik).

After grinding, the solid residues were leached a second time by using two different methods: (i) 5 g of residues were leached by aqua regia in triplicate in the same conditions as the first digestion and (ii) 200 mg of residues were leached by a mixture of 1 mL hydrofluoric acid (Merck, 40%w for analysis), 1 mL hydrochloric acid (Fisher, 32%w certified for analysis) and 3 mL nitric acid (Fisher, 67-69%w, trace metal grade) in duplicate at 150 °C under 125 bars during 20 min and, afterward, at 260 °C under 125 bars during 2 hours. Leachates were analyzed by ICP-MS. Some samples of these residues were also examined by scanning electron microscopy (SEM HIROX-SH 3000 with integrated EDS Bruker Nano).

Results

Sampling

The initial sample of 526 kg was entirely reduced to 30 mm except 41 kg of large pieces, mostly corresponding to heat sinks and coils that were removed because it was not possible to shred them. The ground material was then divided by quartering in 4 samples of respectively 110 kg, 142 kg, 108 kg and 122 kg (see Figure 1b). The differences obtained between the samples masses might be explained by the formation of clusters of copper wires that led to changes of incoming flow rate of materials in the rotary divider. In this step, 3 kg of materials were lost (0.6% of the total mass). Only one of the four samples (122 kg) was afterward used for this study. In the step 2, this sample was entirely shredded to 10 mm and divided to obtain sub-samples of 4 kg. Unlike in step 1, the mass of the sub-samples obtained throughout the different quartering operations of step 2 were similar and closed to targeted value. Clusters were not observed anymore, since copper wires were cut during this step. Consequently, the material was better distributed. During the step 3, 4 of these 4 kg-samples were milled by means of a laboratory knife mill with bottom sieve mesh size of 750 µm. Some particles did not go through the bottom sieve (7,9% in mass) but were gathered with the ground sample. Less than 0.6% of the mass of the initial sample was lost when grinding was performed from 10 mm to 750 µm. This result is promising. For a sake of comparison, Wienold et al.

(2011) reported 0.05% (mass) of material losses during the grinding of 4 kg of PCBs from 1.5 mm to 500 μm with an ultra-centrifugal mill cooled by a continuous flow of liquid nitrogen. Finally, during step 4, some samples were afterward milled with a disk mill or a laboratory knife mill with smaller bottom sieve mesh size (500 µm) in order to further reduce the particle size. 20% of the mass of the sample ground with Retsch SM-2000 with bottom sieve of 500 µm did not go through the sieve, while 2.0% of the material was lost. Concerning the sample ground with the disk mill, the material loss was less than the weighing scale precision (1 g).

Particle size distribution of the samples obtained after the step 3 and the step 4 is given in Figure 2. The d80, 80 th percentile of the particle size distribution, was equal to 750 µm when using the laboratory knife mill with bottom sieve mesh size of 750 µm, equal to 700 µm when using the same device with bottom sieve mesh size of 500 µm and equal to 850 µm after the 4 th milling with disk mill. The use of a bottom sieve mesh size of 500 µm did not enable to reduce the larger particles' size under 700 µm. Even after 2 hours of milling, a large number of particles did not cross the 500 µm-sieve. As said previously, particles that did not cross the bottom sieve were added to the ground sample to preserve the representativeness of the subsample. Conversely, the disk mill had a negative impact, since it was responsible for an increase of the d80 of the ground sample.

The samples used in the next sections of the present paper were those obtained at the end of step 3, using the Retsch SM-2000 with bottom sieve mesh size of 750 µm.

Analyses

As explained above, the grinding methodology proposed in this study did not enable to reduce the size of the spent PCBs particles under 500 µm. Such fine grinding is probably not required to obtain complete accessibility of metals, but it decreased the heterogeneity of composition, which allows reducing the mass of the sample that will be used to perform the chemical analysis of the metal content. In our study, based on the empirical formula [2] used in BRGM (French geological survey) for the characterization of complex materials (unpublished data), 40 grams of samples were analyzed.

𝑆𝑎𝑚𝑝𝑙𝑒 𝑚𝑎𝑠𝑠 𝑓𝑜𝑟 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 = 0.06 × 𝑑 +# (µ𝑚) [2]
Table 2 shows the metal concentrations determined by ICP-AES, ICP-MS and FAAS analyses in three samples of 40 g after aqua regia digestion. For Fe, Cu, Ni, Zn, Co and Pb, leachates concentrations were measured with both FAAS and ICP-AES and it gave similar results. These three samples were taken from two different samples of 4 kg to determine the variability of metal contents, as given in Figure 1b. The sample n°3 was only analyzed by FAAS.

Relative standard deviation (RSD), defined as the ratio of the standard deviation to the mean value, was lower than 2% for Cu and Fe and comprised between 5% and 10%

for Zn, Pb, Ni and Co. When only a duplicate was analyzed, RSD were not calculated but the relative difference between the two obtained values was evaluated. For Al, Ga and Ta, this relative difference was limited (0.9% for Ga, 6.8% for Al, 11.8% for Ta), while high discrepancies in metal concentrations between samples were obtained for Sn and Au (31.3% for Sn and 46.9% for Au).

Metal concentrations obtained after aqua regia digestion were compared with those obtained after digestion with reverse aqua regia, which is supposed to be more oxidative than aqua regia (Figure 3a). The same metal concentrations were obtained except for Ag whose concentration was 60 times lower than that obtained with aqua regia. Likewise, a factor of 15 between aqua regia and reverse aqua regia was observed for Pd. With these results, the use of aqua regia digestion was chosen for further studies.

There is a large amount of data about metal concentrations in spent PCBs in the literature. However, analytical and sampling procedures are often not described (UNEP, 2013). Results greatly depend of the type of WEEE and their origin. Despite this large variability, our data were compared with the literature to be sure that this work was coherent with other studies reported elsewhere. Figure 3b performed on computers PCBs with aqua regia digestion. These results were also compared with those obtained in the present study (Figure 3b).

A generally good agreement was found between the PCBs composition analyzed in the three studies and our present work. There were large amounts of Fe, Cu (>10% in mass)

and Al (6-8% in mass) that came from various parts of PCBs such as heat sinks, copperlayer for conduction, coils, etc. Sn, Zn and Pb, which were mainly used for soldered joint, represented approximately 1% of the mass of PCBs. A huge difference was observed for Ta since Ta content was approximately 5000 times lower in our study than the value reported in the literature.

Analysis of solid residues

After the digestion with aqua regia, 40% of solid residues attributed to non-metal elements of PCBs remained in the reactor. In order to evaluate metal concentration in the residues, they were ground to achieve the most efficient metal accessibility of leaching reactive towards potentially remaining metals. Milling was efficient (particle size <100 μm) and residues' size reduction was more easily achieved than PCBs' size reduction. No metal was detected through SEM-EDX analysis (see supporting information) which revealed on the other hand the presence of Si, Cl, Ca in the residues; these elements are derived from plastics, ceramics and glasses.

The residue remaining after the digestion of the sample n°2 was digested with aqua regia after milling and the residue remaining after the digestion of the sample n°1 was digested using a mixture of HF, HCl and HNO3. During the second digestion with aqua regia, only 10% of the mass of the residue was dissolved. On the contrary, the digestion with hydrofluoric acid enabled to dissolve 100% (in mass) of the residue. Furthermore, the metal concentrations were much lower in the leachate obtained after the second digestion than after the first digestion. Table 3 gathers (i) the metal concentrations in the spent PCBs calculated from the data obtained after the first digestion and after both digestions and (ii) the proportion of metal that was leached during the first digestion vs.

the total mass, derived from the following equation:

% 𝑤𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑚𝑒𝑡𝑎𝑙 𝑙𝑒𝑎𝑐ℎ𝑒𝑑 𝑑𝑢𝑟𝑖𝑛𝑔 𝑡ℎ𝑒 1 JK 𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛 = L M L M NL O × 100 [3]
where 𝑚 -denotes the mass of metal dissolved by the first digestion and 𝑚 P the mass of metal, dissolved by the 2 nd digestion.

The digestion of the residues showed that more than 90% (in mass) of all metals contained in spent PCBs were correctly dissolved by using aqua regia during the first digestion. The digestion was particularly efficient for Cu, Zn, Sn, Pb, Co and Pd, with less than 2% in mass of undissolved metals. In that case, the digestion with HF/HCl/HNO3 enabled to dissolve small quantities of Cu (0.1% in mass), Zn (2.1% in mass), Sn (0.3% in mass), Pb (0.7% in mass), Co (1.1% in mass) and Pd (1.2% in mass).

Samples were ground enough so that these metals were efficiently accessible at 750 µm and easily digested with aqua regia. The digestion with aqua regia was efficient for Ni, Ag, Au and Ga, since more than 93% (in mass) of the metal that was digested.

Concerning Ta, the digestion with aqua regia was not appropriate at all, since only 0.5% of the mass was digested with aqua regia digestion (see section 3.2).

Chemical analysis of size fractions

Metals distribution is heterogeneous and initial sizes of the different elements that constitute PCBs can vary a lot from one PCB to another. In addition, when subjected to mechanical constraints, the different types of PCBs components show various behaviors.

For example, the materials present in PCBs can exhibit different mechanical properties that will cause plastic deformations in some cases, while others will rather break. It leads to heterogeneous grinding: some materials can be easily shredded, with homogeneous particle size reductions, while other materials would be only partially reduced. These phenomena may lead to differences of metal distribution in the sample, which may affect the digestion by aqua regia and the representativeness of the sampling protocol. It is therefore important to evaluate this phenomenon. Moreover, it might be interesting to identify a particular size fraction in which valuable metals are present in order to concentrate them for further extraction.

Consequently, chemical analyses of each size fraction with reverse aqua regia were performed. It is important to point out that Ag and Pd concentrations were underestimated by using this method of characterization of spent PCBs, as previously demonstrated (see section 3.2). Metal concentrations in the genuine sample, called "reconstituted sample", were calculated by using the following equation:

𝑥 JQLRST = ∑ 𝑤 UVQWK)XY ) 𝑥 UVQWK)XY ) Z ),- [4]
where 𝑥 JQLRST , 𝑤 UVQWK)XY ) and 𝑥 UVQWK)XY ) denote the metal concentration in the genuine sample, the mass fraction of the size fraction i and the metal concentration in the size fraction i, respectively. Metal concentrations in the genuine sample, as well as in each size fraction, are given in supporting information.

To estimate the heterogeneity resulting from the size reduction, it was required to calculate the ratio of metal contents of a specific size fraction compared to others and compared to the whole sample content. For most metals (Al, Cu, Pb, Sn, Zn, Ni, Ga and Ta), these ratios were not sufficiently high (<5) to have a noteworthy enrichment of a fraction.

However, for precious metals (Ag, Au and Pd) and for Fe and Co, a particular size fraction was enriched. The highest concentration of metals was found in the coarser fraction for Ag and Fe (size>1000 µm), in fine particles (size<100 µm) for Au and in middle size fractions (100 µm<size<400 µm) for Pd and Co.

The differences of metal contents between 40 g-sample digested by reverse aqua regia and reconstituted sample were calculated and are given in supporting information.

For most metals, the relative difference in metal concentrations between both methods was lower or of the same order of magnitude than the intrinsic variability between replicates (See RSD in Table 2).

Finally, Figure 4 shows the metal distribution as a function of size fractions. Some fractions were enriched in some metals as previously shown. In the fine fraction (size<63 µm), precious metals (Ag and Au) and Ga, which is a strategic metal, were particularly concentrated since it contained 24% Ag, 27% Au and 23% Ga (in mass). Pd and Co were mainly distributed in the fraction -400 µm +200 µm (47% for Pd and 28% for Co in mass). Despite this specific enrichment, all metals were largely distributed in the intermediate size fraction (400 µm < particle size < 800 µm) since this intermediate fraction represented 47% of the total sample mass. In particular, more than 40% of the total mass of Au and Ag were present in this size fraction. This high distribution in the size fraction "-800 µm +400 µm" was also observed for Ga and Cu as it contained at least 38% and up to 59% in mass, respectively.

Discussion

This work aimed at developing a sampling and characterization procedure for the production of reproducible and well-characterized samples. Concerning the sampling procedure, knife mills and disk mills were tested to reduce the particle size below 750 µm. The use of the disk mill increased the particle size, probably because of the formation of clusters observed during this step, due to heat dissipation. Given this result, technologies based on crushing (such as disk mill) were less adapted to reduce PCBs particles' size compared to technologies based on shredding. From the experiments with the knife mill, we can deduce that particles larger than 500 µm were mainly ductile metals and their size cannot be reduced by shredding. The obtained particle sizes may permit a complete accessibility of metals since [START_REF] Yamane | Recycling of WEEE: Characterization of spent printed circuit boards from mobile phones and computers[END_REF] determined that metals are not sufficiently accessible if particles' size is above 1 mm. This accessibility is closely related to the type of insertion and soldered joints of the components in the board.

It must be noticed that the samples obtained after the 3 rd milling step contain 17% (in mass) of fine particles (size<63 µm). They can be easily lost and potentially hazardous: [START_REF] Zhang | Intelligent Liberation and classification of electronic scrap[END_REF] revealed that the fine fraction may be toxic since they contain high Br concentration.

Concerning the characterization, digestion with aqua regia was first compared to digestion with reversed aqua regia. High consistency was observed between results obtained by both methods, except for Ag and Pd. More studies would be required to fully understand the associated mechanisms.

From our study, metals were classified in three different categories: the ones that met our goals (less than 10% of variation between replicates' metal concentrations and more than 98% of the metal digested), the ones that partially met them or the ones for which our procedure did not meet the needs.

Metals for which goals were met

For Cu, Fe, Al, Zn, Pb, and Co, the sampling and characterization procedures were relevant and enabled to obtain reproducible samples: for these metals, RSD and relative differences were lower than 10% for 40-g samples (see Table 2). Moreover, the residues digestion enabled to conclude to an efficient digestion of PCBs samples with aqua regia, since no more than 0.1% to 2% (in mass) of metals remained in the solid residues after digestion.

Metals for which goals were partially met

For some other metals, the sampling and characterization procedure did not enable to obtain high reproducibility and/or high digestion of metals in the PCBs samples.

Concerning reproducibility, despite the low number of replicates, two phenomena could explain the large variation of RSD values: the digestion with aqua regia may depend on metal speciation ; metal is not well-distributed between the different replicates.

Concerning digestion, different hypotheses could explain that a part of these metals was not digested during the first digestion : partial accessibility at 750 µm, too short digestion time or metal speciation. The lack of replicates and variability of samples did not enable to confirm or deny these hypotheses.

However, the accuracy obtained with our procedure could be considered satisfactory depending of the aim of the study.

Regarding Ni and Ga, the variation in the content of the different samples was limited (7.2% RSD and 0.9% of relative difference respectively) which enabled to conclude to a high reproducibility. With 94.5% (in mass) of Ni and 93.0% (in mass) of Ga that were digested by aqua regia, the determination of the samples' concentration was under-evaluated but this could be considered acceptable.

Conversely, Pd was highly digested with aqua regia but its reproducibility met only partially our requirement: the relative difference between a duplicate reached 17%.

Finally, concerning Ag, neither reproducibility, nor high digestion were obtained.

Part of Ag content was still in the residues after aqua regia digestion (92.6% in mass of Ag digested) and the relative difference between a duplicate reached 19.3%. The precipitation of silver chloride may explain these results [START_REF] Petter | Evaluation of gold and silver leaching from printed circuit board of cellphones[END_REF]. The use of nitric acid could improve the dissolution of silver and thus its characterization.

For these metals, metal concentration were under-estimated but this could be acceptable depending on the precision needed. The use of hydrofluoric acid did not seem required. It slightly improved the accuracy of metal analysis but was much more complicated to use due to its toxicity compared to aqua regia. Aqua regia is a good compromise between the simplicity of implementation and the accuracy.

Metals for which goals were not met

For Au, the sampling could not be considered satisfactory, since the relative difference in Au concentration in a duplicate reached 46.9%. This variation could be attributed to the lack of accessibility of fine Au layers in larger particles. 4.8% of the total mass of Au was still present in the residues after the first aqua regia digestion.

For Sn, the sampling and characterization procedure could not be considered satisfactory since the relative difference between the concentrations of a duplicate was 31.3%. The methodology recommended in the standard for the determination of the content in Sn is performed with hydrobromic acid, as Sn is often contained in alloys with Pb and Cd (AFNOR, 2014). Differences in the efficiency of the aqua regia digestion according to the speciation of Sn in the initial PCB could perhaps explain the high relative difference obtained. Moreover, it has been demonstrated that tin slowly precipitates as metastannic acid H2SnO3, which could affect its characterization (Mecucci and Scott, 2002).

Finally, aqua regia digestion was absolutely not suitable for the determination of the concentration in Ta: 99.5% (in mass) of the Ta was present in the residues after digestion.

The use of fluorhydric acid was needed for this metal. It was likely that Ta comes from the capacitors and is present as metal or oxide (Ta2O5), which exhibits low solubility in aqua regia. As an example, [START_REF] Theron | Dissolution and Quantification of Tantalum-Containing Compounds: Comparison with Niobium[END_REF] reported that only 3.9% Ta2O5 and Ta were dissolved in aqua regia, even under microwave.

Overall interest of the developed methodology

Table 4 summarizes, for numerous metals, the overall interest of our procedures for the determination of the metal contents of PCBs samples, taking into account the variation of concentrations between different samples and the efficiency of digestion with aqua regia. These methods seemed fully suitable for Cu, Fe, Al, Zn, Pb and Co. They were slightly less suitable for Ni, Ag, Ga and Pd, but could nevertheless be used depending of the aim of the study. Concerning Au and Sn, the relevance of our procedures was not clearly demonstrated. For Ta, characterization method was undoubtedly not adapted.

After determining the interest of our methodology, each size fraction was analyzed to determine metal distribution. Some enrichments were reported for Ag, Au, Pd, Fe and Co. Several hypotheses can explain this phenomenon: (i) the particle size reduction was heterogeneous for these metals; (ii) they were associated with other elements for which grinding was heterogeneous; (iii) they were included in items of different sizes on PCBs and this impacted the final content of the size fractions of the ground sample. However, the enrichments observed were not large enough to be able to benefit from them.

Therefore, the use of sieving to recover enriched fractions would result in the loss of a large part of the metal in other fractions that would not be kept. In addition, the distribution was not homogeneous enough to use only the fine fraction for the development of metallurgical processes, as it is often performed in some studies [START_REF] Ogunniyi | Chemical composition and liberation characterization of printed circuit board communition fines for beneficiation investigations[END_REF] because the samples would no longer be representative of the initial sample.

Conclusion

This study was devoted to the development of a procedure to produce small samples from large amounts of spent PCBs (more than 500 kg) and to determine their metal contents. The objective was to provide a rigorous sampling and characterization procedure to obtain reproducible and well-characterized subsamples of spent printed circuit boards. These materials are complex and heterogeneous, and even if numerous studies have been published about the development of metallurgical processes to recover the metals they contain, very few data about such procedures are currently available in the literature.

Our strategy to sample and characterize large samples of spent PCBs combined two main procedures: (i) first, a methodology to reduce the particle size, which enabled to release the elements that constitute the PCBs and to divide the initial samples in homogeneous and reproducible subsamples ; (ii) second, the characterization of the subsamples (40 g). The first procedure relied on the use of a shredder to reduce the particle size under 30 mm and 10 mm, and then on a further milling step with a laboratory knife mill to reach 750 µm. The characterization procedure involved first the digestion of the sub-samples with aqua regia, and then the analysis of leachates with ICP-AES, ICP-MS and FAAS. This approach was completed by additional steps to assess the suitability of the methodology: grinding with other laboratory tools, characterization of the solid residues obtained after aqua regia and sieving and characterization of the size fractions.

Regarding the sampling procedure, the use of large shredders followed by a knife milling enabled to reduce particle size to less than 1 mm, threshold below which metals are usually considered to be liberated from polymers, ceramics and glasses, or at least accessible. Material losses were negligible and the measured particle size distribution showed that the d80 reached 750 µm, which demonstrated that the shredding methodology was efficient. Additional milling steps did not enable to reduce further the particle size, mainly because of the presence of ductile metals.

The digestion of the subsamples with aqua regia enabled the dissolution of more than 98% (in mass) of most metals except for Ta which was only partially dissolved by aqua regia (95% of the Ta initially present in the sample remained in the solid residue after digestion). This led to substantial underestimation of its content and demonstrated that the methodology developed in this study was not adapted to the determination of Ta concentration in PCBs. Some issues were also encountered with Ni, Ag, Au and Ga that were not completely dissolved during aqua regia digestion (4 to 7% remained in the solid residues), which led to slight underestimations of their contents.

The combination of the sampling and digestion methodologies was suitable for Cu, Fe, Al, Zn, Pb and Co. For Ni, Ag, Ga and Pd, the methodology was less accurate but remained an efficient tool when it is used to estimate the efficiency of metallurgical processes developed to recover these metals in waste PCBs [START_REF] Hubau | Double-stage continuous bioreactor for the bioleaching of printed circuit boards to recover metals with acidophilic micro-organisms[END_REF]. Other analytical methodologies might be established to complete this characterization in the case of a need of accurate determination of metal content (as XRD; [START_REF] Clearfield | Principles and Applications of Powder Diffraction[END_REF]. For Au and Sn, large variations of their concentration were observed between replicates, while for Ta, the characterization methodology was not adequate.

Finally, the evaluation of the metal composition of each size fraction enabled to conclude that the enrichments observed were not large enough to be able to benefit from them with simple sieving and that the distribution was not homogeneous enough to use only the fine fraction for further studies.

Acknowledgement

The authors acknowledge Eco-systèmes for funding this work in the frame of the ParisTech Chair "Mines Urbaines".

  Organization for Standardization or other organisms published methods for sampling various materials, such as soils (ISO 23909:2008 from AFNOR, 2008), plastic wastes (XP CEN/TS 16010 from AFNOR, 2013), metallurgical powders (ISO 3954:2008 from AFNOR, 2008), particulate materials (ISO 11648-2:2001 from AFNOR, 2001), etc.

  shows the comparison of metal concentrations in spent PCBs reported by the United nations environment programme (UNEP), the United nations university (UNU) and Bizzo et al. in 2013, 2008 and 2014 respectively (Bizzo et al., 2014; UNEP, 2013; UNU, 2008). The study from UNEP does not give information about the type of PCBs and the methodology used for the characterization. The PCBs used in the report published by UNU are from the same category as in the present study, i.e. small WEEE. The study from Bizzo et al. was

Figure 1 :

 1 Figure 1: Comparison between the grinding procedure used in this paper and by

Figure 2 :

 2 Figure 2: Particle size distributions of three samples of 290 g milled with a disk mill

Table 1 : Usual methods for the characterization of spent PCBs. 640

 1

	metal recovery from waste printed circuit boards using cyanide and non-cyanide
	lixiviants -A review, Waste Management, 45, 258-271.
	AFNOR, 2001. Statistical aspects of sampling from bulk materials -Part 2: Sampling of
	particulate materials, standard ISO 11648-2:2001, 107p.
	AFNOR, 2008. Powders for powder metallurgical purposes -Sampling, standard ISO
	3954:2008, 8p.
	AFNOR, 2008. Soil quality -Preparation of laboratory samples from large samples,
	standard ISO 23909:2008, 18p.
	AFNOR, 2013. Plastics -Recycled plastics -Sampling procedures for testing plastics
	waste and recyclates, standard XP CEN/TS 16010, 23p.
	AFNOR, 2014. Determination of certain substances in electrotechnical products -Part 5 :
	cadmium, lead and chromium in polymers and electronics and cadmium and lead in
	metals by AAS, AFS, ICP-OES and ICP-MS, standard NF EN 62321-5, 121p.
	Birloaga, I., De Michelis, I., Ferella, F., Buzatu, M., Vegliò, F., 2013. Study on the
	influence of various factors in the hydrometallurgical processing of waste printed circuit
	boards for copper and gold recovery, Waste Management, 33, 935-941.
	Bizzo, W.A., Figueiredo, R.A., de Andrade, V.F., 2014. Characterization of Printed
	Circuit Boards for Metal and Energy Recovery after Milling and Mechanical Separation,
	Materials, 7, 4555-4566.

Table 3 : Metal concentrations of the sample n°1 and n°2 after a first digestion with aqua regia or after two digestion and ratio (in mass) of metals leached during the 1 st digestion. Concentrations are given in %w and mg.kg -1 .

 3

				Sample n°1			Sample n°2
	Digestion n°1	Method % w/w residues		Aqua regia 40%			Aqua regia 47.5%
	Digestion	Method		HF/HCl/HNO3			Aqua regia
	n°2 on the residues of digestion 1	% w/w residues		0%			90%
	Metal	Unit	Content measured with 1 st digestion	Content after 2 digestions	% recovered from 1 st digestion	Content measured with 1 st digestion	Content after 2 digestions	% recovered from 1 st digestion
	Cu	%w	14.60%	14.61%	99.9%	14.32%	14.34%	99.9%
	Fe	%w	-	-	-	12.34%	12.52%	98.5%
	Al	%w	-	-	-	5.84%	5.96%	98.0%
	Zn	%w	1.81%	1.85%	97.9%	1.52%	1.53%	98.5%
	Sn	%w	1.89%	1.90%	99.7%	1.44%	1.45%	99.3%
	Pb	%w	1.23%	1.24%	99.3%	1.08%	1.09%	98.8%
	Ni	%w	0.32%	0.34%	94.5%	0.32%	0.33%	97.3%
	Co	mg.kg -1	334	338	98.9%	374	376	99.5%
	Ag	mg.kg -1	198	209	94.6%	166	179	92.6%
	Au	mg.kg -1	42.2	44.4	95.2%	62.0	65.9	94.1%
	Pd	mg.kg -1	22.9	23.2	98.8%	26.8	27.3	98.4%
	Ga	mg.kg -1	10.6	11.4	93.0%	10.7	11.5	93.1%
	Ta	mg.kg -1	0.034	7.5	0.5%	0.038	0.045	83.6%

aqua regia at 200 °C at a liquid/solid ratio of 11 mL.g -1 (N.A.= not analyzed).

Concentrations in %w

Concentrations in mg.kg