Christian Serrano
email: christian.serrano@renault.com

Xavier Delorme
email: delorme@emse.fr

Alexandre Dolgui
email: alexandre.dolgui@mines-nantes.fr

Cross-dock distribution and operation planning for overseas delivery consolidation: a case study in the automotive industry

Keywords: Cross-dock, Distribution and operation planning, Integer linear program, Optimization, Automotive industry

Cross-dock distribution and operation planning for overseas delivery consolidation: A case study in the automotive industry

INTRODUCTION

Once major carmakers started to reach almost every market in the globe, automotive industry was pushed to internationalize its activity in order to maintain competitiveness. Economic factors such as the boom of emerging markets or the financial crisis of 2008/09, created the circumstances to back up this kind of strategy. Because of it, most car manufacturers decided to set up industrial facilities outside their home countries in a growing tendency, as many companies are currently investing all around the world [START_REF] Cârstea | Delocalization -the automotive industry's answer to cost reduction[END_REF]. In such a globalized context, oversea logistic flows of Renault have drastically increased, in particular the export of individual parts [START_REF]Atlas Renault[END_REF]. The same tendency revealed in other automotive groups [START_REF] Fleischmann | Strategic planning of BMW's global production network[END_REF][START_REF] Itoh | Investigating variations in the deep-sea sourcing strategies of car manufacturers: Two case studies of parts consolidation centers in Japan[END_REF].

Since this activity entails a major logistics challenge in terms of lead time, quality and cost, as several other automotive firms [START_REF] Carbone | The changing role of ports in supply-chain management: an empirical analysis[END_REF][START_REF] Frigant | Are automotive global production networks becoming more global? Comparison of regional and global integration processes based on auto parts trade data[END_REF], Renault creates cross-dock platforms to consolidate and deliver components. These platforms, Renault calls them International Logistics Networks (ILN), are only used for merging components for international distant deliveries. No cross-dock for inbound logistics is used.

The components are received via inbound logistics, repackaged for transportation by boats and delivered to overseas plants. The main idea of cross-docking strategy here is to transfer incoming deliveries to outgoing vehicles, with almost no storage or treatment in between.

One good example of the situation explained in [START_REF]Atlas Renault[END_REF]. Present in 128 countries, Renault's international development strategy has led to a rise of sales outside of the EU, from 23% in 2004EU, from 23% in to 46% in 2014EU, from 23% in (peak of 51% in 2013)), with Brazil and Russia as key markets of the group. Concerning industrial facilities, by the end of 2013, 8 out of 18 Renault assembly plants were located outside of Europe, accounting for 52% of finished vehicles production.

However, almost 80% of engines and gearboxes were manufactured within EU countries.

Moreover, many suppliers are still located in this region, which means a significant amount of components must be exported to distant industrial sites. To do so, Renault relies on a set of nine multi-modal export-oriented platforms, called ILNs (International Logistics Networks).

These platforms link overseas industrial sites (which will be referred to also as customers) with inland suppliers. Four of them are located in Europe and represented 87% of Renault's exported volume of individual parts in 2013 (3.5 million m 3 ).

From figure 1 it can be noticed that a Renault ILN acts both as information and physical consolidation point. Once customers' delivery orders are received, distribution and operation planning are carried out. Based on this process, orders are generated and transmitted to suppliers. Upon arrival at the ILN, deliveries are unloaded from inbound trucks, then sorted, repacked (10-20% of total volume), moved across the facility and finally loaded onto outbound containers for overseas shipping. As it is possible to observe here, ILN platforms have a double purpose: on the economic level, Renault seeks to reduce overseas transportation costs by consolidating components, on the operational level, this configuration simplifies distant supply management for suppliers since they will not have to deal, each separately, with several aspects such as lead times, customs, time zones, language, cultural environment, etc.

Figure 1. Logistics network of a Renault ILN platform

Based on the analysis of current processes of ILN platforms, this article proposes an alternative method to improve the planning process. In particular, by establishing a better transportation costs assessment and a more accurate daily workload allocation. The purpose of this research is twofold. On the one hand, a specific industrial situation is presented and improved; with still encouraging perspectives in the short term. On the other hand, two research fields, which are mostly treated individually in literature, are brought together: distribution and operation planning in a cross-dock centre.

The rest of paper is organized as follows: in section 2, Renault's ILN information and physical flows are explained in detail; their impact on performance, improvement opportunities, as well as the overall scope of this work are discussed. Accordingly, a literature review and the problem definition are presented in section 3. Afterwards, in section 4, the suggested integer linear programming model and results of numerical experiments are reported with a focus on the methodology. The industrial implementation of a simplified version of the model, which concerns only inbound transportation, is presented in section 5.

Finally, a discussion on results of this research is presented in section 6 and the conclusion, with both industrial and research perspectives, is given in section 7.

RENAULT ILN OPERATION MODE AND PROBLEM DEFINITION

Based on figure 1, this section aims to explain both, information and physical flows at Renault ILNs. A related performance analysis, based on a 5-month period study in [START_REF] Serrano | Planification et ordonnancement des activités dans un centre de crossdock international[END_REF] of one Renault ILN, is presented as well, to assess some improvement opportunities and describe the scope of this work.

Information flow: distribution and operation planning

Figure 2 presents an example of delivery plan for a Renault ILN and a given component. Such a plan is established once a week for all components. The first step is the reception of the customers' orders. For week 11, for example, on Monday it is demanded a quantity of 20, on Tuesday 35 and so on. Second, the shipment schedule is generated based on the corresponding customer lead time, this time includes the time of transportation between the ILN platform and customers (the time from the cargo ship departure to arrival). Moreover, a day of transit at the ILN shop-floor is added to every component to obtain, as a result, the due dates for suppliers of ILN. In the next step, the planning system verifies the inbound transportation days used correspond to those contracted with suppliers and thus the supplier delivery schedules are generated. Figure 2 illustrates how release dates are calculated for ILN and ILN suppliers. In the example shown in figure 2, the contracted delivery days for the corresponding supplier are Monday and Wednesday. Hence, the quantities planned for both Monday and Tuesday will be ordered for Monday (20+35) and the quantities planned for Wednesday, Thursday and Friday will be ordered for Wednesday (20+0+70). Finally, supplier lead time is calculated to obtain the release date for the supplier. In the example in figure 2, the supplier lead time is equal to one week (5 days). Then, daily schedule is inferred from the obtained plan. Based on this information, the workload for each shop-floor activity is calculated resulting in the estimation of the total amount of resources needed for the whole week. Figure 2 illustrates how for a component taking into account the known days for ship departure and arrival as well as other elements of customer lead time, one day ILN lead time and taking into account the contractual day for delivery of components by a supplier and the supplier lead time, the release dates for the supplier and ILN are calculated. In the figure there is no information on how the transportation is executed. In practice, it is executed in a smooth manner taking into account the capacity of transport and the fact that the inventory cost in the harbour is already included in the total transportation cost, so no necessity for Renault to minimize the inventory and inventory cost in harbour.

Cost drivers and impact of the current planning process on performance

A 5-month period was studied at one Renault ILN. The considered platform receives around 150 trucks per week and exports around 100 containers per week. At the shop-floor (10,000 m 2 ) there are 5 inbound doors and 4 outbound doors and a team of around 30 people performs the cargo unloading, repacking, moving and loading activities. Three main operational cost drivers were identified, each one related to one level of the supply chain: truck filling rate (for inbound trucks), daily workload (internal resources, e.g. operators) and container filling rate (outbound deliveries). Storage is allowed at the platform, but since the warehouse is big enough for the maximum possible stock, Renault decided to not consider the internal stock as an operational cost driver. Moreover, as explained in subsection 2.1, the main and only parameter used by Renault planning software to calculate supplier, ILN processing and delivery schedules at the delivery planning step is customers' daily due date. Transportation filling rates (for inbound trucks and outbound containers) and shop-floor workload (internal resources and storage) are not taken into account.

In reality, on the field, good results on container filling rate were observed, with an average value close to 85%. This can be explained by the fact that the shipment schedule is not completely respected. At shop-floor, in order to improve the container filling rate, packages may be retained at the staging zones, on the condition that the cargo ship departure date is respected, i.e. the daily schedule can be violated in order to increase container filling rate if the final constraint on the ship departure date is respected. The latter avoids any negative impact on customer service level. Concerning internal workload level and inbound truck filling rate, the situation becomes less performant. On the one hand, it was noticed high daily workload variability, which results in a non-optimal weekly resources assessment, on the other hand, even if the inbound transportation pre-contracted plan is respected, a low truck filling rate (52% in average) is observed in reality. Both situations are the consequence of the delivery schedule. From suppliers' point of view, there is no flexibility versus given delivery dates and since they are not responsible for inbound transportation, a low truck filling rate is not their concern.

Improvement opportunities and work scope

To sum up the previous description, it is noticeable that the current planning process at Renault ILN does not include its main operational cost drivers. Results on the field study showed an important impact on global performance. Moreover, there is a gap between what is planned and what is actually carried out at shop-floor, due to the time differences between the planned delivery days and the actual cargo ship departure day, whereas this decoupling point could actually be used to optimize the network flows and the internal operation planning.

Table 1 sums up the previous description. Consequently, an alternative method for delivery and operation planning at a cross-dock, based on a Renault ILN configuration, integrating its main parameters, cost drivers and constraints is proposed in this paper.

Table 1. Comparison of operation frequency between the planning process and the physical flows.

ANALYSE OF RELATED LITERATURE

Nowadays, in a globalized industrial environment, supply chain performance is crucial to maintain competitiveness since companies must assure the delivery of products at the right time, with the desired quality level and at the minimum cost. A relatively new strategy to support this objective is cross-docking. A cross dock centre is an intermediate point in a supply chain, in which products from incoming trucks are unloaded, sorted, moved across and

Planning process Theoretical physical flows

Observed on field study (performance)

Shipment

Customer orders received weekly, with daily demands.

Containers sent daily to harbour. Cargo ship departure once a week = weekly deliveries at customers'.

Good CFR (container filling rate): 85%. Flexibility on shipment schedule due to the weekly cargo ship depart. No impact on service level.

Grouping and packaging

Inferred from the shipment schedule. Daily treatment. Daily treatment.

High variability on daily workload (especially on unloading).

Relatively high storage level.

Delivery

Inferred from treatment schedule and consolidated regarding the inbound transportation plan. Supplier orders sent weekly, with daily demands.

Daily deliveries from suppliers.

Low TFR (truck filling rate): 52%.

No flexibility on delivery schedule. Suppliers are not concerned with transportation costs.

ultimately loaded onto outgoing trucks. According to Saddle Creek Logistics Services Report (2011), this logistic solution may result in a reduction of lead times, a decrease of stock levels and economies in transportation. More and more enterprises use cross-docks and the academic literature is also rich, see the state of the art presentations [START_REF] Boysen | Cross dock scheduling: Classification, literature review and research agenda[END_REF]Theophilus et al., 2020). Nevertheless, since few authors present an implementation of their work in a cross-dock centre, a gap between industry practice and current research is observed [START_REF] Ladier | Cross-docking operations: Current research versus industry practice[END_REF]Theophilus et al., 2020). Based on Van Bell et al.'s (2012) classification by decisional level, main study subjects concerning cross-docking are:

 Strategical: geographic location and internal layout.

 Tactical: distribution network planning and vehicle routing (inbound and outbound). The size, the shape and the number of doors impact the internal activities and related models seek to maximize cross-dock performance in terms of travel distances, number of touches and workload. For a review of articles dealing with described subjects, refer to Van Bell et al. (2012). Since our research considers only cross-dock platforms that are already up-andrunning, strategic issues are left out of scope. Vehicle routing for both inbound and outbound flows for ILN can be neglected. The vehicle routing for inbound flows is considered as a given input (based on Renault inbound transportation pre-contracted plan). The vehicle routing for outbound flow is relatively simple. Thus, our study focuses on two fields:

distribution and operation planning in cross-docking. Related literature will be presented next.

Distribution planning

Decisions related to distribution network planning within cross-docking can be considered as an extension of the shipment consolidation problem, which studies a distribution network consisting of a set of supply, transhipment and demand nodes. Product flow quantities, number of facilities and number of trucks are the common decision variables. Different types of constraints can be contemplated like time windows, capacity (storage, treatment, and transportation), direct link between suppliers and customers, among others. Efficient metaheuristics were designed and tested to solve such joint location and scheduling problems in cross-docking environments.

Internal operation planning

Concerning internal operation planning, three strongly linked main subjects have received important attention in current research: truck scheduling, dock-door assignment and shopfloor operation scheduling. A case study at Kodak cross-dock platform is exposed by [START_REF] Palmer | Level loading and cross-docking in a global logistics network[END_REF]. They evaluate the impact of cross-docking level loading on the overall supply chain costs. According to the author, detailed analyses based on distribution network characteristics are required in order to evaluate the trade-offs between inventory policies, service level, transportation costs and workforce size. [START_REF] Ladier | Joint employee weekly timetabling and daily rostering: A decision-support tool for a logistics platform[END_REF] proposed a sequential approach to deal simultaneously with weekly planning and daily rostering of workforce in a logistics platform. To do so, they develop and implement in CPLEX three different mixed-integer linear programs. Industrial data is used for computational experiments and regarding the quality of solutions, the proposed models were adopted by their industrial partner.

Described studies on distribution planning and shipment consolidation presented above could be applied in a single cross-docking network. The reduction of transportation and storage costs is a common objective; however, it seems that ILN characteristics are not jointly represented in any current work. For instance, [START_REF] Chen | Multiple cross-docks with inventory and time windows[END_REF] and [START_REF] Miao | Transhipment problem with penalty in crossdocking distribution networks[END_REF] do not include transportation units, as they consider product shipments as flows. Modelling presented by [START_REF] Gümüs | Cross-docking and its implications in locationdistribution systems[END_REF] and [START_REF] Musa | Ant colony optimization algorithm to solve for the transportation problem of cross-docking network[END_REF] do not allow storage at the cross-dock. Moreover, current research on distribution planning does not include shopfloor activities, such us internal flows, repacking activity or workload assessment. On the other hand, operational models do not take into account inbound and outbound transportation costs, but are focused on truck scheduling and package moving activities at shop-floor. Instead of searching for rule based heuristics, as in majority of existing publications, in this paper, an integer linear program is developed and the commercial solver CPLEX is used to solve it. The advantage is in the possibility to obtain exact optimal solutions and if, at the end of calculation time, the formal proofs of optimality of the best obtained solutions are not terminated by CPLEX, the best solutions obtained are always near to optimum (or even optimal). Therefore, this approach is very practical and perfectly adapted to real life problems as it is demonstrated in the rest of the article.

Cross-dock scheduling

MODELLING AND NUMERICAL EXPERIMENTS

Problem description

A model to combine the distribution and the operation planning at a cross-dock centre is proposed. For a given week, the model seeks to define the daily activities on the inbound, inside of ILN and on the outbound. The main input data is: the daily customer demand, the inbound vehicles capacities, the staging zones capacities, an inbound transportation precontracted plan, the shop-floor processing time per component and all related costs including resource costs (operators, etc.). As a planning model, the granularity considered is the day and, as a consequence, the truck scheduling decisions (time slot, assignment to doors, ...) are outside of the scope of this paper (see [START_REF] Serrano | Scheduling of truck arrivals, truck departures and shop-floor operation in a cross-dock platform, based on trucks loading plans[END_REF], for a description of the subsequent scheduling model used for these decisions). The details on the modelling are given below.

For the distribution planning problem, a single-cross-docking network is considered, with a set of suppliers upstream and a set of customers downstream. Decision variables are related to the component flows within the network and by extension, the number of inbound trucks and outbound containers. As exposed in section 3, few researches on the field consider transportation units. The approximation on the number of vehicles is based on packages dimensions and a cargo loading software is used to evaluate its performance. The latter is detailed in section 4. Particular constraints related to the pre-contracted inbound transportation plan include daily time windows, number of pre-contracted vehicles and penalty costs of cancelling a pre-contracted vehicle or contracting an extra one. Finally, in terms of costs, the model seeks to minimize the total cost composed of inbound transportation costs, linked to the number of trucks and penalty costs, and the outbound transportation costs, based on the number of containers as well as resource costs. This structure of total cost was validated by Renault, taking into account the specificity of their ILNs.

Two main aspects are included to model the shop-floor internal operation. The first one is the workload level induced by package unloading from inbound trucks, component repacking at the workshop (if needed) and package loading into outbound containers. The resulting workload level defines the needs on internal resources needs for the whole week. The second aspect is the storage level at the staging zones. An unlimited capacity (storage/space) for the repacking workshop is considered. In contrast, a limited total capacity is assumed for both inbound and outbound staging areas. The proposed configuration implies that package flow, at the shop-floor, is load-driven since components can be retained in order to improve the container filling rate.

The previous descriptions imply the existence of an important link between the distribution planning decisions and the operation planning decisions. For instance, inbound trucks' arrival schedule defines the unloading workload at the shop-floor and the mix of components impacts the repacking workshop activity. Moreover, the availability of components at the shop-floor, for each final customer, will affect the outbound container loading activity and the storage decisions. The described relationship among tactical and operational levels supports our proposal of a joint model.

Mathematical formulation

For the problem considered with all given assumptions which are specific for Renault ILNs, the following model was developed in this study. This is an integer linear program (integer decision variables and linear objective function and constraints), thus, it can be solved to prove the optimality of the decisions obtained [START_REF] Nemhauser | Integer and Combinatorial Optimization[END_REF]

Si,d

Quantity of component i stored on day d.

Min Z = IT_cost + IA_cost + OT_cost (1)
where:

IT_cost = ∑ d,s [cits × (ITd,s + ITSd,s × cev + wdd × (pvd,s -ITd,s) × cc)] (2) IA_cost = W × cwf + ∑ i,d (Si,d × vi × cst) (3) OT_cost = ∑ d,c (OTd,c × cotc) (4)
Subject to:

∑ d Xi,d = ∑ d qi,d ∀i (5) ∑ d Yi,d = ∑ d qi,d ∀i (6) Xi,d ≤ ∑ d' qi,d' × wdd ∀i,d (7) Yi,d ≤ ∑ d' qi,d' × wdd ∀i,d (8)
∑ i Xi,d * iti,s × vi ≤ (ITd,s + ITSd,s) × vit ∀d,s (9)
∑ i Yi,d × odi,c × vi ≤ OTd,c × voc ∀d,c (10)
ITd,s ≤ pvd,s ∀d,s (11)

Si,1 = Xi,1 -Yi,1 ∀i (12) Si,d = Si,d-1 + Xi,d -Yi,d ∀i, d >1 (13) ∑ i (Si,d × vi) ≤ voz ∀d (14) ∑ i (Xi,d × (uti + rti) + Yi,d × lti) / wh ≤ W ∀d (15) qi,d × ds ≤ Xi,d ∀i, d (16) Xi,d ℕ, Yi,d ℕ, Si,d ℕ, W ℕ ∀i,d (17)
ITd,s ℕ, ITSd,s ℕ, OTd,c ℕ ∀d, c, s (18)

Figure 4 illustrates the defined model. The objective function in (1) seeks to minimize total cost. Equation ( 2) represents the inbound transportation costs, which includes the number of trucks, as well as penalty costs generated by cancelled and additional trucks, (3) characterizes internal activity costs, defined by storage costs and operator (or another resource) costs.

Equation ( 4) represents the outbound transportation costs, based on the number of containers used. Constraints ( 5) and ( 6) guarantee total demand fulfilment, ( 7) and ( 8) ensures the respect of working calendar, ( 9) and ( 10) represent, respectively, the inbound and outbound vehicle capacity and ( 11) denotes the number of pre-contracted inbound vehicles. Equations ( 12) and ( 13) are storage balance constraints that implies the inventory level of a component is equal to its previous inventory level in addition to the received quantity minus the shipped quantity in the current period (assuming that initial storage is zero). Equation ( 14) represents the outbound staging zone capacity, (15) assesses the resource needs for the week: it corresponds to the higher workload level. Constraint (16) will permit, if parameter ds = 1, to respect the delivery schedule generated by the current planning software. Finally, ( 17) and ( 18) are integer constraints.

Numerical experiments

The model presented above was implemented and tested in CPLEX, using a 4GB RAM Intel Celeron P4600 @ 2.00GHz CPU. Table 2 summarizes the general statistics of the experiments, based on a 21-week period of real data from the Renault case of study. Some details are provided next: (1) an aggregation process, based on physical characteristics of components, was applied to group components (i.e. to consider similar components as identical). This process permitted to reduce, in average, 30% the total size of the input data.

(2) Concerning the inbound transport for Renault ILNs there are two main types of trips: a) direct from supplier to ILN platform, and b) indirect, from supplier to a 3PL (third-party logistics) grouping platform, then to ILN. Both types are considered in the model. The first one represents 15% of Renault suppliers which account for 60% of total volume. Hence 85% of suppliers are in indirect transportation (40% of total volume). (3) Finally, no modifications were done regarding final customers.

Figure 5 explains the methodology adopted for the experiments. For each week, two runs were made, with a calculation time limit of three minutes.

The first run simulates the current planning process at Renault ILN: (1) parameter ds is set at 1 so that the original delivery schedule is respected, and (2), as explained in section 2.4, shop-floor operation is driven mainly by container filling performance rather than the daily shipment schedule and therefore the model assumptions for the rest of activities are aligned with the Renault ILN current planning algorithm. For the case ds=1, the calculation time of 3' is sufficient to obtain optimal solutions for the simplified model based on the current Renault planning software rules. If ds=1, many decision variables of the model ( 1) -( 18) are fixed at the values obtained by Renault planning software, and so the model is simplified and solved to optimality (gap =0) for all weeks.

Table 2. Numerical experiments statistics

In the second experimental run, parameter ds is set at 0 and hence the entire developed model is used to propose an optimal planning concerning both delivery and shipment schedules without taking into account the Renault planning software decisions. Considering the dimensions of problems, the theoretical optimality of solutions for the considered cases, was not proved, thus this is a gap which is the difference between the best solution obtained and the theoretical lower bound calculated by the solver (see table 2).

Inbound transportation, internal resources and outbound transportation costs are assessed for each week and for both experimental runs. Since the optimisation model provides an approximation on the number of vehicles (inbound and outbound), CPLEX results were also compared with those obtained with a cargo loading software developed by Renault. In the next section the performance of the optimisation model is evaluated and analysed. In figure 5, three planning approaches are explained: No planning is the situation in Renault before the study considered; Partially optimized planning is the case when the proposed model is only partially used to be compatible with the decisions of No planning approach (some variables are fixed at the values given by the Cargo loading software); and finally, the case of Fully optimized planning when the entire model is used without limitations from Cargo loading software.

Performance analysis

On average, the proposed model showed a total cost reduction of 13% (ds=0 vs ds=1). More accurate conclusions can be drawn by analysing individually each cost driver. Figure 6(a)

compares the inbound transportation costs. First, it is observed that the current planning process (IT_cost_ev) gives the results which are 28% above the lower bound (IT_cost_LB) on average, that estimates the potential profit. Second, it is observed that CPLEX result(IT_cost') is equivalent to the lower bound. However, once evaluated by the cargo software (IT_cost_ev'), the difference between both costs (IT_cost_ev' and IT_cost_LB) is around 11%. The latter can be explained by the fact that the cargo software was developed exclusively for truck load maximisation and includes very complex and detailed constraints, in terms of type of package combination and stacking rules, among others. The model proposed in this paper cover larger scope but gives a much simpler approximation on the number of inbound trucks. Finally, by comparing evaluated inbound transportation costs obtained with both models, it can be concluded that the model proposed in this paper outperforms the current planning process in 17% (average per week, with a standard deviation of 4.7). The relative values of costs are given in figure 6 (due to the confidentiality issue it is not possible to present the corresponding absolute values). The outbound costs were constant; thus they are not reported in the figure. The minimum level of costs in the figure (as in figure 7) is zero. The second cost driver is the internal activity (IA_cost). As specified before, there are two components related to internal activity: storage and resources. Numerical experiments showed that average storage level per week can drop from 30% in the current planning method to less than 1% with the proposed model. At this point of our work, storage costs have not been sharply defined and that is why the model performance was analysed in terms of storage level rather than costs. Figure 6(b) compares resource (e.g. operator) costs. These costs are associated to daily workload peaks, and regarding the current planning process, this effect is more strenuously marked in weeks with public holidays (e.g. weeks 6, 12, 13). In 5-labor-day weeks the proposed model shows an average cost reduction of 13% with a standard deviation of 6.3. An important consequence of internal resources optimization is daily workload smoothing for each shop-floor activity (unloading, repackaging and loading), which will translate into a better organisation and management of ILN internal operations. Finally,

IT_cost_ev' IT_cost_LB IT_cost_ev IT_cost' IA_cost IA_cost'
outbound transportation costs are alike in both original and proposed planning process. This is the consequence of the outbound load-driven configuration combined with the possibility of storage in the outbound staging zone.

INDUSTRIAL IMPLEMENTATION

In real-life context, only a simplified version of the model concerning inbound transportation was implemented and used, because, on the one hand, the results of numerical simulations showed a significant potential on cost reduction in this segment (inbound transportation) and thus, for Renault, this was a priority, and on the other hand, because from an industrial point of view, the modification of the delivery schedule is a conceivable process and can be easily integrated in the existing software employed by Renault, that is not the case for the rest of model. The simplified version, compared to the model presented in section 4, kept only decision variables related to inbound transportation: (X, IT, ITS). Consequently, the objective function concerns only inbound transportation cost (IT_cost) and the constraints (6), ( 8), ( 10), ( 12) -(15) were excluded. The outbound transport is guided by availability of container and vehicles; taking into account the limited numbers of vehicles the transportations are done in a smooth manner. The optimisation of the inbound part did not increase the outbound stocks and costs.

A group of suppliers, representing in average 15% of total weekly volume, was selected for the industrial full-scale test with Renault software. For the concerned scope, the delivery schedule was calculated using the proposed method and afterwards it was incorporated to the Renault information system. The outline is that the delivery orders transmitted to suppliers correspond to the optimized planning generated by the model.

Figure 7 shows the obtained results during a 15-week test. As in figure 6, only relative values are represented. The same methodology from numerical experiments was used and hence both evaluated costs (IT_cost_ev and IT_cost_ev') were compared. Based on the previous offline tests, the expected total cost reduction was 26%. By analysing physical deliveries at ILN, the real inbound transportation costs (IT_cost_REAL) were calculated to asses the performance of the tests: it was observed that, for studied suppliers, inbound transportation costs were reduced by 20%. The difference between expected planning (IT_cost_ev') and real performance (IT_cost_REAL) can be explained on the one hand, by a non-accurate approximation of the number of vehicles in the optimisation model and on the other hand, by external factors such as transportation delay, supplier's capacity, etc. Further analysis is needed in order to cope with described issues.

OPEN ISSUES FOR GENERALISATION

This is the first stage of an extended optimization project concerning planning processes at Renault cross-dock platforms. First of all, concerning strategic issues, even if they are out of scope at this point, the following research perspective should be considered: it might be interesting to include tactical or even operational characteristics in strategic analysis. For

Inbound transportation cost -Industrial test

IT_cost_ev'

IT_cost_REAL IT_cost_ev instance, for the geographic location, all cost drivers must be taken into account: even if the outbound transportation costs reduction seems high, the inbound transportation management might be relatively expensive. The shop-floor layout can be impacted by the transit time, which on its own, can be affected by the diversity of packages and the complexity of outbound trucks loading activity. So, it is necessary to include this kind of aspects when deciding the set-up of a cross-dock centre. Even if this seems to be far away from the presented study, this point is now discussed with Renault for future projects.

The presented work was focused on the tactical and the operational levels. It was concluded that it was necessary to link both decisional levels in order to obtain a better synchronization between the three segments of the logistics network within a cross-dock platform and for that the cost drivers for the inbound, internal and outbound activities were included in the study.

Even though the modelling approach was based on a case of study, it remains relatively generic and can be adapted to other cross-dock platforms and configurations, because the main elements of any platform were considered and the model was proposed after considering several platforms and tested on one of them.

An extension of our field study to other platforms, showed on the one hand, that the three main cost drivers originally identified (inbound, internal and outbound) are found in other

Renault ILN. On the other hand, we saw that both the physical flows and the cost distribution can vary between Renault cross-dock platforms. In the next paragraphs we explain the main differences and how our model can be adapted to respond to different characteristics.

In the inbound segment, one can think that the platform is not responsible for organizing the transportation, which means that the number of trucks is not the accurate cost driver. In order to adapt the presented model, inbound decision variables should be slightly modified: for instance, instead of an approximation on the number of trucks, a fixed cost (related to each order) and a variable cost (related to the volume of the order) would define the inbound cost.

Concerning the internal activity, regarding the type of products, there are some cross-dock centres that do not have temporary staging zones (for instance, in the food industry) and packages are directly moved to the outbound zone. The latter is not an issue, since the presented model can run without an inbound staging zone.

Regarding internal cost configuration, we have that the physical operation can be performed by a 3PL provider. Same for the inbound variables, an adaptation of the model will be needed. The new internal cost driver could be the daily volume and a constraint related to the maximum capacity per day could be added. Finally, on the outbound segment there can be different patterns in terms of transportation frequency. In fact, other than customers on overseas transportation (one cargo ship per week), the cross-dock can ship containers by truck or train, for which the frequency can reach the daily basis. Our model should be adapted to include this constraint, which is translated on customer time windows for the concerned components.

The industrial case study also showed that the container loading activity is a very complex task due to a high diversity on package dimensions and stacking rules. The approximation of number of trucks made in the proposed model results in good solutions for integrated planning at the tactical and operational levels. However, for a shop-floor scheduling, a deeper approximation of number of vehicles will be definitely necessary and will be a future research perspective. A possible approach consists in integrating the cargo loading software solutions into the proposed model.

Another relevant inference from this study is that it might be necessary to assess the impact of environmental factors, such us transportation delay and supply capacity issues. The shop-floor operation planning could also be improved by adding some industrial constraints, such as the capacity of the repacking workshop and a better assessment of the storage capacity and costs.

CONCLUSIONS

Overseas cross-dock platforms play a key role in the automotive supply chain. They connect distant industrial sites with first-tier suppliers, economies in transportation and simplifying overseas procurement management. This work was based on the real life techniques used at Renault for the distribution and operation planning, key activities to assure the performance of the network.

The work presented in this paper follows an operational model in the previous article [START_REF] Serrano | Scheduling of truck arrivals, truck departures and shop-floor operation in a cross-dock platform, based on trucks loading plans[END_REF] where scheduling issues with limited capacities were considered. This two-stage approach (planning and scheduling) was developed and tested for a Renault ILN platform, but can be extended to other cross-docks.

In this paper, a case study at Renault was presented for the planning part, including the current planning method and a field study at one of the Renault ILN platforms. Accordingly, an alternative planning approach to reduce total cost was developed. The cross-dock network The case study presented in this paper reveals, on the one hand, that tactical and operational decisions at a cross-dock are strongly correlated; hence, research on models tackling jointly both decisional levels seems crucial to assure an optimal performance. On the other hand, results on numerical experiments and industrial implementation showed that the proposed approach provides performant solutions for distribution and operation planning. The main contribution of this paper relays on treating jointly two fields related to cross-docking, which are for most cases, apprehended individually.

Figure 2 .Figure 3

 23 Figure 2. Example of distribution and operation planning at Renault ILN for one component.

Figure 3 .

 3 Figure 3. Renault ILN's physical flow scheme.



  Operational: operation planning: truck scheduling (inbound and outbound), dock door assignment (inbound and outbound), shop-floor operation scheduling Main research on strategic decision deals with supply chain design problems, considering one or more cross-docking centres. The main objective is to define network flows including facility location-allocation decisions. Shop-floor layout has also received important attention.

  [START_REF] Miao | Transhipment problem with penalty in crossdocking distribution networks[END_REF] presented a model that seeks to determine product flow quantities and allocation through cross-docks centres including fixed-schedule transportation constraints and tardiness costs in a distribution network. The authors propose a genetic algorithm to minimize total costs (transportation, inventory holding and penalty) and compare their results with CPLEX performance.[START_REF] Chen | Multiple cross-docks with inventory and time windows[END_REF] studied a cross-docking network for which they determine a distribution plan based on expected supplies and demands. Their model minimizes inventory handling costs and transportation costs, satisfying storage capacity and time windows constraints. A set of heuristics methods is proposed and tested, providing quality solutions in realistic timescales. It is hard to compare them because they were developed for different contexts and with different assumptions; nevertheless, the main ideas are very useful for any related problem.An ant colony optimization algorithm which aims to minimize total shipping cost in a crossdocking network is proposed by[START_REF] Musa | Ant colony optimization algorithm to solve for the transportation problem of cross-docking network[END_REF]. The number of trucks and product quantities are determined for each link of the network. Storage is not allowed and direct transportation between suppliers and customers is considered. Results from numerical experiments showed a significant cost reduction and outperformed Branch-and-Bound methods. Ma et al. (2011) studied a global optimization problem in a cross-docking network. A twostage heuristic algorithm which defines TL (truck load) and LTL (less-than-truckload) transportation planning is proposed to determine the quantity of products shipped on each arc of the distribution network. The objective of the model is to minimize transportation and inventory costs. Time windows constraints and truck setup costs are included. Computational experiments showed efficiency in terms of runtimes and solution quality.A two-manufacturer distribution network with cross-docking centres is considered by[START_REF] Gümüs | Cross-docking and its implications in locationdistribution systems[END_REF]. They proposed a mixed integer linear programming model, based on the uncapacitated facility location problem, which seeks to minimize fixed facility, transportation and inventory costs. The latter is considered only at supply and demand nodes.Several numerical experiments were conducted using LINGO and CPLEX software and due to the problem's complexity, they proposed to decompose it into a set of simpler subproblems.[START_REF] Soleimaninanadegany | Product allocation of warehousing and cross docking: a genetic algorithm approach[END_REF] have proposed a genetic algorithm to solve the problem of allocating products to cross-dock or warehouse. The processing costs, customer demand, cross-dock and warehouse capacities and many specific constraints are taken into account.[START_REF] Bienert | Delivery lot splitting as an enabler for cross docking and fast delivery[END_REF] study the impact of split deliveries in crossdocking situations, and their consequences on inventory performances for distribution centres and customer service level.Meysam Mousavi and Tavakkoli-Moghaddam (2013) considered both location and routing scheduling problems. A two-stage mixed-integer programming model for the location of cross-docks and vehicle routing scheduling was developed for retailer distribution networks.

  [START_REF] Li | Crossdocking: JIT Scheduling with Time Windows[END_REF] proposed a model to schedule internal operations based on a well-known parallel machines scheduling problem. At shop-floor, incoming containers are emptied and products are dispatched to the outbound area where outgoing containers are filled. Shop-floor teams are modelled as parallel machines which perform jobs (container loading). Storage is allowed if all machines are busy. The model seeks to minimize the sum of holding costs and penalty costs associated to earliness and tardiness, by defining the inbound and outbound container sequence. A set of heuristics is proposed and compared to an exact method. Results demonstrated that the heuristics provide good solutions in terms of costs and computing time.Yu and Egbelu (2008) studied a cross-docking centre in which the transfer of products at shop-floor is performed by a conveyor belt. A storage area is considered. The authors propose several models to allocate products to outbound trucks and to determine truck sequence at dock-doors. A mathematical modelling approach, a complete enumeration method and a heuristic are developed and tested on randomly-generated instances of different sizes.Truck scheduling at a cross-dock with a zero inventory policy is studied by[START_REF] Boysen | Truck scheduling at zero-inventory cross docking terminals[END_REF]. A completely synchronised inbound and outbound truck schedule is mandatory. They identify three minimization objectives: total flow time, outbound trucks' processing time and tardiness (concerning shipping due dates) and their approaches treat one at time. An exact method based on dynamic programming and a heuristic based on simulated annealing are developed.[START_REF] Carrera | Negotiation models for logistic platform planning and scheduling[END_REF] considered a negotiation model for planning and scheduling at a shoes distribution logistics platform. They propose an integer linear programming model to smooth workload by modifying the dates of arrival (from suppliers) and departure (to costumers).Storage capacity constraints, earliness and tardiness costs and inventory holding costs are considered. The model is implemented and tested with CPLEX, using generated data based on two industrial propositions.

  Zheng et al. (2020) addresses the cold-chain cross-docking truck scheduling problem with two types of products. A mixed-integer linear programming model is developed to minimise the total operational costs that consist of inbound truck arrival penalties for violating contracted time windows, product delivery tardiness penalties, inventory costs and outbound truck transportation costs. The problem is solved in two steps. At the first step, the inbound truck arrival schedule is calculated, then, at the second step, the schedule of outbound truck departure and internal processing schedule are optimized. Heuristics are proposed to generate complete solutions of the considered two-stage problem.[START_REF] Sayed | Integrated cross-dock door assignment and truck scheduling with handling times[END_REF] have studied integrated cross-dock door assignment and truck scheduling problem to simultaneously determine the assignment and scheduling of incoming trucks to inbound doors and outgoing trucks to outbound doors, with the objective of minimizing the total time to process all trucks. Metaheuristics are proposed.In[START_REF] Serrano | Scheduling of truck arrivals, truck departures and shop-floor operation in a cross-dock platform, based on trucks loading plans[END_REF], it was examined a mixed integer linear programming model to schedule hour by hour inbound trucks' arrival times (considering given soft time windows), shop-floor repackaging operations and outbound trucks' departure times. Capacitated temporary storage zones and a capacitated repack workshop are considered at crossdock shopfloor. The model seeks to minimize penalty costs related to inbound trucks' arrival times and consequently unbalanced workload of the repack workshop. Most of problems studied in literature for cross-doc are scheduling problems. An exhaustive state of the art on cross-dock scheduling is presented in (Theophilus et al, 2019). Finally, Kiani Mavi et al (2020) present a bibliometrics analysis of cross-dock problem and show than most of publications are dedicated to one of the following problems: vehicle routing, scheduling, inventory management and distribution management. The studies on joint analysis of all these problems are missing.This research work seeks to establish a link between tactical and operational planning decisions in cross-docking by proposing a distribution planning model that includes shopfloor planning decisions. In contrast to the vast literature in cross-dock optimization where these problems are considered independently, integration and interactions issues taking into account the dependency between them are crucial[START_REF] Dauzère-Pérès | On the importance of sequencing decisions in production planning and scheduling[END_REF][START_REF] Dolgui | Supply chain engineering: Useful methods and techniques[END_REF][START_REF] Kumar | Integrated yet distributed operations planning approach: A next generation manufacturing planning system[END_REF]. This work follows the previous publication[START_REF] Serrano | Planification et ordonnancement des activités dans un centre de crossdock international[END_REF] and both of them represent a new two-step approach (planning and scheduling) for Renault ILN platforms.

cwf

  Internal resource cost per unit (salary cost of one operator, for example). cotc Fixed cost of a container for outbound destination c (the cost which does not depend on the content of a container, it covers the cost of transportation of the container to the destination c, i.e. plant c). ds 1 if delivery schedule from current planning process must be respected, 0 otherwise. Decision variables: Xi,d Incoming quantity of component i on day d. Yi,d Outgoing quantity of component i on day d. ITd,s Approximation of the number of contracted vehicles used for trip s on day d. ITSd,s Approximation of the number of extra vehicles used by trip s on day d. OTd,c Approximation of the number of containers used for destination c on day d. W Number of weekly internal resources (e.g. operators).

Figure 4 .

 4 Figure 4. Proposed model scheme.

Figure 5 .

 5 Figure 5. Numerical experiments methodology.

Figure 6 .

 6 Figure 6. Relative values from numerical experiments.

Figure 7 .

 7 Figure 7. Relative costs evolution over weeks.

  was modelled as an integer linear program integrating inbound, internal and outbound constraints. Both, tactical decisions on distribution planning and operational decisions on shop-floor activities are addressed in the model. The model was implemented and tested with CPLEX for a 21-week period of industrial data from one of the Renault ILNs. In order to get a more precise assessment of transportation costs, a cargo loading software was employed and a performance analysis was conducted based on its results. The proposed model outperformed the current method employed at Renault, reducing total costs by 13%. Each cost driver was analysed individually, finding an important profit potential on inbound transportation and internal activity costs. Outbound transportation costs remain constant. An industrial implementation of a simplified version of the model was tested during 15 weeks. It concerned only the inbound transportation part and it was observed a cost reduction of 20% for the reduced group of suppliers integrated in the test. Results allow to:(1) validate the pertinence of the proposed model, (2) corroborate the gap between the model's approximation on the number of vehicles and reality and (3) notice the influence of external factors on the expected performance. Deeper analysis is needed to better assess items 2 and 3.

  Demand of component i on day d (delivery schedule from current planning process).

	i in I	Components
	d in D	Days of the week [1,5]
	c in C	Customers
	s in S	Suppliers
	Parameters:
	vi	Volume (m 3 ) of component i.
	qi,d
	iti,s	1 if component i is provided by supplier s, 0 otherwise. Where ∑ s iti,s = 1 ∀i.
	uti	Unloading time of component i.
	rti	Repackaging time of component i.
	lti	Loading time of component i.
	wdd 1 if d is a working day, 0 otherwise.
	wh	Worked hours per day.
	vit	Maximum capacity (m 3 ) of inbound trips (per vehicle).
	voc	Maximum capacity (m 3 ) of outbound containers (per container).
	voz	Maximum capacity of the outbound staging zone (m 3 )
	cits	Fixed cost of a contracted vehicle for inbound trip s.
	cc	Cancellation cost (ratio) of a contracted vehicle.
	cev	Extra cost (ratio) of contracting an extra vehicle.
	cst	Storage cost per m 3 and per day.
	Sets:

which is a clear advantage with respect to the heuristics usually used in literature. The proposed model uses the following notation: odi,c 1 if component i is demanded by customer c, 0 otherwise. Where ∑ c odi,c =1 ∀i. pvd,s Number of contracted vehicles in day d, for supplier s.

His works seek to find mathematic models and algorithms to support Decision-Making in Industry, mainly using optimization methods from Operations Research. Therefore, they are related to several scientific areas such as Industrial Engineering, Mathematics or Computer Science. He is currently co-chair of the Organizing Committee of the Congress of the French National


Javier Rubio

Anass Dazazzi
email: dazzazi@chimie.ups-tlse.fr

Yannick Coppel

Patrice Mascalchi

Laurence Salomé

Ahmed Bouhaouss

Myrtil L Kahn

Fabienne Gauffre
email: fabienne.gauffre@univ-rennes1.fr

Transfer of Hydrophobic ZnO Nanocrystals to Water: an Investigation of the Transfer Mechanism and Luminescent Properties

 This strategy was successfully applied to QD

Introduction

The recent development of nanoparticles (Nps) with photoluminescent properties has opened new perspectives in the field of sensing and imaging technologies. Intense research activity is devoted to the elaboration of new luminescent semiconducting nanocrystals (quantum dots, QD). Various physical and chemical procedures were proposed, enabling access to a large variety of materials. The chemical methods appear to be of particular interest since they offer the potential of facile scale-up and occur at lower temperatures than physical methods. Some materials may be synthesized either in water or in organic solvent but so far, better shape control and higher crystallinity are obtained in organic solvents. 1 The most studied luminescent nanomaterials are QD such as CdSe, CdTe, InP or InAs. Indeed, they combine the advantages of high quantum yields (QY) and narrow emission band. 1,2 However, the release of severely toxic ions from these materials is a serious disadvantage for their use for in vivo imaging.

Particularly, cadmium is one of the most toxic metals known to man with major health consequences. 3 Therefore, there is a need to develop luminescent nanocrystals (Ncs) with improved compatibility with living systems. In this context, attention is paid to ZnO semiconducting Ncs as an alternative material compatible with living organisms, even though they still offer relatively poor QY and a lack of control of their optical properties. [4][5][6] We have previously evidenced the interest of organometallic complexes for the preparation of metal oxide Nps of controlled size and shape. [7][8][9] Particularly, luminescent ZnO Ncs were synthesized by a very simple organometallic procedure operated at room temperature. 10,11 In this procedure, the very exothermic reaction of an organometallic precursor with water produces crystalline zinc oxide. Crystal growth is controlled by ligands, 50 typically an alkyl amine or a mixture of alkyl amine and fatty acids. This synthesis yields well-crystallized Nps whose size (typically 2-4 nm) can be varied by tuning the reaction conditions. Nanorods can also be obtained. 12 These luminescent Ncs appear to be good candidates for applications such as 55 imaging due to their thermodynamic and air stabilities. However, the general drawback of the organometallic procedures is that they yield hydrophobic Ncs coated with a layer of alkyl ligands. Thus, a prerequisite for biomedical or other water-based applications is to modify the original hydrophobic layer for a 60 hydrophilic one.

The most common strategy to convert hydrophobic Nps to hydrophilic ones consists in the displacement of the original ligand by hydrophilic compounds. Best results are obtained using hydrophilic ligands with multiple and strong binding, such as 65 polyelectrolytes (or oligomers). [13][14][15][16][17] Encapsulation of the hydrophobic Nps within a polymer or silica shell is also an attractive method, which may provide protection against chemical degradation. 18,19 However, these methods involve a strong alteration of the Np surface which often results in the alteration of without changing their optical properties. 26,27 It is generally accepted that the added surfactant interacts with the pristine ligands via non specific Van der Waals interactions, suggesting that this strategy could universally apply to all types of hydrophobic Nps and surfactants. For example, Yang and coworkers demonstrated that CdTe Ncs coated with the double chain DODAB (dimethyldioctadecylammonium bromide) were efficiently transferred into water, using surfactants with various lengths and head groups. 32 Some authors stated that the hydrophobic Nps are merely embedded within micelles of the added surfactant without perturbing the micellar structure, 27 while others evoked the formation of an interdigitated double layer between the alkyl chain of the surfactant and the alkyl ligands. [START_REF] Khalafalla | Magnetics[END_REF]31 However, to date, there has been no investigation of the molecular organization of the hydrophilic coating.

In an effort to produce functional Nps both water-soluble and of high quality, we have recently proposed the use of polyethylene glycol (PEG) based ligands directly in the organometallic synthesis. Indeed, PEG is soluble in all types of polar solvent, including THF, dichloromethane and water yielding multi-soluble metal and metal oxide Nps in a one step procedure. 33,34 Here, we report an alternative strategy, based on the addition of a new surfactant to ZnO Ncs pre-formed in the presence of octylamine. Single chain surfactants with various non-ionic, anionic or cationic polar groups were evaluated, and we observed that the transfer could be obtained only with long chain alkyl ether of glycolic acid. An insight of the molecular organization of the organic coating was provided using dynamic light scattering, photoluminescence and NMR (DOSY, NOESY) measurements. The optical properties (absorbance and luminescence) of the Ncs after transfer to water were compared to the ones of the pristine ZnO Ncs in organic solvent. Importantly, the Ncs could be transferred to a buffer -an essential step to envisage any biological application-and observed using a wide field fluorescence microscope.

Results and discussion.

The hydrophobic ZnO Ncs were synthesized using octylamine (OA) as a stabilizing agent, following a well established procedure (Figure 1). 10 The synthesis is achieved at room temperature, taking advantage of the very exothermic reaction of 40 water with the organometallic zinc precursor, bis-cyclohexyl zinc, [Zn(Cy) 2 ]. Briefly, OA was mixed with [Zn(Cy) 2 ] (OA/Zn=1), typically in THF, toluene or dichloromethane, under an inert atmosphere. Water was then introduced in the vessel, either in a controlled way or simply by opening the vessel to the 45 air. Interestingly, cyclohexyl evaporates easily during the synthesis or on solvent removal and no other by-products are formed. Therefore, OA is the only organic moiety present in solution. Well-crystallized ZnO Nps of about 3-4 nm were obtained. Nanorods can also be obtained in pure OA. Typical 50 TEM pictures of the isotropic Ncs and of nanorods are displayed Figure 1. Two main broad emission bands are observed in the visible range: one (blue) centered at 440 nm ( 2.82 eV) for an excitation ranging from 360 to 420 nm ( 3.45 to 2.95 eV) and the second one, (yellow) centered at 580 nm ( 2.14 eV) for an 55 excitation range of 280 to 360 nm ( 4.43 to 3.45 eV), respectively (Figure 1). The emission properties of these nanocrystals were previously described in details by Kahn et al. 35 The yellow luminescence of ZnO Ncs is frequently reported in the literature and results from oxygen vacancies in the 60 crystalline structure. 36 The presence of a second emission at higher energies was also observed, but at various wavelengths and its origin is still debated. [START_REF] Somwangthanaroj | Zinc Oxide Materials and Devices[END_REF][START_REF] Djurisic | [END_REF][39][40] We demonstrated that the observed blue emission is due to an electronic density increase at the surface of the Ncs through the electron pair provided by the 65 amine ligand. 41,42 It is therefore of paramount importance to keep the amine ligands at the NP surface to maintain their attractive optical properties. Thus, the so-called "interdigitated double layer" strategy was preferred. In most reported procedures, the new surfactant is added to an organic solution of the Nps, which 70 are further precipitated and recovered as a powder. The resulting double-layer coated Nps are hydrophilic. 26,27,43 However, precipitated Nps do not always readily redisperse. In order to avoid the precipitation step, we decided to transfer the Ncs via the emulsion formation approach. 32 Typically, a small volume (ca. 75 1/10) of a concentrated solution of the ZnO Ncs in dichloromethane was added to an aqueous solution of the surfactant. The mixture was vigorously stirred in order to form an emulsion and the flask left uncovered to allow for the evaporation of the dichloromethane. After evaporation of the organic solvent, 80 a small amount of insoluble material adsorbed on the wall of the vials was observed. The amount of Ncs solubilized in the aqueous phase was evaluated from measurements of the absorbance (see supp Info).

Influence of the molecular structure of the surfactant on the 85 transfer process.

Following the "interdigitated double layer" strategy, a series of anionic and cationic surfactants, all with an alkyl chain length of ca. 12 carbon atoms were evaluated, namely: N-dodecyl pyridinium chloride, lauric acid, lauryl sodium sulfate, 90 polyoxyethylene lauryl ether (C 12 E 4 ) and Lauryl-5 (Figure 2). Only Lauryl-5 led to a transparent water solution after the transfer procedure. The absorbance spectra display moreover a clear evidence of the presence of ZnO Ncs in the water solution. However, the other surfactants yielded more or less turbid 95 aqueous phases with no significant absorbance related to ZnO Ncs. These results clearly demonstrate that not any surfactant could efficiently transfer the ZnO/OA Ncs in water, contrasting with the proposed mechanism of the "interdigitated double layer" method hypothesizing the formation of an interdigitated double 100 layer via non specific Van der Waals interactions. In addition, the presence of micelles in the aqueous solution does not necessarily enable the transfer, since the Ncs were not transferred into a C 12 E 4 solution at a concentration (3 mmoles.L -1 ) largely exceeding the CMC ( 0.1 mmoles.L -1 ).

105

The molecular structure of the polar part of the surfactant obviously played a predominant role, suggesting a specific interaction of the surfactant either with OA or with the ZnO Nc surface. Importantly, we have previously demonstrated by NMR spectroscopy that a large fraction of the capping OA is in fast 110 exchange between the liquid phase and the Ncs surface while another is strongly bonded (at the NMR time scale). 44 Similar observations were reported by different groups for CdSe QD synthesized in the presence of TOP or TOPO ligands. 45,46 Surfactant adsorption can involve various mechanisms such as 115 covalent, electrostatic, hydrogen bonding with the surface groups or Van der Waals interactions between hydrophobic chains. 27,47 Entropic effects arising from the desolvation of the surface or of the polar group of the surfactant also have to be considered. 48 On large surfaces (e.g. porous silica) several regime of adsorption including individual molecules, patches of laterally interacting molecules and hemi-micelles and bicontinuous double layer are observed, depending on the surfactant and on its concentration. 49,50 However little is known in the case of adsorption on nanoparticles. Metal oxide surfaces are generally charged in water, the sign and extent of charging being controlled mainly by the adsorption of H + or OH -ions on amphoteric sites. The isoelectric point of ZnO in water is reached at ca. pH=9. 51 In the present case, we observed that neither the anionic (SDS) nor the cationic (C 12 PyCl) surfactants enabled an efficient transfer, indicating that pure electrostatic interaction is not the main driving force. Poly(oxyethylene) groups were reported to link the surface of metal oxide via hydrogen bonds. 52 However transfer with C 12 E 4 was not successful in our case. Interestingly, a carboxyl function or oxyethylene groups alone (respectively in 20 lauric acid and C 12 E 4 ) did not enable the transfer whereas the combination of both did (Lauryl-5). This suggests that the acid function lowers the pH value at the Nc surface, displacing the Zn-OH/Zn-O -surface equilibrium toward the -OH form, thus favoring hydrogen bonding with the oxyethylene groups of the 25 surfactant. The formation of an ion pair between octylamine and acidic surfactants has also to be considered, however this should lead to a stabilizing effect both for Lauric acid and Lauryl-5.

These results led us to examine more surfactants with ethoxylated glycolic acid head groups (see Figure 3b for the molecular structures). After the transfer procedures using 3 mM solutions of Lauryl-2, Lauryl-5 and Lauryl-10, all the aqueous phases were somewhat turbid and were filtrated. Measurement of the absorbance showed that the ZnO Ncs were successfully transferred to water (~30%) when Lauryl-2 and Lauryl-5 were used (Figure 3a). In contrast, almost no transfer occurred when using Lauryl-10. This can be attributed to the higher solubility of Lauryl-10 in water compared to the ones of Lauryl-2 and Lauryl-5. This higher solubility necessarily decreases the tendency of Lauryl-10 to adsorb at the solid/liquid interface. In addition, 40 ethoxylated surfactants have the tendency to adsorb with the ethoxy groups parallel to the surface, so that the maximum concentration adsorption decreases linearly with the number of ethoxy groups. 50 The influence of the solubility of the surfactant can also be investigated by comparing surfactants with a similar polar group but different alkyl chains. In this context, we selected an oleyl based surfactant (i.e. Oleyl-10) for comparison with Lauryl-10. The oleyl chain was chosen to avoid the low melting point temperature of longer chain surfactants (Figure 3). Interestingly, the aqueous ZnO solutions obtained using Oleyl-10 50 were transparent and did not require to be filtrated. In addition, more than 60% of the Nps were transferred to water, which compares with the best results reported so far. 53 Importantly, ZnO/OA nanorods synthesized using a slightly modified procedure (see experimental section) were also successfully 55 transfered (supporting info, Figure S2), indicating no significant influence of the shape.

To summarize, the choice of the surfactant structure and particularly of its polar part appears of crucial importance. Particularly, the combination of both a carboxyl and ethoxyl 60 groups in the surfactant structure enables an efficient transfer. These results suggest that a specific interaction between the surfactant head group and the ZnO Ncs surface is involved in the building up of the organic corona surrounding the Ncs.

65

Characterization of the organic coating by DLS, zeta potential measurements, and NMR spectroscopy.

In the following, all transfer experiments were achieved using Oleyl-10, which gave the best results in terms of transfer 70 efficiency. After transfer, the pH value is typically in the range 6.5-7. No significant change in their absorbance could be observed during the first three weeks when the solutions were stored at 10 °C in the dark. After this period, the Nps started dissolving (ESI, Figure S3). Interestingly, no troubling or 75 precipitation was observed when NaCl was added up to 0.2 moles.L -1 which compares to physiological conditions. This observation clearly indicated that the Nps were colloidally stable in conditions where electrostatic stabilization is shielded (ESI, Figure S4). 28 This certainly results from the steric stabilization 80 provided by the ethylene oxide groups in the polar head of Oleyl-10. TEM observations of the ZnO Ncs before and after transfer were conducted (Figure 4). In water, the presence of patches of organic material reduced the contrast, but the morphology of the ZnO Ncs (diameter 4.5 ±1.5 nm) is not significantly modified.

85 Dynamic light scattering (DLS), zeta potential measurements, and NMR spectroscopy were used to probe the organic coating of the Ncs. 54 The values of the hydrodynamic diameters and zeta potentials were respectively D h = 10. The diffusion coefficients of OA and Oleyl-10 were measured by DOSY (diffusion ordered spectroscopy). When one moiety exists under states with different dynamics (e.g. monomeric, 115 adsorbed onto particle surface and within micelles) and if the equilibrium between some of these states is fast compared to the NMR time scale, then the observed diffusion coefficient is an averaged value, weighted by the molar fractions of the different states. 55 For instance, in the case of a micellar solution with a "rapid" monomer-micelle equilibrium the observed diffusion coefficient (D) is equal to D = x mon D mon +x mic D mic , where x and D correspond to the molecular fractions and the diffusion coefficients, and the indices "mon" and "mic" refer to the monomeric surfactant and the surfactant within micelles, respectively. If the equilibrium is "slow", then two different diffusion coefficients corresponding to the monomer and micelle states are observed. DOSY measurements of aqueous solutions of OA (8 mM) or Oleyl-10 (2 mM) yielded only one diffusion coefficient for each species, respectively 4.9 ± 0.2 x 10 -10 m 2 /s and 0.37 ± 0.03 x 10 -10 m 2 /s. The value obtained for Oleyl-10 clearly indicates that, at this concentration, the solution is mainly composed of micelles. The value obtained for OA, is one order of magnitude larger, and compares to monomeric species. For comparison, the self-diffusion coefficient of monomeric OA is expected to be ca. 8.9 x 10 -10 m 2 /s in water taking into account the self-diffusion coefficient of monomeric OA in toluene (16.6 ± 0.2 x 10 -10 m 2 /s) and the difference of viscosities between toluene and water (0.57 mPa.s and 1 mPa.s, respectively at 22°C). This suggests that the majority of OA is under monomeric form, in fast 25 equilibrium with a limited amount of micelles. For the OA/Oleyl-10 mixture, the diffusion coefficients of OA and Oleyl-10 are 3.7 ± 0.3 x 10 -10 m 2 /s and 0.39 ± 0.04 x 10 -10 m 2 /s, respectively. The diffusion coefficient of Oleyl-10 was almost unmodified whereas the diffusion coefficient of OA slightly decreased. This observation suggested an interaction between OA and the Oleyl-10 micelles, but could also result from the formation of ammonium carboxylate ion pairs in solution between OA and Oleyl-10. DOSY experiments were also achieved at pH 12 (forbidding the formation of an ammonium carboxylate) and 35 yielded similar diffusion coefficient values (i.e. 3.8 ± 0.3 x 10 -10 m 2 /s and 0.38 ± 0.04 x 10 -10 m 2 /s for OA and Oleyl-10, respectively) confirming that the slowing down of OA diffusion, was not related to the formation of an ion pair. When ZnO Ncs synthesized in the presence of 8 mM of OA were transferred to D 2 O using 2 mM of Oleyl-10, the 1 H NMR spectra of this solution (Figure 5d) evidenced resonance shifts and broadening of the -CH 2 and -CH 2 OA signals (26 Hz and 11 Hz, respectively) compared to the ones obtained for the same ligand mixture but in the absence of ZnO Ncs (vide intra). On the other hand, no significant modification of the Oleyl-10 resonances was detected in the presence of the ZnO/OA Ncs. When the concentration of Oleyl-10 decreased from 2 to 0.5 mM, sharpening of the -CH 2 and -CH 2 OA signals were observed but no significant chemical shift were detected (ESI, Figure S7). 50 Regardless of the sample, the diffusion coefficient of Oleyl-10 remained constant and equal to the one measured in solutions of Oleyl-10 alone (D Oleyl-10 = 0.39 ± 0.04 x 10 -10 m 2 /s). 56 The diffusion coefficient of OA, slightly increased when the concentrations of Oleyl-10 decreases (D OA = 4.4 ± 0.3 x 10 -10 , 4.8 55 ± 0.3 x 10 -10 and 5.0 ± 0.3 x 10 -10 m 2 /s for 2, 1 and 0.5 mM Oleyl-10, respectively). These values were slightly higher than the one measured for the same mixture but in the absence of ZnO Ncs (D OA = 3.8 ± 0.3 x 10 -10 m 2 /s) but remained smaller than the one of the OA micellar solution (D OA = 4.9 ± 0.2 x 10 -10 m 2 /s). 60 Altogether, these DOSY experiments indicate that OA is highly dynamic and is in fast exchange between a slow motion state (i.e. micelles and/or OA on the Np surface) and a monomer state. The fraction of OA in the slow state increased with the concentration of Oleyl-10, suggesting the formation of mixed aggregates of OA 65 and Oleyl-10. Conversely, the low diffusion coefficients measured for Oleyl-10 show that in all samples only a negligible fraction of Oleyl-10 molecules were in the monomeric state.

NOESY experiments showed negative NOE for OA and Oleyl-10 regardless of the sample containing ZnO (Figure 6). In the 70 case of Oleyl-10, this is not surprising as the diffusion coefficient indicated that Oleyl-10 acted as a large molecule. On the other hand, OA showed also negative NOEs even though its diffusion coefficient was ten times faster than the one of Oleyl-10. Such large negative NOEs were measured recently for the pristine ZnO 75 Ncs in the presence of OA in toluene. 44 They were attributed to transferred NOEs in conditions of fast exchange between OA with slow motion at the surface of the ZnO Ncs and OA monomer with fast motions. 54 Furthermore, the NOE cross peaks of OA have stronger amplitudes than the ones of Oleyl-10 (about 80 5 times for 2mM Oleyl-10). This observation indicates a preferential interaction of OA for the ZnO Nps. Indeed, if OA were preferentially interacting with Oleyl-10 aggregates, OA should have NOEs with amplitude equal or even weaker than the Oleyl-10 ones. This result is confirmed by the absence of 85 negative NOE for mixtures of OA and Oleyl-10 in D 2 O (observation of zero quantum artifact 54 ) in the absence of ZnO Ncs (Figure 6). The NOE cross peak between protons in alpha and beta positions of the amine group integrated for 0.04, 0.06 and 0.36 compare to the diagonal peak of alpha protons set to 1, 90 with 0.5, 1 and 2 mM Oleyl-10, respectively. The increase of negative NOE amplitude with the concentration of Oleyl-10 shows a stronger interaction of OA with the ZnO Ncs as the concentration of Oleyl-10 increases. No clear signature of intermolecular NOE between OA and Oleyl-10 could be 95 observed. However, the strong superposition of the proton signals of methylene and methyl from the alkyl chains of OA and of Oleyl-10 could make them difficult to observe.

DOSY and NOESY experiments performed for the same ZnO/OA/Oleyl-10 system at pH 12 exhibited very similar results, 100 namely : i) a slow diffusion coefficient value for Oleyl-10 (0.37 ± 0.04 x 10 -10 m 2 /s), ii) a relatively fast diffusion coefficient for OA, slightly increasing with decreasing the concentration of Oleyl-10 (D OA = 3.3 ± 0.3 x 10 -10 , 3.5 ± 0.3 x 10 -10 and 4.2 ± 0.3 x 10 -10 m 2 /s for 2, 1 and 0.5 mM of Oleyl-10, respectively), iii) 105 negative NOEs are observed for both OA and Oleyl-10 with a stronger amplitude for OA (Transferred NOEs ; ESI, Figure S8).

Both the decrease in the diffusion coefficient of OA and the increase of negative NOE amplitude with increasing the concentration of Oleyl-10 suggest a synergistic effect of OA and 110 Oleyl-10 promoting the adsorption of OA (and possibly of Oleyl-10) at the ZnO Ncs surface. It has been reported that surfactant adsorption can be either enhanced or decreased using mixtures of oppositely charged surfactants, depending on the surface charge and on the surfactants propensity to form mixed micelles. 57 Since charged, co-adsorption of Oleyl-10 with OA on the nanocrystal surfaces reduces the overall surface charge, thus allowing more OA to adsorb.

Altogether, DLS, zeta potential and NMR measurements strongly suggest that the transfer of the ZnO Ncs occurs via the formation of an interdigitated double layer. The localization of OA and Oleyl-10 within the double layer remains difficult to determine. ATR and FT-IR spectra were also recorded but were not informative since no significant difference was observed between mixtures of OA and Oleyl-10 and the ZnO/OA/Oleyl-10 solution after transfer. However, the negative overall surface charge suggests that the outer layer contains predominantly Oleyl-10. The presence of transferred NOE for OA demonstrates that a fraction of the OA is less mobile than Oleyl-10, which is attributed to OA molecules in strong interaction with the Np surface. Such results are in agreement with the optical properties recorded in water (vide intra). Previous study on ZnO NPs stabilized only by the amine ligands has shown that the transferred NOEs are not due to an exchange involving the ligands directly coordinated at the NP surface (first shell) but to 20 an exchange between a second ligand shell surrounding the NPs and the mobile ligands in solution. 54 The first ligand shell (i.e. the one corresponding to the ligands directly adsorbed on the NPs) is not well characterized at this stage. This first shell involved only a small amount of molecules (only few percent 58 ) that probably encounter broadening effects making them difficult to observe or to differentiate from the slowly mobile Oleyl-10 molecules (signal superposition). The first shell can be composed of strongly bonded OA 54 but coordination of carboxylate at the ZnO Ncs surface was also evidenced previously. 59 Consequently, the first ligand shell of the ZnO/OA/Oleyl-10 system in water can also potentially contain Oleyl-10 molecules grafted on its surface.

Optical properties of the ZnO Ncs after their transfer to water. Figure 7a displays a comparison of the optical properties (absorbance and emission) before and after transfer. The absorbance spectra of the aqueous solution were normalized to take into account the loss of Ncs during transfer. The normalization factor (x 2.1) was chosen so that the steep increase in absorbance between 3.28 eV (378 nm) and 3.54 eV (350 nm) 40 was the same for the aqueous and the dichloromethane solutions. Even though the aqueous samples looked transparent by naked eyes (Figure 7b), some scattering was observed in the UV range of the spectra. The normalization factor is however little influenced by light scattering. Importantly, the overall profiles and particularly the onset of absorbance of ZnO Ncs (376 nm, 3.30eV) were not modified by the transfer procedure. At high energies however, the discrepancy between both spectra is more pronounced, due to an increase of light scattering in the UV domain.

The nanocrystals were strongly luminescent in water (Figure 7cd). Compared to the pristine Ncs, both the QY and the relative intensity of the blue and yellow emissions were modified. The spectra recorded for λ exc = 360 nm (3.44 eV) are displayed Figure 7b (for other excitation wavelengths, see ESI Figure S5). In order 55 to compare the organic and aqueous solution emissions, the spectra of the aqueous solutions were multiplied by the same factor as for the absorption spectra. The QY of the pristine ZnO nanocrystals in dichloromethane was ca. 15% for excitations within the range 320-360 nm (3.44-3.87 eV), where only yellow 60 light is emitted. It appeared that the QY associated to the yellow band was enhanced (by a factor up to 1.6) in water regardless the excitation wavelength. The behaviour of the blue emission was more complex. In the low energy excitation range (420-365 nm, 2.95-3.4-eV), the QY associated with the blue emission was 65 slightly inhibited in water. However, whereas the blue emission promptly disappeared for excitation energies higher than 3.4 eV (365 nm) for the pristine ZnO Ncs in dichloromethane, it was present in water up to 3.87 eV (λ exc = 320 nm). Based on previous experiments showing the role of amine ligands on the blue 70 emission, 60 we propose that this behaviour is related to differences in the coordination strength and/or amount of the amine bound to the ZnO surface in water and in dicholoromethane. The luminescence properties remained stable under UV irradiation for several hours. Importantly, the Ncs 75 could be transferred to a buffer -an essential step to envisage any biological application-and observed using a wide field fluorescence microscope (Figure 7d).

Conclusion.

80 Hydrophobic ZnO Ncs were efficiently transferred to water using the "interdigitated double layer" strategy. We demonstrated that the practical implementation of this strategy is not as straightforward as it is usually pictured and that only a few surfactants selected from the family of glycolic acid ether 85 ethoxylates were indeed efficient. Using a complementary set of techniques including DLS and NMR spectroscopy, we demonstrated that the stabilization of the ZnO Ncs involves the formation of an organic layer comprising the pristine ligand (octylamine) and the glycolic acid based surfactants. In addition, 90 a specific interaction between the surfactant and the nanoparticle surface is most likely involved. The water soluble Ncs exhibited good colloidal stability and strong photoluminescence, enabling prospects for use as cheap and safe photoluminescent labels. Further work is underway to extend this strategy to other oxides 95 stabilized with alkylamine ligands such as magnetic iron oxide nanoparticles.

Experimental section.

Material. THF was purchased from Aldrich and purified by distillation before use as solvent for the synthesis of the ZnO Ncs. The THF used to solubilise the Ncs after the synthesis was not distillated. All surfactants used were purchased from Sigma or Aldrich and used without further purification. Commercial names of the ethoxylated surfactants are: Brij 30 (Sigma; C 12 E 4 ), glycolic acid lauryl ether (Aldrich; M n =360; 460 and 690); 105 glycolic acid ethoxylate oleyl ether (Aldrich; M n =700). Synthetic Procedures. Synthesis of the Zn precursor and of the ZnO nanocrystals were performed as described in ref 61 . A THF solution containing (57.9 mg, 0.25 mmoles) of biscyclohexyl zinc and 1eq. of octylamine was first prepared from freshly distilled 110 THF in a glove box under argon. After extraction from the glove box, the solution was exposed to ambient moisture by opening the Schlenk tube. A powder is obtained after solvent evaporation, which readily dispersed into organic solvents such as dichloromethane.

115 Transfer toward water. To the powder sample resulting from the synthesis, 10 mL of dicholoromethane was added and the sample was stirred to yield a transparent solution. Then, 250µL of this solution was added to 2mL of water, forming an emulsion that was vigorously stirred. The vial was then open to air under gentle stir until evaporation of the dichloromethane was completed. The aqueous solutions were not filtrated prior to optical measurements, unless otherwise stated. It was verified using NMR that after evaporation the organic solvent was only present as traces (C< 0.2 mM). For determination of the yield of

Notes and references

The authors acknowledge the financial support of the EU through the Nanotool project (EST n°MEST-CT-2005-020195) and of the LIA and the CNRS-CNRST. to values obtained by different authors and/or methods. b CMC of Lauric acid. 63 c From ref 64 . d The CMC of the glycolic acid ethoxylate lauryl ether surfactants were not available from the literature, but are expected to be slightly larger than the one of C12E4. An anionic surfactant with an ethylene oxide spacer displays an unexpected ability to transfer fluorescent hydrophobic ZnO nanocrystals to water. The chemical composition of the organic interface and the optical properties of the water soluble nanocrystals were characterized.

  7 nm and ζ = 12.3 mV for ZnO/OA in dichloromethane and D h = 14.1 nm and ζ = -40.3 mV 90for ZnO/OA/Oleyl-10 in water. These results are consistent with the building up of a double layer with a strong negative net charge.A first set of NMR experiments was performed either on the ligand alone (i.e. OA or Oleyl-10 in water) or on mixtures of the 95 ligands (i.e. both OA and Oleyl-10 in water) in the absence of ZnO Ncs. Figure5shows the relevant1 H NMR resonances of a) a 8 mM OA water solution, b) a 2 mM Oleyl-10 water solution and c) a mixture of OA (8 mM) and of Oleyl-10 (2 mM). The full spectra are reported in ESI, FigureS6. The signal corresponding 100 to the protons in the alpha ( -CH 2 ) and beta ( -CH 2 ) positions of the amine group were significantly affected by the presence of Oleyl-10, shifting from 2.61 ppm to 2.86 ppm and from 1.41 to 1.57 ppm, respectively. In contrast, no clear change was observed for the Oleyl-10 resonances (modification of the resonance values 105 lower than 5 Hz). The observed shifts in OA resonances may have two origins: (i) the formation of an ammonium carboxylate, and/or (ii) the insertion of OA molecules within Oleyl-10 aggregates. Since OA is in excess compared to Oleyl-10 (4 eq.), the presence of a single set of resonance signals most probably 110 correspond to a fast equilibrium (at the NMR time scale) between free OA and OA in interaction with Oleyl-10.

  100

10 transfer

 10 see ESI. TEM and optical characterization. TEM experiments were prepared by slow evaporation of droplets of colloidal solution of the different samples deposited on carbon supported copper grids. The experiments were performed on a JEOL 1011 operating at 15 100 kV. All optical measurements were achieved using quartz cells of optical pathway 1 cm. Emission spectra were recorded using a Jobin-Yvon spectrofluorometer. Normalized absorbance spectra (fig 5) were obtained by introducing a multiplication factor (x2.3 to the data in water) to adjust the absorbance values 20 at 360 nm. Ncs diluted in Tris buffer were observed through a Zeiss Plan Apochromat 63x/1.40 Oil objective on an Zeiss Axioplan 2 microscope, using an X CITE 120 light source containing a mercury vapor short arc lamp (excitation filter 365/10, emission filter HQ595lp). Images were collected by a Cascade II 512 EM-CCD camera (Roper Scientific). NMR. For NMR samples the transfer procedure was achieved using D 2 O solutions of surfactants. The pH was not controlled or was adjusted to pH~12 by addition of small volumes of a solution of NaOH in D 2 O. 1D and 2D 1 H NMR experiments were recorded on a Bruker Avance 500 spectrometer equipped with a 5 mm triple resonance inverse Z-gradient probe. All chemical shifts are relative to TMS. All diffusion measurements were made using the stimulated echo pulse sequence with bipolar gradient pulses. The 2D NOESY measurements were done with a mixing time of 35 100 ms.

40 a

 40 CNRS, LCC (Laboratoire de Chimie de Coordination), 205 route de Narbonne, F-31077 Toulouse, France. Fax: +33 5 6155 3003;Tel: +33 5 6133 3130 E-mail: myrtil.kahn@lcctoulouse.fr

Figure 1 :

 1 Figure 1: Summary of the main features of the hydrophobic ZnO/OA Ncs used in this study. Top: Reaction scheme of the decomposition of the organometallic precursor. Left: TEM pictures of the nanocrystals (isotropic Ncs and nanorods). Right: Absorbance spectra of isotropic Ncs in dichloromethane and pictures of same solutions under UV irradiation at 280 and 380 nm.

15

 15

20 25Figure 2 :

 202 Figure 2 : Absorbance measurements of the aqueous phases after the transfer procedure using C12 surfactants. (o) C12PyCl; (□) lauric acid; (+) C12E4; (♦) SDS; (…)Lauryl-5. Surfactant concentration: 3mM.

30 35Figure 3 :

 303 Figure 3 : Absorbance of the aqueous phase after transfer using Lauryl alkyl ethoxylate surfactants with 2, 5 and 10 oxyethylene groups and Oleyl-10. Surfactant concentration: 3mM.

40

 40

Figure 4 :

 4 Figure 4 : TEM micrographs and size histograms of ZnO@OA Ncs (1eq) in dichloromethane (left) and after transfer to water (right) using 1mM Oleyl-10. Scale bar : 100 nm. The mean diameter is evaluated by fitting the histogram with a Gaussian curve. The first value correspond to the center of the peak whereas the second one corresponds to twice the45

Figure 5 : 1 H

 51 Figure 5 : 1 H NMR spectra in D 2 O at 293K of OA at 8 mM (a); Oleyl-10 at 2 mM (b); a mixture of OA (8mM) and Oleyl-10 (2mM) (c); and ZnO/OA Nps with 2 mM of Oleyl-10 (d). Only selected areas are shown for clarity. From left to right, resonances of alkene groups of Oleyl-10, of ethylene oxide groups of Oleyl-10, of methylene in alpha position of the amine head group of OA ( -CH 2 ; #) and of other methylenes of OA and Oleyl-10. #: -CH 2 of OA; Full 1 H spectra are shown in ESI, Figure

Figure 6 :

 6 Figure 6: NOESY spectra (mixing time 100ms) in D2O at 293K of ZnO/OA Nps with 2 mM of Oleyl-10 (left) and a mixture of OA (8mM) and Oleyl-10 (2mM) (right). Arrows indicate NOE cross peaks between the α-CH2 and β-CH2 protons of OA. Boxes hightlight NOE cross peaks between protons of ethylene oxide and methylenes groups of Oleyl-10.

Figure 7 :

 7 Figure 7 : (a) Absorbance and emission spectra of ZnO Ncs in dichloromethane and after transfer to water using Oleyl-10, 3 mM. Red line: Spectra in dichloromethane. Blue line: spectra in water after transfer. (b) Photograph of the transparent ZnO Ncs50

Table 1 :

 1 The surfactants investigated in this study.Mn : weight, number average; n : average number of oxyethylene groups. Approximative values from ref,[START_REF] Mukerjee | Critical Micelle Concentration of Aqueous Surfactant Systems[END_REF] the indicated range correspond

Journal of Materials Chemistry Accepted Manuscript

Transfer of Hydrophobic ZnO Nanocrystals to Water: an Investigation of the Transfer Mechanism and Luminescent Properties


Moussa Samb

Maria Bernard

Géraldine Pascal
email: geraldine.pascal@inrae.fr

Functional inference integrated in the FROGS suite

Metabarcoding principle

Determine the diversity of a environment by amplification and sequencing of a genetic marker.

One gene

Assuming that an organism has a metabolic function by the presence in the organism's genome of a known sequence having that function.

What metabolic functions are presents in a microbial community ?

GABI

Our metabarcoding analysis tool PICRUSt2: How does it work?

1. Placement of each sequence OTUs on a reference phylogenetic tree: From a table of OTUs, PICRUSt2 aligns the representative sequence of each OTUs on a phylogenetic tree reconstructed from the sequences of the 16S rRNA gene (or ITS or 18S). The 16S rRNA gene sequence tree is reconstructed from 41,926 16S rRNA gene sequences from IMG database genomes. Phylogenetic placement in PICRUSt2 is based on the output of three tools: HMMER (http://www.hmmer.org) to place OTUs, EPA-ng to determine the optimal position of these placed OTUs in a reference phylogeny, and GAPPA to output a new tree incorporating the OTU placements.

Repeat these steps for each gene Repeat these steps for each OTU -or R package is used for core hidden-state prediction functions. OTUs are corrected by their 16S rRNA gene copy number and then multiplied by their functional predictions to produce a predicted metagenome. PICRUSt2 also provides the OTU contribution of each predicted function, allowing for taxonomy-informed statistical analyses to be conducted.

Pathways

FROGS

  Find, Rapidly, OTUs with Galaxy Solution Software for analysis of metabarcoding data • Analysis of large sets of DNA amplicon sequences (16S, ITS, rpoB…) • Accuracy and speed • Table of Abundance of OTUs (Operational Taxonomic Units) and Taxonomic Affiliations • Graphical user interface under Galaxy platform (user friendly), or command line (python) • 22 bioinformatics and statistics tools • Treatment of ITS sequences (yeasts and fungi) • Management of non-overlapping pairs of readings (ITS, RPOB, LSU or any other marker of size > 600 bases) • + 3500 downloads of FROGS anaconda.org/bioconda/frogs • At least 13 Galaxy platforms around the world offer FROGS • + 25 000 visits on frogs.toulouse.inra.fr PICRUSt2 PICRUSt (Phylogenetic investigation of communities by reconstruction of unobserved states) is an open-source tool to predict metagenomic content from amplicon-based data. The PICRUSt2 method consists of phylogenetic placement, hidden-state prediction and sample-wise gene and pathway abundance tabulation. OTU or ASV sequences and abundances are taken as input and gene family and pathway abundances are output. All necessary reference tree and trait databases for the default workflow are included in the PICRUSt2 implementation. PICRUSt2 is composed of 4 python applications. Users can run PICRUSt2 steps by steps or thanks to an inclusive command line. No graphic interface exists to run PICRUSt2 for non-expert users. Inference functional tool Douglas, G.M., Maffei, V.J., Zaneveld, J.R. et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 38, 685-688 (2020). https://doi.org/10.1038/s41587-020-0548-6

  Prediction of gene family copy numbers of OTUs and 3. Determine gene family abundance per sample. Gene families can be inferred among KEGG Orthologs, EC number, COGs, Pfam and TIGRFAM database for 16S marker and EC number only for ITS and 18S marker. Hidden-state prediction approaches are used in PICRUSt2 to infer the genomic content of sampled sequences. The cast-

	S1 OTU1 3500 OTU2 0 OTU3 400 OTU abundance table S2 6300 210 S3 460 36 700 500 S2 2. S1 OTU1 marker sequence Reference marker tree S3 16S copy number S1 S2 16S copy number prediction table Normalized OTU table	Abundance of genes already known Abundance of ancestral state genes Predicted gene abundance S3
	OTU1 3500 OTU2 0	6300 460	210 36	OTU1 OTU2	7 4	=	OTU1 OTU2	3500/7 0/4	6300/7 460/4	210/7 36/4
	OTU3	400		700	500	OTU3	1			OTU3	400/1	700/1	500/1
	Normalized OTU table			PICRUSt EC number prediction table				EC prediction per sample table
		S1	S2	S3		EC:1.1.1.1 EC:1.1.1.2	EC:1.1.1.3				S1	S2	S3
	OTU1 OTU2	500 0	900 115	30 9	x	OTU1 OTU2	2 1	0 0	2 0		=	EC:1.1.1.1 EC:1.1.1.2	1800 1600	5460 2800	1129 2000
	OTU3	400	700	500		OTU3	2	4	2			EC:1.1.1.3	1800	3200	1060

4. Infer pathway abundance

Lastly, pathway abundances are inferred on the basis of structured pathway mappings. Metabolic pathway are built with MinPath tool

(Ye and Doak, 2009)

. MetaCyc pathway abundances are calculated in PICRUSt2 through structured mappings of EC gene families to pathways.

Acknowledgments:

We are grateful to the genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, doi: 10.15454/1.5572369328961167E12) for providing computing and storage resources.

Objective: include PICRUSt2 in FROGS suite (4 python applications) Objective: include PICRUSt2 in FROGS suite (4 python applications)