Mohamed Hadded
email: mohamed.elhadad@inria.fr

Paul Muhlethaler
email: paul.muhlethaler@inria.fr

Anis Laouiti
email: anis.laouiti@telecom-sudparis.eu

TDMA scheduling strategies for vehicular ad hoc networks: from a distributed to a centralized approach

Vehicular Ad hoc NETworks, known as VANETs, are deployed to reduce the risk of road accidents as well as to improve passenger comfort and safety by allowing vehicles to exchange different kinds of data. Medium Access Control protocols, namely those that are based on TDMA technique play a primary role in providing bounded transmission delay while minimizing data packet loss. However, due to mobility constraints and frequent changes in topology, slot scheduling is a more challenging task in VANETs than in other networks. Many MAC protocols based on TDMA for vehicular networks have been proposed to date. Among them, CTMAC is a centralized scheduling mechanism, while DTMAC, VeMAC and AD-HOCMAC are three distributed TDMA based MAC protocols. In this paper, we evaluate and analyze the performance these four protocols. The scenarios used in the simulation experiments take into account density variation factor that influences protocol performance. We use the MOVE and SUMO tools to generate realistic mobility scenarios. Performance metrics such as access collision, merging collision rate, packet loss and overhead are evaluated using NS-2.34.

I. INTRODUCTION AND MOTIVATION

Vehicular Ad hoc NETworks, known as VANETs, are a promising communication technology that can meet various requirements of Intelligent Transportation System (ITS) applications which aim to improve traffic safety and efficiency [START_REF] Papadimitratos | Vehicular Communication Systems: Enabling Technologies, Applications, and Future Outlook on Intelligent Transportation[END_REF]. Through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, each vehicle can exchange information to warn other vehicles about the current state of the traffic flow or the existence of a potentially dangerous situation such as an accident. Road safety and traffic management applications require a reliable broadcast scheme with minimal transmission delays and collisions, which increases the need for an efficient MAC protocol [START_REF] Hadded | An optimal strategy for collision-free slots allocations in vehicular adhoc networks[END_REF].Recently, Contention-free MAC protocols, notably those that are based on the TDMA technique, have attracted a lot of attention and many protocols have been proposed in the literature in order to handle network access and transmission with minimum packet loss. A number of TDMA-based MAC protocols have been proposed and evaluated for VANETs. In the last few years, several such protocols have been published to offer reliable and real-time communications in VANETs while attenuating the effect of the merging collision and access collision problems [START_REF] Hadded | An optimal strategy for collision-free slots allocations in vehicular adhoc networks[END_REF]. Each protocol has been designed for a specific problem considering a particular mobility scenario. As an example, in [START_REF] Borgonovo | Adhoc MAC: new MAC architecture for ad hoc networks providing efficient and reliable point-to-point and broadcast services[END_REF], Borgonovo et al. propose ADHOCMAC protocol (AD HOC Medium Access Control) to insure an efficient broadcast mechanism for V2V communications and cope with some medium access channel issues, like the hidden-exposed node problem. By doing so they aim to guarantee a certain Quality of Service. The ADHOC MAC protocol is a contention-less medium access protocol based on a dynamic TDMA technique which provides fast access to the channel. This mechanism can be seen as an extension of the Reliable ALOHA technique (R-ALOHA [START_REF] Borgonovo | Rr-Aloha, a reliable r-Aloha broadcast channel for ad-hoc intervehicle communication networks[END_REF]). Each car is able to access the channel in every frame in a random manner to choose a time slot as its Basic CHannel (BCH). The car is guaranteed to access the channel at least once in a given frame.

In [START_REF] Omar | Evaluation of VEMAC for v2v and v2r communications under unbalanced vehicle traffic[END_REF] Omar et al. design and study a protocol called VeMAC. VeMAC proposed for VANETs is a contention-free MAC protocol adapted to the V2V multi-channel radio system that offers efficient one-hop and multi-hop broadcast services on the control channel and eliminates the hidden node problem arising from by vehicle mobility. In addition, VeMAC [START_REF] Omar | Evaluation of VEMAC for v2v and v2r communications under unbalanced vehicle traffic[END_REF] is able to attenuate the merging collision rate by allocating different sets of time slots to cars traveling in different directions (Right; Left) and to RSUs (RoadSide Units). As VeMAC is a fully distributed protocol, an access collision problem may frequently occur between among cars attempting to gain access to the same time slots in high traffic density situations. Another completely distributed TDMA scheduling scheme, named DTMAC which benefits from the linear topology of VANETs is proposed and presented in [START_REF] Hadded | An infrastructure-free slot assignment algorithm for reliable broadcast of periodic messages in vehicular ad hoc networks[END_REF]. This scheme is based on the assumption that the road is partitioned into equal small size areas and the time slots in every TDMA frame are divided into three sets which correspond to cars in three successive areas. In DTMAC, slot allocation and reuse procedures are designed to reduce the effect of collisions that stem from the hidden node problem.

An efficient solution to mitigate the access collision rate while reducing the scheduling overhead consists in using RSUs as primary coordinators to set up and adjust time slot allocation for the cars in their communication range. For instance, in [START_REF] Guo | An adaptive collisionfree mac protocol based on tdma for inter-vehicular communication[END_REF] the authors propose an Adaptive Collision Free MAC (ACFM) protocol based on a centralized and dynamic time slot assignment mechanism. Each frame in ACFM is partitioned into a predefined number of time slots: one RSU time Slot (RS) is dedicated to an RSU to transmit control messages to the cars located in its vicinity and within its communication range. In addition, 36 Data time Slots (DS) can be used by the cars to transmit their data packets to their immediate neighboring vehicles. An RSU periodically diffuses a control message containing the DS allocation schedule for the cars within its radio range along with the time synchronization information. Unfortunately, the protocol does not handle communications between vehicles belonging to two different RSU coverage areas. In [START_REF] Zhang | A novel centralized TDMA-based scheduling protocol for vehicular networks[END_REF], the authors have propose a Unified TDMA-based Schedul-ing Protocol (UTSP) especially designed for Vehicle-to-Infrastructure communications. The main purpose of this research work is to maximize the useful bandwidth for user-related applications in VANETs. Hence, each RSU gathers the required information such as the channel occupancy information, the vehicle velocity, and the Access Category (AC) properties of the cars located in its coverage area. Then it allocates the time slots to the cars according to a weight function that takes into account three elements: speed weight factor, AC (Access Category) weight factor and channel-quality weight factor. However, the authors do not take into consideration the interference problem that may occur between cars in overlapping areas where each area is covered by a different RSU.

This paper compares our protocols DTMAC [START_REF] Hadded | An infrastructure-free slot assignment algorithm for reliable broadcast of periodic messages in vehicular ad hoc networks[END_REF] and CT-MAC [START_REF] Hadded | A centralized TDMA based scheduling algorithm for real-time communications in vehicular ad hoc networks[END_REF], with the contention-free MAC protocol (VeMAC) and AD HOC Medium Access Control (ADHOCMAC) in a highway traffic environment under different conditions. To model realistic vehicular motion patterns, we use the Simulation of Urban Mobility (SUMO) tool. The comparison is made in terms of access collision, merging collision rate, packet loss and overhead. The rest of the paper is organized as follows. Sections 2 and 3 describe respectively the DTMAC and the CTMAC protocols. Section 4 presents the simulation results and performance comparison. Finally, conclusions are reported in Section 5.

II. DISTRIBUTED AND INFRASTRUCTURE-FREE TDMA-BASED MAC PROTOCOL

We begin by presenting our DTMAC protocol where the slot assignment decision is distributed among all the vehicles.

In DTMAC, as Figure 1 shows, the road is divided into N small fixed areas, denoted by x i ; i = 1, …, N, of length equal to R, where R is the communication range. Area IDs can be easily derived using a map and GPS information. The time slots in each TDMA frame are partitioned into three sets S 0 , S 1 and S 2 associated with vehicles in three contiguous areas: x i , x i+1 and x i+2 , respectively. Each frame consists of a constant number of time slots, denoted by and each time slot is of a fixed time duration, denoted by s. Each vehicle can detect the start time of each frame as well as the start time of a time slot.

To acquire this information, messages are exchanged between neighboring vehicles. Furthermore, specific information, called Frame information (FI) is added to each transmitted packet to notify the neighboring vehicles of a time slot assignment. The FI consists of a set of ID Fields (IDFs) of size equal to the number of time slots per frame. Each IDF is dedicated to the corresponding time slot of a frame and it is composed of three fields VC_ID, SLT_ STS and PKT_TYP. The VC_ID field contains the ID of the vehicle that is accessing this slot. Each vehicle is identified by its MAC address. The SLT_STS field contains the status of each slot which indicates whether the slot is Idle, Busy or in Collision. Finally, the PKT_TYP field indicates the type of packet transmitted by the vehicle, i.e., periodic information or eventdriven safety messages.

Let us suppose that an active vehicle v moving within the area x i needs to acquire a time slot on the transmission channel. Vehicle v starts listening to the channel during the set S j (v) of time slots reserved for the area in which it is traveling, where j = (i + 2) mod 3.

 Initially, the status of each slot in the FI is free.  Each vehicle that hears exactly one node transmission in a time slot reserved for its location, will set the status of the slot to "busy" and record the ID of the vehicle accessing the channel in this time slot in the corresponding VC ID field.  If a vehicle does not hear anything during a specific time slot, it will set its status to "free" in the FI.  If a vehicle can not decode the data during a specific time slot, it will set its status to "collision" in the FI.  When a vehicle A has sent data in a given slot, it looks in the field information of the next slots to discover whether its neighbors have correctly received its data. If a neighbor of A reports collision for this slot (in the FI) or even if this slot is reported to be "busy" but being sent by another node (say B in the VC_ID), A considers that its transmission has led to a collision. Actually a node A considers that its transmission is a success if and only if all its neighbors report a success in the FI of their slots specifying that the data was sent by node A. When an access collision occurs among the vehicles that are moving in the same area, the probability of access collision in the next reservation is increased since the choice of available slots will be limited in the new set. In order to ensure channel access continuity, each vehicle should determine the expected available time slots in the set of time slots associated with the next area before leaving the area in which it is currently traveling. In fact, when a vehicle is using a given time slot in the set S j , it should acquire an available time slot in the set S (j+1) mod 3 as its future time slot before leaving its current area. .

III. CENTRALIZED TDMA-BASED MAC PROTOCOL

The key idea of the CTMAC protocol [START_REF] Hadded | A centralized TDMA based scheduling algorithm for real-time communications in vehicular ad hoc networks[END_REF], a TDMA-based protocol, is the use of a collision free scheduling mechanism over adjacent areas. CTMAC prevents vehicles traveling in two adjacent areas from using the same time slot to access the radio channel. In CTMAC, the road is partitioned into several contiguous areas, where each of them is covered by one RSU located in the middle of it. The radio range of an RSU is denoted by R, and the length of an area is equal to 2R. Like traditional TDMA access channel based protocols, the time is divided into successive time frames. In addition, each time frame is divided into two sets of time slots denoted by S1 and S2. These two sets are repeatedly used along the road so that no vehicles located in the same set of two-hop neighbors are using the same time slot. Figure 2 shows a highway divided into several adjacent areas where in each of them, one RSU, having a radio range equal to R, is located in its center. According to CTMAC rules, vehicles traveling in the area covered by RSU1 and those covered by RSU2 are accessing non overlapping sets of time slots. The set S1 of time slots is used by the vehicles that are moving within the coverage area of RSU1, while the set S2 is used by the vehicles that are moving within the coverage area of RSU2. The major benefit of CTMAC is the limited effect of interference between each two successive RSUs. Consequently, the scheduling mechanism is able to reduce the collision rate for vehicles accessing the radio channel without using any complex bandwidth allocation technique. Each time a vehicle enters a new area, it gets a new time slot (see next paragraph for more details). This time slot will be used by the vehicle to access the channel and transmit its data as long as it is traveling within the same area. Each RSU builds and updates a Frame Information (FI) composed of τ fields, τ being the number of time slots in the frame. Each field, denoted as IDF (ID Field) describes the corresponding time slot in the frame. Three subfields are used for this purpose: SLT_STS, VC_ID and PKT_TYP. The SLT_STS subfield indicates whether the slot is being used (occupied), in Collision, or Idle. The VC_ID subfield stores the ID of the car that is using this slot. Whereas the PKT_TYP subfield gives information about the type of packet transmitted: i.e. eventdriven safety messages or periodic packets. In CTMAC and unlike the ADHOC MAC and VeMAC protocols, the frame information is periodically broadcasted only by the RSU and each vehicle updates its local FI based on the FI broadcasted by its RSU. Nevertheless, if an access collision is detected, a vehicle is allowed to broadcast its frame information to its neighbors (and the RSU) to warn them about this event. An RSU u is able to identify the set of available time slots at the end of each frame. This set is denoted by F (u). When free slots are available a Slot Announcement (SA) message along with the FI message are broadcasted during the first slot of the corresponding RSU time slots subset (notice that the first slots of sets S 1 or S 2 are reserved for RSU transmissions). Based on this information, a vehicle v traveling in that area that wishes to access the channel is able to pick one of the available slots at random. It then broadcasts a Slot REQuest message (SREQ) during the selected slot to attract the attention of the RSU and express its intention. When the RSU gets the SREQ message, it checks whether a time slot is available or not. The RSU will send its Slot REPly message (SREP) containing its allocation decision including the allocated time slot index. The decision is simply integrated within the next FI announcement. Either the vehicle v can start using the assigned time slot to transmit its data, or v will reiterate the same reservation steps if a predefined timer expires and no reply has been received from the RSU. An RSU considers that a given vehicle v has left its communication range when it does not receive a packet from tv during its time slot.

As stated before, v will keep using the same time slot while traveling in the same area and without any collision. When a collision occurs, the vehicle will try to get a new time slot as described above. If the moving vehicle v detects different RSU announcements than the current one, it will start a handover procedure to get a new time slot from the newly detected RSU. The vehicle will send an SREQ message to be allocated a new time slot and if it receives an SREP message from the RSU it will free its current time slot and it will resume its transmissions during the time slot newly allocated by the new RSU.

IV. SIMULATION RESULTS AND PERFORMANCE EVALUATION

In this section, we provide the simulation results to evaluate and compare the performance of four TDMA based MAC protocols by varying the number of vehicles.

A. Simulation scenarios and parameters

We use MOVE [START_REF] Karnadi | Rapid generation of realistic mobility models for VANET[END_REF] to generate vehicular traffic scenarios and SUMO [START_REF] Elissa | Title of paper if known[END_REF] to perform real vehicular mobility simulations (see Figure 2). In our simulations, we consider a real highway area digital map to generate a VANET environment close to real highway configurations taking into account different lane directions. In Figure 3 we can see a metropolitan area taken from a Map of San Jose (California) of size 3000m 100m. This map was exported from OpenStreetMap (OSM) and adapted with the help of OpenStreetMap Editor (JOSM). The resulting roads are then populated with vehicles traveling in all directions. Each flow of vehicles is characterized by a set of parameters which consist of the starting and ending time of the flow, the initial point and the destination of the flow and the maximum number of vehicles. In this environment, each vehicle is assigned a random speed be-tween 120km/h and 150km/h. The resulting traffic traces generated by MOVE were injected into the Network Simulator ns2:34. Table I summarizes the simulation parameters that we use in our scenarios.

B. Simulation results

In this section, we compare the performance of DTMAC and CTMAC with VeMAC and ADHOCMAC protocols in terms of merging and access collisions rate and the overhead (the control messages needed to establish and maintain a collision-free schedule). We have used a parameter, called the area occupancy (AO) [START_REF] Omar | Evaluation of VEMAC for v2v and v2r communications under unbalanced vehicle traffic[END_REF]. The protocols are evaluated based on the following metrics:

The access collision rate: is defined as the average number of access collisions per slot per RSU cover-age area.

The merging collision rate: is defined as the aver-age number of merging collisions per slot per RSU coverage area.

Overhead: which is the rate of control packets used to allocate a time slot as well as to maintain the TDMA schedules.

Figure 4 shows the rate of merging collisions for DT-MAC, CTMAC, ADHOCMAC and VeMAC. We can note from this figure that DTMAC prevents more merging collisions than ADHOCMAC, VeMAC and CTMAC, especially for a high Area Occupancy (AO = 0.4). Moreover, we can note that CTMAC achieves a considerably lower rate of merging access collisions compared to VeMAC and AD-HOCMAC. These results can be explained by the fact that DTAMC and CTMAC separates neighboring RSU areas by assigning disjoint sets of time slots to vehicles traveling in these areas. However, in VeMAC, the vehicles that cannot access a time slot from the set of slots reserved for its direction, will attempt to access any available time slot reserved for vehicles moving in the opposite direction. As a result, the merging-collisions occur frequently in VeMAC when traffic density is high, especially when the number of vehicles in each direction is not equal. However, these results might well be expected for the ADHOCMAC protocol since all vehicles randomly acquire a time slot in the frame without considering which direction they are moving in, which could make it susceptible to the merging collisions problem in highway scenarios where the vehicles are moving in opposite directions. The rate of access collisions under different traffic densities is shown in Figure 5. We can note that the CTMAC protocol generates a lower rate of access collisions than the three other protocols, especially for a high traffic load. For instance, at a AO = 0.96, the CTMAC protocol achieves an access collision rate of 0.21%, in contrast to DTMAC, VeMAC and ADHOCMAC which show a rate of 0.352%, 0.4317% and 1.158%, respectively. The reason is that the assignment of time slots to vehicles is performed by the RSUs in a centralized manner. Moreover, CTMAC implements an Access Collision Avoidance mechanism that can prevent the access collision problem occurring more than twice between the same vehicles that are trying to access the channel at the same time. On the other hand, we can note that DTMAC and VeMAC achieve a considerably smaller rate of access collisions than ADHOCMAC, especially for a high AO (≥ 0.5). These results can be explained by the fact that ADHOCMAC has achieved a higher rate of merging collision compared to DTMAC and VeMAC. Indeed, upon detection of merging-collisions, the nodes in collision should release their time slots and request new ones, which can reproduce access-collisions. Figure 6 shows the amount of overhead (in Mega octets) generated by DTMAC, CTMAC, ADHOCMAC and VeMAC. We see that DTMAC, ADHOCMAC and VeMAC have more scheduling overhead than CTMAC for all AO values. These results can be explained by the number of control messages (e.g. frame information) broadcasted by each vehicle in these protocols in order to establish and maintain its schedule table. Moreover, we can also note that the overhead increases for all protocols when the number of vehicles increases. In this paper, we compare the performance two of our TDMA slot scheduling schemes namely CTMAC and DTMAC, with other well-known protocols called ADHOCMAC and VeMAC. The simulation results presented in this paper reveal that CTMAC outperforms other protocols in terms of merging collision ratio, especially for a high vehicle density. Moreover, compared to a centralized solution, the distributed protocols suffer from a higher overhead because it must be aware of the slot allocation of neighboring vehicles. Therefore, we can conclude through these results that CTMAC provides the best performance among the four protocols analyzed for the case of highway scenario, while DTMAC generates a lower merging collision rate and it incurs the least amount of overhead among the three distributed protocols.

Figure 1 .

 1 Figure 1. TDMA slots scheduling principle.

Figure 3 .

 3 Figure 3. Simulation framework.

Figure 3 .

 3 Figure 3. VANET network topology captured from Google MAP.

Figure 4 .

 4 Figure 4. Merging collision rate vs. Area Occupancy (AO)

Figure 5 .

 5 Figure 5. Access collision rate vs. Area Occupancy (AO)

Figure 6 .

 6 Figure 6. Overhead en Mega octets

Table 1 .

 1 Simulation parameters

	Parameter	Value
	Simulation duration	120 (s)
	Speed	120 (km/h)
	Speed standard deviation	30 (km/h)
	Number of slots per frame	100
	Slot duration	0:001 (s)
	Highway length	2.5 (km)
	The number of lanes per direction	2
	The radio range (R)	310 (m)


Abdallah Sobehy
email: sobehy@telecom-sudparis.eu

Eric Renault
email: eric.renault@telecom-sudparis.eu

Paul Muhlethaler
email: paul.muhlethaler@inria.fr

Position Certainty Propagation: A location service for MANETs

Keywords: Location Service, MANETs, WSNs

Localization in Mobile Ad-hoc Networks (MANETs) and Wireless Sensor Networks (WSNs) is an issue of great interest, especially in applications such as the IoT and VANETs. We propose a solution that overcomes two limiting characteristics of these types of networks. The first is the high cost of nodes with a location sensor (such as GPS) which we will refer to as anchor nodes. The second is the low computational capability of nodes in the network. The proposed algorithm addresses two issues; self-localization where each non-anchor node should discover its own position, and global localization where a node establishes knowledge of the position of all the nodes in the network. We address the problem as a graph where vertices are nodes in the network and edges indicate connectivity between nodes. The weights of edges represent the Euclidean distance between the nodes. Given a graph with at least three anchor nodes and knowing the maximum communication range for each node, we are able to localize nodes using fairly simple computations in a moderately dense graph.

Introduction

Localization in MANETs has been the focus of attention in recent years as it is used in various applications such as routing, autonomous air-vehicles, network security, environment surveillance for military purposes, etc. For instance, in [START_REF] Jung | QGeo: Q-Learning-Based Geographic Ad Hoc Routing Protocol for Unmanned Robotic Networks[END_REF][START_REF] Terao | A proposal on void zone aware Greedy Forwarding method over MANET[END_REF][START_REF] Ko | Location Aided Routing (LAR) in mobile ad hoc networks[END_REF], the location information is used in various ways to improve routing decisions. A straightforward method to locate network nodes is to equip each of them with a location sensor such as a GPS and broadcast the location information. In SFLS location service [START_REF] Renault | Semiflooding location service[END_REF], the location information is forwarded with higher frequency to close neighbors in terms of the number of hops and lower frequency to further nodes to decrease bandwidth consumption while maintaining adequate knowledge of neighbors' positions. Since IoT networks have limiting characteristics, it is rather expensive to equip each node with a GPS sensor. Thus, different solutions attempt to locate nodes where only a subset of network nodes are equipped with a location sensor.

To estimate the position of non-anchor nodes, additional information is required to relate nodes to each other. According to the classification specified in [START_REF] Huang | Monte Carlo localization of wireless sensor networks with a single mobile beacon[END_REF], node relations can be as simple as being in connection with anchor nodes or not. This is used in [START_REF] Bulusu | GPS-less low-cost outdoor localization for very small devices[END_REF] where a node's position is assumed to be the average of the known positions of the other nodes. Another approach is range-based where the distance between nodes is computed either through RSSI (Received Signal Strength Indicator), TOA (Time Of Arrival) or TDOA (Time Difference Of Arrival) [START_REF] Chen | A localization method for the Internet of Things[END_REF]. TOA and TSOA require external hardware to synchronize a transmitter and a receiver, while RSSI does not since the distance is derived from a signal attenuation model that relates the signal strength to the distance [START_REF] Chen | A localization method for the Internet of Things[END_REF]. Knowing the relative distances is obviously an added benefit that helps to improve the accuracy of the location. However, using RSSI is subject to errors due to environmental factors (indoors or outdoors), multi-path fading and noise [START_REF] Chen | A localization method for the Internet of Things[END_REF]. In [START_REF] Sallouha | Localization in longrange ultra narrow band IoT networks using RSSI[END_REF], RSSI was found to vary consistently with a distance up to 50 meters. In [START_REF] Huang | Monte Carlo localization of wireless sensor networks with a single mobile beacon[END_REF][START_REF] Peng | Robust, probabilistic, constraint-based localization for wireless sensor networks[END_REF][START_REF] Sichitiu | Localization of wireless sensor networks with a mobile beacon[END_REF], the probability distribution is integrated in the location process instead of concluding a single position for each node. Such a method allows the uncertainty of measurements to be taken into account, be it is the GPS position or the relative distance between nodes [START_REF] Huang | Monte Carlo localization of wireless sensor networks with a single mobile beacon[END_REF].

Related Work

Consider the location problem as an undirected graph G = (V, E) where V is the set of the nodes in the network and E is the set of edges connecting two nodes located within communication range of each other. In this context, the given information is the position of anchor nodes and edge weights which are relative distances between nodes within communication range. The aim is to compute the position of all non-anchor nodes. This can be seen as an optimization problem that can be solved using numerical techniques such as SDP [START_REF] Biswas | Semidefinite programming for ad hoc wireless sensor network localization[END_REF] or linear programming [START_REF] Larsson | Cramer-Rao bound analysis of distributed positioning in sensor networks[END_REF]. To avoid inaccuracies of signal propagation models when using RSSI for distance measurements, some studies [START_REF] Mourad | Guaranteed boxed localization in manets by interval analysis and constraints propagation techniques[END_REF][START_REF] Mourad | Modelfree interval-based localization in manets[END_REF][START_REF] Mourad | Interval-based localization using RSSI comparison in MANETs[END_REF] have used intervalanalysis. In interval-based analysis, instead of computing direct distances from RSSI, a set of inequalities is formulated to indicate that a node is on a ring between 2 radii based on its relative position to other nodes. Then, the defined areas for nodes can be further restrained using the Waltz algorithm [START_REF] Waltz | Generating semantic descriptions from drawings of scenes with shadows[END_REF].

Some approaches divide the problem into sub problems as in [START_REF] Sallouha | Localization in longrange ultra narrow band IoT networks using RSSI[END_REF] where 1) base stations classify ordinary nodes into clusters based on their proximity to anchor nodes and 2) within each cluster, a node seen by three anchor nodes is located using a simple geometrical computation. In [START_REF] Chen | A localization method for the Internet of Things[END_REF], the region where the network is deployed is divided into rectangular grids as a first step, then within each small grid the location is refined. However, in the previously mentioned approaches the number of GPS nodes needs to be high in order to satisfy the necessary constraints.

Generally speaking, if the distance of a non-anchor node to three anchor nodes is known, it can be located with simple geometric computations except in the rare cases where the nodes are collinear or when some nodes overlap. This might imply a conclusion that at least three GPS nodes in the network are needed for location. However, in [START_REF] Huang | Monte Carlo localization of wireless sensor networks with a single mobile beacon[END_REF], the proposed solution attempts to locate the network with a single anchor node. Each node starts with a uniform probability distribution over the deployment region and as the roaming anchor node passes by the ordinary nodes it tweaks the distribution to locate nodes. In this context, the authors experiment their solution where the anchor nodes use a random model to traverse the network and have a reasonable claim that traversing the network can be optimized to improve the location process. In [START_REF] Peng | Robust, probabilistic, constraint-based localization for wireless sensor networks[END_REF][START_REF] Huang | Monte Carlo localization of wireless sensor networks with a single mobile beacon[END_REF] negative information about the absence of a node in the proximity of the anchor nodes is used in the location process. This kind of information is used as a basis for our algorithm.

An approach which is seemingly far from the mentioned methods, yea interesting to address the node location problem, is Graph Layout Algorithms such as FDP [START_REF] Fruchterman | Graph drawing by force directed placement[END_REF] and neato [START_REF] Kamada | An algorithm for drawing general undirected graphs[END_REF]. Even though the objective of these algorithms is generally to create an easy-on-the-eye graph, some variations make it possible to fix the positions of some nodes (the anchor nodes) and to set the suitable edge length (i.e., is the distance between nodes). We have tested these algorithms using graphviz [START_REF] Ellson | Graphviz-open source graph drawing tools[END_REF] and the estimated node positions are satisfactory.

Other methods, which use a set of distance equations and optimization techniques usually require high computation power, which is impractical for these networks. In this case, the computation is done remotely in a centralized fashion. The graph structure might not yield a unique solution. Even if the graph has a unique solution, finding this solution is proved to be NP-hard [START_REF] Eren | Rigidity, computation, and randomization in network localization[END_REF].

We address the problem from the graph point of view aiming at pinpointing the location of as many anchor nodes as possible. We first introduce our algorithm. Then we move on to explain how it can be implemented distributively over the nodes.

In Section 3, the system model is presented and the centralized version of the algorithm is introduced. In Section 4, the distributed version of the algorithm is explained and how each node acquires awareness of its location and the location of other nodes. Next, the algorithm is compared with an existing method in section 5. Section 6 contains the conclusion, and possible future work.

System Model

Overview

Let us consider the network as an undirected graph G = (V, E) where V = {0, 1, ..., n} are the nodes of the network and E = {(0, 1), (0, 2)...} where each pair represents an edge connecting two nodes located within the communication range of each other. Each edge is assigned a weight indicating the distance between the two nodes. We assume a subset of the network nodes m are anchor nodes where m includes at least three nodes of the network and for our evaluation we use exactly three anchor nodes. We also assume there is at least one node within communication range of at least three anchor nodes.

Position Certainty Propagation algorithm

To illustrate the algorithm, let's relate its steps to a 15-node network with three anchor nodes in a region of 20×20 square meters and a maximum communication range of 8 meters. Figure 1 presents an example of such a network. The algorithm starts by computing the position of the nodes with the most anchor nodes (or nodes with found positions) in their vicinity. Thurs, the first step is computing for each node the number of the neighbours with known position (whether anchor nodes or computed position nodes). Recall that for the algorithm to start, there must exist at least one node with at least three anchor nodes in its vicinity. Each anchor node forms a circle centered at the anchor node with a radius equal to the edge weight between the anchor node and the node in question. The node position is chosen to be the intersection of the three circles. Whenever a node position is found, it changes its state to a known position node. Whenever a node position is computed, all nodes recompute the number of neighbors with known positions in their vicinity. Then, the algorithm computes the position of the other nodes with the most known position nodes in their vicinity. Figure 2 illustrates the step of computing the number of nodes with known positions in vicinity (left) and the computation of the position of node 12 by computing the intersection of the three circles formed by the known position nodes: 3, 10 and 13 (right).

As the position of nodes are computed, the probability of finding nodes with known positions in the vicinity increases. Continuing to compute the position of nodes, one can reach the case where the maximum number of known position nodes in the vicinity is 2 for all nodes in the network. In this case, the intersection between the two circles formed by the two known position nodes in the vicinity would generally yield two possible positions. To choose one of these two positions, the node is assumed to be at both positions and the set of neighbors from the known position nodes that are within a radius of maximum communication range is computed at each of the two potential positions. The position where wrong nodes would have been neighbors, i.e. if one potential node position would be within the communication range of another node n while n is not actually sensed as a one-hop neighbor, is eliminated and the other position is chosen for this node. Figure 3 shows this case and the simulated two positions for node 6 are presented as blue octagons. The lower of the two possible positions can be eliminated because at this position node 6 would have had other neighbors which are not it actual neighbors in the network.

There might be cases where the elimination is not needed e.g. when the two generated circles are tangent at one point or when of the two positions are out of the deployment region. The algorithm continues to compute the position of nodes with the two previously mentioned cases until no more positions can be estimated. This is the case when a node sees two known position nodes within its vicinity but neither of the two possible positions introduces wrong neighbors and thus cannot be eliminated. Also, when a node is seen only by one known position neighbor, it may be located anywhere on the circle centered on the known position node and of radius the distance between the two nodes. Therefore, its position cannot be effectively established. This can be seen when attempting to compute the position of node 11, where its only known position neighbor is node 8. The final outcome of the algorithm in figure 4 shows that only node 11's position cannot be computed. The main advantage of this algorithm is its lightness as it comes down to a series of triangulation steps. The next section discusses the implementation of the proposed solution in a distributed manner.

Distributed implementation of the algorithm

To cope with the distributive nature of MANETs, we introduce here a possible algorithm to run at each node either to establish its own position or the position of all the nodes in the network. First, we assume that a node with a known position broadcasts its own position through the network. To mitigate overloading the network bandwidth, position updates are spread over the network using a bandwidth conserving method like SFLS [START_REF] Renault | Semiflooding location service[END_REF]. This method is based on sending the updates with the highest frequency to one-hop neighbors, and for each increment of hop-count the frequency of updates is halved. This method is compatible with our approach since the information needed to eliminate wrong positions relies on neighbors which are a few hops away. A node that needs to compute its own position receives position information from other nodes. It also computes the distance to one hop neighbors using any of the previously mentioned methods. When sufficient information has been received, it computes its own position as shown in Algorithm 1. Let N 1 be the list of detected known position neighbors within communication range of the node. When N 1 includes two or three nodes, the position of the node can be made certain using the intersection method. If N 1 includes two nodes, the intersection method returns the two possible positions from which one is to be eliminated if possible. Let N be the list containing position information of all received positions of network nodes. The receive method is called when a message is received from a sender node within communication range that contains the node ID n and the corresponding position pos. Id 1 contains the list of IDs of all one-hop neighbors whether their position is known or not.

Algorithm 1 Position Certainty Propagation

When positions are computed, the nodes start broadcasting their own position using SFLS [START_REF] Renault | Semiflooding location service[END_REF]. Also, to take into account the continuous change of node positions, nodes should re-run the position computation algorithm whenever a change in the parameters used to compute its position is received. To make it clearer, if any of the three nodes used to compute a node's position send a message indicating a position change, the node should update its own position.

Performance Evaluation

Comparable method: Gps-free

In order to assess our algorithm we compare it with an existing solution [START_REF] Čapkun | GPS-free positioning in mobile ad hoc networks[END_REF], which we will refer to as Gps-free. We implemented the first part of the Gps-free algorithm where node positions are computed in a local coordinate system of one of the nodes in the network. The paper further explains how to choose a stable coordinate system for the network, which does not concern us since we evaluate the solution for a static instance of the network. The algorithm compromises the following steps:

1. Each node creates a local map composed of itself and the maximum possible number of 1-hop neighbours via triangulation making itself the origin. 2. Node k can transfer its coordinate system to node i if both nodes exist in the local map of one another in addition to a third node common in both local maps. 3. All nodes in the network attempt to transfer their coordinate system to node i so that all node positions are computed in one common coordinate system

We refer the reader to the Gps-free article [START_REF] Čapkun | GPS-free positioning in mobile ad hoc networks[END_REF] for further details of how these steps are executed. Observing the behaviour of Gps-free in some graphs we figured an improvement that can increase the percentage of localized nodes. The improvement concerns the condition that only nodes who cannot build a local map are transferred to another coordinate system via triangulation. We quote from the Gps-free paper: "The nodes that are not able to build their local coordinate system but communicate with three nodes that already computed their positions in the referent coordinate system can obtain their position in the Network Coordinate System by triangulisation" [START_REF] Čapkun | GPS-free positioning in mobile ad hoc networks[END_REF]. We extended this behaviour to nodes who built their local map but still cannot transfer their local coordinate system to the referent coordinate system; only the node compute its position in the referent coordinate system.

Experimental setup

Our objective is to compute the positions in the global coordinate system using GPS information of three nodes. In the Gps-free method, even though the positions are computed in a local coordinate system, it is possible to transfer the node positions to the global coordinate system if the 3 GPS nodes have their positions computed. Each simulation run is a connected geometric graph (aka disc graph) where nodes are randomly positioned in a 100m×100m grid. Three anchor nodes are randomly chosen so that at least one non-anchor node is within their communication range. Our algorithm is run to compute the positions of the non-anchor nodes as previously described. Gps-free is run where the node chosen to have all nodes transferred to its coordinate system is the one the maximizes the number of estimated nodes, this ensures extracting the best possible result from the Gps-free method without taking into account whether the GPS nodes are among the nodes with the computed positions. The success percentage is the percentage of nodes with their position computed. Two experiments are conducted, one varying the maximum communication range [14, 15, 16.., 23] while keeping the average number of nodes constant at 100 nodes. In the other experiment the communication range is kept constant at 14m while the average number of nodes is varied [100,115,130,145,160]. The number of nodes in each experiment follows a poisson distribution with the given average. In each graph configuration the experiment is repeated 1000 times and the confidence interval is shown as a vertical bar around the point.

Experimental Results

Figure 5 shows the comparison between PCP, Gps-free and Gps-free extended version. We start with a maximum communication range of 14m and increment by 1 meter for each new configuration. We show on the graph the average node degree at each maximum communication range. Put differently, for the first configuration each node has a maximum communication range of 14 meters and the average node degree over the 1000 simulations is approximately 5.5, the next configuration is with a range of 15 meters which gives an average node degree of 6.3 etc. It can be clearly seen that the maximum communication range has a direct impact on the number of localized nodes. As the the maximum communication range increases and consequently the average node degree, it is possible to estimate positions of more nodes. When the average degree is ≈ 10, our algorithm is able to compute the positions of ≈ 90% of nodes. Also, our algorithm shows a higher success percent at all configurations compared to Gpsfree and the extended version. In another attempt to study the effect of varying the number of nodes keeping the maximum communication range constant at 14 meters, increasing the number of nodes has a similar effect to increasing the maximum communication range. Here, the node density (nodes/meter) is shown against the success rate.

Conclusion

This article presented a simple algorithm to compute the positions of nodes when a network has a very limited number of anchor nodes (the minimum is set to 3). The algorithm has an initial condition, which is the presence of a node that is within the vicinity of at least three anchor nodes. It might appear that the starting condition is hindering the utility of the approach, however it can be partially mitigated by assuming three non-anchor nodes that are within each other's vicinity and satisfy the initial condition as anchor nodes. These virtual anchor nodes are given assumed positions so that they form a triangle from the known distance between them. For instance, assume non-collinear nodes i, j, k see each other and at least a forth node. Node i is positioned at the origin, node j is on the horizontal axis with at a distance equal to dist(i, j) and node k is added to have a positive y-value using triangluation. The algorithm then treat them as anchor nodes and compute the position of the rest of the nodes as previously explained. When at least three real anchor node positions are computed, all the nodes can then be transferred to the global coordinate system using there computed positions and actual positions [START_REF] Čapkun | GPS-free positioning in mobile ad hoc networks[END_REF]. It has been shown statistically that with a fairly dense network (average degree ≈ 10), the algorithm is able to quickly and efficiently compute the position of ≈ 90% the nodes whose positions are not known. The next step is to test our algorithm with the inevitable uncertainty of measurements using a tool such as NS3. Additionally, the impact of the node's velocity, delay of receiving information on the accuracy of the estimation of the node's position are also to be considered.

Fig. 1 .

 1 Fig. 1. 15-node network example

Fig. 2 .

 2 Fig. 2. Counting number of known position neighbours (left). Computing position from the intersection of three circles (right)

Fig. 3 .Fig. 4 .

 34 Fig. 3. Counting number of known position neighbours (left). Computing position from the intersection of two circles (right)

Fig. 5 .

 5 Fig. 5. Comparing PCP to Gps-free and Gps-free ext methods

1 :

 1 N (list of received node positions) and N1 (1-hop neighbours with known positions) 2: Id1 list of IDs of all one-hop neighbors 3: procedure receive(sender, n, pos, timeStamp)

	8:	update N with n, pos and timeStamp
	9:	end if
	10: end procedure
	11: do
	12:	receive(sender, n, pos, time())
	13:	if N1 has 3 or more nodes then
	14:	self P os = intersection(N1)
	15:	else if N1 has 2 nodes then
	16:	[pos1, pos2] = intersection(N1)
	17:
	20:	pos2IsCompatible ⇐⇒ for each n ∈ P 2N eighbors, n ∈ Id1
	21:	if pos1IsCompatible and not pos2IsCompatible then
	22:	self P os = pos1
	23:	else if pos2IsCompatible and not pos1IsCompatible then
	24:	self P os = pos2
	25:	end if
	26:	end if
	27: while self P os not found

4: if sender == n and (n not in N1 or timeStamp > N1[n].timeStamp) then 5: update N1 with n, pos and timeStamp 6: end if 7: if n not in N or timeStamp > N [n].timeStamp then P 1N eighbors = n for each n ∈ N where distance(n, pos1) <= CommRange 18: P 2N eighbors = n for each n ∈ N where distance(n, pos2) <= CommRange 19: pos1IsCompatible ⇐⇒ for each n ∈ P 1N eighbors, n ∈ Id1


Pascal Guillo

Olivier Hamelin

Pinar Batat

Gediminas Jonusauskas

Nathan D Mcclenaghan

Steṕhane Meńage

Photocatalyzed Sulfide Oxygenation with Water as the Unique Oxygen Atom Source

In our research program aiming to develop new ruthenium based polypyridine catalysts for oxidation we were interested in combining a photosensitizer and a catalytic fragment within the same complex to achieve catalytic light driven oxidation. To respond to the lack of such conjugates, we report here a new catalytic system capable of using light to activate water molecules in order to perform selective sulfide oxygenation into sulfoxide via an oxygen atom transfer from H 2 O to the substrate with a TON of up to 197 ± 6. On the basis of electrochemical and photophysical studies, a proton coupled electron transfer process yielding to an oxidant Ru(IV)-oxo species was proposed. In particular, the synergistic effect between both partners in the dyad yielding a more efficient catalyst compared to the bimolecular system is highlighted.

INTRODUCTION

In the past decade, a tremendous amount of effort has been devoted to solar energy to chemical energy conversion to promote chemical reactions of interest. 1 To perform such a transformation, ruthenium polypyridine complexes, thanks to their interesting photophysical properties, have emerged as candidates of choice. 2 Since the pioneering work of Deronzier et al. about 20 years ago, development of photocatalytic systems for organic synthesis has attracted very little attention until now. 3 Currently, the necessary search for new energies has stimulated researchers to design new photocatalysts. As a consequence, in the past few years the number of photoredox catalysts has then increased spectacularly in the literature. 4 Among others, one can cite the very efficient systems reported by T. P. Yoon 4g and C. R. J. Stephenson 4f for [2 + 2] cycloaddition of enones and reductive halogenation, respec tively. By combining an inorganic catalyst ([Ru(bpy) 3 ] 2+ ) as photoredox catalyst with a chiral amine as an organocatalyst to perform efficient enantioselective alkylation of aldehydes, MacMillan et al. reported probably the most elegant example of photoredox catalysis to date. 4i On the other hand, inspired by photosystem II, some heterogeneous and homogeneous systems were developed to perform one of the most challenging reactions, photooxidation of water into dioxygen. 5 In most of these cases, a ruthenium polypyridine complex acting as catalyst was associated to ruthenium tris(diimine) complexes as photosensitizers. In such catalytic systems it is proposed that after initiation by light an oxidizing ruthenium oxo species is formed due to an intermolecular electron transfer from the catalyst to the sacrificial electron acceptor via the chromophore. In the field of redox catalysis, such high valent species also oxidize a wide range of organic substrates such as alkanes, alkenes, and sulfides. 6 In the course of the development of new, eco aware catalytic systems and to achieve efficient catalytic light driven oxygenation of organic substrates, we were interested in the design of a photocatalyst combining a photosensitizer and a catalytic fragment within the same entity in order to promote direct electron transfer between both partners. To the best of our knowledge, only two reports using this approach have been published, both very recently. First, during the course of this work, Rocha and co workers reported the use of a dinuclear ruthenium based complex to perform photocatalytic alcohol oxidation into the corresponding aldehyde or ketone. 7 However, unlike sulfide and alkane oxygenation, in that case no oxygen atom incorporation into the substrate is required. Very recently we also showed that a similar dyad selectively oxidizes sulfides into sulfoxides. 8 A proton coupled electron transfer (PCET) process resulting in an oxygen atom transfer from the water molecule to the substrate was involved. On the basis of our previous work using a [(bpy) 2 Ru(bpym)] (bpym = 2,2′ bipyrimidine) fragment as a chiral metalloligand for ketone reduction, 9a we report here a revisited synthesis and full characterization of the dinuclear complex [(bpy) 2 Ru(bpym)Ru(tpy)OH 2 ] 4+ (designated Ru phot -Ru cat -OH 2 ) recently published by T. J. Meyer 10 while this work was under progress as catalyst for water oxidation using Ce(IV) as oxidant. In this paper we showed that this catalyst also has the ability to selectively photooxidize sulfide into sulfoxide using water as the unique source of oxygen atom (Scheme 1). In particular, a synergistic ef fect was observed between both partners of the dyad compared to the bimolecular system. Finally, based on electrochemical and photophysical studies and on literature reports a mechanism involving formation of a Ru(IV)O species thanks to a proton coupled electron transfer process was proposed. 13 (named Ru cat -OH 2 ) were prepared according to literature methods. The purity of each complex was confirmed by NMR spectroscopy on the basis of published data.

EXPERIMENTAL SECTION

1 H NMR spectra were recorded on a Bruker Avance DPX 300 MHz NMR spectrometer at room temperature.

Elemental analyses were conducted at the Service Central d'Analyze CNRS, Solaize, France using the ICP AES method for metal titration.

Electrospray ionization mass spectrometry measurements were performed on a LXQ linear ion trap (Thermo Scientific, San Jose, CA) equipped with an electrospray source in an aqueous or aqueous/ acetone mixture. Electrospray full scan spectra in the range m/z 50-2000 amu were obtained by infusion through fused silica tubing at 2-10 μL•min -1 . The LXQ calibration was achieved according to the standard calibration procedure from the manufacturer (mixture of caffeine/MRFA and Ultramark 1621). The temperature of the heated capillary of the LXQ was set to the range 150-200 °C; the ion spray voltage was in the range 1-3 kV. The experimental isotopic profile was compared in each case to the theoretical one.

Absorption spectra were recorded with a Varian Cary 1Bio spectrophotometer.

Cyclic voltammetry experiments were performed on a computer controlled Biologic VMP2 potentiostat. Electrochemical measure ments were carried out under nitrogen. A standard three electrode configuration was used consisting of a basal carbon (3 mm in diameter) disk as the working electrode, an auxiliary platinum wire, and an Ag/AgCl/aqueous AgCl sat + KCl 3 mol•L -1 (hereafter named Ag/AgCl) reference electrode closed by a Vicor frit and directly dipped into the solution. The working electrode was polished on a MD Nap polishing pad with a 1 μm monocrystalline diamond DP suspension and activated using a reported procedure. 14 Solution concentrations were 1 mM for the complex and 0.1 M for the pH 6.8 sodium phosphate buffer. RDE experiments were carried out with a EG&G PAR 273A potentiostat at 20 mV•s -1 with a glassy carbon rotating disk electrode (3 mm in diameter; 500 rpm).

Photooxidation of thioethers was investigated under irradiation with a 180 W xenon arc lamp equipped with a 390 nm cutoff filter and a water jacket to remove UV and IR radiation, respectively. During the experiments, samples were placed at 15 cm from the lamp.

Optical properties: Dilute solutions studied were either air equilibrated or degassed by multicycle freeze-pump-thaw cycles.

The transient absorption/time resolved fluorescence setup was built as follows. A frequency tripled Nd:YAG amplified laser system (30 ps, 30 mJ at 1064 nm, 20 Hz, Ekspla model PL 2143) output was used to pump an optical parametric generator (Ekspla model PG 401) producing tunable excitation pulses in the range 410-2300 nm. The residual of fundamental laser radiation was focused in a high pressure Xe filled breakdown cell where a white light pulse for sample probing was produced. All light signals were analyzed by a spectrograph (Princeton Instruments Acton model SP2300) coupled with a high dynamic range streak camera (Hamamatsu C7700). Accumulated sequences (sample emission, probe without and with excitation) of pulses were recorded and treated by HPDTA (Hamamatsu) software to produce two dimensional maps (wavelength vs delay) of transient absorption intensity in the range 300-800 nm. Typical measurement error was better than 10 -3 O.D.

Transient absorption on the subpicosecond time scale: A Ti:Sapphire laser system emitting pulses of 0.6 mJ and 30 fs at 800 nm and 1 kHz pulse repetition rate (Femtopower Compact Pro) with an optical parametric generator (Light Conversion Topas C) and sequential frequency mixers was used to excite samples at the maximum of the steady state absorption band. White light continuum (360-1000 nm) pulses generated in a 5 mm D 2 O cell were used as a probe. The variable delay time between excitation and probe pulses was obtained using a delay line with 0.1 μm resolution. The solutions were placed in a 1 mm circulating cell. White light signal and reference spectra were recorded using a two channel fiber spectrometer (Avantes Avaspec 2048 2). A home written acquisition and experi ment control program in LabView made recording transient spectra with an average error less than 10 -3 of optical density for all wavelengths possible. The temporal resolution of our setup was better than 50 fs. A temporal chirp of the probe pulse was corrected by a computer program with respect to a Lawrencian fit of a Kerr signal generated in a 0.2 mm glass plate used in place of the sample.

Scheme 1. Homodinuclear Ruthenium Based Catalyst for Sulfide Photooxidation

Actinomery experiments were performed using potassium ferriox alate as an actinometer according to a reported procedure. 15 A 150 W xenon lamp equipped with a monochromator (λ = 436 nm) was used. The light intensity of the monochromatic light was determined as 7.3 × 10 -11 einstein•s -1 . The variation of the absorbance at 510 nm of the solution as a function of irradiation time is shown in Figure S1, Supporting Information.

The photochemical photooxygenation was performed in a square quartz cuvette (10 mm path length) containing a mixture of catalyst, 4 bromophenyl methyl sulfide substrate, and Co(III) salt in a 0.1:50:100 mM ratio in 0.1 M phosphate buffer (pH 6.8). Formation of the product was monitored by gas chromatography (Perkin Elmer Autosystem XL) taking aliquots from the reaction mixture and using benzophenone as reference. A quantum yield of 0.32 was determined in the conditions described above.

Synthesis and Characterization.

[(bpy) 2 Ru(bpym)Ru(tpy)Cl]- [PF 6 ] 3 ([Ru phot -Ru cat -Cl][PF 6 ] 3 ).
To a solution of [(bpy) 2 Ru (bpym)][PF 6 ] 2 (300 mg, 0.35 mmol) in 24 mL of a 2:1 ethanol-water mixture, Ru(tpy)Cl 3 (230 mg, 0.52 mmol) was added. The resulting solution was refluxed for 3 h. After cooling to room temperature, an excess of NH 4 PF 6 was then added to precipitate the complex (addition of water was sometimes required to maximize precipitation). After 1 h at -20 °C the resulting solid was filtered and then washed with cold water. After dissolution of the residue in a minimum amount of acetone the complex was precipitated once again by addition of the solution to a large volume of diethyl ether. After filtration, [Ru phot -Ru cat -Cl][PF 6 ] 3 (470 mg, 98% yield) was obtained as a green powder.

1 H NMR (300 MHz, acetone d 6 ) δ (ppm) = 10.38 (dd, 1H, 1.5 Hz, 5.7 Hz), 8.89 (d, 1H, 3.6 Hz), 8.87 (d, 1H, 3.9 Hz), 8.79-8.61 (m, 9H), 8.34-8.03 (m, 12H), 7.95 (dd, 2H, 5.1 Hz, 5.1 Hz), 7.76 (dd, 1H, 6 Hz, 6 Hz), 7.66 (dd, 1H, 7.2 Hz, 7.2 Hz), 7.59-7.50 (m, 3H), 7.43 (dd, 1H, 6.9 Hz), 7. 26 (t, 1H, 5.7 Hz) P, 6.58. Found: C, 36.59; H, 2.59; N, 11.02; P, 6.75.

. ESI MS (m/z) (relative intensity) 1232, {[Ru phot -Ru cat -Cl][PF 6 ] 2 } + (5); 544, {[Ru phot - Ru cat -Cl][PF 6 ]} 2+ (28); 314, {[Ru phot -Ru cat -Cl]} 3+ (100). UV-vis (H 2 O, λ max , nm, (ε, M -1 .cm -1
[(bpy) 2 Ru(bpym)Ru(tpy

)(OH 2 )][PF 6 ] 4 ([Ru phot -Ru cat -OH 2 ][PF 6 ] 4 ). To a solution of [(bpy) 2 Ru(bpym)Ru(tpy)Cl][PF 6 ] 3 ([Ru phot - Ru cat -Cl][PF 6 ]
3 ) (200 mg, 0.15 mmol) in 20 mL of water silver trifluoromethanesulfonate (373 mg, 1.5 mmol) was added and the solution was refluxed for 2 h. After cooling to room temperature, an excess of NH 4 PF 6 was then added to precipitate the complex (addition of water is sometimes required to maximize precipitation). After 1 h at 0 °C the solid was filtered and then washed with cold water. The complex was then dissolved in a minimum of acetone and then reprecipitated by addition of the solution to a large volume of diethyl ether. After filtration, [Ru phot -Ru cat -OH 2 ][PF 6 ] 4 (200 mg, 92% yield) was obtained as a green powder. ] 4 in a 0.1 M sodium phosphate buffer at pH 6.8 was prepared and stored at -20 °C.

To 5 mL of this solution in a Schlenk system 1000 equiv of [Co(NH 3 ) 5 Cl]Cl 2 (25 mg, 100 μmol) was added. The solution was degassed for 15 min by Ar bubbling, and then 500 equiv (50 μmol) of substrate was added under an inert atmosphere. The sample was irradiated at 180 W for 24 h. After extraction of the organic products by dichloromethane (×2) and diethyl ether (×2) a known quantity of 3,4,5 trimethoxybenzaldehyde was added as reference. After evapo ration, products were characterized and quantified using ] 2 -H + } + , respectively. The electronic properties of both complexes were investigated and compared to those of the Ru phot system (Table 1, Figure S1, Supporting Information) whose transitions were assigned on the basis of literature reports. 9b,12,13 Assignments of the bands at higher energy (280-300 nm) were ascribed to ligand centered transitions ( 1 LC) with π-π* transitions. Two metal to ligand charge transfer ( 1 MLCT) bands were also observed at lower energy in the visible region of the electronic 16 Photocatalytic Oxygenation. While various oxidants are used to oxidize sulfides into sulfoxides 17 very few photocatalytic systems were reported to date to perform such a transformation. 8,18 Thus, photooxidation of 4 bromophenyl methyl sulfide to the corresponding sulfoxide by Ru phot -Ru cat -OH 2 using [Co(NH 3 )Cl]Cl 2 as an electron acceptor was investigated as the probe reaction with a complex:substrate:Co(III) 1:500:1000 ratio in deoxygenated water (0.1 M phosphate buffer; pH 6.8). All photocatalytic experiments were carried out at least three times and under an inert atmosphere in order to prevent photochemical formation of 1 O 2 as potential oxidant. The resulting solution was irradiated with a Xenon lamp (180 W) equipped with a UV filter (λ exc > 390 nm). The resulting products were then extracted from the reaction mixture, characterized, and quantified by 1 H NMR spectroscopy. The results are summarized in Table 2. Figure S1, Supporting Information, shows the plot of the turnover number as a function of time for photocatalytic oxidation using the complex Ru phot -Ru cat -OH 2 . Up to 131 ± 6 TON was achieved after 24 h of light irradiation (entry 2) with an excellent selectivity since no evidence for formation of sulfone could be obtained. Control experiments showed that in the absence of light (entry 3), catalyst, or electron acceptor no product was formed. Similarly to the photosensitizer fragment [(bpy) 2 Ru(bpym)] 2+ (Ru phot ), 9 which alone was not able to oxidize the substrate (entry 4), the cation [(bpym)Ru(tpy)OH 2 ] 2+ (Ru cat -OH 2 ) 13 showed poor activity under light exposition with only 10 ± 2 TON (entries 5 and 6). Interestingly, a stoichiometric bimolecular combination of both partners resulted in the formation of the sulfoxide but with a lower efficiency than in the dyad (entry 7). Moreover, both complexes (Ru phot -Ru cat -Cl and Ru phot -Ru cat -OH 2 ) showed almost similar activity with a TON of about 135 (entries 1 and 2). This may be attributed to conversion of the Ru phot -Ru cat -Cl precatalyst to the Ru phot -Ru cat -OH 2 catalyst by a fast Cl -/H 2 O ligand exchange which could occur in the catalytic conditions. Finally, the quantum yield of the photochemical photooxygenation was determined by actinometry using potassium ferrioxalate as actinometer under irradiation with a monochromatic light (λ = 436 nm; light intensity 7.3 × 10 -11 einstein•s -1 ). 15 For the catalytic system:substrate:Co(III) salt, 0.1:50:100 mM in 0.1 M phosphate buffer (pH 6.8), a quantum yield of 0.32 of the photocatalytic oxygenation was determined.

The photocatalytic activity of the catalyst Ru phot -Ru cat -OH 2 was then assayed during oxidation of a variety of sulfides (Table 3). With the exception of the 4 nitrophenyl methyl sulfide, all substrates were efficiently converted into their corresponding sulfoxide with TON ranging from 124 ± 4 to 197 ± 6 and with excellent selectivities since no overoxidation products could be detected. Even though the solubility properties of the various substrates are different in aqueous conditions, it seems that the reactivity is directly correlated to the electronic effect of the para substituent on the substrate. Only 51 ± 5 TON was achieved after 24 h of irradiation with the NO 2 electron withdrawing substituent, whereas more than 197 ± 6 TON was obtained when the para substituent is the methoxy electron donating group. In the same conditions, lower catalytic activity was observed during alkenes and alcohol oxidation. As an example, only 25 ± 3 and 3 TON were achieved during oxidation of 1 phenylethanol and trans β methyl styrene into the corresponding ketone and trans epoxide, respectively. No oxidized product could be detected with electron poor alkenes such as cis cyclooctene and methyl trans cinnamate.

The stability of the catalyst toward light was confirmed, first, by irradiation of a solution of the catalyst in phosphate buffer. On the basis of electronic absorption, after 24 h of light exposure more than 70% of the catalyst remains unchanged (Figure S1, Supporting Information). Second, when a second portion of 1000 equiv of Co(III) salt was added to the catalytic mixture after 24 h of irradiation and the resulting solution was irradiated for additional 24 h, the catalytic activity increased about 50% from 131 ± 6 to 195 ± 8 TON (Table 2, entry 8). Electrochemical Studies. The electrochemical behavior of both Ru phot -Ru cat -OH 2 and Ru phot -Ru cat -Cl complexes was investigated in a 0.1 M phosphate buffer (pH 6.8; Figure 1). Data are reported in S8, Supporting Information). For all complexes a cathodic wave attributed to the one electron reversible reduction of the bpym ligand was observed. 12 As a consequence of the Lewis acidity of the ruthenium cation, coordination of a second subunit to the bpym ligand resulted in a shift of this wave to a more positive potential (from -0.66 to -0.42 V vs NHE).

The cyclic voltammograms (CV) of Ru phot -Ru cat -Cl and Ru phot -Ru cat -OH 2 showed, in addition to reduction of the bridging bpym ligand, two irreversible oxidation processes at +1.29 and +0.92 V vs NHE, respectively (Figure 1). These waves were attributed to oxidation of the Ru cat center by comparison with the oxidation of [Ru(diimine) 3 ] 2+ systems which usually arise at higher potentials (in the range 1.4-1.9 V vs NHE, i.e., +1.51 and +1.46 V vs NHE for the Ru phot and Ru phot -Ru cat -OH 2 complexes, respectively). 2b Only for Ru phot -Ru cat -OH 2 the reversibility of the oxidation wave of the catalytic center can be observed at 200 mV.s -1 (Figures S9 andS10, Supporting Information). In the CV of the Ru phot -Ru cat -Cl complex a small wave at +0.92 V vs NHE becoming largely predominant after 2 days was assigned to the aquo species (Figure S11, Supporting Information). It is noteworthy that in such conditions, avoiding light exposure and Co(III) salt, the Cl -/H 2 O ligand exchange proved to be largely slower than in the photocatalytic conditions used for sulfide oxygenation.

An experiment using a rotating disk electrode (RDE, 500 rpm, inset Figure 1) showed unambiguously that oxidation of the Ru cat (II)-OH 2 subunit is a two electron process yielding a Ru cat (IV)O species. This was confirmed by analysis of its relative current intensity (integrated area) compared to the one of the bpym reduction, which is a one electron process. Moreover, it was also observed that this oxidation potential decreased linearly with pH over the range pH 1-7 with a slope of -0.056 V/pH unit (Figure S12, Supporting Information). These data are in agreement with a PCET process involving two protons and two electrons avoiding charge build up during oxidation. As a consequence, the oxidation potential of the Ru cat (IV)O/ Ru cat (II)-OH 2 couple is lower than that corresponding to the Ru cat (III)-Cl/Ru cat (II)-Cl couple for which no PCET process is involved, even though the Cl -ligand is negatively charged and a strong donor. 19 Finally, as the potential of the Ru phot (III)/Ru phot (II) couple is higher than the one of the Ru cat (IV)/Ru cat (II) couple in the Ru phot -Ru cat -OH 2 complex oxidation of the Ru cat (II)-OH 2 fragment into a versatile oxidant Ru cat (IV)O species by the photogenerated Ru phot (III) is favored.

Oxygen Atom Transfer from H 2 O to the Substrate. In order to highlight the oxygen atom transfer from the water molecule to the substrate, an isotopic labeling study employing a 1:1 mixture of H 2 18 O and H 2 16 O was carried out. After the usual extractions the product mixture was analyzed by GC MS, and the results were compared to the unlabeled sulfoxide displaying fragments at m/z 218 and 220 (Figure 2). The mass spectra of the mixture disclosed formation of a 1:1 mixture of both labeled and unlabeled sulfoxide with fragments at m/z 220, 222 and 218, 220 respectively. This result is fully consistent with an oxygen atom transfer from water to the substrate.

Photophysical Studies. To gain better insight into the photo physical behavior of the nonemissive dinuclear catalyst transient absorption spectroscopy was employed, exciting into different MLCT bands in the visible region under pseudocatalytic conditions in the absence of Co(III) salt. The excited behavior of model [{(bpy) 2 Ru} 2 (bpym)] 4+20 (named Ru phot -Ru phot ) and Ru phot -Ru cat -OH 2 was seen to be rather different (Figures 3 and4). Exciting Ru phot -Ru phot gave rise to a transient absorption signature attributed to a low lying MLCT state involving the easily reduced bridging ligand and an adjacent ruthenium with a lifetime of 1.8 ns in air equilibrated buffer solution. On the other hand, on exciting the dinuclear aquo complex a similar absorption signature is formed showing a similar intermediate based on the Ru phot subunit, regardless of excitation wavelength. The lifetime is only 80 ps (and insensitive to the presence of oxygen). The induced quenching may be attributed to an intramolecular photoinduced electron transfer (PET) reaction as suggested by the catalytic results. However, other quenching mechanisms cannot be entirely ruled out. Assuming the dominant quenching pathway is a PET process from Ru cat to the excited state of the Ru phot subunit, a rate constant of 1.2 × 10 10 s -1 can be estimated, with a small net driving force of ca. -0.1 eV. 21 In spite of the short lived excited state under degassed conditions in the presence of 1000 equiv of cobalt salt this excited state lifetime was further shortened to around 55 ps, showing rapid diffusion, approaching the diffusion limit and/or static quenching, permitting electron transfer to the electron acceptor, potentially an important enabler in the catalytic reaction.

DISCUSSION

To date, it is well known that [(diimine)Ru(tpy)-OH 2 ] 2+ systems have an extensive and well defined catalytic oxidation chemistry involving the high valent [(diimine)Ru(tpy)O] 2+ species. 6i This system was shown to be particularly efficient for catalytic oxidation of alcohols, aldehydes, and unsaturated hydrocarbons. 22 Through the pioneering work of T. J. Meyer it was also shown that the molecular assembly of a [(diimine) Ru(tpy)-OH 2 ] based catalyst with a chromophore allowed photodehydrogenation of propanol on TiO 2 . 23 With the system described here, by combining a photosensitizer and a [(bpy)Ru(tpy)-OH 2 ] like fragment we showed that the sulfoxide can be selectively and catalytically obtained without further overoxidation into the corresponding sulfone with a TON in the range of the ruthenium based bimolecular and binuclear photocatalysts reported by Rocha 7 and very recently by Sun 8b and our group. 8a The system reported here shows similar behavior with the [(bpy) 2 Ru(tpphen)Ru(bpy)(OH 2 )] 4+ system reported very recently by us in terms of both the stability and the catalytic efficiency during sulfide oxygenation in the same conditions. This can be attributed to the relative structural homology between both dyads. More importantly, the results collected in Table 2 highlight the absolute necessity to associate both partners (chromophore and catalytic fragment) to be able to perform the photocatalytic reaction. Indeed, no product could be detected when they are used independently, whereas up to 56 ± 4 TON was reached by the bimolecular system. The dyad proved to be even more efficient with up to 131 ± 6 TON emphasizing the advantage of combining both partners within a unique photocatalytic entity for a better communication between them. This suggests that the (inter vs intra)molecular electron transfer from the catalyst to the photosensitizer directly impacts the efficiency of the photocatalytic system.

As far as the Ru phot -Ru cat -Cl precursor is concerned, it was observed that several days are needed for its conversion into its corresponding aquo compound in water in the absence of silver salt (Figure S11, Supporting Information) while a few hours are required for the cation [Ru(tpy)(bpy)Cl] + . 19a,24 This difference in behavior can be attributed to the significant electron attracting effect of the Ru phot moiety as it was noted by the group of C. P. Berlinguette that observed the rate of halide substitution by a water molecule is slower when bpy ligands bearing electron withdrawing substituents were used. 19a How ever, when the Ru phot -Ru cat -Cl complex was used for sulfide oxidation, an activity comparable to the aquo complex is observed after 24 h. This could be ascribed to the rate acceleration of the Cl -/H 2 O exchange in the photocatalytic conditions affording the aquo photocatalyst from the Ru phot -Ru cat -Cl precatalyst. However, it was observed that light is not the only parameter allowing this rate enhancement. Indeed, the Ru phot -Ru cat -Cl system showed high stability (i) after several hours of light irradiation without the presence of the electron acceptor and also (ii) in water in the presence of the sulfide under dark conditions (data not shown). This suggests that all these parameters (light, Co(III), and maybe substrate also) have to be combined to allow this substitution in the experimental time window, leading to formation of the aquo catalyst.

In the four photocatalytic systems based on use of the [(diimine)Ru(tpy)-OH 2 ] like compound as catalyst in associ ation with a photosensitizer 7,8a,b,23 a mechanism involving the two electron oxidation of the catalytic center thanks to a PCET process initiated by photon absorption by the chromophore fragment can be proposed (Scheme 3.). A similar mechanism occurs with the dyad system reported here. Excitation of the Ru phot moiety by irradiation in the visible region corresponding to its MLCT absorption initiates an intramolecular electron transfer from the Ru cat moiety to Ru phot in its excited state, yielding formation of the [Ru phot (I)-Ru cat (III)-OH (or OH 2 )] 3+(or 4+) species as it was underlined by the photophysical studies in the absence of Co(III) salt. However, addition of a large excess of electron acceptor resulted in a small but significant diminution of the excited state lifetime (from 80 to 55 ps). Thus, an oxidative quenching involving intervention of a fast intermolecular electron transfer from Ru phot in its excited state to the sacrificial electron acceptor generating the [Ru phot (III)-Ru cat (II)-OH 2 )] 4+ species may be proposed. This mechanism is generally accepted. 8a,c Thanks to the higher Ru phot potential relative to the Ru cat -OH 2 potential observed by cyclic voltammetry, the resulting Ru phot (III) fragment is thermodynamically able to oxidize the neighboring Ru cat (II)-OH 2 subunit by an intramolecular electron transfer. A proton is also released from the aquo ligand to avoid unfavorable charge build up during the oxidation process. In light of the reported mechanism for the [Ru(tpy)(bpym)-OH 2 ] 2+ catalyst (Ru cat -OH 2 ) 13,19a,24 and on the basis of the two electron oxidation observed for the Ru(II) cat moiety (Figure 1 studies. 10 Surprisingly, the CV of the Ru phot -Ru cat -OH 2 complex recorded in our laboratory in the same conditions (at pH 4.4; acetate buffer) showed significant differences with the one published (Figure S13, Supporting Information). 10 While two waves were observed at +0.75 and +1.2 V vs NHE and attributed to successive one electron oxidation processes Ru(II)-OH 2 → Ru(III)-OH → Ru(IV)O, a single wave at +1.05 V vs NHE corresponding to a two electron oxidation process was observed in our case. Our data (CV, RDE, and E pH experiments) strongly support a two electron two proton process, unambiguously attributed to oxidation of the Ru cat (II)-OH 2 fragment into a Ru cat (IV)O fragment, as also observed for the [Ru(tpy)(bpym)OH 2 ] 2+ (Ru cat -OH 2 ), 13,19a,24 [(bpy)Ru( i PrPybox)OH 2 ] 2+ , 25 and trans [Ru(II) pyrpy O)(tpy)-OH 2 ] +26 complexes ( i PrPybox = 2,6 bis[4 isopropyl 2 oxazolin 2 yl]pyridine; pyrpy = 3,5 dimethyl 2 (2 pyridyl)pyrrolate).

Then, similar to the proposed mechanism for water oxidation, the Ru(IV)O species undergoes nucleophilic attack of the sulfide to give the corresponding sulfoxide as a product with regeneration of the ruthenium aquo catalyst. Such a mechanism was also proposed during dimethylsulfide oxidation by [(bpy) 2 Ru(py)O] 2+ . 27 This mechanism was also suggested by the effect of the substituents on the phenyl ring of the sulfide with regard to the reactivity (Table 3) even though each substrate has a different solubility in the aqueous environment. However, as expected, the more electron donating the aryl substituent the more efficient the reaction.

In conclusion, we report herein the synthesis of a ruthenium based photocatalyst for sulfide photooxygenation. Similar to the first dyad system reported recently by our group, 8a it was shown that combination of a light absorbing photosensitizing fragment and a catalytic subunit within the same entity affords a better catalytic activity compared to the bimolecular system. This emphasizes a more efficient synergistic effect between both partners in the dyad. This finding should represent an important consideration in the design of future catalysts for various oxygenation reactions.

  )): 236 (49 800), 280 (71 200), 411 (21 800), 469 (12 400), 622 (7800). Anal. Calcd for [Ru phot -Ru cat -Cl][PF 6 ] 3 (C 43 H 33 ClF 18 N 11 P 3 Ru 2 •2H 2 O): C, 36.5 ; H, 2.64; N, 10.91;

  1 H NMR (300 MHz, acetone d 6 ) δ (ppm) = 10.07(d, 1H, 5.7 Hz), 8.91-8.87 (m, 5H), 8.79-8.68 (m, 5H), 8.58(d, 1H, 5.4 Hz), 8.48 (t, 1H, 8.1 Hz), 8.33-8.16 (m, 9H), 8.08 (dd, 2H, 5.4 Hz, 12.6 Hz), 7.93 (d, 2H, 5.7 Hz), 7.74 (dd, 1H, 6.3 Hz, 6.3 Hz), 7.69-7.61 (m, 2H), 7.58-7.50 (m, 3H), 7.28 (t, 1H, 5.7 Hz), 6.27 (s large, 2H). ESI MS (m/z) (relative intensity) 1216, {[ Ru phot -Ru cat -OH 2 ][PF 6 ] 2 -H + } + (7); 534.5, { [Ru phot -Ru cat -OH 2 ][PF 6 ] -H + } 2+ (100). UV-vis (H 2 O, λ max , nm, (ε, M -1 •cm -1 )): 278 (65 900), 303 (29 900), 331 (12 800), 409 (20 500), 445 (12 500), 610 (6900

  1 H NMR spectroscopy by comparison of the integral ratio of the methyl signals of the product and those of the methoxy signals of the reference. Synthesis and Characterization of the Catalyst. In 2009 the synthesis of the Ru phot -Ru cat -OH 2 complex was achieved in two steps in moderate overall yield (55%) by condensation of Ru(bpy) 2 Cl 2 and [(bpym)Ru(tpy)] 2+ to yield the Ru phot -Ru cat -Cl precursor. 10 Subsequent Cl -/H 2 O exchange was achieved via formation of the [(bpy) 2 Ru(bpym)Ru(tpy)(OTf)] 3+ complex (OTf = trifluorometha nesulfonate) as an intermediate. However, reaction of [(bpy) 2 Ru (bpym)][PF 6 ] 2 9 (named Ru phot ) and Ru(tpy)Cl 3 11 in a refluxing mixture of 2:1 ethanol-water and subsequent direct substitution of the chloro ligand by a water molecule using Ag(OTf) in refluxing water (Scheme 2) proved to be more efficient affording the desired Ru phot -Ru cat -OH 2 dinuclear complex in higher yield (90% for two steps). The presence of the water molecule as ligand in the final product was confirmed by 1 H NMR in acetone d 6 as a broad singlet at 6.27 ppm and integrated for two protons. This signal disappeared immediately after addition of a few drops of D 2 O as a result of H 2 O/D 2 O exchange (Figure S1, Supporting Information). Both dinuclear systems were fully characterized by 1 H NMR spectroscopy, ESI mass spectrometry, and elemental analyses. ESI mass spectra displayed fragments at 1232 and 1216 m/z corresponding to the monocations {[Ru phot -Ru cat -Cl][PF 6 ] 2 } + and {[Ru phot -Ru cat -OH 2 ][PF 6

Scheme 2 .

 2 Scheme 2. Synthesis of [Ru phot -Ru cat -Cl][PF 6 ] 3 and [Ru phot -Ru cat -OH 2 ][PF 6 ] 4 a

a

  Complex:Substrate: Co(III) 0.02:10:20 mM in 0.1 M phosphate buffer (pH 6.8) for 24 h. Sun: Xenon lamp (180 W). Moon: in the absence of light. b [Ru phot ] = [Ru cat -OH 2 ] = 0.02 mM. c After addition of a second portion of Co(III) salt (1000 equiv) and additional 24 h of irradiation.

Figure 1 .

 1 Figure 1. Cyclic voltammograms of the Ru phot -Ru cat -Cl (a) and Ru phot -Ru cat -OH 2 (b) complexes ([concentration] = 1 mM) in 0.1 M sodium phosphate buffer at pH 6.8; 20 mV•s -1 . (Inset) RDE experiment (500 rpm); voltammogram of the Ru phot -Ru cat -OH 2 complex ([concentration] = 0.5 mM) in 0.1 M sodium phosphate buffer at pH 6.8.

Figure 2 .

 2 Figure 2. Experimental mass spectra of (a) the corresponding sulfoxide obtained using a standard procedure, (b) the corresponding labeled and unlabeled sulfoxides obtained in an unbuffered deoxygenated H 2 16 O-H 2 18 O 1:1 mixture (instead of the buffered solution), and (c) simulated spectra of unlabeled 4 bromophenyl methyl sulfoxide (top) and 18 O labeled 4 bromophenyl methyl sulfoxide (bottom).

Figure 3 .

 3 Figure 3. (1) (a) Transient absorption spectrum of Ru phot -Ru phot , and (b) calculated excited state absorption of Ru phot -Ru phot . λ exc = 430 nm in aqueous buffer solution. (2) (a) Transient absorption spectrum of Ru phot -Ru cat -OH 2 , and (b) calculated excited state absorption of Ru phot -Ru cat -OH 2 . λ exc = 430 nm in aqueous buffer solution.

Figure 4 .

 4 Figure 4. Kinetics model of transient absorption spectral changes at 430 nm: (a) Ru phot -Ru phot (τ = 1.8 ns), (b) Ru phot -Ru cat -OH 2 (τ = 86 ps), and (c) Ru phot -Ru cat -OH 2 in the presence of electron acceptor (Co(III) salt 1000 equiv; τ = 57 ps). λ exc = 600 nm in aqueous buffer solution.

  Scheme 3.

  ). Anal. Calcd for [Ru phot -Ru cat -OH 2 ][PF 6 ] 4 •2H 2 O (C 43 H 39 F 24 N 11 O 3 P 4 Ru 2 ): C, 33.54; H, 2.55; N, 10.01. Found: C, 33.42; H, 2.59; N, 9.82.

Standard Conditions for Photocatalytic Sulfide Oxidation. A 0.02 mM solution of [Ru phot -Ru cat -OH 2 ][PF 6

Table 1 .

 1 Spectroscopic Data for [(bpy) 2 Ru(bpym)][PF 6 ] 2 ([Ru phot ][PF 6 ] 2 ), [(bpy) 2 Ru(bpym)Ru(tpy)Cl][PF 6 ] 3 ([Ru phot -Ru cat -Cl][PF 6 ] 3 ), and [(bpy) 2 Ru(bpym)Ru(tpy)OH 2 ][PF 6 ] 4 ([Ru phot -Ru cat -OH 2 ][PF 6 ] 4 ) a λ max /nm (ε/M -1 cm -1 )

	compound	π→π*	dπ Ruphot, Rucat →π*
	[Ru phot ][PF 6 ] 2	283 (53 000)	417 (12 300)
			475 (6000)
	[PF 6 ] 3 [Ru phot Ru cat Cl]	280 (71 200)	411 (21 800)	622 (7800) b
			469 (12 400)
	[PF 6 ] 4 [Ru phot Ru cat OH 2 ]	278 (65 900)	409 (20 500)	610 (6900) b
		303 (29 900)	445 (12 500)
		331 (12 800

a 10 μM in water. b dπ Rucat → π* bpym transition.

Table 2 .

 2 Photocatalytic Oxidation of 4 Bromophenyl Methyl Sulfide a

Table 3 .

 3 Photocatalytic Oxidation of a Variety of Sulfides Using Ru phot -Ru cat -OH 2 as Catalyst a

		substrate R 1	TON
	R 1 S CH 3	R 1 = phenyl	124 ± 4
		R 1 = 4-bromophenyl	131 ± 6
		R 1 = 2-bromophenyl	133 ± 4
		R 1 = 4-methoxyphenyl	197 ± 6
		R 1 = 4-nitrophenyl	51 ± 5
	alcohol	1-phenylethanol	25 ± 3
	alkene	trans-β-methyl styrene	3
		methyl trans cinnamate	0
		cis-cyclooctene	0

a Catalyst:Substrate:Co(III) 0.02:10:20 mM in 0.1 M phosphate buffer (pH 6.8); Xenon lamp (180 W) for

24 h.

  Table 4 and compared to those of [(bpy) 2 Ru(bpym)] 2+ and [Ru(tpy)(bpym)(OH 2 or Cl)] 2+/+ model cations for the Ru phot and Ru cat -(OH 2 13 or Cl 12 ) subunits, respectively (Figures S6-

Table 4 .

 4 Electrochemical Data for All Complexes (in V vs NHE) a

			E 1/2
	compound	bpym/ bpym .	Ru cat (ox)/ Ru cat (red)	Ru phot (III)/ Ru phot (II)
	Ru cat Cl	0.70	+1.00
	Ru cat OH 2	0.70	+0.86
	Ru phot	0.66		+1.51 b
	Ru phot Ru cat Cl	0.42	+1.29 b
	Ru phot Ru cat	0.41	+0.92 b	+1.46 b
	OH 2
	a [complex] = 1 mM in a 0.1 M phosphate buffer (pH 6.8).

b

Irreversible wave; anodic peak.

ACKNOWLEDGMENTS

Financial support from the CNRS, MENRT, UniversiteB ordeaux I, Reǵion Aquitaine is gratefully acknowledged.

AUTHOR INFORMATION

Corresponding Author *E mail: olivier.hamelin@cea.fr.